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</script></head><body><div id="package-header"><ul class="links" id="page-menu"><li><a href="index.html">Contents</a></li><li><a href="doc-index.html">Index</a></li></ul><p class="caption">tensorflow-core-ops-0.1.0.0: Haskell wrappers for Core Tensorflow Ops.</p></div><div id="content"><div id="module-header"><table class="info"><tr><th>Safe Haskell</th><td>None</td></tr><tr><th>Language</th><td>Haskell2010</td></tr></table><p class="caption">TensorFlow.GenOps.Core</p></div><div id="synopsis"><p id="control.syn" class="caption expander" onclick="toggleSection('syn')">Synopsis</p><ul id="section.syn" class="hide" onclick="toggleSection('syn')"><li class="src short"><a href="#v:_HostRecv">_HostRecv</a> :: <span class="keyword">forall</span> tensor_type. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tensor_type => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tensor_type</li><li class="src short"><a href="#v:_Recv">_Recv</a> :: <span class="keyword">forall</span> tensor_type. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tensor_type => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tensor_type</li><li class="src short"><a href="#v:_Send">_Send</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:_Arg">_Arg</a> :: <span class="keyword">forall</span> t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyRMSProp">sparseApplyRMSProp</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 v9 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyAdam">applyAdam</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v10 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyMomentum">sparseApplyMomentum</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyMomentum">applyMomentum</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyFtrl">applyFtrl</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyAdagradDA">sparseApplyAdagradDA</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 v9 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyAdagrad">sparseApplyAdagrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyProximalAdagrad">applyProximalAdagrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyAdagrad">applyAdagrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyAdadelta">applyAdadelta</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyProximalGradientDescent">sparseApplyProximalGradientDescent</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyProximalGradientDescent">applyProximalGradientDescent</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:encodeBase64">encodeBase64</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:stringSplit">stringSplit</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:stringJoin">stringJoin</a> :: [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:asString">asString</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:stringToHashBucketStrong">stringToHashBucketStrong</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:scatterMul">scatterMul</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:reduceJoin">reduceJoin</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:scatterSub">scatterSub</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:scatterAdd">scatterAdd</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:scatterUpdate">scatterUpdate</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:assignSub">assignSub</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:assignAdd">assignAdd</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseSegmentMeanGrad">sparseSegmentMeanGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseSoftmax">sparseSoftmax</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixSolve">matrixSolve</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:selfAdjointEigV2">selfAdjointEigV2</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:selfAdjointEig">selfAdjointEig</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyGradientDescent">applyGradientDescent</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stackPush">stackPush</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cholesky">cholesky</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:dynamicStitch">dynamicStitch</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>] -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readerNumWorkUnitsCompleted">readerNumWorkUnitsCompleted</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:readerRead">readerRead</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>)</li><li class="src short"><a href="#v:fFT2D">fFT2D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:fixedLengthRecordReader">fixedLengthRecordReader</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:placeholder">placeholder</a> :: <span class="keyword">forall</span> dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:scalarSummary">scalarSummary</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:softmax">softmax</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:shardedFilename">shardedFilename</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:_HostSend">_HostSend</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:sigmoidGrad">sigmoidGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:nonMaxSuppression">nonMaxSuppression</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:identityReader">identityReader</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:extractGlimpse">extractGlimpse</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:conv3DBackpropInput">conv3DBackpropInput</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixSolveLs">matrixSolveLs</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:rGBToHSV">rGBToHSV</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:decodeGif">decodeGif</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a></li><li class="src short"><a href="#v:adjustContrast">adjustContrast</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:depthToSpace">depthToSpace</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixSolve">batchMatrixSolve</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:erfc">erfc</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:resizeBilinearGrad">resizeBilinearGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fact">fact</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:deleteSessionTensor">deleteSessionTensor</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:logicalOr">logicalOr</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:getSessionTensor">getSessionTensor</a> :: <span class="keyword">forall</span> v1 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:batchMatrixInverse">batchMatrixInverse</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:shardedFilespec">shardedFilespec</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:decodeBase64">decodeBase64</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:getSessionHandle">getSessionHandle</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:initializeTable">initializeTable</a> :: <span class="keyword">forall</span> v1 v2 v3 tkey tval. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tkey, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tval) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tkey -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tval -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:tan">tan</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tanh">tanh</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyAdagradDA">applyAdagradDA</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stringToHashBucket">stringToHashBucket</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:eluGrad">eluGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fractionalAvgPoolGrad">fractionalAvgPoolGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixTriangularSolve">matrixTriangularSolve</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:editDistance">editDistance</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:barrierIncompleteSize">barrierIncompleteSize</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:threadUnsafeUnigramCandidateSampler">threadUnsafeUnigramCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:barrierReadySize">barrierReadySize</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:barrierClose">barrierClose</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:textLineReader">textLineReader</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:fFT3D">fFT3D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:refExit">refExit</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:exp">exp</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:restoreSlice">restoreSlice</a> :: <span class="keyword">forall</span> v1 v2 v3 dt. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dt => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dt</li><li class="src short"><a href="#v:conj">conj</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:resizeNearestNeighborGrad">resizeNearestNeighborGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArrayClose">tensorArrayClose</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:atan">atan</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArraySize">tensorArraySize</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:tensorArrayConcat">tensorArrayConcat</a> :: <span class="keyword">forall</span> v1 v2 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:lRN">lRN</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stringToHashBucketFast">stringToHashBucketFast</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:tensorArrayPack">tensorArrayPack</a> :: <span class="keyword">forall</span> v1 v2 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:concatOffset">concatOffset</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>] -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>]</li><li class="src short"><a href="#v:refEnter">refEnter</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:softsign">softsign</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArrayWrite">tensorArrayWrite</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:diag">diag</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixDiagPart">matrixDiagPart</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:queueSize">queueSize</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:decodePng">decodePng</a> :: <span class="keyword">forall</span> v1 dtype. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` dtype) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:ceil">ceil</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:priorityQueue">priorityQueue</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:placeholderWithDefault">placeholderWithDefault</a> :: <span class="keyword">forall</span> v1 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:cropAndResizeGradImage">cropAndResizeGradImage</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readerReset">readerReset</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:extractImagePatches">extractImagePatches</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixSetDiag">batchMatrixSetDiag</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stackClose">stackClose</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:quantizeAndDequantize">quantizeAndDequantize</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:isNan">isNan</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:where-39-">where'</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:listDiff">listDiff</a> :: <span class="keyword">forall</span> v1 v2 t out_idx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</li><li class="src short"><a href="#v:stridedSlice">stridedSlice</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 index t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:randomShuffleQueue">randomShuffleQueue</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:tileGrad">tileGrad</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stridedSliceAssign">stridedSliceAssign</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 index t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:reshape">reshape</a> :: <span class="keyword">forall</span> v1 v2 t tshape. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tshape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tshape) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tshape -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fIFOQueue">fIFOQueue</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:learnedUnigramCandidateSampler">learnedUnigramCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:fractionalAvgPool">fractionalAvgPool</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:randomCrop">randomCrop</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:_HostCast">_HostCast</a> :: <span class="keyword">forall</span> v1 dstT srcT. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dstT, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> srcT) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 srcT -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dstT</li><li class="src short"><a href="#v:queueClose">queueClose</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:slice">slice</a> :: <span class="keyword">forall</span> v1 v2 v3 index t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stridedSliceGrad">stridedSliceGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 index t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseTensorDenseAdd">sparseTensorDenseAdd</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:size">size</a> :: <span class="keyword">forall</span> v1 t out_type. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</li><li class="src short"><a href="#v:barrier">barrier</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:lgamma">lgamma</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:decodeJpeg">decodeJpeg</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a></li><li class="src short"><a href="#v:shapeN">shapeN</a> :: <span class="keyword">forall</span> v1 t out_type. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type) => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t] -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type]</li><li class="src short"><a href="#v:uniformCandidateSampler">uniformCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:unique">unique</a> :: <span class="keyword">forall</span> v1 t out_idx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</li><li class="src short"><a href="#v:drawBoundingBoxes">drawBoundingBoxes</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArraySplit">tensorArraySplit</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:split">split</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</li><li class="src short"><a href="#v:segmentMax">segmentMax</a> :: <span class="keyword">forall</span> v1 v2 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:abort">abort</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:sparseReorder">sparseReorder</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:rsqrtGrad">rsqrtGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:reverseSequence">reverseSequence</a> :: <span class="keyword">forall</span> v1 v2 t tlen. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tlen, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tlen) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tlen -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readerNumRecordsProduced">readerNumRecordsProduced</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:deserializeManySparse">deserializeManySparse</a> :: <span class="keyword">forall</span> v1 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:immutableConst">immutableConst</a> :: <span class="keyword">forall</span> dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:minimum">minimum</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:initializeTableFromTextFile">initializeTableFromTextFile</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:diagPart">diagPart</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:log">log</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArrayScatter">tensorArrayScatter</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:rank">rank</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></li><li class="src short"><a href="#v:identity">identity</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:adjustContrastv2">adjustContrastv2</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:sparseApplyProximalAdagrad">sparseApplyProximalAdagrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:gather">gather</a> :: <span class="keyword">forall</span> v1 v2 tindices tparams. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tparams) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tparams -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tparams</li><li class="src short"><a href="#v:isVariableInitialized">isVariableInitialized</a> :: <span class="keyword">forall</span> v1 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:concat">concat</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:randomUniformInt">randomUniformInt</a> :: <span class="keyword">forall</span> v1 v2 v3 t tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tout -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:stopGradient">stopGradient</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:avgPool">avgPool</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:wholeFileReader">wholeFileReader</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:switch">switch</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:randomStandardNormal">randomStandardNormal</a> :: <span class="keyword">forall</span> v1 t dtype. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:sigmoid">sigmoid</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sampleDistortedBoundingBox">sampleDistortedBoundingBox</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:greater">greater</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:refNextIteration">refNextIteration</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:spaceToDepth">spaceToDepth</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:controlTrigger">controlTrigger</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:scatterDiv">scatterDiv</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:copy">copy</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cropAndResizeGradBoxes">cropAndResizeGradBoxes</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:sparseSegmentMean">sparseSegmentMean</a> :: <span class="keyword">forall</span> v1 v2 v3 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:assign">assign</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:restore">restore</a> :: <span class="keyword">forall</span> v1 v2 dt. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dt => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dt</li><li class="src short"><a href="#v:maxPoolGradWithArgmax">maxPoolGradWithArgmax</a> :: <span class="keyword">forall</span> v1 v2 v3 t targmax. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> targmax, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` targmax) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 targmax -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:checkNumerics">checkNumerics</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:zerosLike">zerosLike</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readFile">readFile</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:transpose">transpose</a> :: <span class="keyword">forall</span> v1 v2 t tperm. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tperm, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tperm) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tperm -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:parseTensor">parseTensor</a> :: <span class="keyword">forall</span> v1 out_type. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</li><li class="src short"><a href="#v:acos">acos</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:bitcast">bitcast</a> :: <span class="keyword">forall</span> v1 t type'. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> type', <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` type') => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> type'</li><li class="src short"><a href="#v:lookupTableImport">lookupTableImport</a> :: <span class="keyword">forall</span> v1 v2 v3 tin tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:biasAddGrad">biasAddGrad</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchSelfAdjointEig">batchSelfAdjointEig</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:prod">prod</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:resizeBilinear">resizeBilinear</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:tensorArrayUnpack">tensorArrayUnpack</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:batchMatrixDeterminant">batchMatrixDeterminant</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sum">sum</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:iFFT2D">iFFT2D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:fill">fill</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fixedUnigramCandidateSampler">fixedUnigramCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:dilation2D">dilation2D</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:polygamma">polygamma</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:refIdentity">refIdentity</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:encodePng">encodePng</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:lookupTableInsert">lookupTableInsert</a> :: <span class="keyword">forall</span> v1 v2 v3 tin tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:batchIFFT2D">batchIFFT2D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:uniqueWithCounts">uniqueWithCounts</a> :: <span class="keyword">forall</span> v1 t out_idx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</li><li class="src short"><a href="#v:gatherNd">gatherNd</a> :: <span class="keyword">forall</span> v1 v2 tindices tparams. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tparams) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tparams -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tparams</li><li class="src short"><a href="#v:tensorArrayRead">tensorArrayRead</a> :: <span class="keyword">forall</span> v1 v2 v3 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:readerReadUpTo">readerReadUpTo</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>)</li><li class="src short"><a href="#v:betainc">betainc</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixBandPart">batchMatrixBandPart</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:depthwiseConv2dNativeBackpropInput">depthwiseConv2dNativeBackpropInput</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:refSelect">refSelect</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:exit">exit</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:lookupTableFind">lookupTableFind</a> :: <span class="keyword">forall</span> v1 v2 v3 tin tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:squeeze">squeeze</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:mean">mean</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:spaceToBatchND">spaceToBatchND</a> :: <span class="keyword">forall</span> v1 v2 v3 t tblock_shape tpaddings. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tblock_shape -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tpaddings -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:spaceToBatch">spaceToBatch</a> :: <span class="keyword">forall</span> v1 v2 t tpaddings. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cTCGreedyDecoder">cTCGreedyDecoder</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:batchToSpaceND">batchToSpaceND</a> :: <span class="keyword">forall</span> v1 v2 v3 t tblock_shape tcrops. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tcrops, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tcrops) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tblock_shape -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tcrops -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:pack">pack</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:oneHot">oneHot</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t tI. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tI, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` tI) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tI -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:broadcastGradientArgs">broadcastGradientArgs</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:matrixSetDiag">matrixSetDiag</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:applyRMSProp">applyRMSProp</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:const">const</a> :: <span class="keyword">forall</span> dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:enter">enter</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:debugIdentity">debugIdentity</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:debugNanCount">debugNanCount</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:batchNormWithGlobalNormalization">batchNormWithGlobalNormalization</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixDiag">batchMatrixDiag</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:unpack">unpack</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</li><li class="src short"><a href="#v:sparseSplit">sparseSplit</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> ([<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>])</li><li class="src short"><a href="#v:mirrorPad">mirrorPad</a> :: <span class="keyword">forall</span> v1 v2 t tpaddings. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixDiagPart">batchMatrixDiagPart</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fractionalMaxPoolGrad">fractionalMaxPoolGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matchingFiles">matchingFiles</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:tile">tile</a> :: <span class="keyword">forall</span> v1 v2 t tmultiples. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tmultiples, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tmultiples) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tmultiples -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseSparseMinimum">sparseSparseMinimum</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:allCandidateSampler">allCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:refSwitch">refSwitch</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:mergeSummary">mergeSummary</a> :: [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:logicalNot">logicalNot</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:lRNGrad">lRNGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stringToNumber">stringToNumber</a> :: <span class="keyword">forall</span> v1 out_type. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` out_type) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</li><li class="src short"><a href="#v:sparseMatMul">sparseMatMul</a> :: <span class="keyword">forall</span> v1 v2 ta tb. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> ta, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` ta, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tb, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tb) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 ta -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tb -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:merge">merge</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t] -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</li><li class="src short"><a href="#v:choleskyGrad">choleskyGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchCholeskyGrad">batchCholeskyGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tensorArrayGather">tensorArrayGather</a> :: <span class="keyword">forall</span> v1 v2 v3 dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:resizeNearestNeighbor">resizeNearestNeighbor</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:negTrain">negTrain</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:tensorArrayGrad">tensorArrayGrad</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:audioSummary">audioSummary</a> :: <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:noOp">noOp</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:nextIteration">nextIteration</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:softplusGrad">softplusGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:svd">svd</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:hSVToRGB">hSVToRGB</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:parameterizedTruncatedNormal">parameterizedTruncatedNormal</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t dtype. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 dtype -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:square">square</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:elu">elu</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:lookupTableExport">lookupTableExport</a> :: <span class="keyword">forall</span> v1 tkeys tvalues. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tkeys, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tvalues) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tkeys, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tvalues)</li><li class="src short"><a href="#v:lookupTableSize">lookupTableSize</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:avgPoolGrad">avgPoolGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:computeAccidentalHits">computeAccidentalHits</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:cTCLoss">cTCLoss</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:avgPool3D">avgPool3D</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:inv">inv</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:stackPop">stackPop</a> :: <span class="keyword">forall</span> v1 elem_type. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> elem_type => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> elem_type</li><li class="src short"><a href="#v:paddingFIFOQueue">paddingFIFOQueue</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:batchSelfAdjointEigV2">batchSelfAdjointEigV2</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:batchMatrixTriangularSolve">batchMatrixTriangularSolve</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatrixSolveLs">batchMatrixSolveLs</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchSvd">batchSvd</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:tensorSummary">tensorSummary</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:sparseSoftmaxCrossEntropyWithLogits">sparseSoftmaxCrossEntropyWithLogits</a> :: <span class="keyword">forall</span> v1 v2 t tlabels. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tlabels, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tlabels) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tlabels -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:maxPoolWithArgmax">maxPoolWithArgmax</a> :: <span class="keyword">forall</span> v1 t targmax. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> targmax, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` targmax) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> targmax)</li><li class="src short"><a href="#v:fFT">fFT</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:histogramSummary">histogramSummary</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:pad">pad</a> :: <span class="keyword">forall</span> v1 v2 t tpaddings. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchIFFT3D">batchIFFT3D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:imageSummary">imageSummary</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:segmentSum">segmentSum</a> :: <span class="keyword">forall</span> v1 v2 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:encodeJpeg">encodeJpeg</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:batchNormWithGlobalNormalizationGrad">batchNormWithGlobalNormalizationGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:biasAddV1">biasAddV1</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:invertPermutation">invertPermutation</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:mirrorPadGrad">mirrorPadGrad</a> :: <span class="keyword">forall</span> v1 v2 t tpaddings. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:reverse">reverse</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv2D">conv2D</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv2DBackpropInput">conv2DBackpropInput</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readerSerializeState">readerSerializeState</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:temporaryVariable">temporaryVariable</a> :: <span class="keyword">forall</span> dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:cropAndResize">cropAndResize</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:maxPoolGrad">maxPoolGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:fusedResizeAndPadConv2D">fusedResizeAndPadConv2D</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:randomUniform">randomUniform</a> :: <span class="keyword">forall</span> v1 t dtype. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:depthwiseConv2dNative">depthwiseConv2dNative</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyAdadelta">sparseApplyAdadelta</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:depthwiseConv2dNativeBackpropFilter">depthwiseConv2dNativeBackpropFilter</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv3D">conv3D</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:greaterEqual">greaterEqual</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:sparseDenseCwiseAdd">sparseDenseCwiseAdd</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv3DBackpropFilter">conv3DBackpropFilter</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv3DBackpropInputV2">conv3DBackpropInputV2</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:mod">mod</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:refMerge">refMerge</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t] -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</li><li class="src short"><a href="#v:conv3DBackpropFilterV2">conv3DBackpropFilterV2</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:serializeManySparse">serializeManySparse</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:avgPool3DGrad">avgPool3DGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:maxPool3DGrad">maxPool3DGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseReduceSum">sparseReduceSum</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:relu">relu</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:l2Loss">l2Loss</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:readerRestoreState">readerRestoreState</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:shape">shape</a> :: <span class="keyword">forall</span> v1 t out_type. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</li><li class="src short"><a href="#v:softmaxCrossEntropyWithLogits">softmaxCrossEntropyWithLogits</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:maxPool">maxPool</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:dilation2DBackpropInput">dilation2DBackpropInput</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:equal">equal</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:dilation2DBackpropFilter">dilation2DBackpropFilter</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:reluGrad">reluGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:relu6">relu6</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:resizeBicubic">resizeBicubic</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:relu6Grad">relu6Grad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseTensorDenseMatMul">sparseTensorDenseMatMul</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:softplus">softplus</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchMatMul">batchMatMul</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:softsignGrad">softsignGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:lessEqual">lessEqual</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:logSoftmax">logSoftmax</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:inTopK">inTopK</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:matrixDiag">matrixDiag</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:maxPool3D">maxPool3D</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:topK">topK</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</li><li class="src short"><a href="#v:topKV2">topKV2</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</li><li class="src short"><a href="#v:fractionalMaxPool">fractionalMaxPool</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:matrixBandPart">matrixBandPart</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:decodeRaw">decodeRaw</a> :: <span class="keyword">forall</span> v1 out_type. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` out_type) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</li><li class="src short"><a href="#v:decodeJSONExample">decodeJSONExample</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:truncatedNormal">truncatedNormal</a> :: <span class="keyword">forall</span> v1 t dtype. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:randomShuffle">randomShuffle</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:multinomial">multinomial</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:randomGamma">randomGamma</a> :: <span class="keyword">forall</span> v1 v2 s t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> s, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` s, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 s -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:addN">addN</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t] -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:max">max</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:_Retval">_Retval</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:destroyTemporaryVariable">destroyTemporaryVariable</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cast">cast</a> :: <span class="keyword">forall</span> v1 dstT srcT. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dstT, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> srcT) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 srcT -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dstT</li><li class="src short"><a href="#v:countUpTo">countUpTo</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:abs">abs</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:neg">neg</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseSparseMaximum">sparseSparseMaximum</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:invGrad">invGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sqrt">sqrt</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixInverse">matrixInverse</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sqrtGrad">sqrtGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:expandDims">expandDims</a> :: <span class="keyword">forall</span> v1 v2 t tdim. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tdim, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tdim) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tdim -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:all">all</a> :: <span class="keyword">forall</span> v1 v2 tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:cTCBeamSearchDecoder">cTCBeamSearchDecoder</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> ([<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:rsqrt">rsqrt</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tanhGrad">tanhGrad</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sin">sin</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matrixDeterminant">matrixDeterminant</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cos">cos</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchToSpace">batchToSpace</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseToDense">sparseToDense</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:asin">asin</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:argMin">argMin</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:isInf">isInf</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:sign">sign</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:add">add</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseApplyFtrl">sparseApplyFtrl</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 v8 v9 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sub">sub</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchFFT3D">batchFFT3D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:sparseReduceSumSparse">sparseReduceSumSparse</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:biasAdd">biasAdd</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:mul">mul</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:div">div</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:loopCond">loopCond</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:squaredDifference">squaredDifference</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:maximum">maximum</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:logUniformCandidateSampler">logUniformCandidateSampler</a> :: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:less">less</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:pow">pow</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:igammac">igammac</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:igamma">igamma</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:zeta">zeta</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:imag">imag</a> :: <span class="keyword">forall</span> v1 t tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:complex">complex</a> :: <span class="keyword">forall</span> v1 v2 t tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:notEqual">notEqual</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:complexAbs">complexAbs</a> :: <span class="keyword">forall</span> v1 t tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:logicalAnd">logicalAnd</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:batchFFT">batchFFT</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:select">select</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:matMul">matMul</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:digamma">digamma</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:conv2DBackpropFilter">conv2DBackpropFilter</a> :: <span class="keyword">forall</span> v1 v2 v3 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:min">min</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:isFinite">isFinite</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:argMax">argMax</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></li><li class="src short"><a href="#v:segmentMean">segmentMean</a> :: <span class="keyword">forall</span> v1 v2 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cumprod">cumprod</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:segmentMin">segmentMin</a> :: <span class="keyword">forall</span> v1 v2 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:unsortedSegmentSum">unsortedSegmentSum</a> :: <span class="keyword">forall</span> v1 v2 v3 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:tFRecordReader">tFRecordReader</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:sparseSegmentSum">sparseSegmentSum</a> :: <span class="keyword">forall</span> v1 v2 v3 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseSegmentSqrtN">sparseSegmentSqrtN</a> :: <span class="keyword">forall</span> v1 v2 v3 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:copyHost">copyHost</a> :: <span class="keyword">forall</span> v1 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:variable">variable</a> :: <span class="keyword">forall</span> dtype. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</li><li class="src short"><a href="#v:sparseSegmentSqrtNGrad">sparseSegmentSqrtNGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:range">range</a> :: <span class="keyword">forall</span> v1 v2 v3 tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tidx</li><li class="src short"><a href="#v:any">any</a> :: <span class="keyword">forall</span> v1 v2 tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></li><li class="src short"><a href="#v:linSpace">linSpace</a> :: <span class="keyword">forall</span> v1 v2 v3 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:resizeArea">resizeArea</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></li><li class="src short"><a href="#v:real">real</a> :: <span class="keyword">forall</span> v1 t tout. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</li><li class="src short"><a href="#v:iFFT">iFFT</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:iFFT3D">iFFT3D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:cross">cross</a> :: <span class="keyword">forall</span> v1 v2 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:cumsum">cumsum</a> :: <span class="keyword">forall</span> v1 v2 t tidx. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchIFFT">batchIFFT</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:erf">erf</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:barrierInsertMany">barrierInsertMany</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></li><li class="src short"><a href="#v:floor">floor</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:batchFFT2D">batchFFT2D</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>) -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</li><li class="src short"><a href="#v:sparseAddGrad">sparseAddGrad</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</li><li class="src short"><a href="#v:sparseAdd">sparseAdd</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 v5 v6 v7 t treal. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> treal, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` treal) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 treal -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:batchCholesky">batchCholesky</a> :: <span class="keyword">forall</span> v1 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:dynamicPartition">dynamicPartition</a> :: <span class="keyword">forall</span> v1 v2 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a> -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</li><li class="src short"><a href="#v:serializeSparse">serializeSparse</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></li><li class="src short"><a href="#v:sparseConcat">sparseConcat</a> :: <span class="keyword">forall</span> v1 v2 v3 t. <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t => <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>] -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t] -> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>] -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:segmentProd">segmentProd</a> :: <span class="keyword">forall</span> v1 v2 t tindices. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseReshape">sparseReshape</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</li><li class="src short"><a href="#v:sparseDenseCwiseMul">sparseDenseCwiseMul</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li><li class="src short"><a href="#v:sparseDenseCwiseDiv">sparseDenseCwiseDiv</a> :: <span class="keyword">forall</span> v1 v2 v3 v4 t. (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t) => <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a> -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t -> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</li></ul></div><div id="interface"><h1>Documentation</h1><div class="top"><p class="src"><a name="v:_HostRecv" class="def">_HostRecv</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tensor_type</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>send_device_incarnation</strong>: The current incarnation of send_device.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tensor_type</td><td class="doc"><p><strong>tensor</strong>: The tensor to receive.</p></td></tr></table></div><div class="doc"><p>Receives the named tensor from send_device on recv_device.</p><p>_HostRecv requires its input on host memory whereas _Recv requires its
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input on device memory.</p></div></div><div class="top"><p class="src"><a name="v:_Recv" class="def">_Recv</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tensor_type</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>send_device_incarnation</strong>: The current incarnation of send_device.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tensor_type</td><td class="doc"><p><strong>tensor</strong>: The tensor to receive.</p></td></tr></table></div><div class="doc"><p>Receives the named tensor from send_device on recv_device.</p></div></div><div class="top"><p class="src"><a name="v:_Send" class="def">_Send</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>send_device_incarnation</strong>: The current incarnation of send_device.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong>: The tensor to send.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Sends the named tensor from send_device to recv_device.</p></div></div><div class="top"><p class="src"><a name="v:_Arg" class="def">_Arg</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>index</strong>: This argument is the index-th argument of the function.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The argument.</p></td></tr></table></div><div class="doc"><p>A graph node which represents an argument to a function.</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyRMSProp" class="def">sparseApplyRMSProp</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>ms</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>mom</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>rho</strong>: Decay rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>momentum</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>epsilon</strong>: Ridge term. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var, ms and mom.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the RMSProp algorithm.</p><p>Note that in dense implement of this algorithm, ms and mom will
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update even if the grad is zero, but in this sparse implement, ms
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and mom will not update in iterations the grad is zero.</p><p>mean_square = decay * mean_square + (1-decay) * gradient ** 2
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Delta = learning_rate * gradient / sqrt(mean_square + epsilon)</p><p>ms <- rho * ms_{t-1} + (1-rho) * grad * grad
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mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
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var <- var - mom</p></div></div><div class="top"><p class="src"><a name="v:applyAdam" class="def">applyAdam</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>m</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>v</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>beta1_power</strong>: Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>beta2_power</strong>: Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>beta1</strong>: Momentum factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>beta2</strong>: Momentum factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 t</td><td class="doc"><p><strong>epsilon</strong>: Ridge term. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v10 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the Adam algorithm.</p><p>lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
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m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t
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v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t
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variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyMomentum" class="def">sparseApplyMomentum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Learning rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>momentum</strong>: Momentum. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update relevant entries in '*var' and '*accum' according to the momentum scheme.</p><p>Set use_nesterov = True if you want to use Nesterov momentum.</p><p>That is for rows we have grad for, we update var and accum as follows:</p><p>accum = accum * momentum + grad
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var -= lr * accum</p></div></div><div class="top"><p class="src"><a name="v:applyMomentum" class="def">applyMomentum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>momentum</strong>: Momentum. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the momentum scheme. Set use_nesterov = True if you</p><p>want to use Nesterov momentum.</p><p>accum = accum * momentum + grad
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var -= lr * accum</p></div></div><div class="top"><p class="src"><a name="v:applyFtrl" class="def">applyFtrl</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>linear</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>l1</strong>: L1 regulariation. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>l2</strong>: L2 regulariation. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>lr_power</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the Ftrl-proximal scheme.</p><p>accum_new = accum + grad * grad
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linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
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|
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
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var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
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accum = accum_new</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyAdagradDA" class="def">sparseApplyAdagradDA</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>gradient_accumulator</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>gradient_squared_accumulator</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>lr</strong>: Learning rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>global_step</strong>: Training step number. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update entries in '*var' and '*accum' according to the proximal adagrad scheme.</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyAdagrad" class="def">sparseApplyAdagrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Learning rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update relevant entries in '*var' and '*accum' according to the adagrad scheme.</p><p>That is for rows we have grad for, we update var and accum as follows:
|
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accum += grad * grad
|
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var -= lr * grad * (1 / sqrt(accum))</p></div></div><div class="top"><p class="src"><a name="v:applyProximalAdagrad" class="def">applyProximalAdagrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.</p><p>accum += grad * grad
|
|
prox_v = var - lr * grad * (1 / sqrt(accum))
|
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var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}</p></div></div><div class="top"><p class="src"><a name="v:applyAdagrad" class="def">applyAdagrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the adagrad scheme.</p><p>accum += grad * grad
|
|
var -= lr * grad * (1 / sqrt(accum))</p></div></div><div class="top"><p class="src"><a name="v:applyAdadelta" class="def">applyAdadelta</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>accum_update</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>rho</strong>: Decay factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>epsilon</strong>: Constant factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the adadelta scheme.</p><p>accum = rho() * accum + (1 - rho()) * grad.square();
|
|
update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
|
|
update_accum = rho() * update_accum + (1 - rho()) * update.square();
|
|
var -= update;</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyProximalGradientDescent" class="def">sparseApplyProximalGradientDescent</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>alpha</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Sparse update '*var' as FOBOS algorithm with fixed learning rate.</p><p>That is for rows we have grad for, we update var as follows:
|
|
prox_v = var - alpha * grad
|
|
var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}</p></div></div><div class="top"><p class="src"><a name="v:applyProximalGradientDescent" class="def">applyProximalGradientDescent</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>alpha</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>delta</strong>: The change.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' as FOBOS algorithm with fixed learning rate.</p><p>prox_v = var - alpha * delta
|
|
var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}</p></div></div><div class="top"><p class="src"><a name="v:encodeBase64" class="def">encodeBase64</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>input</strong>: Strings to be encoded.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>output</strong>: Input strings encoded in base64.</p></td></tr></table></div><div class="doc"><p>Encode strings into web-safe base64 format.</p><p>Refer to the following article for more information on base64 format:
|
|
en.wikipedia.org<em>wiki</em>Base64. Base64 strings may have padding with '=' at the
|
|
end so that the encoded has length multiple of 4. See Padding section of the
|
|
link above.</p><p>Web-safe means that the encoder uses - and _ instead of + and /.</p></div></div><div class="top"><p class="src"><a name="v:stringSplit" class="def">stringSplit</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>input</strong>: 1-D. Strings to split.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>delimiter</strong>: 0-D. Delimiter character, or empty string.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>indices</strong>, <strong>values</strong>, <strong>shape</strong>)</p><ul><li><strong>indices</strong>: A dense matrix of int64 representing the indices of the sparse tensor.</li><li><strong>values</strong>: A vector of strings corresponding to the splited values.</li><li><strong>shape</strong>: a length-2 vector of int64 representing the shape of the sparse
|
|
tensor, where the first value is N and the second value is the maximum number
|
|
of tokens in a single input entry.</li></ul></td></tr></table></div><div class="doc"><p>Split elements of <code>input</code> based on <code>delimiter</code> into a <code>SparseTensor</code>.</p><p>Let N be the size of source (typically N will be the batch size). Split each
|
|
element of <code>input</code> based on <code>delimiter</code> and return a <code>SparseTensor</code>
|
|
containing the splitted tokens. Empty tokens are ignored.</p><p><code>delimiter</code> can be empty or a single character. If <code>delimiter</code> is an empty
|
|
string, each element of <code>input</code> is split into individual 1 character strings.</p><p>For example:
|
|
N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output
|
|
will be</p><p>indices = [0, 0;
|
|
0, 1;
|
|
1, 0;
|
|
1, 1;
|
|
1, 2]
|
|
shape = [2, 3]
|
|
values = [<code>hello</code>, <code>world</code>, <code>a</code>, <code>b</code>, <code>c</code>]</p></div></div><div class="top"><p class="src"><a name="v:stringJoin" class="def">stringJoin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>]</td><td class="doc"><p><strong>inputs</strong>: A list of string tensors. The tensors must all have the same shape,
|
|
or be scalars. Scalars may be mixed in; these will be broadcast to the shape
|
|
of non-scalar inputs.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Joins the strings in the given list of string tensors into one tensor;</p><p>with the given separator (default is an empty separator).</p></div></div><div class="top"><p class="src"><a name="v:asString" class="def">asString</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Converts each entry in the given tensor to strings. Supports many numeric</p><p>types and boolean.</p></div></div><div class="top"><p class="src"><a name="v:stringToHashBucketStrong" class="def">stringToHashBucketStrong</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_buckets</strong>: The number of buckets.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>input</strong>: The strings to assign a hash bucket.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong>: A Tensor of the same shape as the input <code>string_tensor</code>.</p></td></tr></table></div><div class="doc"><p>Converts each string in the input Tensor to its hash mod by a number of buckets.</p><p>The hash function is deterministic on the content of the string within the
|
|
process. The hash function is a keyed hash function, where attribute <code>key</code>
|
|
defines the key of the hash function. <code>key</code> is an array of 2 elements.</p><p>A strong hash is important when inputs may be malicious, e.g. URLs with
|
|
additional components. Adversaries could try to make their inputs hash to the
|
|
same bucket for a denial-of-service attack or to skew the results. A strong
|
|
hash prevents this by making it dificult, if not infeasible, to compute inputs
|
|
that hash to the same bucket. This comes at a cost of roughly 4x higher compute
|
|
time than tf.string_to_hash_bucket_fast.</p></div></div><div class="top"><p class="src"><a name="v:scatterMul" class="def">scatterMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: A tensor of indices into the first dimension of <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>updates</strong>: A tensor of updated values to multiply to <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as <code>ref</code>. Returned as a convenience for operations that want
|
|
to use the updated values after the update is done.</p></td></tr></table></div><div class="doc"><p>Multiplies sparse updates into a variable reference.</p><p>This operation computes</p><p># Scalar indices
|
|
ref[indices, ...] *= updates[...]</p><p># Vector indices (for each i)
|
|
ref[indices[i], ...] *= updates[i, ...]</p><p># High rank indices (for each i, ..., j)
|
|
ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]</p><p>This operation outputs <code>ref</code> after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p><p>Duplicate entries are handled correctly: if multiple <code>indices</code> reference
|
|
the same location, their contributions multiply.</p><p>Requires `updates.shape = indices.shape + ref.shape[1:]`.</p></div></div><div class="top"><p class="src"><a name="v:reduceJoin" class="def">reduceJoin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>inputs</strong>: The input to be joined. All reduced indices must have non-zero size.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce over. Dimensions are reduced in the
|
|
order specified. Omitting <code>reduction_indices</code> is equivalent to passing
|
|
`[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>output</strong>: Has shape equal to that of the input with reduced dimensions removed or
|
|
set to `1` depending on <code>keep_dims</code>.</p></td></tr></table></div><div class="doc"><p>Joins a string Tensor across the given dimensions.</p><p>Computes the string join across dimensions in the given string Tensor of shape
|
|
`[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input
|
|
strings with the given separator (default: empty string). Negative indices are
|
|
counted backwards from the end, with `-1` being equivalent to `n - 1`. Passing
|
|
an empty <code>reduction_indices</code> joins all strings in linear index order and outputs
|
|
a scalar string.</p><p>For example:</p><p>```
|
|
# tensor <code>a</code> is [["a", "b"], ["c", "d"]]
|
|
tf.reduce_join(a, 0) ==> ["ac", "bd"]
|
|
tf.reduce_join(a, 1) ==> ["ab", "cd"]
|
|
tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"]
|
|
tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"]
|
|
tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]]
|
|
tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]]
|
|
tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"]
|
|
tf.reduce_join(a, [0, 1]) ==> ["acbd"]
|
|
tf.reduce_join(a, [1, 0]) ==> ["abcd"]
|
|
tf.reduce_join(a, []) ==> ["abcd"]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:scatterSub" class="def">scatterSub</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: A tensor of indices into the first dimension of <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>updates</strong>: A tensor of updated values to subtract from <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as <code>ref</code>. Returned as a convenience for operations that want
|
|
to use the updated values after the update is done.</p></td></tr></table></div><div class="doc"><p>Subtracts sparse updates to a variable reference.</p><p># Scalar indices
|
|
ref[indices, ...] -= updates[...]</p><p># Vector indices (for each i)
|
|
ref[indices[i], ...] -= updates[i, ...]</p><p># High rank indices (for each i, ..., j)
|
|
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]</p><p>This operation outputs <code>ref</code> after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p><p>Duplicate entries are handled correctly: if multiple <code>indices</code> reference
|
|
the same location, their (negated) contributions add.</p><p>Requires `updates.shape = indices.shape + ref.shape[1:]`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/ScatterSub.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:scatterAdd" class="def">scatterAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: A tensor of indices into the first dimension of <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>updates</strong>: A tensor of updated values to add to <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as <code>ref</code>. Returned as a convenience for operations that want
|
|
to use the updated values after the update is done.</p></td></tr></table></div><div class="doc"><p>Adds sparse updates to a variable reference.</p><p>This operation computes</p><p># Scalar indices
|
|
ref[indices, ...] += updates[...]</p><p># Vector indices (for each i)
|
|
ref[indices[i], ...] += updates[i, ...]</p><p># High rank indices (for each i, ..., j)
|
|
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]</p><p>This operation outputs <code>ref</code> after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p><p>Duplicate entries are handled correctly: if multiple <code>indices</code> reference
|
|
the same location, their contributions add.</p><p>Requires `updates.shape = indices.shape + ref.shape[1:]`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/ScatterAdd.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:scatterUpdate" class="def">scatterUpdate</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: A tensor of indices into the first dimension of <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>updates</strong>: A tensor of updated values to store in <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as <code>ref</code>. Returned as a convenience for operations that want
|
|
to use the updated values after the update is done.</p></td></tr></table></div><div class="doc"><p>Applies sparse updates to a variable reference.</p><p>This operation computes</p><p># Scalar indices
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|
ref[indices, ...] = updates[...]</p><p># Vector indices (for each i)
|
|
ref[indices[i], ...] = updates[i, ...]</p><p># High rank indices (for each i, ..., j)
|
|
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]</p><p>This operation outputs <code>ref</code> after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p><p>If values in <code>ref</code> is to be updated more than once, because there are
|
|
duplicate entires in <code>indices</code>, the order at which the updates happen
|
|
for each value is undefined.</p><p>Requires `updates.shape = indices.shape + ref.shape[1:]`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/ScatterUpdate.png" alt</a>
|
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<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:assignSub" class="def">assignSub</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The value to be subtracted to the variable.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as "ref". Returned as a convenience for operations that want
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|
to use the new value after the variable has been updated.</p></td></tr></table></div><div class="doc"><p>Update <code>ref</code> by subtracting <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> from it.</p><p>This operation outputs "ref" after the update is done.
|
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This makes it easier to chain operations that need to use the reset value.</p></div></div><div class="top"><p class="src"><a name="v:assignAdd" class="def">assignAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The value to be added to the variable.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as "ref". Returned as a convenience for operations that want
|
|
to use the new value after the variable has been updated.</p></td></tr></table></div><div class="doc"><p>Update <code>ref</code> by adding <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> to it.</p><p>This operation outputs "ref" after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p></div></div><div class="top"><p class="src"><a name="v:sparseSegmentMeanGrad" class="def">sparseSegmentMeanGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>grad</strong>: gradient propagated to the SparseSegmentMean op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>indices</strong>: indices passed to the corresponding SparseSegmentMean op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>segment_ids</strong>: segment_ids passed to the corresponding SparseSegmentMean op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>output_dim0</strong>: dimension 0 of "data" passed to SparseSegmentMean op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes gradients for SparseSegmentMean.</p><p>Returns tensor "output" with same shape as grad, except for dimension 0 whose
|
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value is output_dim0.</p></div></div><div class="top"><p class="src"><a name="v:sparseSoftmax" class="def">sparseSoftmax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_indices</strong>: 2-D. `NNZ x R` matrix with the indices of non-empty values in a
|
|
SparseTensor, in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sp_values</strong>: 1-D. <code>NNZ</code> non-empty values corresponding to <code>sp_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D. The <code>NNZ</code> values for the result <code>SparseTensor</code>.</p></td></tr></table></div><div class="doc"><p>Applies softmax to a batched N-D <code>SparseTensor</code>.</p><p>The inputs represent an N-D SparseTensor with logical shape `[..., B, C]`
|
|
(where `N >= 2`), and with indices sorted in the canonical lexicographic order.</p><p>This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
|
|
logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
|
|
zero elements do not participate*. Specifically, the algorithm is equivalent
|
|
to the following:</p><ol><li>Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
|
|
with shape `[B, C]`, along the size-C dimension;</li><li>Masks out the original implicitly-zero locations;</li><li>Renormalizes the remaining elements.</li></ol><p>Hence, the <code>SparseTensor</code> result has exactly the same non-zero indices and
|
|
shape.</p></div></div><div class="top"><p class="src"><a name="v:matrixSolve" class="def">matrixSolve</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong>: Shape is `[..., M, K]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., M, K]`.</p></td></tr></table></div><div class="doc"><p>Solves systems of linear equations.</p><p><code>Matrix</code> is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
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|
form square matrices. <code>Rhs</code> is a tensor of shape `[..., M, K]`. The <code>output</code> is
|
|
a tensor shape `[..., M, K]`. If <code>adjoint</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> then each output matrix
|
|
satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
|
|
If <code>adjoint</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code> then each output matrix satisfies
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|
`adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.</p></div></div><div class="top"><p class="src"><a name="v:selfAdjointEigV2" class="def">selfAdjointEigV2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> input of shape `[N, N]`.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>e</strong>, <strong>v</strong>)</p><ul><li><strong>e</strong>: Eigenvalues. Shape is `[N]`.</li><li><strong>v</strong>: Eigenvectors. Shape is `[N, N]`.</li></ul></td></tr></table></div><div class="doc"><p>Computes the eigen decomposition of one or more square self-adjoint matrices.</p><p>Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in
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|
<code>input</code> such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`.</p><p>```prettyprint
|
|
# a is a tensor.
|
|
# e is a tensor of eigenvalues.
|
|
# v is a tensor of eigenvectors.
|
|
e, v = self_adjoint_eig(a)
|
|
e = self_adjoint_eig(a, compute_v=False)
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:selfAdjointEig" class="def">selfAdjointEig</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., M+1, M]`.</p></td></tr></table></div><div class="doc"><p>Computes the Eigen Decomposition of a batch of square self-adjoint matrices.</p><p>The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
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|
form square matrices, with the same constraints as the single matrix
|
|
SelfAdjointEig.</p><p>The result is a [..., M+1, M] matrix with [..., 0,:] containing the
|
|
eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.</p></div></div><div class="top"><p class="src"><a name="v:applyGradientDescent" class="def">applyGradientDescent</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>alpha</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>delta</strong>: The change.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' by subtracting <code>alpha</code> * <code>delta</code> from it.</p></div></div><div class="top"><p class="src"><a name="v:stackPush" class="def">stackPush</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a stack.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>elem</strong>: The tensor to be pushed onto the stack.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as the input <code><a href="../base-4.8.2.0/Data-Foldable.html#v:elem">elem</a></code>.</p></td></tr></table></div><div class="doc"><p>Push an element onto the stack.</p></div></div><div class="top"><p class="src"><a name="v:cholesky" class="def">cholesky</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., M, M]`.</p></td></tr></table></div><div class="doc"><p>Computes the Cholesky decomposition of one or more square matrices.</p><p>The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
|
|
form square matrices, with the same constraints as the single matrix Cholesky
|
|
decomposition above. The output is a tensor of the same shape as the input
|
|
containing the Cholesky decompositions for all input submatrices `[..., :, :]`.</p></div></div><div class="top"><p class="src"><a name="v:dynamicStitch" class="def">dynamicStitch</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>]</td><td class="doc"><p><strong>indices</strong></p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t]</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>merged</strong></p></td></tr></table></div><div class="doc"><p>Interleave the values from the `data` tensors into a single tensor.</p><p>Builds a merged tensor such that</p><p>merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]</p><p>For example, if each `indices[m]` is scalar or vector, we have</p><p># Scalar indices
|
|
merged[indices[m], ...] = data[m][...]</p><p># Vector indices
|
|
merged[indices[m][i], ...] = data[m][i, ...]</p><p>Each `data[i].shape` must start with the corresponding `indices[i].shape`,
|
|
and the rest of `data[i].shape` must be constant w.r.t. <code>i</code>. That is, we
|
|
must have `data[i].shape = indices[i].shape + constant`. In terms of this
|
|
<code>constant</code>, the output shape is</p><p>merged.shape = [max(indices)] + constant</p><p>Values are merged in order, so if an index appears in both `indices[m][i]` and
|
|
`indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the
|
|
merged result.</p><p>For example:</p><p>indices[0] = 6
|
|
indices[1] = [4, 1]
|
|
indices[2] = [[5, 2], [0, 3]]
|
|
data[0] = [61, 62]
|
|
data[1] = [[41, 42], [11, 12]]
|
|
data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
|
|
merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
|
|
[51, 52], [61, 62]]</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/DynamicStitch.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:readerNumWorkUnitsCompleted" class="def">readerNumWorkUnitsCompleted</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>units_completed</strong></p></td></tr></table></div><div class="doc"><p>Returns the number of work units this Reader has finished processing.</p></div></div><div class="top"><p class="src"><a name="v:readerRead" class="def">readerRead</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>queue_handle</strong>: Handle to a Queue, with string work items.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>)</td><td class="doc"><p>(<strong>key</strong>, <strong>value</strong>)</p><ul><li><strong>key</strong>: A scalar.</li><li><strong>value</strong>: A scalar.</li></ul></td></tr></table></div><div class="doc"><p>Returns the next record (key, value pair) produced by a Reader.</p><p>Will dequeue from the input queue if necessary (e.g. when the
|
|
Reader needs to start reading from a new file since it has finished
|
|
with the previous file).</p></div></div><div class="top"><p class="src"><a name="v:fFT2D" class="def">fFT2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most 2
|
|
dimensions of <code>input</code> are replaced with their 2D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the 2-dimensional discrete Fourier Transform over the inner-most</p><p>2 dimensions of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:fixedLengthRecordReader" class="def">fixedLengthRecordReader</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>record_bytes</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: The handle to reference the Reader.</p></td></tr></table></div><div class="doc"><p>A Reader that outputs fixed-length records from a file.</p></div></div><div class="top"><p class="src"><a name="v:placeholder" class="def">placeholder</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A placeholder tensor that must be replaced using the feed mechanism.</p></td></tr></table></div><div class="doc"><p>A placeholder op for a value that will be fed into the computation.</p><p>N.B. This operation will fail with an error if it is executed. It is
|
|
intended as a way to represent a value that will always be fed, and to
|
|
provide attrs that enable the fed value to be checked at runtime.</p></div></div><div class="top"><p class="src"><a name="v:scalarSummary" class="def">scalarSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tags</strong>: Tags for the summary.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>values</strong>: Same shape as `tags. Values for the summary.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong>: Scalar. Serialized <code>Summary</code> protocol buffer.</p></td></tr></table></div><div class="doc"><p>Outputs a <code>Summary</code> protocol buffer with scalar values.</p><p>The input <code>tags</code> and <code>values</code> must have the same shape. The generated summary
|
|
has a summary value for each tag-value pair in <code>tags</code> and <code>values</code>.</p></div></div><div class="top"><p class="src"><a name="v:softmax" class="def">softmax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>logits</strong>: 2-D with shape `[batch_size, num_classes]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>softmax</strong>: Same shape as <code>logits</code>.</p></td></tr></table></div><div class="doc"><p>Computes softmax activations.</p><p>For each batch <code>i</code> and class <code>j</code> we have</p><p>softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))</p></div></div><div class="top"><p class="src"><a name="v:shardedFilename" class="def">shardedFilename</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>basename</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>shard</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>num_shards</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>filename</strong></p></td></tr></table></div><div class="doc"><p>Generate a sharded filename. The filename is printf formatted as</p><p>%s-%05d-of-%05d, basename, shard, num_shards.</p></div></div><div class="top"><p class="src"><a name="v:_HostSend" class="def">_HostSend</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>send_device_incarnation</strong>: The current incarnation of send_device.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong>: The tensor to send.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Sends the named tensor from send_device to recv_device.</p><p>_HostSend requires its input on host memory whereas _Send requires its
|
|
input on device memory.</p></div></div><div class="top"><p class="src"><a name="v:sigmoidGrad" class="def">sigmoidGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradient of the sigmoid of <code>x</code> wrt its input.</p><p>Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
|
|
<code>dy</code> is the corresponding input gradient.</p></div></div><div class="top"><p class="src"><a name="v:nonMaxSuppression" class="def">nonMaxSuppression</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>boxes</strong>: A 2-D float tensor of shape `[num_boxes, 4]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>scores</strong>: A 1-D float tensor of shape `[num_boxes]` representing a single
|
|
score corresponding to each box (each row of boxes).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>max_output_size</strong>: A scalar integer tensor representing the maximum number of
|
|
boxes to be selected by non max suppression.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>selected_indices</strong>: A 1-D integer tensor of shape `[M]` representing the selected
|
|
indices from the boxes tensor, where `M <= max_output_size`.</p></td></tr></table></div><div class="doc"><p>Greedily selects a subset of bounding boxes in descending order of score,</p><p>pruning away boxes that have high intersection-over-union (IOU) overlap
|
|
with previously selected boxes. Bounding boxes are supplied as
|
|
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
|
|
diagonal pair of box corners and the coordinates can be provided as normalized
|
|
(i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
|
|
is agnostic to where the origin is in the coordinate system. Note that this
|
|
algorithm is invariant to orthogonal transformations and translations
|
|
of the coordinate system; thus translating or reflections of the coordinate
|
|
system result in the same boxes being selected by the algorithm.</p><p>The output of this operation is a set of integers indexing into the input
|
|
collection of bounding boxes representing the selected boxes. The bounding
|
|
box coordinates corresponding to the selected indices can then be obtained
|
|
using the tf.gather operation. For example:</p><p>selected_indices = tf.image.non_max_suppression(
|
|
boxes, scores, max_output_size, iou_threshold)
|
|
selected_boxes = tf.gather(boxes, selected_indices)</p></div></div><div class="top"><p class="src"><a name="v:identityReader" class="def">identityReader</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: The handle to reference the Reader.</p></td></tr></table></div><div class="doc"><p>A Reader that outputs the queued work as both the key and value.</p><p>To use, enqueue strings in a Queue. ReaderRead will take the front
|
|
work string and output (work, work).</p></div></div><div class="top"><p class="src"><a name="v:extractGlimpse" class="def">extractGlimpse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>input</strong>: A 4-D float tensor of shape `[batch_size, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: A 1-D tensor of 2 elements containing the size of the glimpses
|
|
to extract. The glimpse height must be specified first, following
|
|
by the glimpse width.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>offsets</strong>: A 2-D integer tensor of shape `[batch_size, 2]` containing
|
|
the x, y locations of the center of each window.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>glimpse</strong>: A tensor representing the glimpses `[batch_size,
|
|
glimpse_height, glimpse_width, channels]`.</p></td></tr></table></div><div class="doc"><p>Extracts a glimpse from the input tensor.</p><p>Returns a set of windows called glimpses extracted at location
|
|
<code>offsets</code> from the input tensor. If the windows only partially
|
|
overlaps the inputs, the non overlapping areas will be filled with
|
|
random noise.</p><p>The result is a 4-D tensor of shape `[batch_size, glimpse_height,
|
|
glimpse_width, channels]`. The channels and batch dimensions are the
|
|
same as that of the input tensor. The height and width of the output
|
|
windows are specified in the <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> parameter.</p><p>The argument <code>normalized</code> and <code>centered</code> controls how the windows are built:</p><ul><li>If the coordinates are normalized but not centered, 0.0 and 1.0
|
|
correspond to the minimum and maximum of each height and width
|
|
dimension.</li><li>If the coordinates are both normalized and centered, they range from</li><li>1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
|
|
left corner, the lower right corner is located at (1.0, 1.0) and the
|
|
center is at (0, 0).</li><li>If the coordinates are not normalized they are interpreted as
|
|
numbers of pixels.</li></ul></div></div><div class="top"><p class="src"><a name="v:conv3DBackpropInput" class="def">conv3DBackpropInput</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, depth, rows, cols, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: Shape `[depth, rows, cols, in_channels, out_channels]`.
|
|
<code>in_channels</code> must match between <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
|
|
out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradients of 3-D convolution with respect to the input.</p></div></div><div class="top"><p class="src"><a name="v:matrixSolveLs" class="def">matrixSolveLs</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong>: Shape is `[..., M, N]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong>: Shape is `[..., M, K]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a></td><td class="doc"><p><strong>l2_regularizer</strong>: Scalar tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., N, K]`.</p></td></tr></table></div><div class="doc"><p>Solves one or more linear least-squares problems.</p><p><code>matrix</code> is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions
|
|
form matrices of size `[M, N]`. Rhs is a tensor of shape `[..., M, K]`.
|
|
The output is a tensor shape `[..., N, K]` where each output matrix solves
|
|
each of the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]
|
|
in the least squares sense.</p><p>matrix and right-hand sides in the batch:</p><p><code>matrix</code>=\(A in Re^{m times n}\),
|
|
<code>rhs</code>=\(B in Re^{m times k}\),
|
|
<code>output</code>=\(X in Re^{n times k}\),
|
|
<code>l2_regularizer</code>=\(lambda\).</p><p>If <code>fast</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, then the solution is computed by solving the normal
|
|
equations using Cholesky decomposition. Specifically, if \(m ge n\) then
|
|
\(X = (A^T A + lambda I)^{-1} A^T B\), which solves the least-squares
|
|
problem \(X = mathrm{argmin}_{Z in Re^{n times k}} ||A Z - B||_F^2 +
|
|
lambda ||Z||_F^2\). If \(m lt n\) then <code>output</code> is computed as
|
|
\(X = A^T (A A^T + lambda I)^{-1} B\), which (for \(lambda = 0\)) is the
|
|
minimum-norm solution to the under-determined linear system, i.e.
|
|
\(X = mathrm{argmin}_{Z in Re^{n times k}} ||Z||_F^2 \), subject to
|
|
\(A Z = B\). Notice that the fast path is only numerically stable when
|
|
\(A\) is numerically full rank and has a condition number
|
|
\(mathrm{cond}(A) lt frac{1}{sqrt{epsilon_{mach}}}\) or\(lambda\) is
|
|
sufficiently large.</p><p>If <code>fast</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> an algorithm based on the numerically robust complete
|
|
orthogonal decomposition is used. This computes the minimum-norm
|
|
least-squares solution, even when \(A\) is rank deficient. This path is
|
|
typically 6-7 times slower than the fast path. If <code>fast</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> then
|
|
<code>l2_regularizer</code> is ignored.</p></div></div><div class="top"><p class="src"><a name="v:rGBToHSV" class="def">rGBToHSV</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 1-D or higher rank. RGB data to convert. Last dimension must be size 3.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: <code>images</code> converted to HSV.</p></td></tr></table></div><div class="doc"><p>Converts one or more images from RGB to HSV.</p><p>Outputs a tensor of the same shape as the <code>images</code> tensor, containing the HSV
|
|
value of the pixels. The output is only well defined if the value in <code>images</code>
|
|
are in `[0,1]`.</p><p>`output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
|
|
`output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
|
|
corresponds to pure red, hue 1<em>3 is pure green, and 2</em>3 is pure blue.</p></div></div><div class="top"><p class="src"><a name="v:decodeGif" class="def">decodeGif</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong>: 0-D. The GIF-encoded image.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a></td><td class="doc"><p><strong>image</strong>: 4-D with shape `[num_frames, height, width, 3]`. RGB order</p></td></tr></table></div><div class="doc"><p>Decode the first frame of a GIF-encoded image to a uint8 tensor.</p><p>GIF with frame or transparency compression are not supported
|
|
convert animated GIF from compressed to uncompressed by:</p><p>convert $src.gif -coalesce $dst.gif</p></div></div><div class="top"><p class="src"><a name="v:adjustContrast" class="def">adjustContrast</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>contrast_factor</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>min_value</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>max_value</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Deprecated. Disallowed in GraphDef version >= 2.</p></div></div><div class="top"><p class="src"><a name="v:depthToSpace" class="def">depthToSpace</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>block_size</strong>: The size of the spatial block, same as in Space2Depth.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>DepthToSpace for tensors of type T.</p><p>Rearranges data from depth into blocks of spatial data.
|
|
This is the reverse transformation of SpaceToDepth. More specifically,
|
|
this op outputs a copy of the input tensor where values from the <code>depth</code>
|
|
dimension are moved in spatial blocks to the <code>height</code> and <code>width</code> dimensions.
|
|
The attr <code>block_size</code> indicates the input block size and how the data is moved.</p><ul><li>Chunks of data of size `block_size * block_size` from depth are rearranged
|
|
into non-overlapping blocks of size `block_size x block_size`</li><li>The width the output tensor is `input_depth * block_size`, whereas the
|
|
height is `input_height * block_size`.</li><li>The depth of the input tensor must be divisible by
|
|
`block_size * block_size`.</li></ul><p>That is, assuming the input is in the shape:
|
|
`[batch, height, width, depth]`,
|
|
the shape of the output will be:
|
|
`[batch, height*block_size, width*block_size, depth/(block_size*block_size)]`</p><p>This operation requires that the input tensor be of rank 4, and that
|
|
<code>block_size</code> be >=1 and that `block_size * block_size` be a divisor of the
|
|
input depth.</p><p>This operation is useful for resizing the activations between convolutions
|
|
(but keeping all data), e.g. instead of pooling. It is also useful for training
|
|
purely convolutional models.</p><p>For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2:</p><p>```prettyprint
|
|
x = [[[[1, 2, 3, 4]]]]</p><p>```</p><p>This operation will output a tensor of shape `[1, 2, 2, 1]`:</p><p>```prettyprint
|
|
[[[[1], [2]],
|
|
[[3], [4]]]]
|
|
```</p><p>Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
|
|
the corresponding output will have 2x2 elements and will have a depth of
|
|
1 channel (1 = `4 / (block_size * block_size)`).
|
|
The output element shape is `[2, 2, 1]`.</p><p>For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.</p><p>```prettyprint
|
|
x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
|
|
```</p><p>This operation, for block size of 2, will return the following tensor of shape
|
|
`[1, 2, 2, 3]`</p><p>```prettyprint
|
|
[[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]</p><p>```</p><p>Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:</p><p>```prettyprint
|
|
x = [[[[1, 2, 3, 4],
|
|
[5, 6, 7, 8]],
|
|
[[9, 10, 11, 12],
|
|
[13, 14, 15, 16]]]]
|
|
```</p><p>the operator will return the following tensor of shape `[1 4 4 1]`:</p><p>```prettyprint
|
|
x = [[ [1], [2], [5], [6]],
|
|
[ [3], [4], [7], [8]],
|
|
[ [9], [10], [13], [14]],
|
|
[ [11], [12], [15], [16]]]</p><p>```</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixSolve" class="def">batchMatrixSolve</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:erfc" class="def">erfc</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the complementary error function of <code>x</code> element-wise.</p></div></div><div class="top"><p class="src"><a name="v:resizeBilinearGrad" class="def">resizeBilinearGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>grads</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>original_image</strong>: 4-D with shape `[batch, orig_height, orig_width, channels]`,
|
|
The image tensor that was resized.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, orig_height, orig_width, channels]`.
|
|
Gradients with respect to the input image. Input image must have been
|
|
float or double.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of bilinear interpolation.</p></div></div><div class="top"><p class="src"><a name="v:fact" class="def">fact</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>fact</strong></p></td></tr></table></div><div class="doc"><p>Output a fact about factorials.</p></div></div><div class="top"><p class="src"><a name="v:deleteSessionTensor" class="def">deleteSessionTensor</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle for a tensor stored in the session state.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Delete the tensor specified by its handle in the session.</p></div></div><div class="top"><p class="src"><a name="v:logicalOr" class="def">logicalOr</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of x OR y element-wise.</p><ul><li>NOTE*: <code>LogicalOr</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:getSessionTensor" class="def">getSessionTensor</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle for a tensor stored in the session state.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>value</strong>: The tensor for the given handle.</p></td></tr></table></div><div class="doc"><p>Get the value of the tensor specified by its handle.</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixInverse" class="def">batchMatrixInverse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:shardedFilespec" class="def">shardedFilespec</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>basename</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>num_shards</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>filename</strong></p></td></tr></table></div><div class="doc"><p>Generate a glob pattern matching all sharded file names.</p></div></div><div class="top"><p class="src"><a name="v:decodeBase64" class="def">decodeBase64</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>input</strong>: Base64 strings to decode.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>output</strong>: Decoded strings.</p></td></tr></table></div><div class="doc"><p>Decode web-safe base64-encoded strings.</p><p>Input may or may not have padding at the end. See EncodeBase64 for padding.
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Web-safe means that input must use - and _ instead of + and /.</p></div></div><div class="top"><p class="src"><a name="v:getSessionHandle" class="def">getSessionHandle</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: The tensor to be stored.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle for the tensor stored in the session state.</p></td></tr></table></div><div class="doc"><p>Store the input tensor in the state of the current session.</p></div></div><div class="top"><p class="src"><a name="v:initializeTable" class="def">initializeTable</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tkey, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tval)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to a table which will be initialized.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tkey</td><td class="doc"><p><strong>keys</strong>: Keys of type Tkey.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tval</td><td class="doc"><p><strong>values</strong>: Values of type Tval.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Table initializer that takes two tensors for keys and values respectively.</p></div></div><div class="top"><p class="src"><a name="v:tan" class="def">tan</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes tan of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:tanh" class="def">tanh</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes hyperbolic tangent of <code>x</code> element-wise.</p></div></div><div class="top"><p class="src"><a name="v:applyAdagradDA" class="def">applyAdagradDA</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>gradient_accumulator</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>gradient_squared_accumulator</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>global_step</strong>: Training step number. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the proximal adagrad scheme.</p></div></div><div class="top"><p class="src"><a name="v:stringToHashBucket" class="def">stringToHashBucket</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_buckets</strong>: The number of buckets.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>string_tensor</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong>: A Tensor of the same shape as the input <code>string_tensor</code>.</p></td></tr></table></div><div class="doc"><p>Converts each string in the input Tensor to its hash mod by a number of buckets.</p><p>The hash function is deterministic on the content of the string within the
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process.</p><p>Note that the hash function may change from time to time.
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This functionality will be deprecated and it's recommended to use
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`tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`.</p></div></div><div class="top"><p class="src"><a name="v:eluGrad" class="def">eluGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>gradients</strong>: The backpropagated gradients to the corresponding Elu operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>outputs</strong>: The outputs of the corresponding Elu operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>backprops</strong>: The gradients: `gradients * (outputs + 1)` if outputs < 0,
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<code>gradients</code> otherwise.</p></td></tr></table></div><div class="doc"><p>Computes gradients for the exponential linear (Elu) operation.</p></div></div><div class="top"><p class="src"><a name="v:fractionalAvgPoolGrad" class="def">fractionalAvgPoolGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>orig_input_tensor_shape</strong>: Original input tensor shape for <code>fractional_avg_pool</code></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, height, width, channels]`. Gradients
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w.r.t. the output of <code>fractional_avg_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>row_pooling_sequence</strong>: row pooling sequence, form pooling region with
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col_pooling_sequence.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>col_pooling_sequence</strong>: column pooling sequence, form pooling region with
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row_pooling sequence.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D. Gradients w.r.t. the input of <code>fractional_avg_pool</code>.</p></td></tr></table></div><div class="doc"><p>Computes gradient of the FractionalAvgPool function.</p><p>Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
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FractionalAvgPoolGrad, we just need to evenly back-propagate each element of
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out_backprop to those indices that form the same pooling cell. Therefore, we
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just need to know the shape of original input tensor, instead of the whole
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tensor.</p></div></div><div class="top"><p class="src"><a name="v:matrixTriangularSolve" class="def">matrixTriangularSolve</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong>: Shape is `[..., M, K]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., M, K]`.</p></td></tr></table></div><div class="doc"><p>Solves systems of linear equations with upper or lower triangular matrices by</p><p>backsubstitution.</p><p><code>matrix</code> is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form
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square matrices. If <code>lower</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code> then the strictly upper triangular part
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of each inner-most matrix is assumed to be zero and not accessed.
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If <code>lower</code> is False then the strictly lower triangular part of each inner-most
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matrix is assumed to be zero and not accessed.
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<code>rhs</code> is a tensor of shape `[..., M, K]`.</p><p>The output is a tensor of shape `[..., M, K]`. If <code>adjoint</code> is
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<code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code> then the innermost matrices in output` satisfy matrix equations
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`matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`.
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If <code>adjoint</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> then the strictly then the innermost matrices in
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<code>output</code> satisfy matrix equations
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`adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.</p></div></div><div class="top"><p class="src"><a name="v:editDistance" class="def">editDistance</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>hypothesis_indices</strong>: The indices of the hypothesis list SparseTensor.
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This is an N x R int64 matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>hypothesis_values</strong>: The values of the hypothesis list SparseTensor.
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This is an N-length vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>hypothesis_shape</strong>: The shape of the hypothesis list SparseTensor.
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This is an R-length vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>truth_indices</strong>: The indices of the truth list SparseTensor.
|
|
This is an M x R int64 matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>truth_values</strong>: The values of the truth list SparseTensor.
|
|
This is an M-length vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>truth_shape</strong>: truth indices, vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>output</strong>: A dense float tensor with rank R - 1.</p><p>For the example input:</p><p>// hypothesis represents a 2x1 matrix with variable-length values:
|
|
// (0,0) = ["a"]
|
|
// (1,0) = ["b"]
|
|
hypothesis_indices = [[0, 0, 0],
|
|
[1, 0, 0]]
|
|
hypothesis_values = ["a", "b"]
|
|
hypothesis_shape = [2, 1, 1]</p><p>// truth represents a 2x2 matrix with variable-length values:
|
|
// (0,0) = []
|
|
// (0,1) = ["a"]
|
|
// (1,0) = ["b", "c"]
|
|
// (1,1) = ["a"]
|
|
truth_indices = [[0, 1, 0],
|
|
[1, 0, 0],
|
|
[1, 0, 1],
|
|
[1, 1, 0]]
|
|
truth_values = ["a", "b", "c", "a"]
|
|
truth_shape = [2, 2, 2]
|
|
normalize = true</p><p>The output will be:</p><p>// output is a 2x2 matrix with edit distances normalized by truth lengths.
|
|
output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis
|
|
[0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis</p></td></tr></table></div><div class="doc"><p>Computes the (possibly normalized) Levenshtein Edit Distance.</p><p>The inputs are variable-length sequences provided by SparseTensors
|
|
(hypothesis_indices, hypothesis_values, hypothesis_shape)
|
|
and
|
|
(truth_indices, truth_values, truth_shape).</p><p>The inputs are:</p></div></div><div class="top"><p class="src"><a name="v:barrierIncompleteSize" class="def">barrierIncompleteSize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a barrier.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: The number of incomplete elements (i.e. those with some of their value
|
|
components not set) in the barrier.</p></td></tr></table></div><div class="doc"><p>Computes the number of incomplete elements in the given barrier.</p></div></div><div class="top"><p class="src"><a name="v:threadUnsafeUnigramCandidateSampler" class="def">threadUnsafeUnigramCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to randomly sample per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>range_max</strong>: The sampler will sample integers from the interval [0, range_max).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
|
|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
|
|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
|
|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
|
|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a learned unigram distribution.</p><p>See explanations of candidate sampling and the data formats at
|
|
go/candidate-sampling.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
|
|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:barrierReadySize" class="def">barrierReadySize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a barrier.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: The number of complete elements (i.e. those with all of their value
|
|
components set) in the barrier.</p></td></tr></table></div><div class="doc"><p>Computes the number of complete elements in the given barrier.</p></div></div><div class="top"><p class="src"><a name="v:barrierClose" class="def">barrierClose</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a barrier.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Closes the given barrier.</p><p>This operation signals that no more new elements will be inserted in the
|
|
given barrier. Subsequent InsertMany that try to introduce a new key will fail.
|
|
Subsequent InsertMany operations that just add missing components to already
|
|
existing elements will continue to succeed. Subsequent TakeMany operations will
|
|
continue to succeed if sufficient completed elements remain in the barrier.
|
|
Subsequent TakeMany operations that would block will fail immediately.</p></div></div><div class="top"><p class="src"><a name="v:textLineReader" class="def">textLineReader</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: The handle to reference the Reader.</p></td></tr></table></div><div class="doc"><p>A Reader that outputs the lines of a file delimited by '\n'.</p></div></div><div class="top"><p class="src"><a name="v:fFT3D" class="def">fFT3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most 3
|
|
dimensions of <code>input</code> are replaced with their 3D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the 3-dimensional discrete Fourier Transform over the inner-most 3</p><p>dimensions of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:refExit" class="def">refExit</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the parent frame.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Exits the current frame to its parent frame.</p><p>Exit makes its input `data` available to the parent frame.</p></div></div><div class="top"><p class="src"><a name="v:exp" class="def">exp</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes exponential of x element-wise. \(y = e^x\).</p></div></div><div class="top"><p class="src"><a name="v:restoreSlice" class="def">restoreSlice</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dt</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>file_pattern</strong>: Must have a single element. The pattern of the files from
|
|
which we read the tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tensor_name</strong>: Must have a single element. The name of the tensor to be
|
|
restored.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>shape_and_slice</strong>: Scalar. The shapes and slice specifications to use when
|
|
restoring a tensors.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dt</td><td class="doc"><p><strong>tensor</strong>: The restored tensor.</p></td></tr></table></div><div class="doc"><p>Restores a tensor from checkpoint files.</p><p>This is like <code>Restore</code> except that restored tensor can be listed as filling
|
|
only a slice of a larger tensor. <code>shape_and_slice</code> specifies the shape of the
|
|
larger tensor and the slice that the restored tensor covers.</p><p>The <code>shape_and_slice</code> input has the same format as the
|
|
elements of the <code>shapes_and_slices</code> input of the <code>SaveSlices</code> op.</p></div></div><div class="top"><p class="src"><a name="v:conj" class="def">conj</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the complex conjugate of a complex number.</p><p>Given a tensor <code>input</code> of complex numbers, this operation returns a tensor of
|
|
complex numbers that are the complex conjugate of each element in <code>input</code>. The
|
|
complex numbers in <code>input</code> must be of the form \(a + bj\), where *a* is the
|
|
real part and *b* is the imaginary part.</p><p>The complex conjugate returned by this operation is of the form \(a - bj\).</p><p>For example:</p><p>```
|
|
# tensor <code>input</code> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:resizeNearestNeighborGrad" class="def">resizeNearestNeighborGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>grads</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: = A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The
|
|
original input size.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients
|
|
with respect to the input image.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of nearest neighbor interpolation.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayClose" class="def">tensorArrayClose</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Delete the TensorArray from its resource container. This enables</p><p>the user to close and release the resource in the middle of a step/run.</p></div></div><div class="top"><p class="src"><a name="v:atan" class="def">atan</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes atan of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:tensorArraySize" class="def">tensorArraySize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray (output of TensorArray or TensorArrayGrad).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: The current size of the TensorArray.</p></td></tr></table></div><div class="doc"><p>Get the current size of the TensorArray.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayConcat" class="def">tensorArrayConcat</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>value</strong>, <strong>lengths</strong>)</p><ul><li><strong>value</strong>: All of the elements in the TensorArray, concatenated along the first
|
|
axis.</li><li><strong>lengths</strong>: A vector of the row sizes of the original T elements in the
|
|
value output. In the example above, this would be the values:
|
|
`(n1, n2, ..., n(T-1))`.</li></ul></td></tr></table></div><div class="doc"><p>Concat the elements from the TensorArray into value <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><p>Takes <code>T</code> elements of shapes</p><p>```
|
|
(n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...)
|
|
```</p><p>and concatenates them into a Tensor of shape:</p><p>```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```</p><p>All elements must have the same shape (excepting the first dimension).</p></div></div><div class="top"><p class="src"><a name="v:lRN" class="def">lRN</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Local Response Normalization.</p><p>The 4-D <code>input</code> tensor is treated as a 3-D array of 1-D vectors (along the last
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|
dimension), and each vector is normalized independently. Within a given vector,
|
|
each component is divided by the weighted, squared sum of inputs within
|
|
<code>depth_radius</code>. In detail,</p><p>sqr_sum[a, b, c, d] =
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|
sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
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|
output = input / (bias + alpha * sqr_sum) ** beta</p><p>For details, see <a href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks">Krizhevsky et al., ImageNet classification with deep
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|
convolutional neural networks (NIPS 2012)</a>.</p></div></div><div class="top"><p class="src"><a name="v:stringToHashBucketFast" class="def">stringToHashBucketFast</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_buckets</strong>: The number of buckets.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>input</strong>: The strings to assign a hash bucket.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong>: A Tensor of the same shape as the input <code>string_tensor</code>.</p></td></tr></table></div><div class="doc"><p>Converts each string in the input Tensor to its hash mod by a number of buckets.</p><p>The hash function is deterministic on the content of the string within the
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|
process and will never change. However, it is not suitable for cryptography.
|
|
This function may be used when CPU time is scarce and inputs are trusted or
|
|
unimportant. There is a risk of adversaries constructing inputs that all hash
|
|
to the same bucket. To prevent this problem, use a strong hash function with
|
|
`tf.string_to_hash_bucket_strong`.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayPack" class="def">tensorArrayPack</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>value</strong>: All of the elements in the TensorArray, concatenated along a new
|
|
axis (the new dimension 0).</p></td></tr></table></div><div class="doc"><p>Pack the elements from the TensorArray into output <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><ul><li>*WARNING: This op is deprecated.**</li></ul><p>Instead of this op, use <code>TensorArrayGather</code> with
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|
`indices = RangeOp(0, TensorArraySizeOp)`.</p><p>All elements must have the same shape.</p></div></div><div class="top"><p class="src"><a name="v:concatOffset" class="def">concatOffset</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>concat_dim</strong>: The dimension along which to concatenate.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>]</td><td class="doc"><p><strong>shape</strong>: The <code>N</code> int32 vectors representing shape of tensors being concatenated.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>]</td><td class="doc"><p><strong>offset</strong>: The <code>N</code> int32 vectors representing the starting offset
|
|
of input tensors within the concatenated output.</p><p>This is typically used by gradient computations for a concat operation.</p></td></tr></table></div><div class="doc"><p>Computes offsets of concat inputs within its output.</p><p>For example:</p><p>```prettyprint
|
|
# <code>x</code> is [2, 2, 7]
|
|
# <code>y</code> is [2, 3, 7]
|
|
# <code>z</code> is [2, 5, 7]
|
|
concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:refEnter" class="def">refEnter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the child frame.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Creates or finds a child frame, and makes `data` available to the child frame.</p><p>The unique <code>frame_name</code> is used by the <code>Executor</code> to identify frames. If
|
|
<code>is_constant</code> is true, <code>output</code> is a constant in the child frame; otherwise
|
|
it may be changed in the child frame. At most <code>parallel_iterations</code> iterations
|
|
are run in parallel in the child frame.</p></div></div><div class="top"><p class="src"><a name="v:softsign" class="def">softsign</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>activations</strong></p></td></tr></table></div><div class="doc"><p>Computes softsign: `features / (abs(features) + 1)`.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayWrite" class="def">tensorArrayWrite</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>index</strong>: The position to write to inside the TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>value</strong>: The tensor to write to the TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_out</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr></table></div><div class="doc"><p>Push an element onto the tensor_array.</p></div></div><div class="top"><p class="src"><a name="v:diag" class="def">diag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>diagonal</strong>: Rank k tensor where k is at most 3.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns a diagonal tensor with a given diagonal values.</p><p>Given a <code>diagonal</code>, this operation returns a tensor with the <code>diagonal</code> and
|
|
everything else padded with zeros. The diagonal is computed as follows:</p><p>Assume <code>diagonal</code> has dimensions [D1,..., Dk], then the output is a tensor of
|
|
rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:</p><p>`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.</p><p>For example:</p><p>```prettyprint
|
|
# <code>diagonal</code> is [1, 2, 3, 4]
|
|
tf.diag(diagonal) ==> [[1, 0, 0, 0]
|
|
[0, 2, 0, 0]
|
|
[0, 0, 3, 0]
|
|
[0, 0, 0, 4]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:matrixDiagPart" class="def">matrixDiagPart</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Rank <code>k</code> tensor where `k >= 2` and the last two dimensions are equal.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>diagonal</strong>: The extracted diagonal(s) having shape
|
|
`diagonal.shape = input.shape[:-1]`.</p></td></tr></table></div><div class="doc"><p>Returns the batched diagonal part of a batched tensor.</p><p>This operation returns a tensor with the <code>diagonal</code> part
|
|
of the batched <code>input</code>. The <code>diagonal</code> part is computed as follows:</p><p>Assume <code>input</code> has <code>k</code> dimensions `[I, J, K, ..., N, N]`, then the output is a
|
|
tensor of rank `k - 1` with dimensions `[I, J, K, ..., N]` where:</p><p>`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.</p><p>The input must be at least a matrix.</p><p>For example:</p><p>```prettyprint
|
|
# <code>input</code> is [[[1, 0, 0, 0]
|
|
[0, 2, 0, 0]
|
|
[0, 0, 3, 0]
|
|
[0, 0, 0, 4]],
|
|
[[5, 0, 0, 0]
|
|
[0, 6, 0, 0]
|
|
[0, 0, 7, 0]
|
|
[0, 0, 0, 8]]]</p><p>and input.shape = (2, 4, 4)</p><p>tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]</p><p>which has shape (2, 4)
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:queueSize" class="def">queueSize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a queue.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: The number of elements in the given queue.</p></td></tr></table></div><div class="doc"><p>Computes the number of elements in the given queue.</p></div></div><div class="top"><p class="src"><a name="v:decodePng" class="def">decodePng</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` dtype)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong>: 0-D. The PNG-encoded image.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>image</strong>: 3-D with shape `[height, width, channels]`.</p></td></tr></table></div><div class="doc"><p>Decode a PNG-encoded image to a uint8 or uint16 tensor.</p><p>The attr <code>channels</code> indicates the desired number of color channels for the
|
|
decoded image.</p><p>Accepted values are:</p><ul><li>0: Use the number of channels in the PNG-encoded image.</li><li>1: output a grayscale image.</li><li>3: output an RGB image.</li><li>4: output an RGBA image.</li></ul><p>If needed, the PNG-encoded image is transformed to match the requested number
|
|
of color channels.</p></div></div><div class="top"><p class="src"><a name="v:ceil" class="def">ceil</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns element-wise smallest integer in not less than x.</p></div></div><div class="top"><p class="src"><a name="v:priorityQueue" class="def">priorityQueue</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the queue.</p></td></tr></table></div><div class="doc"><p>A queue that produces elements sorted by the first component value.</p><p>Note that the PriorityQueue requires the first component of any element
|
|
to be a scalar int64, in addition to the other elements declared by
|
|
component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue
|
|
and DequeueMany) on a PriorityQueue will all require (resp. output) one extra
|
|
entry in their input (resp. output) lists.</p></div></div><div class="top"><p class="src"><a name="v:placeholderWithDefault" class="def">placeholderWithDefault</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 dtype</td><td class="doc"><p><strong>input</strong>: The default value to produce when <code>output</code> is not fed.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A placeholder tensor that defaults to <code>input</code> if it is not fed.</p></td></tr></table></div><div class="doc"><p>A placeholder op that passes though <code>input</code> when its output is not fed.</p></div></div><div class="top"><p class="src"><a name="v:cropAndResizeGradImage" class="def">cropAndResizeGradImage</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>grads</strong>: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>boxes</strong>: A 2-D tensor of shape `[num_boxes, 4]`. The <code>i</code>-th row of the tensor
|
|
specifies the coordinates of a box in the `box_ind[i]` image and is specified
|
|
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
|
|
<code>y</code> is mapped to the image coordinate at `y * (image_height - 1)`, so as the
|
|
`[0, 1]` interval of normalized image height is mapped to
|
|
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
|
|
which case the sampled crop is an up-down flipped version of the original
|
|
image. The width dimension is treated similarly. Normalized coordinates
|
|
outside the `[0, 1]` range are allowed, in which case we use
|
|
<code>extrapolation_value</code> to extrapolate the input image values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>box_ind</strong>: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
|
|
The value of `box_ind[i]` specifies the image that the <code>i</code>-th box refers to.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>image_size</strong>: A 1-D tensor with value `[batch, image_height, image_width, depth]`
|
|
containing the original image size. Both <code>image_height</code> and <code>image_width</code> need
|
|
to be positive.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of the crop_and_resize op wrt the input image tensor.</p></div></div><div class="top"><p class="src"><a name="v:readerReset" class="def">readerReset</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Restore a Reader to its initial clean state.</p></div></div><div class="top"><p class="src"><a name="v:extractImagePatches" class="def">extractImagePatches</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>patches</strong>: 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
|
|
ksize_cols * depth]` containing image patches with size
|
|
`ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension.</p></td></tr></table></div><div class="doc"><p>Extract <code>patches</code> from <code>images</code> and put them in the "depth" output dimension.</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixSetDiag" class="def">batchMatrixSetDiag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>diagonal</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:stackClose" class="def">stackClose</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a stack.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Delete the stack from its resource container.</p></div></div><div class="top"><p class="src"><a name="v:quantizeAndDequantize" class="def">quantizeAndDequantize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Tensor to quantize and then dequantize.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Quantizes then dequantizes a tensor.</p><p>This op simulates the precision loss from the quantized forward pass by:
|
|
1. Quantizing the tensor to fixed point numbers, which should match the target
|
|
quantization method when it is used in inference.
|
|
2. Dequantizing it back to floating point numbers for the following ops, most
|
|
likely matmul.</p><p>There are different ways to quantize. This version does not use the full range
|
|
of the output type, choosing to elide the lowest possible value for symmetry
|
|
(e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit
|
|
quantization), so that 0.0 maps to 0.</p><p>To perform this op, we first find the range of values in our tensor. The range
|
|
we use is always centered on 0, so we find m such that</p><ol><li>m = max(abs(input_min), abs(input_max)) if range_given is true,</li><li>m = max(max(abs(min_elem(input)), abs(max_elem(input))) otherwise.</li></ol><p>Our input tensor range is then [-m, m].</p><p>Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].
|
|
If signed_input is true, this is</p><dl><dt>min_fixed, max_fixed </dt><dd>=</dd><dt>-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1</dt><dd>.</dd></dl><p>Otherwise, if signed_input is false, the fixed-point range is</p><dl><dt>min_fixed, max_fixed</dt><dd>= [0, (1 << num_bits) - 1].</dd></dl><p>From this we compute our scaling factor, s:</p><p>s = (max_fixed - min_fixed) / (2 * m).</p><p>Now we can quantize and dequantize the elements of our tensor. An element e
|
|
is transformed into e':</p><p>e' = (e * s).round_to_nearest() / s.</p><p>Note that we have a different number of buckets in the signed vs. unsigned
|
|
cases. For example, if num_bits == 8, we get 254 buckets in the signed case
|
|
vs. 255 in the unsigned case.</p><p>For example, suppose num_bits = 8 and m = 1. Then</p><dl><dt>min_fixed, max_fixed</dt><dd>= [-127, 127], and
|
|
s = (127 + 127) / 2 = 127.</dd></dl><p>Given the vector {-1, -0.5, 0, 0.3}, this is quantized to
|
|
{-127, -63, 0, 38}, and dequantized to {-1, -63.0<em>127, 0, 38.0</em>127}.</p></div></div><div class="top"><p class="src"><a name="v:isNan" class="def">isNan</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns which elements of x are NaN.</p></div></div><div class="top"><p class="src"><a name="v:where-39-" class="def">where'</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>index</strong></p></td></tr></table></div><div class="doc"><p>Returns locations of true values in a boolean tensor.</p><p>This operation returns the coordinates of true elements in <code>input</code>. The
|
|
coordinates are returned in a 2-D tensor where the first dimension (rows)
|
|
represents the number of true elements, and the second dimension (columns)
|
|
represents the coordinates of the true elements. Keep in mind, the shape of
|
|
the output tensor can vary depending on how many true values there are in
|
|
<code>input</code>. Indices are output in row-major order.</p><p>For example:</p><p>```prettyprint
|
|
# <code>input</code> tensor is [[True, False]
|
|
# [True, False]]
|
|
# <code>input</code> has two true values, so output has two coordinates.
|
|
# <code>input</code> has rank of 2, so coordinates have two indices.
|
|
where(input) ==> [[0, 0],
|
|
[1, 0]]</p><p># <code>input</code> tensor is [[[True, False]
|
|
# [True, False]]
|
|
# [[False, True]
|
|
# [False, True]]
|
|
# [[False, False]
|
|
# [False, True]]]
|
|
# <code>input</code> has 5 true values, so output has 5 coordinates.
|
|
# <code>input</code> has rank of 3, so coordinates have three indices.
|
|
where(input) ==> [[0, 0, 0],
|
|
[0, 1, 0],
|
|
[1, 0, 1],
|
|
[1, 1, 1],
|
|
[2, 1, 1]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:listDiff" class="def">listDiff</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: 1-D. Values to keep.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong>: 1-D. Values to remove.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</td><td class="doc"><p>(<strong>out</strong>, <strong>idx</strong>)</p><ul><li><strong>out</strong>: 1-D. Values present in <code>x</code> but not in <code>y</code>.</li><li><strong>idx</strong>: 1-D. Positions of <code>x</code> values preserved in <code>out</code>.</li></ul></td></tr></table></div><div class="doc"><p>Computes the difference between two lists of numbers or strings.</p><p>Given a list <code>x</code> and a list <code>y</code>, this operation returns a list <code>out</code> that
|
|
represents all values that are in <code>x</code> but not in <code>y</code>. The returned list <code>out</code>
|
|
is sorted in the same order that the numbers appear in <code>x</code> (duplicates are
|
|
preserved). This operation also returns a list <code>idx</code> that represents the
|
|
position of each <code>out</code> element in <code>x</code>. In other words:</p><p>`out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`</p><p>For example, given this input:</p><p>```prettyprint
|
|
x = [1, 2, 3, 4, 5, 6]
|
|
y = [1, 3, 5]
|
|
```</p><p>This operation would return:</p><p>```prettyprint
|
|
out ==> [2, 4, 6]
|
|
idx ==> [1, 3, 5]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:stridedSlice" class="def">stridedSlice</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index</td><td class="doc"><p><strong>begin</strong>: `begin[i]` specifies the offset into the <code>i</code>th dimension of
|
|
<code>input</code> to slice from.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index</td><td class="doc"><p><strong>end</strong>: `end[i]` specifies the first offset into the <code>i</code>th dimension of
|
|
<code>input</code> that will not be extracted. Out or range values are
|
|
clamped to `[0,dim[i]) if slice[i] > 0` or `[-1,dim[i]-1]`
|
|
`if slice[i] < 0`</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index</td><td class="doc"><p><strong>strides</strong>: `strides[i]` specifies the increment in the <code>i</code>th dimension
|
|
after extracting a given element. Negative indices will reverse
|
|
the original order. Out or range values are
|
|
clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Return a strided slice from <code>input</code>.</p><p>The output tensor is a tensor with dimensions implied by <code>begin</code>,
|
|
<code>end</code>, and <code>strides</code>, whose values are extracted from <code>begin</code>.</p><p>Specifically, the result tensor at index `(i[0], i[1], ..., i[n-1])`
|
|
will obtain the value `input[begin[0] + i[0] * stride[0], ..., `
|
|
`begin[n-1] + i[n-1] * stride[n-1])]`.</p><ul><li>Requirements*:
|
|
`0 != strides[i] for i in [0, n)`</li></ul></div></div><div class="top"><p class="src"><a name="v:randomShuffleQueue" class="def">randomShuffleQueue</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the queue.</p></td></tr></table></div><div class="doc"><p>A queue that randomizes the order of elements.</p></div></div><div class="top"><p class="src"><a name="v:tileGrad" class="def">tileGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>multiples</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the gradient of <code>Tile</code>.</p><p>Since <code>Tile</code> takes an input and repeats the input <code>multiples</code> times
|
|
along each dimension, <code>TileGrad</code> takes in <code>multiples</code> and aggregates
|
|
each repeated tile of <code>input</code> into <code>output</code>.</p></div></div><div class="top"><p class="src"><a name="v:stridedSliceAssign" class="def">stridedSliceAssign</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index</td><td class="doc"><p><strong>begin</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index</td><td class="doc"><p><strong>end</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index</td><td class="doc"><p><strong>strides</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>value</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong></p></td></tr></table></div><div class="doc"><p>Assign <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> to the sliced l-value reference of <code>ref</code>.</p><p>The values of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> are assigned to the positions in the variable
|
|
<code>ref</code> that are selected by the slice parameters. The slice parameters
|
|
`begin, <code>end</code>, <code>strides</code>, etc. work exactly as in <code>StridedSlice</code>.</p><p>NOTE this op currently does not support broadcasting and so <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>'s
|
|
shape must be exactly the shape produced by the slice of <code>ref</code>.</p></div></div><div class="top"><p class="src"><a name="v:reshape" class="def">reshape</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tshape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tshape)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tshape</td><td class="doc"><p><strong>shape</strong>: Defines the shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Reshapes a tensor.</p><p>Given <code>tensor</code>, this operation returns a tensor that has the same values
|
|
as <code>tensor</code> with shape <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code>.</p><p>If one component of <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> is the special value -1, the size of that dimension
|
|
is computed so that the total size remains constant. In particular, a <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code>
|
|
of `[-1]` flattens into 1-D. At most one component of <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> can be -1.</p><p>If <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> is 1-D or higher, then the operation returns a tensor with shape
|
|
<code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> filled with the values of <code>tensor</code>. In this case, the number of elements
|
|
implied by <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> must be the same as the number of elements in <code>tensor</code>.</p><p>For example:</p><p>```prettyprint
|
|
# tensor <code>t</code> is [1, 2, 3, 4, 5, 6, 7, 8, 9]
|
|
# tensor <code>t</code> has shape [9]
|
|
reshape(t, [3, 3]) ==> [[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]]</p><p># tensor <code>t</code> is [[[1, 1], [2, 2]],
|
|
# [[3, 3], [4, 4]]]
|
|
# tensor <code>t</code> has shape [2, 2, 2]
|
|
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
|
|
[3, 3, 4, 4]]</p><p># tensor <code>t</code> is [[[1, 1, 1],
|
|
# [2, 2, 2]],
|
|
# [[3, 3, 3],
|
|
# [4, 4, 4]],
|
|
# [[5, 5, 5],
|
|
# [6, 6, 6]]]
|
|
# tensor <code>t</code> has shape [3, 2, 3]
|
|
# pass '[-1]' to flatten <code>t</code>
|
|
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]</p><p># -1 can also be used to infer the shape</p><p># -1 is inferred to be 9:
|
|
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
|
|
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
|
|
# -1 is inferred to be 2:
|
|
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
|
|
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
|
|
# -1 is inferred to be 3:
|
|
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
|
|
[2, 2, 2],
|
|
[3, 3, 3]],
|
|
[[4, 4, 4],
|
|
[5, 5, 5],
|
|
[6, 6, 6]]]</p><p># tensor <code>t</code> is [7]
|
|
# shape `[]` reshapes to a scalar
|
|
reshape(t, []) ==> 7
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:fIFOQueue" class="def">fIFOQueue</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the queue.</p></td></tr></table></div><div class="doc"><p>A queue that produces elements in first-in first-out order.</p></div></div><div class="top"><p class="src"><a name="v:learnedUnigramCandidateSampler" class="def">learnedUnigramCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to randomly sample per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>range_max</strong>: The sampler will sample integers from the interval [0, range_max).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
|
|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
|
|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
|
|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
|
|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a learned unigram distribution.</p><p>See explanations of candidate sampling and the data formats at
|
|
go/candidate-sampling.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
|
|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:fractionalAvgPool" class="def">fractionalAvgPool</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>output</strong>, <strong>row_pooling_sequence</strong>, <strong>col_pooling_sequence</strong>)</p><ul><li><strong>output</strong>: output tensor after fractional avg pooling.</li><li><strong>row_pooling_sequence</strong>: row pooling sequence, needed to calculate gradient.</li><li><strong>col_pooling_sequence</strong>: column pooling sequence, needed to calculate gradient.</li></ul></td></tr></table></div><div class="doc"><p>Performs fractional average pooling on the input.</p><p>Fractional average pooling is similar to Fractional max pooling in the pooling
|
|
region generation step. The only difference is that after pooling regions are
|
|
generated, a mean operation is performed instead of a max operation in each
|
|
pooling region.</p></div></div><div class="top"><p class="src"><a name="v:randomCrop" class="def">randomCrop</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>image</strong>: 3-D of shape `[height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>size</strong>: 1-D of length 2 containing: <code>crop_height</code>, <code>crop_width</code>..</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 3-D of shape `[crop_height, crop_width, channels].`</p></td></tr></table></div><div class="doc"><p>Randomly crop <code>image</code>.</p><p><code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> is a 1-D int64 tensor with 2 elements representing the crop height and
|
|
width. The values must be non negative.</p><p>This Op picks a random location in <code>image</code> and crops a <code>height</code> by <code>width</code>
|
|
rectangle from that location. The random location is picked so the cropped
|
|
area will fit inside the original image.</p></div></div><div class="top"><p class="src"><a name="v:_HostCast" class="def">_HostCast</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dstT, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> srcT)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 srcT</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dstT</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Cast x of type SrcT to y of DstT.</p><p>_HostCast requires its input and produces its output in host memory.</p></div></div><div class="top"><p class="src"><a name="v:queueClose" class="def">queueClose</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a queue.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Closes the given queue.</p><p>This operation signals that no more elements will be enqueued in the
|
|
given queue. Subsequent Enqueue(Many) operations will fail.
|
|
Subsequent Dequeue(Many) operations will continue to succeed if
|
|
sufficient elements remain in the queue. Subsequent Dequeue(Many)
|
|
operations that would block will fail immediately.</p></div></div><div class="top"><p class="src"><a name="v:slice" class="def">slice</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index</td><td class="doc"><p><strong>begin</strong>: begin[i] specifies the offset into the <code>i</code>th dimension of
|
|
<code>input</code> to slice from.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index</td><td class="doc"><p><strong>size</strong>: size[i] specifies the number of elements of the <code>i</code>th dimension
|
|
of <code>input</code> to slice. If size[i] is -1, all remaining elements in dimension
|
|
i are included in the slice (i.e. this is equivalent to setting
|
|
size[i] = input.dim_size(i) - begin[i]).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Return a slice from <code>input</code>.</p><p>The output tensor is a tensor with dimensions described by <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code>
|
|
whose values are extracted from <code>input</code> starting at the offsets in
|
|
<code>begin</code>.</p><ul><li>Requirements*:
|
|
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)</li></ul></div></div><div class="top"><p class="src"><a name="v:stridedSliceGrad" class="def">stridedSliceGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` index, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 index</td><td class="doc"><p><strong>shape</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 index</td><td class="doc"><p><strong>begin</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 index</td><td class="doc"><p><strong>end</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 index</td><td class="doc"><p><strong>strides</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>dy</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the gradient of <code>StridedSlice</code>.</p><p>Since <code>StridedSlice</code> cuts out pieces of its <code>input</code> which is size
|
|
<code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code>, its gradient will have the same shape (which is passed here
|
|
as <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code>). The gradient will be zero in any element that the slice
|
|
does not select.</p><p>Arguments are the same as StridedSliceGrad with the exception that
|
|
<code>dy</code> is the input gradient to be propagated and <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> is the
|
|
shape of <code>StridedSlice</code>'s <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:sparseTensorDenseAdd" class="def">sparseTensorDenseAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tindices</td><td class="doc"><p><strong>a_indices</strong>: 2-D. The <code>indices</code> of the <code>SparseTensor</code>, with shape `[nnz, ndims]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>a_values</strong>: 1-D. The <code>values</code> of the <code>SparseTensor</code>, with shape `[nnz]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tindices</td><td class="doc"><p><strong>a_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the <code>SparseTensor</code>, with shape `[ndims]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>b</strong>: <code>ndims</code>-D Tensor. With shape <code>a_shape</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Adds up a <code>SparseTensor</code> and a dense <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code>, producing a dense <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code>.</p><p>This Op does not require <code>a_indices</code> be sorted in standard lexicographic order.</p></div></div><div class="top"><p class="src"><a name="v:size" class="def">size</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the size of a tensor.</p><p>This operation returns an integer representing the number of elements in
|
|
<code>input</code>.</p><p>For example:</p><p>```prettyprint
|
|
# <code>t</code> is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
|
|
size(t) ==> 12
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:barrier" class="def">barrier</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the barrier.</p></td></tr></table></div><div class="doc"><p>Defines a barrier that persists across different graph executions.</p><p>A barrier represents a key-value map, where each key is a string, and
|
|
each value is a tuple of tensors.</p><p>At runtime, the barrier contains <code>complete</code> and <code>incomplete</code>
|
|
elements. A complete element has defined tensors for all components of
|
|
its value tuple, and may be accessed using BarrierTakeMany. An
|
|
incomplete element has some undefined components in its value tuple,
|
|
and may be updated using BarrierInsertMany.</p></div></div><div class="top"><p class="src"><a name="v:lgamma" class="def">lgamma</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the log of the absolute value of `Gamma(x)` element-wise.</p></div></div><div class="top"><p class="src"><a name="v:decodeJpeg" class="def">decodeJpeg</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong>: 0-D. The JPEG-encoded image.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a></td><td class="doc"><p><strong>image</strong>: 3-D with shape `[height, width, channels]`..</p></td></tr></table></div><div class="doc"><p>Decode a JPEG-encoded image to a uint8 tensor.</p><p>The attr <code>channels</code> indicates the desired number of color channels for the
|
|
decoded image.</p><p>Accepted values are:</p><ul><li>0: Use the number of channels in the JPEG-encoded image.</li><li>1: output a grayscale image.</li><li>3: output an RGB image.</li></ul><p>If needed, the JPEG-encoded image is transformed to match the requested number
|
|
of color channels.</p><p>The attr <code>ratio</code> allows downscaling the image by an integer factor during
|
|
decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
|
|
downscaling the image later.</p></div></div><div class="top"><p class="src"><a name="v:shapeN" class="def">shapeN</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type)</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t]</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type]</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns shape of tensors.</p><p>This operation returns N 1-D integer tensors representing shape of `input[i]s`.</p></div></div><div class="top"><p class="src"><a name="v:uniformCandidateSampler" class="def">uniformCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to randomly sample per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>range_max</strong>: The sampler will sample integers from the interval [0, range_max).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
|
|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
|
|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
|
|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
|
|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a uniform distribution.</p><p>See explanations of candidate sampling and the data formats at
|
|
go/candidate-sampling.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
|
|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:unique" class="def">unique</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: 1-D.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</td><td class="doc"><p>(<strong>y</strong>, <strong>idx</strong>)</p><ul><li><strong>y</strong>: 1-D.</li><li><strong>idx</strong>: 1-D.</li></ul></td></tr></table></div><div class="doc"><p>Finds unique elements in a 1-D tensor.</p><p>This operation returns a tensor <code>y</code> containing all of the unique elements of <code>x</code>
|
|
sorted in the same order that they occur in <code>x</code>. This operation also returns a
|
|
tensor <code>idx</code> the same size as <code>x</code> that contains the index of each value of <code>x</code>
|
|
in the unique output <code>y</code>. In other words:</p><p>`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`</p><p>For example:</p><p>```prettyprint
|
|
# tensor <code>x</code> is [1, 1, 2, 4, 4, 4, 7, 8, 8]
|
|
y, idx = unique(x)
|
|
y ==> [1, 2, 4, 7, 8]
|
|
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:drawBoundingBoxes" class="def">drawBoundingBoxes</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D with shape `[batch, height, width, depth]`. A batch of images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>boxes</strong>: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
|
|
boxes.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with the same shape as <code>images</code>. The batch of input images with
|
|
bounding boxes drawn on the images.</p></td></tr></table></div><div class="doc"><p>Draw bounding boxes on a batch of images.</p><p>Outputs a copy of <code>images</code> but draws on top of the pixels zero or more bounding
|
|
boxes specified by the locations in <code>boxes</code>. The coordinates of the each
|
|
bounding box in <code>boxes</code> are encoded as `[y_min, x_min, y_max, x_max]`. The
|
|
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
|
|
height of the underlying image.</p><p>For example, if an image is 100 x 200 pixels and the bounding box is
|
|
`[0.1, 0.2, 0.5, 0.9]`, the bottom-left and upper-right coordinates of the
|
|
bounding box will be `(10, 40)` to `(50, 180)`.</p><p>Parts of the bounding box may fall outside the image.</p></div></div><div class="top"><p class="src"><a name="v:tensorArraySplit" class="def">tensorArraySplit</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The concatenated tensor to write to the TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>lengths</strong>: The vector of lengths, how to split the rows of value into the
|
|
TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_out</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr></table></div><div class="doc"><p>Split the data from the input value into TensorArray elements.</p><p>Assuming that <code>lengths</code> takes on values</p><p>```(n0, n1, ..., n(T-1))```</p><p>and that <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> has shape</p><p>```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```,</p><p>this splits values into a TensorArray with T tensors.</p><p>TensorArray index t will be the subtensor of values with starting position</p><p>```(n0 + n1 + ... + n(t-1), 0, 0, ...)```</p><p>and having size</p><p>```nt x d0 x d1 x ...```</p></div></div><div class="top"><p class="src"><a name="v:split" class="def">split</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_split</strong>: The number of ways to split. Must evenly divide
|
|
`value.shape[split_dim]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>split_dim</strong>: 0-D. The dimension along which to split. Must be in the range
|
|
`[0, rank(value))`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The tensor to split.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</td><td class="doc"><p><strong>output</strong>: They are identically shaped tensors, whose shape matches that of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>
|
|
except along <code>split_dim</code>, where their sizes are
|
|
`values.shape[split_dim] / num_split`.</p></td></tr></table></div><div class="doc"><p>Splits a tensor into <code>num_split</code> tensors along one dimension.</p></div></div><div class="top"><p class="src"><a name="v:segmentMax" class="def">segmentMax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor whose rank is equal to the rank of `data`'s
|
|
first dimension. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the maximum along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on Segmentation</a>
|
|
for an explanation of segments.</p><p>Computes a tensor such that
|
|
\(output_i = max_j(data_j)\) where <code><a href="../base-4.8.2.0/Data-Ord.html#v:max">max</a></code> is over <code>j</code> such
|
|
that `segment_ids[j] == i`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/SegmentMax.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:abort" class="def">abort</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></p><div class="doc"><p>Raise a exception to abort the process when called.</p><p>Returns nothing but an exception.</p></div></div><div class="top"><p class="src"><a name="v:sparseReorder" class="def">sparseReorder</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
|
|
SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>input_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>input_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>)</p><ul><li><strong>output_indices</strong>: 2-D. `N x R` matrix with the same indices as input_indices, but
|
|
in canonical row-major ordering.</li><li><strong>output_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>output_indices</code>.</li></ul></td></tr></table></div><div class="doc"><p>Reorders a SparseTensor into the canonical, row-major ordering.</p><p>Note that by convention, all sparse ops preserve the canonical ordering along
|
|
increasing dimension number. The only time ordering can be violated is during
|
|
manual manipulation of the indices and values vectors to add entries.</p><p>Reordering does not affect the shape of the SparseTensor.</p><p>If the tensor has rank <code>R</code> and <code>N</code> non-empty values, <code>input_indices</code> has
|
|
shape `[N, R]`, input_values has length <code>N</code>, and input_shape has length <code>R</code>.</p></div></div><div class="top"><p class="src"><a name="v:rsqrtGrad" class="def">rsqrtGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradient for the rsqrt of <code>x</code> wrt its input.</p><p>Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and <code>dy</code>
|
|
is the corresponding input gradient.</p></div></div><div class="top"><p class="src"><a name="v:reverseSequence" class="def">reverseSequence</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tlen, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tlen)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>seq_dim</strong>: The dimension which is partially reversed.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The input to reverse.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tlen</td><td class="doc"><p><strong>seq_lengths</strong>: 1-D with length `input.dims(batch_dim)` and
|
|
`max(seq_lengths) < input.dims(seq_dim)`</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The partially reversed input. It has the same shape as <code>input</code>.</p></td></tr></table></div><div class="doc"><p>Reverses variable length slices.</p><p>This op first slices <code>input</code> along the dimension <code>batch_dim</code>, and for each
|
|
slice <code>i</code>, reverses the first `seq_lengths[i]` elements along
|
|
the dimension <code>seq_dim</code>.</p><p>The elements of <code>seq_lengths</code> must obey `seq_lengths[i] < input.dims[seq_dim]`,
|
|
and <code>seq_lengths</code> must be a vector of length `input.dims[batch_dim]`.</p><p>The output slice <code>i</code> along dimension <code>batch_dim</code> is then given by input
|
|
slice <code>i</code>, with the first `seq_lengths[i]` slices along dimension
|
|
<code>seq_dim</code> reversed.</p><p>For example:</p><p>```prettyprint
|
|
# Given this:
|
|
batch_dim = 0
|
|
seq_dim = 1
|
|
input.dims = (4, 8, ...)
|
|
seq_lengths = [7, 2, 3, 5]</p><p># then slices of input are reversed on seq_dim, but only up to seq_lengths:
|
|
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
|
|
output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
|
|
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
|
|
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]</p><p># while entries past seq_lens are copied through:
|
|
output[0, 7:, :, ...] = input[0, 7:, :, ...]
|
|
output[1, 2:, :, ...] = input[1, 2:, :, ...]
|
|
output[2, 3:, :, ...] = input[2, 3:, :, ...]
|
|
output[3, 2:, :, ...] = input[3, 2:, :, ...]
|
|
```</p><p>In contrast, if:</p><p>```prettyprint
|
|
# Given this:
|
|
batch_dim = 2
|
|
seq_dim = 0
|
|
input.dims = (8, ?, 4, ...)
|
|
seq_lengths = [7, 2, 3, 5]</p><p># then slices of input are reversed on seq_dim, but only up to seq_lengths:
|
|
output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
|
|
output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
|
|
output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
|
|
output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]</p><p># while entries past seq_lens are copied through:
|
|
output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
|
|
output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
|
|
output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
|
|
output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:readerNumRecordsProduced" class="def">readerNumRecordsProduced</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>records_produced</strong></p></td></tr></table></div><div class="doc"><p>Returns the number of records this Reader has produced.</p><p>This is the same as the number of ReaderRead executions that have
|
|
succeeded.</p></div></div><div class="top"><p class="src"><a name="v:deserializeManySparse" class="def">deserializeManySparse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>serialized_sparse</strong>: 2-D, The <code>N</code> serialized <code>SparseTensor</code> objects.
|
|
Must have 3 columns.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>sparse_indices</strong>, <strong>sparse_values</strong>, <strong>sparse_shape</strong>)</p><ul><li><strong>sparse_indices</strong></li><li><strong>sparse_values</strong></li><li><strong>sparse_shape</strong></li></ul></td></tr></table></div><div class="doc"><p>Deserialize and concatenate <code>SparseTensors</code> from a serialized minibatch.</p><p>The input <code>serialized_sparse</code> must be a string matrix of shape `[N x 3]` where
|
|
<code>N</code> is the minibatch size and the rows correspond to packed outputs of
|
|
<code>SerializeSparse</code>. The ranks of the original <code>SparseTensor</code> objects
|
|
must all match. When the final <code>SparseTensor</code> is created, it has rank one
|
|
higher than the ranks of the incoming <code>SparseTensor</code> objects
|
|
(they have been concatenated along a new row dimension).</p><p>The output <code>SparseTensor</code> object's shape values for all dimensions but the
|
|
first are the max across the input <code>SparseTensor</code> objects' shape values
|
|
for the corresponding dimensions. Its first shape value is <code>N</code>, the minibatch
|
|
size.</p><p>The input <code>SparseTensor</code> objects' indices are assumed ordered in
|
|
standard lexicographic order. If this is not the case, after this
|
|
step run <code>SparseReorder</code> to restore index ordering.</p><p>For example, if the serialized input is a `[2 x 3]` matrix representing two
|
|
original <code>SparseTensor</code> objects:</p><p>index = [ 0]
|
|
[10]
|
|
[20]
|
|
values = [1, 2, 3]
|
|
shape = [50]</p><p>and</p><p>index = [ 2]
|
|
[10]
|
|
values = [4, 5]
|
|
shape = [30]</p><p>then the final deserialized <code>SparseTensor</code> will be:</p><p>index = [0 0]
|
|
[0 10]
|
|
[0 20]
|
|
[1 2]
|
|
[1 10]
|
|
values = [1, 2, 3, 4, 5]
|
|
shape = [2 50]</p></div></div><div class="top"><p class="src"><a name="v:immutableConst" class="def">immutableConst</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>tensor</strong></p></td></tr></table></div><div class="doc"><p>Returns immutable tensor from memory region.</p><p>The current implementation memmaps the tensor from a file.</p></div></div><div class="top"><p class="src"><a name="v:minimum" class="def">minimum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the min of x and y (i.e. x < y ? x : y) element-wise.</p><ul><li>NOTE*: <code>Minimum</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:initializeTableFromTextFile" class="def">initializeTableFromTextFile</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>key_index</strong>: Column index in a line to get the table <code>key</code> values from.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>value_index</strong>: Column index that represents information of a line to get the table
|
|
<code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> values from.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to a table which will be initialized.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>filename</strong>: Filename of a vocabulary text file.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Initializes a table from a text file.</p><p>It inserts one key-value pair into the table for each line of the file.
|
|
The key and value is extracted from the whole line content, elements from the
|
|
split line based on <code>delimiter</code> or the line number (starting from zero).
|
|
Where to extract the key and value from a line is specified by <code>key_index</code> and
|
|
<code>value_index</code>.</p><ul><li>A value of -1 means use the line number(starting from zero), expects <code>int64</code>.</li><li>A value of -2 means use the whole line content, expects <code>string</code>.</li><li>A value >= 0 means use the index (starting at zero) of the split line based
|
|
on <code>delimiter</code>.</li></ul></div></div><div class="top"><p class="src"><a name="v:diagPart" class="def">diagPart</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Rank k tensor where k is 2, 4, or 6.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>diagonal</strong>: The extracted diagonal.</p></td></tr></table></div><div class="doc"><p>Returns the diagonal part of the tensor.</p><p>This operation returns a tensor with the <code>diagonal</code> part
|
|
of the <code>input</code>. The <code>diagonal</code> part is computed as follows:</p><p>Assume <code>input</code> has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
|
|
tensor of rank <code>k</code> with dimensions `[D1,..., Dk]` where:</p><p>`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.</p><p>For example:</p><p>```prettyprint
|
|
# <code>input</code> is [[1, 0, 0, 0]
|
|
[0, 2, 0, 0]
|
|
[0, 0, 3, 0]
|
|
[0, 0, 0, 4]]</p><p>tf.diag_part(input) ==> [1, 2, 3, 4]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:log" class="def">log</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes natural logarithm of x element-wise.</p><p>I.e., \(y = log_e x\).</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayScatter" class="def">tensorArrayScatter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>indices</strong>: The locations at which to write the tensor elements.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>value</strong>: The concatenated tensor to write to the TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_out</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr></table></div><div class="doc"><p>Scatter the data from the input value into specific TensorArray elements.</p><p><code>indices</code> must be a vector, its length must match the first dim of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:rank" class="def">rank</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the rank of a tensor.</p><p>This operation returns an integer representing the rank of <code>input</code>.</p><p>For example:</p><p>```prettyprint
|
|
# <code>t</code> is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
|
|
# shape of tensor <code>t</code> is [2, 2, 3]
|
|
rank(t) ==> 3
|
|
```</p><ul><li>*Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
|
|
of a tensor is the number of indices required to uniquely select each element
|
|
of the tensor. Rank is also known as "order", "degree", or "ndims."</li></ul></div></div><div class="top"><p class="src"><a name="v:identity" class="def">identity</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Return a tensor with the same shape and contents as the input tensor or value.</p></div></div><div class="top"><p class="src"><a name="v:adjustContrastv2" class="def">adjustContrastv2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>images</strong>: Images to adjust. At least 3-D.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>contrast_factor</strong>: A float multiplier for adjusting contrast.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>output</strong>: The contrast-adjusted image or images.</p></td></tr></table></div><div class="doc"><p>Adjust the contrast of one or more images.</p><p><code>images</code> is a tensor of at least 3 dimensions. The last 3 dimensions are
|
|
interpreted as `[height, width, channels]`. The other dimensions only
|
|
represent a collection of images, such as `[batch, height, width, channels].`</p><p>Contrast is adjusted independently for each channel of each image.</p><p>For each channel, the Op first computes the mean of the image pixels in the
|
|
channel and then adjusts each component of each pixel to
|
|
`(x - mean) * contrast_factor + mean`.</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyProximalAdagrad" class="def">sparseApplyProximalAdagrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>lr</strong>: Learning rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.</p><p>That is for rows we have grad for, we update var and accum as follows:
|
|
accum += grad * grad
|
|
prox_v = var
|
|
prox_v -= lr * grad * (1 / sqrt(accum))
|
|
var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}</p></div></div><div class="top"><p class="src"><a name="v:gather" class="def">gather</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tparams)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tparams</td><td class="doc"><p><strong>params</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tparams</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Gather slices from <code>params</code> according to <code>indices</code>.</p><p><code>indices</code> must be an integer tensor of any dimension (usually 0-D or 1-D).
|
|
Produces an output tensor with shape `indices.shape + params.shape[1:]` where:</p><p># Scalar indices
|
|
output[:, ..., :] = params[indices, :, ... :]</p><p># Vector indices
|
|
output[i, :, ..., :] = params[indices[i], :, ... :]</p><p># Higher rank indices
|
|
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]</p><p>If <code>indices</code> is a permutation and `len(indices) == params.shape[0]` then
|
|
this operation will permute <code>params</code> accordingly.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/Gather.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:isVariableInitialized" class="def">isVariableInitialized</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 dtype</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node. May be uninitialized.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>is_initialized</strong></p></td></tr></table></div><div class="doc"><p>Checks whether a tensor has been initialized.</p><p>Outputs boolean scalar indicating whether the tensor has been initialized.</p></div></div><div class="top"><p class="src"><a name="v:concat" class="def">concat</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>concat_dim</strong>: 0-D. The dimension along which to concatenate. Must be in the
|
|
range [0, rank(values)).</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t]</td><td class="doc"><p><strong>values</strong>: The <code>N</code> Tensors to concatenate. Their ranks and types must match,
|
|
and their sizes must match in all dimensions except <code>concat_dim</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: A <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> with the concatenation of values stacked along the
|
|
<code>concat_dim</code> dimension. This tensor's shape matches that of <code>values</code> except
|
|
in <code>concat_dim</code> where it has the sum of the sizes.</p></td></tr></table></div><div class="doc"><p>Concatenates tensors along one dimension.</p></div></div><div class="top"><p class="src"><a name="v:randomUniformInt" class="def">randomUniformInt</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>shape</strong>: The shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tout</td><td class="doc"><p><strong>minval</strong>: 0-D. Inclusive lower bound on the generated integers.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout</td><td class="doc"><p><strong>maxval</strong>: 0-D. Exclusive upper bound on the generated integers.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>output</strong>: A tensor of the specified shape filled with uniform random integers.</p></td></tr></table></div><div class="doc"><p>Outputs random integers from a uniform distribution.</p><p>The generated values are uniform integers in the range `[minval, maxval)`.
|
|
The lower bound <code>minval</code> is included in the range, while the upper bound
|
|
<code>maxval</code> is excluded.</p><p>The random integers are slightly biased unless `maxval - minval` is an exact
|
|
power of two. The bias is small for values of `maxval - minval` significantly
|
|
smaller than the range of the output (either `2^32` or `2^64`).</p></div></div><div class="top"><p class="src"><a name="v:stopGradient" class="def">stopGradient</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Stops gradient computation.</p><p>When executed in a graph, this op outputs its input tensor as-is.</p><p>When building ops to compute gradients, this op prevents the contribution of
|
|
its inputs to be taken into account. Normally, the gradient generator adds ops
|
|
to a graph to compute the derivatives of a specified <code>loss</code> by recursively
|
|
finding out inputs that contributed to its computation. If you insert this op
|
|
in the graph it inputs are masked from the gradient generator. They are not
|
|
taken into account for computing gradients.</p><p>This is useful any time you want to compute a value with TensorFlow but need
|
|
to pretend that the value was a constant. Some examples include:</p><ul><li>The *EM* algorithm where the *M-step* should not involve backpropagation
|
|
through the output of the *E-step*.</li><li>Contrastive divergence training of Boltzmann machines where, when
|
|
differentiating the energy function, the training must not backpropagate
|
|
through the graph that generated the samples from the model.</li><li>Adversarial training, where no backprop should happen through the adversarial
|
|
example generation process.</li></ul></div></div><div class="top"><p class="src"><a name="v:avgPool" class="def">avgPool</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The average pooled output tensor.</p></td></tr></table></div><div class="doc"><p>Performs average pooling on the input.</p><p>Each entry in <code>output</code> is the mean of the corresponding size <code>ksize</code>
|
|
window in <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:wholeFileReader" class="def">wholeFileReader</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: The handle to reference the Reader.</p></td></tr></table></div><div class="doc"><p>A Reader that outputs the entire contents of a file as a value.</p><p>To use, enqueue filenames in a Queue. The output of ReaderRead will
|
|
be a filename (key) and the contents of that file (value).</p></div></div><div class="top"><p class="src"><a name="v:switch" class="def">switch</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be forwarded to the appropriate output.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>pred</strong>: A scalar that specifies which output port will receive data.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>output_false</strong>, <strong>output_true</strong>)</p><ul><li><strong>output_false</strong>: If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is false, data will be forwarded to this output.</li><li><strong>output_true</strong>: If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is true, data will be forwarded to this output.</li></ul></td></tr></table></div><div class="doc"><p>Forwards `data` to the output port determined by <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code>.</p><p>If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is true, the `data` input is forwarded to <code>output_true</code>. Otherwise,
|
|
the data goes to <code>output_false</code>.</p><p>See also <code>RefSwitch</code> and <code>Merge</code>.</p></div></div><div class="top"><p class="src"><a name="v:randomStandardNormal" class="def">randomStandardNormal</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>shape</strong>: The shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A tensor of the specified shape filled with random normal values.</p></td></tr></table></div><div class="doc"><p>Outputs random values from a normal distribution.</p><p>The generated values will have mean 0 and standard deviation 1.</p></div></div><div class="top"><p class="src"><a name="v:sigmoid" class="def">sigmoid</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes sigmoid of <code>x</code> element-wise.</p><p>Specifically, `y = 1 / (1 + exp(-x))`.</p></div></div><div class="top"><p class="src"><a name="v:sampleDistortedBoundingBox" class="def">sampleDistortedBoundingBox</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>image_size</strong>: 1-D, containing `[height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>bounding_boxes</strong>: 3-D with shape `[batch, N, 4]` describing the N bounding boxes
|
|
associated with the image.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>begin</strong>, <strong>size</strong>, <strong>bboxes</strong>)</p><ul><li><strong>begin</strong>: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
|
|
`tf.slice`.</li><li><strong>size</strong>: 1-D, containing `[target_height, target_width, -1]`. Provide as input to
|
|
`tf.slice`.</li><li><strong>bboxes</strong>: 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
|
|
Provide as input to `tf.image.draw_bounding_boxes`.</li></ul></td></tr></table></div><div class="doc"><p>Generate a single randomly distorted bounding box for an image.</p><p>Bounding box annotations are often supplied in addition to ground-truth labels
|
|
in image recognition or object localization tasks. A common technique for
|
|
training such a system is to randomly distort an image while preserving
|
|
its content, i.e. *data augmentation*. This Op outputs a randomly distorted
|
|
localization of an object, i.e. bounding box, given an <code>image_size</code>,
|
|
<code>bounding_boxes</code> and a series of constraints.</p><p>The output of this Op is a single bounding box that may be used to crop the
|
|
original image. The output is returned as 3 tensors: <code>begin</code>, <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> and
|
|
<code>bboxes</code>. The first 2 tensors can be fed directly into `tf.slice` to crop the
|
|
image. The latter may be supplied to `tf.image.draw_bounding_box` to visualize
|
|
what the bounding box looks like.</p><p>Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
|
|
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
|
|
height of the underlying image.</p><p>For example,</p><p># Generate a single distorted bounding box.
|
|
begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
|
|
tf.shape(image),
|
|
bounding_boxes=bounding_boxes)</p><p># Draw the bounding box in an image summary.
|
|
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
|
|
bbox_for_draw)
|
|
tf.image_summary(<code>images_with_box</code>, image_with_box)</p><p># Employ the bounding box to distort the image.
|
|
distorted_image = tf.slice(image, begin, size)</p><p>Note that if no bounding box information is available, setting
|
|
`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
|
|
bounding box covering the whole image. If <code>use_image_if_no_bounding_boxes</code> is
|
|
false and no bounding boxes are supplied, an error is raised.</p></div></div><div class="top"><p class="src"><a name="v:greater" class="def">greater</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x > y) element-wise.</p><ul><li>NOTE*: <code>Greater</code> supports broadcasting. More about broadcasting
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|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:refNextIteration" class="def">refNextIteration</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the next iteration.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Makes its input available to the next iteration.</p></div></div><div class="top"><p class="src"><a name="v:spaceToDepth" class="def">spaceToDepth</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>block_size</strong>: The size of the spatial block.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>SpaceToDepth for tensors of type T.</p><p>Rearranges blocks of spatial data, into depth. More specifically,
|
|
this op outputs a copy of the input tensor where values from the <code>height</code>
|
|
and <code>width</code> dimensions are moved to the <code>depth</code> dimension.
|
|
The attr <code>block_size</code> indicates the input block size and how the data is moved.</p><ul><li>Non-overlapping blocks of size `block_size x block size` are rearranged
|
|
into depth at each location.</li><li>The depth of the output tensor is `input_depth * block_size * block_size`.</li><li>The input tensor's height and width must be divisible by block_size.</li></ul><p>That is, assuming the input is in the shape:
|
|
`[batch, height, width, depth]`,
|
|
the shape of the output will be:
|
|
`[batch, height<em>block_size, width</em>block_size, depth*block_size*block_size]`</p><p>This operation requires that the input tensor be of rank 4, and that
|
|
<code>block_size</code> be >=1 and a divisor of both the input <code>height</code> and <code>width</code>.</p><p>This operation is useful for resizing the activations between convolutions
|
|
(but keeping all data), e.g. instead of pooling. It is also useful for training
|
|
purely convolutional models.</p><p>For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2:</p><p>```prettyprint
|
|
x = [[[[1], [2]],
|
|
[[3], [4]]]]
|
|
```</p><p>This operation will output a tensor of shape `[1, 1, 1, 4]`:</p><p>```prettyprint
|
|
[[[[1, 2, 3, 4]]]]
|
|
```</p><p>Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`,
|
|
the corresponding output will have a single element (i.e. width and height are
|
|
both 1) and will have a depth of 4 channels (1 * block_size * block_size).
|
|
The output element shape is `[1, 1, 4]`.</p><p>For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g.</p><p>```prettyprint
|
|
x = [[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]
|
|
```</p><p>This operation, for block_size of 2, will return the following tensor of shape
|
|
`[1, 1, 1, 12]`</p><p>```prettyprint
|
|
[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
|
|
```</p><p>Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2:</p><p>```prettyprint
|
|
x = [[[[1], [2], [5], [6]],
|
|
[[3], [4], [7], [8]],
|
|
[[9], [10], [13], [14]],
|
|
[[11], [12], [15], [16]]]]
|
|
```</p><p>the operator will return the following tensor of shape `[1 2 2 4]`:</p><p>```prettyprint
|
|
x = [[[[1, 2, 3, 4],
|
|
[5, 6, 7, 8]],
|
|
[[9, 10, 11, 12],
|
|
[13, 14, 15, 16]]]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:controlTrigger" class="def">controlTrigger</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></p><div class="doc"><p>Does nothing. Serves as a control trigger for scheduling.</p><p>Only useful as a placeholder for control edges.</p></div></div><div class="top"><p class="src"><a name="v:scatterDiv" class="def">scatterDiv</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: A tensor of indices into the first dimension of <code>ref</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>updates</strong>: A tensor of values that <code>ref</code> is divided by.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as <code>ref</code>. Returned as a convenience for operations that want
|
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to use the updated values after the update is done.</p></td></tr></table></div><div class="doc"><p>Divides a variable reference by sparse updates.</p><p>This operation computes</p><p># Scalar indices
|
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ref[indices, ...] /= updates[...]</p><p># Vector indices (for each i)
|
|
ref[indices[i], ...] /= updates[i, ...]</p><p># High rank indices (for each i, ..., j)
|
|
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]</p><p>This operation outputs <code>ref</code> after the update is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p><p>Duplicate entries are handled correctly: if multiple <code>indices</code> reference
|
|
the same location, their contributions divide.</p><p>Requires `updates.shape = indices.shape + ref.shape[1:]`.</p></div></div><div class="top"><p class="src"><a name="v:copy" class="def">copy</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Input tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Output tensor, deep-copied from input.</p></td></tr></table></div><div class="doc"><p>Copy Op.</p><p>Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the
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device on which the tensor is allocated.</p><p>Unlike the CopyHost Op, this op does not have HostMemory constraint on its
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input or output.</p></div></div><div class="top"><p class="src"><a name="v:cropAndResizeGradBoxes" class="def">cropAndResizeGradBoxes</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>grads</strong>: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>image</strong>: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
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Both <code>image_height</code> and <code>image_width</code> need to be positive.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>boxes</strong>: A 2-D tensor of shape `[num_boxes, 4]`. The <code>i</code>-th row of the tensor
|
|
specifies the coordinates of a box in the `box_ind[i]` image and is specified
|
|
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
|
|
<code>y</code> is mapped to the image coordinate at `y * (image_height - 1)`, so as the
|
|
`[0, 1]` interval of normalized image height is mapped to
|
|
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
|
|
which case the sampled crop is an up-down flipped version of the original
|
|
image. The width dimension is treated similarly. Normalized coordinates
|
|
outside the `[0, 1]` range are allowed, in which case we use
|
|
<code>extrapolation_value</code> to extrapolate the input image values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>box_ind</strong>: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
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|
The value of `box_ind[i]` specifies the image that the <code>i</code>-th box refers to.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>output</strong>: A 2-D tensor of shape `[num_boxes, 4]`.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of the crop_and_resize op wrt the input boxes tensor.</p></div></div><div class="top"><p class="src"><a name="v:sparseSegmentMean" class="def">sparseSegmentMean</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>indices</strong>: A 1-D tensor. Has same rank as <code>segment_ids</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the mean along sparse segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Like <code>SegmentMean</code>, but <code>segment_ids</code> can have rank less than `data`'s first
|
|
dimension, selecting a subset of dimension 0, specified by <code>indices</code>.</p></div></div><div class="top"><p class="src"><a name="v:assign" class="def">assign</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a <code>Variable</code> node. May be uninitialized.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The value to be assigned to the variable.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output_ref</strong>: = Same as "ref". Returned as a convenience for operations that want
|
|
to use the new value after the variable has been reset.</p></td></tr></table></div><div class="doc"><p>Update <code>ref</code> by assigning <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> to it.</p><p>This operation outputs "ref" after the assignment is done.
|
|
This makes it easier to chain operations that need to use the reset value.</p></div></div><div class="top"><p class="src"><a name="v:restore" class="def">restore</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dt</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>file_pattern</strong>: Must have a single element. The pattern of the files from
|
|
which we read the tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tensor_name</strong>: Must have a single element. The name of the tensor to be
|
|
restored.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dt</td><td class="doc"><p><strong>tensor</strong>: The restored tensor.</p></td></tr></table></div><div class="doc"><p>Restores a tensor from checkpoint files.</p><p>Reads a tensor stored in one or several files. If there are several files (for
|
|
instance because a tensor was saved as slices), <code>file_pattern</code> may contain
|
|
wildcard symbols (<code><a href="../base-4.8.2.0/Prelude.html#v:-42-">*</a></code> and <code>?</code>) in the filename portion only, not in the
|
|
directory portion.</p><p>If a <code>file_pattern</code> matches several files, <code>preferred_shard</code> can be used to hint
|
|
in which file the requested tensor is likely to be found. This op will first
|
|
open the file at index <code>preferred_shard</code> in the list of matching files and try
|
|
to restore tensors from that file. Only if some tensors or tensor slices are
|
|
not found in that first file, then the Op opens all the files. Setting
|
|
<code>preferred_shard</code> to match the value passed as the <code>shard</code> input
|
|
of a matching <code>Save</code> Op may speed up Restore. This attribute only affects
|
|
performance, not correctness. The default value -1 means files are processed in
|
|
order.</p><p>See also <code>RestoreSlice</code>.</p></div></div><div class="top"><p class="src"><a name="v:maxPoolGradWithArgmax" class="def">maxPoolGradWithArgmax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> targmax, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` targmax)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The original input.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>grad</strong>: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the
|
|
output of <code>max_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 targmax</td><td class="doc"><p><strong>argmax</strong>: The indices of the maximum values chosen for each output of <code>max_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Gradients w.r.t. the input of <code>max_pool</code>.</p></td></tr></table></div><div class="doc"><p>Computes gradients of the maxpooling function.</p></div></div><div class="top"><p class="src"><a name="v:checkNumerics" class="def">checkNumerics</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Checks a tensor for NaN and Inf values.</p><p>When run, reports an <code>InvalidArgument</code> error if <code>tensor</code> has any values
|
|
that are not a number (NaN) or infinity (Inf). Otherwise, passes <code>tensor</code> as-is.</p></div></div><div class="top"><p class="src"><a name="v:zerosLike" class="def">zerosLike</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: a tensor of type T.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong>: a tensor of the same shape and type as x but filled with zeros.</p></td></tr></table></div><div class="doc"><p>Returns a tensor of zeros with the same shape and type as x.</p></div></div><div class="top"><p class="src"><a name="v:readFile" class="def">readFile</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>filename</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong></p></td></tr></table></div><div class="doc"><p>Reads and outputs the entire contents of the input filename.</p></div></div><div class="top"><p class="src"><a name="v:transpose" class="def">transpose</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tperm, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tperm)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tperm</td><td class="doc"><p><strong>perm</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Shuffle dimensions of x according to a permutation.</p><p>The output <code>y</code> has the same rank as <code>x</code>. The shapes of <code>x</code> and <code>y</code> satisfy:
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|
`y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`</p></div></div><div class="top"><p class="src"><a name="v:parseTensor" class="def">parseTensor</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>serialized</strong>: A scalar string containing a serialized TensorProto proto.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</td><td class="doc"><p><strong>output</strong>: A Tensor of type <code>out_type</code>.</p></td></tr></table></div><div class="doc"><p>Transforms a serialized tensorflow.TensorProto proto into a Tensor.</p></div></div><div class="top"><p class="src"><a name="v:acos" class="def">acos</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes acos of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:bitcast" class="def">bitcast</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> type', <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` type')</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> type'</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Bitcasts a tensor from one type to another without copying data.</p><p>Given a tensor <code>input</code>, this operation returns a tensor that has the same buffer
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data as <code>input</code> with datatype `type`.</p><p>If the input datatype <code>T</code> is larger than the output datatype `type` then the
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shape changes from [...] to [..., sizeof(<code>T</code>)/sizeof(`type`)].</p><p>If <code>T</code> is smaller than `type`, the operator requires that the rightmost
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dimension be equal to sizeof(`type`)/sizeof(<code>T</code>). The shape then goes from
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[..., sizeof(`type`)/sizeof(<code>T</code>)] to [...].</p><ul><li>NOTE*: Bitcast is implemented as a low-level cast, so machines with different
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endian orderings will give different results.</li></ul></div></div><div class="top"><p class="src"><a name="v:lookupTableImport" class="def">lookupTableImport</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to the table.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin</td><td class="doc"><p><strong>keys</strong>: Any shape. Keys to look up.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout</td><td class="doc"><p><strong>values</strong>: Values to associate with keys.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Replaces the contents of the table with the specified keys and values.</p><p>The tensor <code>keys</code> must be of the same type as the keys of the table.
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The tensor <code>values</code> must be of the type of the table values.</p></div></div><div class="top"><p class="src"><a name="v:biasAddGrad" class="def">biasAddGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>out_backprop</strong>: Any number of dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D with size the feature dimension of <code>out_backprop</code>.</p></td></tr></table></div><div class="doc"><p>The backward operation for <a href="BiasAdd.html">BiasAdd</a> on the "bias" tensor.</p><p>It accumulates all the values from out_backprop into the feature dimension.
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For NHWC data format, the feature dimension is the last. For NCHW data format,
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the feature dimension is the third-to-last.</p></div></div><div class="top"><p class="src"><a name="v:batchSelfAdjointEig" class="def">batchSelfAdjointEig</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:prod" class="def">prod</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the product of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
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<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
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<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
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retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:resizeBilinear" class="def">resizeBilinear</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
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new size for the images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>resized_images</strong>: 4-D with shape
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`[batch, new_height, new_width, channels]`.</p></td></tr></table></div><div class="doc"><p>Resize <code>images</code> to <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> using bilinear interpolation.</p><p>Input images can be of different types but output images are always float.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayUnpack" class="def">tensorArrayUnpack</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: The concatenated tensor to write to the TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_out</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr></table></div><div class="doc"><p>Unpack the data from the input value into TensorArray elements.</p><ul><li>*WARNING: This op is deprecated.**</li></ul><p>Instead of this op, use <code>TensorArrayScatter</code> with
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`indices = RangeOp(0, SizeOp(value)[0])`.</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixDeterminant" class="def">batchMatrixDeterminant</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:sum" class="def">sum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the sum of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
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|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:iFFT2D" class="def">iFFT2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most 2
|
|
dimensions of <code>input</code> are replaced with their inverse 2D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most</p><p>2 dimensions of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:fill" class="def">fill</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>dims</strong>: 1-D. Represents the shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>value</strong>: 0-D (scalar). Value to fill the returned tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Creates a tensor filled with a scalar value.</p><p>This operation creates a tensor of shape <code>dims</code> and fills it with <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><p>For example:</p><p>```prettyprint
|
|
# Output tensor has shape [2, 3].
|
|
fill([2, 3], 9) ==> [[9, 9, 9]
|
|
[9, 9, 9]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:fixedUnigramCandidateSampler" class="def">fixedUnigramCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to randomly sample per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>range_max</strong>: The sampler will sample integers from the interval [0, range_max).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
|
|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
|
|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
|
|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
|
|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a learned unigram distribution.</p><p>A unigram sampler could use a fixed unigram distribution read from a
|
|
file or passed in as an in-memory array instead of building up the distribution
|
|
from data on the fly. There is also an option to skew the distribution by
|
|
applying a distortion power to the weights.</p><p>The vocabulary file should be in CSV-like format, with the last field
|
|
being the weight associated with the word.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
|
|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:dilation2D" class="def">dilation2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: 3-D with shape `[filter_height, filter_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, out_height, out_width, depth]`.</p></td></tr></table></div><div class="doc"><p>Computes the grayscale dilation of 4-D <code>input</code> and 3-D <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> tensors.</p><p>The <code>input</code> tensor has shape `[batch, in_height, in_width, depth]` and the
|
|
<code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> tensor has shape `[filter_height, filter_width, depth]`, i.e., each
|
|
input channel is processed independently of the others with its own structuring
|
|
function. The <code>output</code> tensor has shape
|
|
`[batch, out_height, out_width, depth]`. The spatial dimensions of the output
|
|
tensor depend on the <code>padding</code> algorithm. We currently only support the default
|
|
<a href="NHWC.html">NHWC</a> <code>data_format</code>.</p><p>In detail, the grayscale morphological 2-D dilation is the max-sum correlation
|
|
(for consistency with <code>conv2d</code>, we use unmirrored filters):</p><p>output[b, y, x, c] =
|
|
max_{dy, dx} input[b,
|
|
strides[1] * y + rates[1] * dy,
|
|
strides[2] * x + rates[2] * dx,
|
|
c] +
|
|
filter[dy, dx, c]</p><p>Max-pooling is a special case when the filter has size equal to the pooling
|
|
kernel size and contains all zeros.</p><p>Note on duality: The dilation of <code>input</code> by the <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> is equal to the
|
|
negation of the erosion of `-input` by the reflected <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:polygamma" class="def">polygamma</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Compute the polygamma function \(psi^{(n)}(x)\).</p><p>The polygamma function is defined as:</p><p>```
|
|
psi^{(n)}(x) = frac{d^n}{dx^n} psi(x)
|
|
```
|
|
where \(psi(x)\) is the digamma function.</p></div></div><div class="top"><p class="src"><a name="v:refIdentity" class="def">refIdentity</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Return the same ref tensor as the input ref tensor.</p></div></div><div class="top"><p class="src"><a name="v:encodePng" class="def">encodePng</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>image</strong>: 3-D with shape `[height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong>: 0-D. PNG-encoded image.</p></td></tr></table></div><div class="doc"><p>PNG-encode an image.</p><p><code>image</code> is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
|
|
where <code>channels</code> is:</p><ul><li>1: for grayscale.</li><li>2: for grayscale + alpha.</li><li>3: for RGB.</li><li>4: for RGBA.</li></ul><p>The ZLIB compression level, <code>compression</code>, can be -1 for the PNG-encoder
|
|
default or a value from 0 to 9. 9 is the highest compression level, generating
|
|
the smallest output, but is slower.</p></div></div><div class="top"><p class="src"><a name="v:lookupTableInsert" class="def">lookupTableInsert</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to the table.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin</td><td class="doc"><p><strong>keys</strong>: Any shape. Keys to look up.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout</td><td class="doc"><p><strong>values</strong>: Values to associate with keys.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Updates the table to associates keys with values.</p><p>The tensor <code>keys</code> must be of the same type as the keys of the table.
|
|
The tensor <code>values</code> must be of the type of the table values.</p></div></div><div class="top"><p class="src"><a name="v:batchIFFT2D" class="def">batchIFFT2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:uniqueWithCounts" class="def">uniqueWithCounts</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_idx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: 1-D.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_idx)</td><td class="doc"><p>(<strong>y</strong>, <strong>idx</strong>, <strong>count</strong>)</p><ul><li><strong>y</strong>: 1-D.</li><li><strong>idx</strong>: 1-D.</li><li><strong>count</strong>: 1-D.</li></ul></td></tr></table></div><div class="doc"><p>Finds unique elements in a 1-D tensor.</p><p>This operation returns a tensor <code>y</code> containing all of the unique elements of <code>x</code>
|
|
sorted in the same order that they occur in <code>x</code>. This operation also returns a
|
|
tensor <code>idx</code> the same size as <code>x</code> that contains the index of each value of <code>x</code>
|
|
in the unique output <code>y</code>. Finally, it returns a third tensor <code>count</code> that
|
|
contains the count of each element of <code>y</code> in <code>x</code>. In other words:</p><p>`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`</p><p>For example:</p><p>```prettyprint
|
|
# tensor <code>x</code> is [1, 1, 2, 4, 4, 4, 7, 8, 8]
|
|
y, idx, count = unique_with_counts(x)
|
|
y ==> [1, 2, 4, 7, 8]
|
|
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
|
|
count ==> [2, 1, 3, 1, 2]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:gatherNd" class="def">gatherNd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tparams)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tparams</td><td class="doc"><p><strong>params</strong>: `M-D`. The tensor from which to gather values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>indices</strong>: `(N+1)-D`. Index tensor having shape `[d_0, ..., d_N, R]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tparams</td><td class="doc"><p><strong>output</strong>: `(N+M-R)-D`. Values from <code>params</code> gathered from indices given by
|
|
<code>indices</code>.</p></td></tr></table></div><div class="doc"><p>Gather values or slices from <code>params</code> according to <code>indices</code>.</p><p><code>params</code> is a Tensor of rank <code>R</code> and <code>indices</code> is a Tensor of rank <code>M</code>.</p><p><code>indices</code> must be integer tensor, containing indices into <code>params</code>.
|
|
It must be shape `[d_0, ..., d_N, R]` where `0 < R <= M`.</p><p>The innermost dimension of <code>indices</code> (with length <code>R</code>) corresponds to
|
|
indices into elements (if `R = M`) or slices (if `R < M`) along the <code>N</code>th
|
|
dimension of <code>params</code>.</p><p>Produces an output tensor with shape</p><dl><dt>d_0, ..., d_{n-1}, params.shape[R</dt><dd>, ..., params.shape[M-1]].</dd></dl><p>Some examples below.</p><p>Simple indexing into a matrix:</p><p>indices = [[0, 0], [1, 1]]
|
|
params = [[<code>a</code>, <code>b</code>], [<code>c</code>, <code>d</code>]]
|
|
output = [<code>a</code>, <code>d</code>]</p><p>Slice indexing into a matrix:</p><p>indices = [[1], [0]]
|
|
params = [[<code>a</code>, <code>b</code>], [<code>c</code>, <code>d</code>]]
|
|
output = [[<code>c</code>, <code>d</code>], [<code>a</code>, <code>b</code>]]</p><p>Indexing into a 3-tensor:</p><p>indices = [[1]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]</p><p>indices = [[0, 1], [1, 0]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [[<code>c0</code>, <code>d0</code>], [<code>a1</code>, <code>b1</code>]]</p><p>indices = [[0, 0, 1], [1, 0, 1]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [<code>b0</code>, <code>b1</code>]</p><p>Batched indexing into a matrix:</p><p>indices = [[[0, 0]], [[0, 1]]]
|
|
params = [[<code>a</code>, <code>b</code>], [<code>c</code>, <code>d</code>]]
|
|
output = [[<code>a</code>], [<code>b</code>]]</p><p>Batched slice indexing into a matrix:</p><p>indices = [[[1]], [[0]]]
|
|
params = [[<code>a</code>, <code>b</code>], [<code>c</code>, <code>d</code>]]
|
|
output = [[[<code>c</code>, <code>d</code>]], [[<code>a</code>, <code>b</code>]]]</p><p>Batched indexing into a 3-tensor:</p><p>indices = [[[1]], [[0]]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [[[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]],
|
|
[[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]]]]</p><p>indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [[[<code>c0</code>, <code>d0</code>], [<code>a1</code>, <code>b1</code>]],
|
|
[[<code>a0</code>, <code>b0</code>], [<code>c1</code>, <code>d1</code>]]]</p><p>indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
|
|
params = [[[<code>a0</code>, <code>b0</code>], [<code>c0</code>, <code>d0</code>]],
|
|
[[<code>a1</code>, <code>b1</code>], [<code>c1</code>, <code>d1</code>]]]
|
|
output = [[<code>b0</code>, <code>b1</code>], [<code>d0</code>, <code>c1</code>]]</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayRead" class="def">tensorArrayRead</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>index</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>value</strong>: The tensor that is read from the TensorArray.</p></td></tr></table></div><div class="doc"><p>Read an element from the TensorArray into output <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:readerReadUpTo" class="def">readerReadUpTo</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a <code>Reader</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>queue_handle</strong>: Handle to a <code>Queue</code>, with string work items.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_records</strong>: number of records to read from <code>Reader</code>.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>)</td><td class="doc"><p>(<strong>keys</strong>, <strong>values</strong>)</p><ul><li><strong>keys</strong>: A 1-D tensor.</li><li><strong>values</strong>: A 1-D tensor.</li></ul></td></tr></table></div><div class="doc"><p>Returns up to <code>num_records</code> (key, value) pairs produced by a Reader.</p><p>Will dequeue from the input queue if necessary (e.g. when the
|
|
Reader needs to start reading from a new file since it has finished
|
|
with the previous file).
|
|
It may return less than <code>num_records</code> even before the last batch.</p></div></div><div class="top"><p class="src"><a name="v:betainc" class="def">betainc</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>b</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Compute the regularized incomplete beta integral \(I_x(a, b)\).</p><p>The regularized incomplete beta integral is defined as:</p><p>```
|
|
I_x(a, b) = frac{B(x; a, b)}{B(a, b)}
|
|
```
|
|
where</p><p>```
|
|
B(x; a, b) = int_0^x t^{a-1} (1 - t)^{b-1} dt
|
|
```</p><p>is the incomplete beta function and \(B(a, b)\) is the *complete*
|
|
beta function.</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixBandPart" class="def">batchMatrixBandPart</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_lower</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_upper</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>band</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:depthwiseConv2dNativeBackpropInput" class="def">depthwiseConv2dNativeBackpropInput</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>input_sizes</strong>: An integer vector representing the shape of <code>input</code>,
|
|
where <code>input</code> is a 4-D `[batch, height, width, channels]` tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: 4-D with shape
|
|
`[filter_height, filter_width, in_channels, depthwise_multiplier]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, out_channels]`.
|
|
Gradients w.r.t. the output of the convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient
|
|
w.r.t. the input of the convolution.</p></td></tr></table></div><div class="doc"><p>Computes the gradients of depthwise convolution with respect to the input.</p></div></div><div class="top"><p class="src"><a name="v:refSelect" class="def">refSelect</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>index</strong>: A scalar that determines the input that gets selected.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t]</td><td class="doc"><p><strong>inputs</strong>: A list of ref tensors, one of which will be forwarded to <code>output</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The forwarded tensor.</p></td></tr></table></div><div class="doc"><p>Forwards the <code>index</code>th element of <code>inputs</code> to <code>output</code>.</p></div></div><div class="top"><p class="src"><a name="v:exit" class="def">exit</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the parent frame.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Exits the current frame to its parent frame.</p><p>Exit makes its input `data` available to the parent frame.</p></div></div><div class="top"><p class="src"><a name="v:lookupTableFind" class="def">lookupTableFind</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tin, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to the table.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tin</td><td class="doc"><p><strong>keys</strong>: Any shape. Keys to look up.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tout</td><td class="doc"><p><strong>default_value</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>values</strong>: Same shape as <code>keys</code>. Values found in the table, or <code>default_values</code>
|
|
for missing keys.</p></td></tr></table></div><div class="doc"><p>Looks up keys in a table, outputs the corresponding values.</p><p>The tensor <code>keys</code> must of the same type as the keys of the table.
|
|
The output <code>values</code> is of the type of the table values.</p><p>The scalar <code>default_value</code> is the value output for keys not present in the
|
|
table. It must also be of the same type as the table values.</p></div></div><div class="top"><p class="src"><a name="v:squeeze" class="def">squeeze</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The <code>input</code> to squeeze.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Contains the same data as <code>input</code>, but has one or more dimensions of
|
|
size 1 removed.</p></td></tr></table></div><div class="doc"><p>Removes dimensions of size 1 from the shape of a tensor.</p><p>Given a tensor <code>input</code>, this operation returns a tensor of the same type with
|
|
all dimensions of size 1 removed. If you don't want to remove all size 1
|
|
dimensions, you can remove specific size 1 dimensions by specifying
|
|
<code>squeeze_dims</code>.</p><p>For example:</p><p>```prettyprint
|
|
# <code>t</code> is a tensor of shape [1, 2, 1, 3, 1, 1]
|
|
shape(squeeze(t)) ==> [2, 3]
|
|
```</p><p>Or, to remove specific size 1 dimensions:</p><p>```prettyprint
|
|
# <code>t</code> is a tensor of shape [1, 2, 1, 3, 1, 1]
|
|
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:mean" class="def">mean</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the mean of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
|
|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:spaceToBatchND" class="def">spaceToBatchND</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
|
|
where spatial_shape has <code>M</code> dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tblock_shape</td><td class="doc"><p><strong>block_shape</strong>: 1-D with shape `[M]`, all values must be >= 1.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tpaddings</td><td class="doc"><p><strong>paddings</strong>: 2-D with shape `[M, 2]`, all values must be >= 0.
|
|
`paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension
|
|
`i + 1`, which corresponds to spatial dimension <code>i</code>. It is required that
|
|
`block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.</p><p>This operation is equivalent to the following steps:</p><ol><li>Zero-pad the start and end of dimensions `[1, ..., M]` of the
|
|
input according to <code>paddings</code> to produce <code>padded</code> of shape <code>padded_shape</code>.</li><li>Reshape <code>padded</code> to <code>reshaped_padded</code> of shape:
|
|
[batch] +
|
|
[padded_shape[1] / block_shape[0],
|
|
block_shape[0],
|
|
...,
|
|
padded_shape[M] / block_shape[M-1],
|
|
block_shape[M-1]] +
|
|
remaining_shape</li><li>Permute dimensions of <code>reshaped_padded</code> to produce
|
|
<code>permuted_reshaped_padded</code> of shape:
|
|
block_shape +
|
|
[batch] +
|
|
[padded_shape[1] / block_shape[0],
|
|
...,
|
|
padded_shape[M] / block_shape[M-1]] +
|
|
remaining_shape</li><li>Reshape <code>permuted_reshaped_padded</code> to flatten <code>block_shape</code> into the batch
|
|
dimension, producing an output tensor of shape:
|
|
[batch * prod(block_shape)] +
|
|
[padded_shape[1] / block_shape[0],
|
|
...,
|
|
padded_shape[M] / block_shape[M-1]] +
|
|
remaining_shape</li></ol><p>Some examples:</p><ol><li>For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and
|
|
`paddings = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2]], [[3], [4]]]]
|
|
```</p><p>The output tensor has shape `[4, 1, 1, 1]` and value:</p><p>```prettyprint
|
|
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
|
|
```</p><ol><li>For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and
|
|
`paddings = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]
|
|
```</p><p>The output tensor has shape `[4, 1, 1, 3]` and value:</p><p>```prettyprint
|
|
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
|
|
```</p><ol><li>For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and
|
|
`paddings = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]],
|
|
[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]]
|
|
```</p><p>The output tensor has shape `[4, 2, 2, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [3]], [[5], [7]]],
|
|
[[[2], [4]], [[10], [12]]],
|
|
[[[5], [7]], [[13], [15]]],
|
|
[[[6], [8]], [[14], [16]]]]
|
|
```</p><ol><li>For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and
|
|
paddings = `[[0, 0], [2, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]]],
|
|
[[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]]
|
|
```</p><p>The output tensor has shape `[8, 1, 3, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
|
|
[[[0], [2], [4]]], [[[0], [10], [12]]],
|
|
[[[0], [5], [7]]], [[[0], [13], [15]]],
|
|
[[[0], [6], [8]]], [[[0], [14], [16]]]]
|
|
```</p><p>Among others, this operation is useful for reducing atrous convolution into
|
|
regular convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>SpaceToBatch for N-D tensors of type T.</p><p>This operation divides "spatial" dimensions `[1, ..., M]` of the input into a
|
|
grid of blocks of shape <code>block_shape</code>, and interleaves these blocks with the
|
|
"batch" dimension (0) such that in the output, the spatial dimensions
|
|
`[1, ..., M]` correspond to the position within the grid, and the batch
|
|
dimension combines both the position within a spatial block and the original
|
|
batch position. Prior to division into blocks, the spatial dimensions of the
|
|
input are optionally zero padded according to <code>paddings</code>. See below for a
|
|
precise description.</p></div></div><div class="top"><p class="src"><a name="v:spaceToBatch" class="def">spaceToBatch</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>block_size</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, height, width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings</td><td class="doc"><p><strong>paddings</strong>: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
|
|
the padding of the input with zeros across the spatial dimensions as follows:</p><p>paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]</p><p>The effective spatial dimensions of the zero-padded input tensor will be:</p><p>height_pad = pad_top + height + pad_bottom
|
|
width_pad = pad_left + width + pad_right</p><p>The attr <code>block_size</code> must be greater than one. It indicates the block size.</p><ul><li>Non-overlapping blocks of size `block_size x block size` in the height and
|
|
width dimensions are rearranged into the batch dimension at each location.</li><li>The batch of the output tensor is `batch * block_size * block_size`.</li><li>Both height_pad and width_pad must be divisible by block_size.</li></ul><p>The shape of the output will be:</p><p>[batch*block_size*block_size, height_pad<em>block_size, width_pad</em>block_size,
|
|
depth]</p><p>Some examples:</p><ol><li>For the following input of shape `[1, 2, 2, 1]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2]], [[3], [4]]]]
|
|
```</p><p>The output tensor has shape `[4, 1, 1, 1]` and value:</p><p>```prettyprint
|
|
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
|
|
```</p><ol><li>For the following input of shape `[1, 2, 2, 3]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]
|
|
```</p><p>The output tensor has shape `[4, 1, 1, 3]` and value:</p><p>```prettyprint
|
|
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
|
|
```</p><ol><li>For the following input of shape `[1, 4, 4, 1]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]],
|
|
[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]]
|
|
```</p><p>The output tensor has shape `[4, 2, 2, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [3]], [[5], [7]]],
|
|
[[[2], [4]], [[10], [12]]],
|
|
[[[5], [7]], [[13], [15]]],
|
|
[[[6], [8]], [[14], [16]]]]
|
|
```</p><ol><li>For the following input of shape `[2, 2, 4, 1]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]]],
|
|
[[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]]
|
|
```</p><p>The output tensor has shape `[8, 1, 2, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
|
|
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
|
|
```</p><p>Among others, this operation is useful for reducing atrous convolution into
|
|
regular convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>SpaceToBatch for 4-D tensors of type T.</p><p>This is a legacy version of the more general SpaceToBatchND.</p><p>Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
|
|
More specifically, this op outputs a copy of the input tensor where values from
|
|
the <code>height</code> and <code>width</code> dimensions are moved to the <code>batch</code> dimension. After
|
|
the zero-padding, both <code>height</code> and <code>width</code> of the input must be divisible by the
|
|
block size.</p></div></div><div class="top"><p class="src"><a name="v:cTCGreedyDecoder" class="def">cTCGreedyDecoder</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>inputs</strong>: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>sequence_length</strong>: A vector containing sequence lengths, size `(batch_size)`.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>decoded_indices</strong>, <strong>decoded_values</strong>, <strong>decoded_shape</strong>, <strong>log_probability</strong>)</p><ul><li><strong>decoded_indices</strong>: Indices matrix, size `(total_decoded_outputs x 2)`,
|
|
of a `SparseTensor<a href="int64,">2</a>`. The rows store: [batch, time].</li><li><strong>decoded_values</strong>: Values vector, size: `(total_decoded_outputs)`,
|
|
of a `SparseTensor<a href="int64,">2</a>`. The vector stores the decoded classes.</li><li><strong>decoded_shape</strong>: Shape vector, size `(2)`, of the decoded SparseTensor.
|
|
Values are: `[batch_size, max_decoded_length]`.</li><li><strong>log_probability</strong>: Matrix, size `(batch_size x 1)`, containing sequence
|
|
log-probabilities.</li></ul></td></tr></table></div><div class="doc"><p>Performs greedy decoding on the logits given in inputs.</p><p>A note about the attribute merge_repeated: if enabled, when
|
|
consecutive logits' maximum indices are the same, only the first of
|
|
these is emitted. Labeling the blank <code><a href="../base-4.8.2.0/Prelude.html#v:-42-">*</a></code>, the sequence "A B B * B B"
|
|
becomes "A B" if merge_repeated = True and "A B B B B" if
|
|
merge_repeated = False.</p><p>Regardless of the value of merge_repeated, if the maximum index of a given
|
|
time and batch corresponds to the blank, index `(num_classes - 1)`, no new
|
|
element is emitted.</p></div></div><div class="top"><p class="src"><a name="v:batchToSpaceND" class="def">batchToSpaceND</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tblock_shape, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tcrops, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tcrops)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
|
|
where spatial_shape has M dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tblock_shape</td><td class="doc"><p><strong>block_shape</strong>: 1-D with shape `[M]`, all values must be >= 1.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tcrops</td><td class="doc"><p><strong>crops</strong>: 2-D with shape `[M, 2]`, all values must be >= 0.
|
|
`crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
|
|
dimension `i + 1`, which corresponds to spatial dimension <code>i</code>. It is
|
|
required that
|
|
`crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.</p><p>This operation is equivalent to the following steps:</p><ol><li>Reshape <code>input</code> to <code>reshaped</code> of shape:
|
|
[block_shape[0], ..., block_shape[M-1],
|
|
batch / prod(block_shape),
|
|
input_shape[1], ..., input_shape[N-1]]</li><li>Permute dimensions of <code>reshaped</code> to produce <code>permuted</code> of shape
|
|
[batch / prod(block_shape),</li></ol><p>input_shape[1], block_shape[0],
|
|
...,
|
|
input_shape[M], block_shape[M-1],</p><p>input_shape[M+1], ..., input_shape[N-1]]</p><ol><li>Reshape <code>permuted</code> to produce <code>reshaped_permuted</code> of shape
|
|
[batch / prod(block_shape),</li></ol><p>input_shape[1] * block_shape[0],
|
|
...,
|
|
input_shape[M] * block_shape[M-1],</p><p>input_shape[M+1],
|
|
...,
|
|
input_shape[N-1]]</p><ol><li>Crop the start and end of dimensions `[1, ..., M]` of
|
|
<code>reshaped_permuted</code> according to <code>crops</code> to produce the output of shape:
|
|
[batch / prod(block_shape),</li></ol><p>input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
|
|
...,
|
|
input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],</p><p>input_shape[M+1], ..., input_shape[N-1]]</p><p>Some examples:</p><ol><li>For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and
|
|
`crops = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
|
|
```</p><p>The output tensor has shape `[1, 2, 2, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [2]], [[3], [4]]]]
|
|
```</p><ol><li>For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
|
|
`crops = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
|
|
```</p><p>The output tensor has shape `[1, 2, 2, 3]` and value:</p><p>```prettyprint
|
|
x = [[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]
|
|
```</p><ol><li>For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
|
|
`crops = [[0, 0], [0, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[1], [3]], [[5], [7]]],
|
|
[[[2], [4]], [[10], [12]]],
|
|
[[[5], [7]], [[13], [15]]],
|
|
[[[6], [8]], [[14], [16]]]]
|
|
```</p><p>The output tensor has shape `[1, 4, 4, 1]` and value:</p><p>```prettyprint
|
|
x = [[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]],
|
|
[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]
|
|
```</p><ol><li>For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
|
|
`crops = [[0, 0], [2, 0]]`:</li></ol><p>```prettyprint
|
|
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
|
|
[[[0], [2], [4]]], [[[0], [10], [12]]],
|
|
[[[0], [5], [7]]], [[[0], [13], [15]]],
|
|
[[[0], [6], [8]]], [[[0], [14], [16]]]]
|
|
```</p><p>The output tensor has shape `[2, 2, 4, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]]],
|
|
[[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]]
|
|
```</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>BatchToSpace for N-D tensors of type T.</p><p>This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape
|
|
`block_shape + [batch]`, interleaves these blocks back into the grid defined by
|
|
the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as
|
|
the input. The spatial dimensions of this intermediate result are then
|
|
optionally cropped according to <code>crops</code> to produce the output. This is the
|
|
reverse of SpaceToBatch. See below for a precise description.</p></div></div><div class="top"><p class="src"><a name="v:pack" class="def">pack</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t]</td><td class="doc"><p><strong>values</strong>: Must be of same shape and type.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The packed tensor.</p></td></tr></table></div><div class="doc"><p>Packs a list of <code>N</code> rank-<code>R</code> tensors into one rank-`(R+1)` tensor.</p><p>Packs the <code>N</code> tensors in <code>values</code> into a tensor with rank one higher than each
|
|
tensor in <code>values</code>, by packing them along the <code>axis</code> dimension.
|
|
Given a list of tensors of shape `(A, B, C)`;</p><p>if `axis == 0` then the <code>output</code> tensor will have the shape `(N, A, B, C)`.
|
|
if `axis == 1` then the <code>output</code> tensor will have the shape `(A, N, B, C)`.
|
|
Etc.</p><p>For example:</p><p>```prettyprint
|
|
# <code>x</code> is [1, 4]
|
|
# <code>y</code> is [2, 5]
|
|
# <code>z</code> is [3, 6]
|
|
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
|
|
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
|
|
```</p><p>This is the opposite of <code><a href="TensorFlow-GenOps-Core.html#v:unpack">unpack</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:oneHot" class="def">oneHot</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tI, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>]` tI)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tI</td><td class="doc"><p><strong>indices</strong>: A tensor of indices.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>depth</strong>: A scalar defining the depth of the one hot dimension.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>on_value</strong>: A scalar defining the value to fill in output when `indices[j] = i`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>off_value</strong>: A scalar defining the value to fill in output when `indices[j] != i`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The one-hot tensor.</p></td></tr></table></div><div class="doc"><p>Returns a one-hot tensor.</p><p>The locations represented by indices in <code>indices</code> take value <code>on_value</code>,
|
|
while all other locations take value <code>off_value</code>.</p><p>If the input <code>indices</code> is rank <code>N</code>, the output will have rank `N+1`,
|
|
The new axis is created at dimension <code>axis</code> (default: the new axis is
|
|
appended at the end).</p><p>If <code>indices</code> is a scalar the output shape will be a vector of length <code>depth</code>.</p><p>If <code>indices</code> is a vector of length <code>features</code>, the output shape will be:
|
|
```
|
|
features x depth if axis == -1
|
|
depth x features if axis == 0
|
|
```</p><p>If <code>indices</code> is a matrix (batch) with shape `[batch, features]`,
|
|
the output shape will be:
|
|
```
|
|
batch x features x depth if axis == -1
|
|
batch x depth x features if axis == 1
|
|
depth x batch x features if axis == 0
|
|
```</p><p>Examples
|
|
=========</p><p>Suppose that</p><p>```
|
|
indices = [0, 2, -1, 1]
|
|
depth = 3
|
|
on_value = 5.0
|
|
off_value = 0.0
|
|
axis = -1
|
|
```</p><p>Then output is `[4 x 3]`:</p><p>```output =
|
|
[5.0 0.0 0.0] // one_hot(0)
|
|
[0.0 0.0 5.0] // one_hot(2)
|
|
[0.0 0.0 0.0] // one_hot(-1)
|
|
[0.0 5.0 0.0] // one_hot(1)
|
|
```</p><p>Suppose that</p><p>```
|
|
indices = [0, 2, -1, 1]
|
|
depth = 3
|
|
on_value = 0.0
|
|
off_value = 3.0
|
|
axis = 0
|
|
```</p><p>Then output is `[3 x 4]`:</p><p>```output =
|
|
[0.0 3.0 3.0 3.0]
|
|
[3.0 3.0 3.0 0.0]
|
|
[3.0 3.0 3.0 3.0]
|
|
[3.0 0.0 3.0 3.0]
|
|
// ^ one_hot(0)
|
|
// ^ one_hot(2)
|
|
// ^ one_hot(-1)
|
|
// ^ one_hot(1)
|
|
```
|
|
Suppose that</p><p>```
|
|
indices = [[0, 2], [1, -1]]
|
|
depth = 3
|
|
on_value = 1.0
|
|
off_value = 0.0
|
|
axis = -1
|
|
```</p><p>Then output is `[2 x 2 x 3]`:</p><p>```output =
|
|
[
|
|
[1.0, 0.0, 0.0] // one_hot(0)
|
|
[0.0, 0.0, 1.0] // one_hot(2)
|
|
][
|
|
[0.0, 1.0, 0.0] // one_hot(1)
|
|
[0.0, 0.0, 0.0] // one_hot(-1)
|
|
]```</p></div></div><div class="top"><p class="src"><a name="v:broadcastGradientArgs" class="def">broadcastGradientArgs</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>s0</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>s1</strong></p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>r0</strong>, <strong>r1</strong>)</p><ul><li><strong>r0</strong></li><li><strong>r1</strong></li></ul></td></tr></table></div><div class="doc"><p>Return the reduction indices for computing gradients of s0 op s1 with broadcast.</p><p>This is typically used by gradient computations for a broadcasting operation.</p></div></div><div class="top"><p class="src"><a name="v:matrixSetDiag" class="def">matrixSetDiag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Rank `k+1`, where `k >= 1`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>diagonal</strong>: Rank <code>k</code>, where `k >= 1`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Rank `k+1`, with `output.shape = input.shape`.</p></td></tr></table></div><div class="doc"><p>Returns a batched matrix tensor with new batched diagonal values.</p><p>Given <code>input</code> and <code>diagonal</code>, this operation returns a tensor with the
|
|
same shape and values as <code>input</code>, except for the diagonals of the innermost
|
|
matrices. These will be overwritten by the values in <code>diagonal</code>.
|
|
The batched matrices must be square.</p><p>The output is computed as follows:</p><p>Assume <code>input</code> has `k+1` dimensions `[I, J, K, ..., N, N]` and <code>diagonal</code> has
|
|
<code>k</code> dimensions `[I, J, K, ..., N]`. Then the output is a
|
|
tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:</p><ul><li>`output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.</li><li>`output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.</li></ul></div></div><div class="top"><p class="src"><a name="v:applyRMSProp" class="def">applyRMSProp</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>ms</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>mom</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>rho</strong>: Decay rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>momentum</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>epsilon</strong>: Ridge term. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update '*var' according to the RMSProp algorithm.</p><p>Note that in dense implement of this algorithm, ms and mom will
|
|
update even if the grad is zero, but in this sparse implement, ms
|
|
and mom will not update in iterations the grad is zero.</p><p>mean_square = decay * mean_square + (1-decay) * gradient ** 2
|
|
Delta = learning_rate * gradient / sqrt(mean_square + epsilon)</p><p>ms <- rho * ms_{t-1} + (1-rho) * grad * grad
|
|
mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
|
|
var <- var - mom</p></div></div><div class="top"><p class="src"><a name="v:const" class="def">const</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns a constant tensor.</p></div></div><div class="top"><p class="src"><a name="v:enter" class="def">enter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the child frame.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Creates or finds a child frame, and makes `data` available to the child frame.</p><p>This op is used together with <code>Exit</code> to create loops in the graph.
|
|
The unique <code>frame_name</code> is used by the <code>Executor</code> to identify frames. If
|
|
<code>is_constant</code> is true, <code>output</code> is a constant in the child frame; otherwise
|
|
it may be changed in the child frame. At most <code>parallel_iterations</code> iterations
|
|
are run in parallel in the child frame.</p></div></div><div class="top"><p class="src"><a name="v:debugIdentity" class="def">debugIdentity</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Input tensor, non-Reference type.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Output tensor that equals the input tensor.</p></td></tr></table></div><div class="doc"><p>Debug Identity Op.</p><p>Provides an identity mapping of the non-Ref type input tensor for debugging.</p></div></div><div class="top"><p class="src"><a name="v:debugNanCount" class="def">debugNanCount</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Input tensor, non-Reference type.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong>: An integer output tensor that is the number of NaNs in the input.</p></td></tr></table></div><div class="doc"><p>Debug NaN Value Counter Op</p><p>Counts number of NaNs in the input tensor, for debugging.</p></div></div><div class="top"><p class="src"><a name="v:batchNormWithGlobalNormalization" class="def">batchNormWithGlobalNormalization</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>scale_after_normalization</strong>: A bool indicating whether the resulted tensor
|
|
needs to be multiplied with gamma.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>variance_epsilon</strong>: A small float number to avoid dividing by 0.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>t</strong>: A 4D input Tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>m</strong>: A 1D mean Tensor with size matching the last dimension of t.
|
|
This is the first output from tf.nn.moments,
|
|
or a saved moving average thereof.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>v</strong>: A 1D variance Tensor with size matching the last dimension of t.
|
|
This is the second output from tf.nn.moments,
|
|
or a saved moving average thereof.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>beta</strong>: A 1D beta Tensor with size matching the last dimension of t.
|
|
An offset to be added to the normalized tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>gamma</strong>: A 1D gamma Tensor with size matching the last dimension of t.
|
|
If "scale_after_normalization" is true, this tensor will be multiplied
|
|
with the normalized tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>result</strong></p></td></tr></table></div><div class="doc"><p>Batch normalization.</p><p>This op is deprecated. Prefer `tf.nn.batch_normalization`.</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixDiag" class="def">batchMatrixDiag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>diagonal</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:unpack" class="def">unpack</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: 1-D or higher, with <code>axis</code> dimension size equal to <code>num</code>.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</td><td class="doc"><p><strong>output</strong>: The list of tensors unpacked from <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></td></tr></table></div><div class="doc"><p>Unpacks a given dimension of a rank-<code>R</code> tensor into <code>num</code> rank-`(R-1)` tensors.</p><p>Unpacks <code>num</code> tensors from <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> by chipping it along the <code>axis</code> dimension.
|
|
For example, given a tensor of shape `(A, B, C, D)`;</p><p>If `axis == 0` then the i'th tensor in <code>output</code> is the slice `value[i, :, :, :]`
|
|
and each tensor in <code>output</code> will have shape `(B, C, D)`. (Note that the
|
|
dimension unpacked along is gone, unlike <code><a href="TensorFlow-GenOps-Core.html#v:split">split</a></code>).</p><p>If `axis == 1` then the i'th tensor in <code>output</code> is the slice `value[:, i, :, :]`
|
|
and each tensor in <code>output</code> will have shape `(A, C, D)`.
|
|
Etc.</p><p>This is the opposite of <code><a href="TensorFlow-GenOps-Core.html#v:pack">pack</a></code>.</p></div></div><div class="top"><p class="src"><a name="v:sparseSplit" class="def">sparseSplit</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_split</strong>: The number of ways to split.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>split_dim</strong>: 0-D. The dimension along which to split. Must be in the range
|
|
`[0, rank(shape))`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>indices</strong>: 2-D tensor represents the indices of the sparse tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>values</strong>: 1-D tensor represents the values of the sparse tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>shape</strong>: 1-D. tensor represents the shape of the sparse tensor.
|
|
output indices: A list of 1-D tensors represents the indices of the output
|
|
sparse tensors.</p></td></tr><tr><td class="src">-> ([<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>])</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>, <strong>output_shape</strong>)</p><ul><li><strong>output_indices</strong></li><li><strong>output_values</strong>: A list of 1-D tensors represents the values of the output sparse
|
|
tensors.</li><li><strong>output_shape</strong>: A list of 1-D tensors represents the shape of the output sparse
|
|
tensors.</li></ul></td></tr></table></div><div class="doc"><p>Split a <code>SparseTensor</code> into <code>num_split</code> tensors along one dimension.</p><p>If the `shape[split_dim]` is not an integer multiple of <code>num_split</code>. Slices
|
|
`[0 : shape[split_dim] % num_split]` gets one extra dimension.
|
|
For example, if `split_dim = 1` and `num_split = 2` and the input is</p><p>input_tensor = shape = [2, 7]
|
|
[ a d e ]
|
|
[b c ]</p><p>Graphically the output tensors are:</p><p>output_tensor[0] = shape = [2, 4]
|
|
[ a ]
|
|
[b c ]</p><p>output_tensor[1] = shape = [2, 3]
|
|
[ d e ]
|
|
[ ]</p></div></div><div class="top"><p class="src"><a name="v:mirrorPad" class="def">mirrorPad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The input tensor to be padded.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings</td><td class="doc"><p><strong>paddings</strong>: A two-column matrix specifying the padding sizes. The number of
|
|
rows must be the same as the rank of <code>input</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The padded tensor.</p></td></tr></table></div><div class="doc"><p>Pads a tensor with mirrored values.</p><p>This operation pads a <code>input</code> with mirrored values according to the <code>paddings</code>
|
|
you specify. <code>paddings</code> is an integer tensor with shape `[n, 2]`, where n is
|
|
the rank of <code>input</code>. For each dimension D of <code>input</code>, `paddings[D, 0]` indicates
|
|
how many values to add before the contents of <code>input</code> in that dimension, and
|
|
`paddings[D, 1]` indicates how many values to add after the contents of <code>input</code>
|
|
in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater
|
|
than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if <code>copy_border</code> is true
|
|
(if false, respectively).</p><p>The padded size of each dimension D of the output is:</p><p>`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`</p><p>For example:</p><p>```prettyprint
|
|
# <code>t</code> is [[1, 2, 3], [4, 5, 6]].
|
|
# <code>paddings</code> is [[1, 1]], [2, 2]].
|
|
# <code>mode</code> is SYMMETRIC.
|
|
# rank of <code>t</code> is 2.
|
|
pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
|
|
[2, 1, 1, 2, 3, 3, 2]
|
|
[5, 4, 4, 5, 6, 6, 5]
|
|
[5, 4, 4, 5, 6, 6, 5]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:batchMatrixDiagPart" class="def">batchMatrixDiagPart</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>diagonal</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:fractionalMaxPoolGrad" class="def">fractionalMaxPoolGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>orig_input</strong>: Original input for <code>fractional_max_pool</code></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>orig_output</strong>: Original output for <code>fractional_max_pool</code></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, height, width, channels]`. Gradients
|
|
w.r.t. the output of <code>fractional_max_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>row_pooling_sequence</strong>: row pooling sequence, form pooling region with
|
|
col_pooling_sequence.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>col_pooling_sequence</strong>: column pooling sequence, form pooling region with
|
|
row_pooling sequence.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D. Gradients w.r.t. the input of <code>fractional_max_pool</code>.</p></td></tr></table></div><div class="doc"><p>Computes gradient of the FractionalMaxPool function.</p></div></div><div class="top"><p class="src"><a name="v:matchingFiles" class="def">matchingFiles</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>pattern</strong>: A (scalar) shell wildcard pattern.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>filenames</strong>: A vector of matching filenames.</p></td></tr></table></div><div class="doc"><p>Returns the set of files matching a pattern.</p><p>Note that this routine only supports wildcard characters in the
|
|
basename portion of the pattern, not in the directory portion.</p></div></div><div class="top"><p class="src"><a name="v:tile" class="def">tile</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tmultiples, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tmultiples)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 1-D or higher.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tmultiples</td><td class="doc"><p><strong>multiples</strong>: 1-D. Length must be the same as the number of dimensions in <code>input</code></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Constructs a tensor by tiling a given tensor.</p><p>This operation creates a new tensor by replicating <code>input</code> <code>multiples</code> times.
|
|
The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
|
|
and the values of <code>input</code> are replicated `multiples[i]` times along the <code>i</code>th
|
|
dimension. For example, tiling `[a b c d]` by `[2]` produces
|
|
`[a b c d a b c d]`.</p></div></div><div class="top"><p class="src"><a name="v:sparseSparseMinimum" class="def">sparseSparseMinimum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
|
|
SparseTensor, in the canonical lexicographic ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>a_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>a_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_indices</strong>: counterpart to <code>a_indices</code> for the other operand.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>b_values</strong>: counterpart to <code>a_values</code> for the other operand; must be of the same dtype.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_shape</strong>: counterpart to <code>a_shape</code> for the other operand; the two shapes must be equal.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>)</p><ul><li><strong>output_indices</strong>: 2-D. The indices of the output SparseTensor.</li><li><strong>output_values</strong>: 1-D. The values of the output SparseTensor.</li></ul></td></tr></table></div><div class="doc"><p>Returns the element-wise min of two SparseTensors.</p><p>Assumes the two SparseTensors have the same shape, i.e., no broadcasting.</p></div></div><div class="top"><p class="src"><a name="v:allCandidateSampler" class="def">allCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to produce per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
|
|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
|
|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
|
|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
|
|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a learned unigram distribution.</p><p>See explanations of candidate sampling and the data formats at
|
|
go/candidate-sampling.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
|
|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:refSwitch" class="def">refSwitch</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The ref tensor to be forwarded to the appropriate output.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>pred</strong>: A scalar that specifies which output port will receive data.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>output_false</strong>, <strong>output_true</strong>)</p><ul><li><strong>output_false</strong>: If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is false, data will be forwarded to this output.</li><li><strong>output_true</strong>: If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is true, data will be forwarded to this output.</li></ul></td></tr></table></div><div class="doc"><p>Forwards the ref tensor `data` to the output port determined by <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code>.</p><p>If <code><a href="../base-4.8.2.0/Prelude.html#v:pred">pred</a></code> is true, the `data` input is forwarded to <code>output_true</code>. Otherwise,
|
|
the data goes to <code>output_false</code>.</p><p>See also <code>Switch</code> and <code>Merge</code>.</p></div></div><div class="top"><p class="src"><a name="v:mergeSummary" class="def">mergeSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>]</td><td class="doc"><p><strong>inputs</strong>: Can be of any shape. Each must contain serialized <code>Summary</code> protocol
|
|
buffers.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong>: Scalar. Serialized <code>Summary</code> protocol buffer.</p></td></tr></table></div><div class="doc"><p>Merges summaries.</p><p>This op creates a
|
|
<a href="https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto">`Summary`</a>
|
|
protocol buffer that contains the union of all the values in the input
|
|
summaries.</p><p>When the Op is run, it reports an <code>InvalidArgument</code> error if multiple values
|
|
in the summaries to merge use the same tag.</p></div></div><div class="top"><p class="src"><a name="v:logicalNot" class="def">logicalNot</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of NOT x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:lRNGrad" class="def">lRNGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input_grads</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>input_image</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>output_image</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The gradients for LRN.</p></td></tr></table></div><div class="doc"><p>Gradients for Local Response Normalization.</p></div></div><div class="top"><p class="src"><a name="v:stringToNumber" class="def">stringToNumber</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` out_type)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>string_tensor</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</td><td class="doc"><p><strong>output</strong>: A Tensor of the same shape as the input <code>string_tensor</code>.</p></td></tr></table></div><div class="doc"><p>Converts each string in the input Tensor to the specified numeric type.</p><p>(Note that int32 overflow results in an error while float overflow
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results in a rounded value.)</p></div></div><div class="top"><p class="src"><a name="v:sparseMatMul" class="def">sparseMatMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> ta, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` ta, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tb, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tb)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 ta</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tb</td><td class="doc"><p><strong>b</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>product</strong></p></td></tr></table></div><div class="doc"><p>Multiply matrix "a" by matrix "b".</p><p>The inputs must be two-dimensional matrices and the inner dimension of "a" must
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match the outer dimension of "b". This op is optimized for the case where at
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least one of "a" or "b" is sparse. The breakeven for using this versus a dense
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matrix multiply on one platform was 30% zero values in the sparse matrix.</p></div></div><div class="top"><p class="src"><a name="v:merge" class="def">merge</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t]</td><td class="doc"><p><strong>inputs</strong>: The input tensors, exactly one of which will become available.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</td><td class="doc"><p>(<strong>output</strong>, <strong>value_index</strong>)</p><ul><li><strong>output</strong>: Will be set to the available input tensor.</li><li><strong>value_index</strong>: The index of the chosen input tensor in <code>inputs</code>.</li></ul></td></tr></table></div><div class="doc"><p>Forwards the value of an available tensor from <code>inputs</code> to <code>output</code>.</p><p><code>Merge</code> waits for at least one of the tensors in <code>inputs</code> to become available.
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It is usually combined with <code>Switch</code> to implement branching.</p><p><code>Merge</code> forwards the first tensor for become available to <code>output</code>, and sets
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<code>value_index</code> to its index in <code>inputs</code>.</p></div></div><div class="top"><p class="src"><a name="v:choleskyGrad" class="def">choleskyGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>l</strong>: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`.
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Algorithm depends only on lower triangular part of the innermost matrices of
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this tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>grad</strong>: df/dl where f is some scalar function. Shape is `[..., M, M]`.
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Algorithm depends only on lower triangular part of the innermost matrices of
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this tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Symmetrized version of df/dA . Shape is `[..., M, M]`</p></td></tr></table></div><div class="doc"><p>Computes the reverse mode backpropagated gradient of the Cholesky algorithm.</p><p>For an explanation see "Differentiation of the Cholesky algorithm" by
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Iain Murray <a href="http://arxiv.org/abs/1602.07527">http://arxiv.org/abs/1602.07527</a>.</p></div></div><div class="top"><p class="src"><a name="v:batchCholeskyGrad" class="def">batchCholeskyGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>l</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>grad</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:tensorArrayGather" class="def">tensorArrayGather</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>indices</strong>: The locations in the TensorArray from which to read tensor elements.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>value</strong>: All of the elements in the TensorArray, concatenated along a new
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axis (the new dimension 0).</p></td></tr></table></div><div class="doc"><p>Gather specific elements from the TensorArray into output <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><p>All elements selected by <code>indices</code> must have the same shape.</p></div></div><div class="top"><p class="src"><a name="v:resizeNearestNeighbor" class="def">resizeNearestNeighbor</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
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new size for the images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>resized_images</strong>: 4-D with shape
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`[batch, new_height, new_width, channels]`.</p></td></tr></table></div><div class="doc"><p>Resize <code>images</code> to <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> using nearest neighbor interpolation.</p></div></div><div class="top"><p class="src"><a name="v:negTrain" class="def">negTrain</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_negative_samples</strong>: Number of negative samples per example.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>w_in</strong>: input word embedding.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>w_out</strong>: output word embedding.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>examples</strong>: A vector of word ids.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>labels</strong>: A vector of word ids.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>lr</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Training via negative sampling.</p></div></div><div class="top"><p class="src"><a name="v:tensorArrayGrad" class="def">tensorArrayGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the forward TensorArray.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>flow_in</strong>: A float scalar that enforces proper chaining of operations.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>grad_handle</strong></p></td></tr></table></div><div class="doc"><p>Creates a TensorArray for storing the gradients of values in the given handle.</p><p>If the given TensorArray gradient already exists, returns a reference to it.</p><p>Locks the size of the original TensorArray by disabling its dynamic size flag.</p><ul><li>*A note about the input flow_in:**</li></ul><p>The handle flow_in forces the execution of the gradient lookup to occur
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only after certain other operations have occurred. For example, when
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the forward TensorArray is dynamically sized, writes to this TensorArray
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may resize the object. The gradient TensorArray is statically sized based
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on the size of the forward TensorArray when this operation executes.
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Furthermore, the size of the forward TensorArray is frozen by this call.
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As a result, the flow is used to ensure that the call to generate the gradient
|
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TensorArray only happens after all writes are executed.</p><p>In the case of dynamically sized TensorArrays, gradient computation should
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only be performed on read operations that have themselves been chained via
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flow to occur only after all writes have executed. That way the final size
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of the forward TensorArray is known when this operation is called.</p><ul><li>*A note about the source attribute:**</li></ul><p>TensorArray gradient calls use an accumulator TensorArray object. If
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|
multiple gradients are calculated and run in the same session, the multiple
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gradient nodes may accidentally flow throuth the same accumulator TensorArray.
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This double counts and generally breaks the TensorArray gradient flow.</p><p>The solution is to identify which gradient call this particular
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TensorArray gradient is being called in. This is performed by identifying
|
|
a unique string (e.g. "gradients", "gradients_1", ...) from the input
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gradient Tensor's name. This string is used as a suffix when creating
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the TensorArray gradient object here (the attribute <code>source</code>).</p><p>The attribute <code>source</code> is added as a suffix to the forward TensorArray's
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name when performing the creation / lookup, so that each separate gradient
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calculation gets its own TensorArray accumulator.</p></div></div><div class="top"><p class="src"><a name="v:audioSummary" class="def">audioSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>sample_rate</strong>: The sample rate of the signal in hertz.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tag</strong>: Scalar. Used to build the <code>tag</code> attribute of the summary values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>tensor</strong>: 2-D of shape `[batch_size, frames]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong>: Scalar. Serialized <code>Summary</code> protocol buffer.</p></td></tr></table></div><div class="doc"><p>Outputs a <code>Summary</code> protocol buffer with audio.</p><p>The summary has up to <code>max_outputs</code> summary values containing audio. The
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audio is built from <code>tensor</code> which must be 3-D with shape `[batch_size,
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frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
|
|
assumed to be in the range of `[-1.0, 1.0]` with a sample rate of <code>sample_rate</code>.</p><p>The <code>tag</code> argument is a scalar <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> of type <code>string</code>. It is used to
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build the <code>tag</code> of the summary values:</p><ul><li>If <code>max_outputs</code> is 1, the summary value tag is '*tag*/audio'.</li><li>If <code>max_outputs</code> is greater than 1, the summary value tags are
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generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.</li></ul></div></div><div class="top"><p class="src"><a name="v:noOp" class="def">noOp</a> :: <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></p><div class="doc"><p>Does nothing. Only useful as a placeholder for control edges.</p></div></div><div class="top"><p class="src"><a name="v:nextIteration" class="def">nextIteration</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong>: The tensor to be made available to the next iteration.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same tensor as `data`.</p></td></tr></table></div><div class="doc"><p>Makes its input available to the next iteration.</p></div></div><div class="top"><p class="src"><a name="v:softplusGrad" class="def">softplusGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>gradients</strong>: The backpropagated gradients to the corresponding softplus operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>features</strong>: The features passed as input to the corresponding softplus operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>backprops</strong>: The gradients: `gradients / (1 + exp(-features))`.</p></td></tr></table></div><div class="doc"><p>Computes softplus gradients for a softplus operation.</p></div></div><div class="top"><p class="src"><a name="v:svd" class="def">svd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
|
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form matrices of size `[M, N]`. Let <code>P</code> be the minimum of <code>M</code> and <code>N</code>.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>s</strong>, <strong>u</strong>, <strong>v</strong>)</p><ul><li><strong>s</strong>: Singular values. Shape is `[..., P]`.</li><li><strong>u</strong>: Left singular vectors. If <code>full_matrices</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> then shape is
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|
`[..., M, M]`; if <code>full_matrices</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code> then shape is
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`[..., M, P]`. Undefined if <code>compute_uv</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code>.</li><li><strong>v</strong>: Left singular vectors. If <code>full_matrices</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code> then shape is
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`[..., N, N]`. If <code>full_matrices</code> is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code> then shape is `[..., N, P]`.
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Undefined if <code>compute_uv</code> is false.</li></ul></td></tr></table></div><div class="doc"><p>Computes the singular value decompositions of one or more matrices.</p><p>Computes the SVD of each inner matrix in <code>input</code> such that
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`input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])`</p><p>```prettyprint
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|
# a is a tensor containing a batch of matrices.
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# s is a tensor of singular values for each matrix.
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# u is the tensor containing of left singular vectors for each matrix.
|
|
# v is the tensor containing of right singular vectors for each matrix.
|
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s, u, v = svd(a)
|
|
s, _, _ = svd(a, compute_uv=False)
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```</p></div></div><div class="top"><p class="src"><a name="v:hSVToRGB" class="def">hSVToRGB</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 1-D or higher rank. HSV data to convert. Last dimension must be size 3.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: <code>images</code> converted to RGB.</p></td></tr></table></div><div class="doc"><p>Convert one or more images from HSV to RGB.</p><p>Outputs a tensor of the same shape as the <code>images</code> tensor, containing the RGB
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value of the pixels. The output is only well defined if the value in <code>images</code>
|
|
are in `[0,1]`.</p><p>See <code>rgb_to_hsv</code> for a description of the HSV encoding.</p></div></div><div class="top"><p class="src"><a name="v:parameterizedTruncatedNormal" class="def">parameterizedTruncatedNormal</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>shape</strong>: The shape of the output tensor. Batches are indexed by the 0th dimension.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 dtype</td><td class="doc"><p><strong>means</strong>: The mean parameter of each batch.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 dtype</td><td class="doc"><p><strong>stdevs</strong>: The standard deviation parameter of each batch. Must be greater than 0.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 dtype</td><td class="doc"><p><strong>minvals</strong>: The minimum cutoff. May be -infinity.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 dtype</td><td class="doc"><p><strong>maxvals</strong>: The maximum cutoff. May be +infinity, and must be more than the minval
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for each batch.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A matrix of shape num_batches x samples_per_batch, filled with random
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truncated normal values using the parameters for each row.</p></td></tr></table></div><div class="doc"><p>Outputs random values from a normal distribution. The parameters may each be a</p><p>scalar which applies to the entire output, or a vector of length shape[0] which
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stores the parameters for each batch.</p></div></div><div class="top"><p class="src"><a name="v:square" class="def">square</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes square of x element-wise.</p><p>I.e., \(y = x * x = x^2\).</p></div></div><div class="top"><p class="src"><a name="v:elu" class="def">elu</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>activations</strong></p></td></tr></table></div><div class="doc"><p>Computes exponential linear: `exp(features) - 1` if < 0, <code>features</code> otherwise.</p><p>See <a href="http://arxiv.org/abs/1511.07289">Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)</a></p></div></div><div class="top"><p class="src"><a name="v:lookupTableExport" class="def">lookupTableExport</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tkeys, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tvalues)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to the table.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tkeys, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tvalues)</td><td class="doc"><p>(<strong>keys</strong>, <strong>values</strong>)</p><ul><li><strong>keys</strong>: Vector of all keys present in the table.</li><li><strong>values</strong>: Tensor of all values in the table. Indexed in parallel with <code>keys</code>.</li></ul></td></tr></table></div><div class="doc"><p>Outputs all keys and values in the table.</p></div></div><div class="top"><p class="src"><a name="v:lookupTableSize" class="def">lookupTableSize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>table_handle</strong>: Handle to the table.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>size</strong>: Scalar that contains number of elements in the table.</p></td></tr></table></div><div class="doc"><p>Computes the number of elements in the given table.</p></div></div><div class="top"><p class="src"><a name="v:avgPoolGrad" class="def">avgPoolGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>orig_input_shape</strong>: 1-D. Shape of the original input to <code>avg_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>grad</strong>: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t.
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the output of <code>avg_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D. Gradients w.r.t. the input of <code>avg_pool</code>.</p></td></tr></table></div><div class="doc"><p>Computes gradients of the average pooling function.</p></div></div><div class="top"><p class="src"><a name="v:computeAccidentalHits" class="def">computeAccidentalHits</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: The true_classes output of UnpackSparseLabels.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sampled_candidates</strong>: The sampled_candidates output of CandidateSampler.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>indices</strong>, <strong>ids</strong>, <strong>weights</strong>)</p><ul><li><strong>indices</strong>: A vector of indices corresponding to rows of true_candidates.</li><li><strong>ids</strong>: A vector of IDs of positions in sampled_candidates that match a true_label
|
|
for the row with the corresponding index in indices.</li><li><strong>weights</strong>: A vector of the same length as indices and ids, in which each element
|
|
is -FLOAT_MAX.</li></ul></td></tr></table></div><div class="doc"><p>Computes the ids of the positions in sampled_candidates that match true_labels.</p><p>When doing log-odds NCE, the result of this op should be passed through a
|
|
SparseToDense op, then added to the logits of the sampled candidates. This has
|
|
the effect of <code>removing</code> the sampled labels that match the true labels by
|
|
making the classifier sure that they are sampled labels.</p></div></div><div class="top"><p class="src"><a name="v:cTCLoss" class="def">cTCLoss</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>inputs</strong>: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>labels_indices</strong>: The indices of a `SparseTensor<a href="int32,">2</a>`.
|
|
`labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for
|
|
`(batch b, time t)`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>labels_values</strong>: The values (labels) associated with the given batch and time.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>sequence_length</strong>: A vector containing sequence lengths (batch).</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>loss</strong>, <strong>gradient</strong>)</p><ul><li><strong>loss</strong>: A vector (batch) containing log-probabilities.</li><li><strong>gradient</strong>: The gradient of <code>loss</code>. 3-D, shape:
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|
`(max_time x batch_size x num_classes)`.</li></ul></td></tr></table></div><div class="doc"><p>Calculates the CTC Loss (log probability) for each batch entry. Also calculates</p><p>the gradient. This class performs the softmax operation for you, so inputs
|
|
should be e.g. linear projections of outputs by an LSTM.</p></div></div><div class="top"><p class="src"><a name="v:avgPool3D" class="def">avgPool3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The average pooled output tensor.</p></td></tr></table></div><div class="doc"><p>Performs 3D average pooling on the input.</p></div></div><div class="top"><p class="src"><a name="v:inv" class="def">inv</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the reciprocal of x element-wise.</p><p>I.e., \(y = 1 / x\).</p></div></div><div class="top"><p class="src"><a name="v:stackPop" class="def">stackPop</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> elem_type</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a stack.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> elem_type</td><td class="doc"><p><strong>elem</strong>: The tensor that is popped from the top of the stack.</p></td></tr></table></div><div class="doc"><p>Pop the element at the top of the stack.</p></div></div><div class="top"><p class="src"><a name="v:paddingFIFOQueue" class="def">paddingFIFOQueue</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to the queue.</p></td></tr></table></div><div class="doc"><p>A queue that produces elements in first-in first-out order.</p><p>Variable-size shapes are allowed by setting the corresponding shape dimensions
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to 0 in the shape attr. In this case DequeueMany will pad up to the maximum
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size of any given element in the minibatch. See below for details.</p></div></div><div class="top"><p class="src"><a name="v:batchSelfAdjointEigV2" class="def">batchSelfAdjointEigV2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>e</strong>, <strong>v</strong>)</p><ul><li><strong>e</strong></li><li><strong>v</strong></li></ul></td></tr></table></div></div><div class="top"><p class="src"><a name="v:batchMatrixTriangularSolve" class="def">batchMatrixTriangularSolve</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:batchMatrixSolveLs" class="def">batchMatrixSolveLs</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>matrix</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>rhs</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a></td><td class="doc"><p><strong>l2_regularizer</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:batchSvd" class="def">batchSvd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>s</strong>, <strong>u</strong>, <strong>v</strong>)</p><ul><li><strong>s</strong></li><li><strong>u</strong></li><li><strong>v</strong></li></ul></td></tr></table></div></div><div class="top"><p class="src"><a name="v:tensorSummary" class="def">tensorSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong>: A tensor to serialize.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong></p></td></tr></table></div><div class="doc"><p>Outputs a <code>Summary</code> protocol buffer with a tensor.</p></div></div><div class="top"><p class="src"><a name="v:sparseSoftmaxCrossEntropyWithLogits" class="def">sparseSoftmaxCrossEntropyWithLogits</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tlabels, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tlabels)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong>: batch_size x num_classes matrix</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tlabels</td><td class="doc"><p><strong>labels</strong>: batch_size vector with values in [0, num_classes).
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This is the label for the given minibatch entry.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>loss</strong>, <strong>backprop</strong>)</p><ul><li><strong>loss</strong>: Per example loss (batch_size vector).</li><li><strong>backprop</strong>: backpropagated gradients (batch_size x num_classes matrix).</li></ul></td></tr></table></div><div class="doc"><p>Computes softmax cross entropy cost and gradients to backpropagate.</p><p>Unlike <code>SoftmaxCrossEntropyWithLogits</code>, this operation does not accept
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a matrix of label probabilities, but rather a single label per row
|
|
of features. This label is considered to have probability 1.0 for the
|
|
given row.</p><p>Inputs are the logits, not probabilities.</p></div></div><div class="top"><p class="src"><a name="v:maxPoolWithArgmax" class="def">maxPoolWithArgmax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> targmax, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` targmax)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, height, width, channels]`. Input to pool over.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> targmax)</td><td class="doc"><p>(<strong>output</strong>, <strong>argmax</strong>)</p><ul><li><strong>output</strong>: The max pooled output tensor.</li><li><strong>argmax</strong>: 4-D. The flattened indices of the max values chosen for each output.</li></ul></td></tr></table></div><div class="doc"><p>Performs max pooling on the input and outputs both max values and indices.</p><p>The indices in <code>argmax</code> are flattened, so that a maximum value at position
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`[b, y, x, c]` becomes flattened index
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`((b * height + y) * width + x) * channels + c`.</p></div></div><div class="top"><p class="src"><a name="v:fFT" class="def">fFT</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most
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dimension of <code>input</code> is replaced with its 1D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the 1-dimensional discrete Fourier Transform over the inner-most</p><p>dimension of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:histogramSummary" class="def">histogramSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tag</strong>: Scalar. Tag to use for the <code><a href="Summary.html#v:Value">Value</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>values</strong>: Any shape. Values to use to build the histogram.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong>: Scalar. Serialized <code>Summary</code> protocol buffer.</p></td></tr></table></div><div class="doc"><p>Outputs a <code>Summary</code> protocol buffer with a histogram.</p><p>The generated
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<a href="https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto">`Summary`</a>
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|
has one summary value containing a histogram for <code>values</code>.</p><p>This op reports an <code>InvalidArgument</code> error if any value is not finite.</p></div></div><div class="top"><p class="src"><a name="v:pad" class="def">pad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings</td><td class="doc"><p><strong>paddings</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Pads a tensor with zeros.</p><p>This operation pads a <code>input</code> with zeros according to the <code>paddings</code> you
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specify. <code>paddings</code> is an integer tensor with shape `[Dn, 2]`, where n is the
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|
rank of <code>input</code>. For each dimension D of <code>input</code>, `paddings[D, 0]` indicates
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|
how many zeros to add before the contents of <code>input</code> in that dimension, and
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|
`paddings[D, 1]` indicates how many zeros to add after the contents of <code>input</code>
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|
in that dimension.</p><p>The padded size of each dimension D of the output is:</p><p>`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`</p><p>For example:</p><p>```prettyprint
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# <code>t</code> is [[1, 1], [2, 2]]
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# <code>paddings</code> is [[1, 1], [2, 2]]
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# rank of <code>t</code> is 2
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pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
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[0, 0, 1, 1, 0, 0]
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[0, 0, 2, 2, 0, 0]
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|
[0, 0, 0, 0, 0, 0]]
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|
```</p></div></div><div class="top"><p class="src"><a name="v:batchIFFT3D" class="def">batchIFFT3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:imageSummary" class="def">imageSummary</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>tag</strong>: Scalar. Used to build the <code>tag</code> attribute of the summary values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>tensor</strong>: 4-D of shape `[batch_size, height, width, channels]` where
|
|
<code>channels</code> is 1, 3, or 4.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>summary</strong>: Scalar. Serialized <code>Summary</code> protocol buffer.</p></td></tr></table></div><div class="doc"><p>Outputs a <code>Summary</code> protocol buffer with images.</p><p>The summary has up to <code>max_images</code> summary values containing images. The
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images are built from <code>tensor</code> which must be 4-D with shape `[batch_size,
|
|
height, width, channels]` and where <code>channels</code> can be:</p><ul><li>1: <code>tensor</code> is interpreted as Grayscale.</li><li>3: <code>tensor</code> is interpreted as RGB.</li><li>4: <code>tensor</code> is interpreted as RGBA.</li></ul><p>The images have the same number of channels as the input tensor. For float
|
|
input, the values are normalized one image at a time to fit in the range
|
|
`[0, 255]`. <code>uint8</code> values are unchanged. The op uses two different
|
|
normalization algorithms:</p><ul><li>If the input values are all positive, they are rescaled so the largest one
|
|
is 255.</li><li>If any input value is negative, the values are shifted so input value 0.0
|
|
is at 127. They are then rescaled so that either the smallest value is 0,
|
|
or the largest one is 255.</li></ul><p>The <code>tag</code> argument is a scalar <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> of type <code>string</code>. It is used to
|
|
build the <code>tag</code> of the summary values:</p><ul><li>If <code>max_images</code> is 1, the summary value tag is '*tag*/image'.</li><li>If <code>max_images</code> is greater than 1, the summary value tags are
|
|
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.</li></ul><p>The <code>bad_color</code> argument is the color to use in the generated images for
|
|
non-finite input values. It is a <code>unit8</code> 1-D tensor of length <code>channels</code>.
|
|
Each element must be in the range `[0, 255]` (It represents the value of a
|
|
pixel in the output image). Non-finite values in the input tensor are
|
|
replaced by this tensor in the output image. The default value is the color
|
|
red.</p></div></div><div class="top"><p class="src"><a name="v:segmentSum" class="def">segmentSum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor whose rank is equal to the rank of `data`'s
|
|
first dimension. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
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|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the sum along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on Segmentation</a>
|
|
for an explanation of segments.</p><p>Computes a tensor such that
|
|
\(output_i = sum_j data_j\) where sum is over <code>j</code> such
|
|
that `segment_ids[j] == i`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/SegmentSum.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:encodeJpeg" class="def">encodeJpeg</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a></td><td class="doc"><p><strong>image</strong>: 3-D with shape `[height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>contents</strong>: 0-D. JPEG-encoded image.</p></td></tr></table></div><div class="doc"><p>JPEG-encode an image.</p><p><code>image</code> is a 3-D uint8 Tensor of shape `[height, width, channels]`.</p><p>The attr <code>format</code> can be used to override the color format of the encoded
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|
output. Values can be:</p><ul><li>`''`: Use a default format based on the number of channels in the image.</li><li><code>grayscale</code>: Output a grayscale JPEG image. The <code>channels</code> dimension
|
|
of <code>image</code> must be 1.</li><li><code>rgb</code>: Output an RGB JPEG image. The <code>channels</code> dimension
|
|
of <code>image</code> must be 3.</li></ul><p>If <code>format</code> is not specified or is the empty string, a default format is picked
|
|
in function of the number of channels in <code>image</code>:</p><ul><li>1: Output a grayscale image.</li><li>3: Output an RGB image.</li></ul></div></div><div class="top"><p class="src"><a name="v:batchNormWithGlobalNormalizationGrad" class="def">batchNormWithGlobalNormalizationGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>scale_after_normalization</strong>: A bool indicating whether the resulted tensor
|
|
needs to be multiplied with gamma.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>variance_epsilon</strong>: A small float number to avoid dividing by 0.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>t</strong>: A 4D input Tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>m</strong>: A 1D mean Tensor with size matching the last dimension of t.
|
|
This is the first output from tf.nn.moments,
|
|
or a saved moving average thereof.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>v</strong>: A 1D variance Tensor with size matching the last dimension of t.
|
|
This is the second output from tf.nn.moments,
|
|
or a saved moving average thereof.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>gamma</strong>: A 1D gamma Tensor with size matching the last dimension of t.
|
|
If "scale_after_normalization" is true, this Tensor will be multiplied
|
|
with the normalized Tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>backprop</strong>: 4D backprop Tensor.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>dx</strong>, <strong>dm</strong>, <strong>dv</strong>, <strong>db</strong>, <strong>dg</strong>)</p><ul><li><strong>dx</strong>: 4D backprop tensor for input.</li><li><strong>dm</strong>: 1D backprop tensor for mean.</li><li><strong>dv</strong>: 1D backprop tensor for variance.</li><li><strong>db</strong>: 1D backprop tensor for beta.</li><li><strong>dg</strong>: 1D backprop tensor for gamma.</li></ul></td></tr></table></div><div class="doc"><p>Gradients for batch normalization.</p><p>This op is deprecated. See `tf.nn.batch_normalization`.</p></div></div><div class="top"><p class="src"><a name="v:biasAddV1" class="def">biasAddV1</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: Any number of dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>bias</strong>: 1-D with size the last dimension of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Broadcasted sum of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> and <code>bias</code>.</p></td></tr></table></div><div class="doc"><p>Adds <code>bias</code> to <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><p>This is a deprecated version of BiasAdd and will be soon removed.</p><p>This is a special case of `tf.add` where <code>bias</code> is restricted to be 1-D.
|
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Broadcasting is supported, so <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> may have any number of dimensions.</p></div></div><div class="top"><p class="src"><a name="v:invertPermutation" class="def">invertPermutation</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: 1-D.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong>: 1-D.</p></td></tr></table></div><div class="doc"><p>Computes the inverse permutation of a tensor.</p><p>This operation computes the inverse of an index permutation. It takes a 1-D
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|
integer tensor <code>x</code>, which represents the indices of a zero-based array, and
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|
swaps each value with its index position. In other words, for an output tensor
|
|
<code>y</code> and an input tensor <code>x</code>, this operation computes the following:</p><p>`y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`</p><p>The values must include 0. There can be no duplicate values or negative values.</p><p>For example:</p><p>```prettyprint
|
|
# tensor <code>x</code> is [3, 4, 0, 2, 1]
|
|
invert_permutation(x) ==> [2, 4, 3, 0, 1]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:mirrorPadGrad" class="def">mirrorPadGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tpaddings, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tpaddings)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The input tensor to be folded.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tpaddings</td><td class="doc"><p><strong>paddings</strong>: A two-column matrix specifying the padding sizes. The number of
|
|
rows must be the same as the rank of <code>input</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The folded tensor.</p></td></tr></table></div><div class="doc"><p>Gradient op for <code>MirrorPad</code> op. This op folds a mirror-padded tensor.</p><p>This operation folds the padded areas of <code>input</code> by <code>MirrorPad</code> according to the
|
|
<code>paddings</code> you specify. <code>paddings</code> must be the same as <code>paddings</code> argument
|
|
given to the corresponding <code>MirrorPad</code> op.</p><p>The folded size of each dimension D of the output is:</p><p>`input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`</p><p>For example:</p><p>```prettyprint
|
|
# <code>t</code> is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
|
|
# <code>paddings</code> is [[0, 1]], [0, 1]].
|
|
# <code>mode</code> is SYMMETRIC.
|
|
# rank of <code>t</code> is 2.
|
|
pad(t, paddings) ==> [[ 1, 5]
|
|
[11, 28]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:reverse" class="def">reverse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>tensor</strong>: Up to 8-D.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>dims</strong>: 1-D. The dimensions to reverse.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The same shape as <code>tensor</code>.</p></td></tr></table></div><div class="doc"><p>Reverses specific dimensions of a tensor.</p><p>Given a <code>tensor</code>, and a <code>bool</code> tensor <code>dims</code> representing the dimensions
|
|
of <code>tensor</code>, this operation reverses each dimension i of <code>tensor</code> where
|
|
`dims[i]` is <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>.</p><p><code>tensor</code> can have up to 8 dimensions. The number of dimensions
|
|
of <code>tensor</code> must equal the number of elements in <code>dims</code>. In other words:</p><p>`rank(tensor) = size(dims)`</p><p>For example:</p><p>```prettyprint
|
|
# tensor <code>t</code> is [[[[ 0, 1, 2, 3],
|
|
# [ 4, 5, 6, 7],
|
|
# [ 8, 9, 10, 11]],
|
|
# [[12, 13, 14, 15],
|
|
# [16, 17, 18, 19],
|
|
# [20, 21, 22, 23]]]]
|
|
# tensor <code>t</code> shape is [1, 2, 3, 4]</p><p># <code>dims</code> is [False, False, False, True]
|
|
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
|
|
[ 7, 6, 5, 4],
|
|
[ 11, 10, 9, 8]],
|
|
[[15, 14, 13, 12],
|
|
[19, 18, 17, 16],
|
|
[23, 22, 21, 20]]]]</p><p># <code>dims</code> is [False, True, False, False]
|
|
reverse(t, dims) ==> [[[[12, 13, 14, 15],
|
|
[16, 17, 18, 19],
|
|
[20, 21, 22, 23]
|
|
[[ 0, 1, 2, 3],
|
|
[ 4, 5, 6, 7],
|
|
[ 8, 9, 10, 11]]]]</p><p># <code>dims</code> is [False, False, True, False]
|
|
reverse(t, dims) ==> [[[[8, 9, 10, 11],
|
|
[4, 5, 6, 7],
|
|
[0, 1, 2, 3]]
|
|
[[20, 21, 22, 23],
|
|
[16, 17, 18, 19],
|
|
[12, 13, 14, 15]]]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:conv2D" class="def">conv2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes a 2-D convolution given 4-D <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> tensors.</p><p>Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
|
|
and a filter / kernel tensor of shape
|
|
`[filter_height, filter_width, in_channels, out_channels]`, this op
|
|
performs the following:</p><ol><li>Flattens the filter to a 2-D matrix with shape
|
|
`[filter_height * filter_width * in_channels, output_channels]`.</li><li>Extracts image patches from the input tensor to form a *virtual*
|
|
tensor of shape `[batch, out_height, out_width,
|
|
filter_height * filter_width * in_channels]`.</li><li>For each patch, right-multiplies the filter matrix and the image patch
|
|
vector.</li></ol><p>In detail, with the default NHWC format,</p><p>output[b, i, j, k] =
|
|
sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
|
|
filter[di, dj, q, k]</p><p>Must have `strides[0] = strides[3] = 1`. For the most common case of the same
|
|
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.</p></div></div><div class="top"><p class="src"><a name="v:conv2DBackpropInput" class="def">conv2DBackpropInput</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>input_sizes</strong>: An integer vector representing the shape of <code>input</code>,
|
|
where <code>input</code> is a 4-D `[batch, height, width, channels]` tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: 4-D with shape
|
|
`[filter_height, filter_width, in_channels, out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, out_channels]`.
|
|
Gradients w.r.t. the output of the convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient
|
|
w.r.t. the input of the convolution.</p></td></tr></table></div><div class="doc"><p>Computes the gradients of convolution with respect to the input.</p></div></div><div class="top"><p class="src"><a name="v:readerSerializeState" class="def">readerSerializeState</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>state</strong></p></td></tr></table></div><div class="doc"><p>Produce a string tensor that encodes the state of a Reader.</p><p>Not all Readers support being serialized, so this can produce an
|
|
Unimplemented error.</p></div></div><div class="top"><p class="src"><a name="v:temporaryVariable" class="def">temporaryVariable</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>ref</strong>: A reference to the variable tensor.</p></td></tr></table></div><div class="doc"><p>Returns a tensor that may be mutated, but only persists within a single step.</p><p>This is an experimental op for internal use only and it is possible to use this
|
|
op in unsafe ways. DO NOT USE unless you fully understand the risks.</p><p>It is the caller's responsibility to ensure that <code>ref</code> is eventually passed to a
|
|
matching <code>DestroyTemporaryVariable</code> op after all other uses have completed.</p><p>Outputs a ref to the tensor state so it may be read or modified.</p><p>E.g.
|
|
var = state_ops._temporary_variable([1, 2], types.float_)
|
|
var_name = var.op.name
|
|
var = state_ops.assign(var, [[4.0, 5.0]])
|
|
var = state_ops.assign_add(var, [[6.0, 7.0]])
|
|
final = state_ops._destroy_temporary_variable(var, var_name=var_name)</p></div></div><div class="top"><p class="src"><a name="v:cropAndResize" class="def">cropAndResize</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>image</strong>: A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
|
|
Both <code>image_height</code> and <code>image_width</code> need to be positive.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>boxes</strong>: A 2-D tensor of shape `[num_boxes, 4]`. The <code>i</code>-th row of the tensor
|
|
specifies the coordinates of a box in the `box_ind[i]` image and is specified
|
|
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
|
|
<code>y</code> is mapped to the image coordinate at `y * (image_height - 1)`, so as the
|
|
`[0, 1]` interval of normalized image height is mapped to
|
|
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
|
|
which case the sampled crop is an up-down flipped version of the original
|
|
image. The width dimension is treated similarly. Normalized coordinates
|
|
outside the `[0, 1]` range are allowed, in which case we use
|
|
<code>extrapolation_value</code> to extrapolate the input image values.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>box_ind</strong>: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
|
|
The value of `box_ind[i]` specifies the image that the <code>i</code>-th box refers to.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>crop_size</strong>: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
|
|
cropped image patches are resized to this size. The aspect ratio of the image
|
|
content is not preserved. Both <code>crop_height</code> and <code>crop_width</code> need to be
|
|
positive.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>crops</strong>: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.</p></td></tr></table></div><div class="doc"><p>Extracts crops from the input image tensor and bilinearly resizes them (possibly</p><p>with aspect ratio change) to a common output size specified by <code>crop_size</code>. This
|
|
is more general than the <code>crop_to_bounding_box</code> op which extracts a fixed size
|
|
slice from the input image and does not allow resizing or aspect ratio change.</p><p>Returns a tensor with <code>crops</code> from the input <code>image</code> at positions defined at the
|
|
bounding box locations in <code>boxes</code>. The cropped boxes are all resized (with
|
|
bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The
|
|
result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`.</p></div></div><div class="top"><p class="src"><a name="v:maxPoolGrad" class="def">maxPoolGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>orig_input</strong>: The original input tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>orig_output</strong>: The original output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>grad</strong>: 4-D. Gradients w.r.t. the output of <code>max_pool</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Gradients w.r.t. the input to <code>max_pool</code>.</p></td></tr></table></div><div class="doc"><p>Computes gradients of the maxpooling function.</p></div></div><div class="top"><p class="src"><a name="v:fusedResizeAndPadConv2D" class="def">fusedResizeAndPadConv2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
|
|
new size for the images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>paddings</strong>: A two-column matrix specifying the padding sizes. The number of
|
|
rows must be the same as the rank of <code>input</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>filter</strong>: 4-D with shape
|
|
`[filter_height, filter_width, in_channels, out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Performs a resize and padding as a preprocess during a convolution.</p><p>It's often possible to do spatial transformations more efficiently as part of
|
|
the packing stage of a convolution, so this op allows for an optimized
|
|
implementation where these stages are fused together. This prevents the need to
|
|
write out the intermediate results as whole tensors, reducing memory pressure,
|
|
and we can get some latency gains by merging the transformation calculations.
|
|
The data_format attribute for Conv2D isn't supported by this op, and defaults to
|
|
<code>NHWC</code> order.
|
|
Internally this op uses a single per-graph scratch buffer, which means that it
|
|
will block if multiple versions are being run in parallel. This is because this
|
|
operator is primarily an optimization to minimize memory usage.</p></div></div><div class="top"><p class="src"><a name="v:randomUniform" class="def">randomUniform</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>shape</strong>: The shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A tensor of the specified shape filled with uniform random values.</p></td></tr></table></div><div class="doc"><p>Outputs random values from a uniform distribution.</p><p>The generated values follow a uniform distribution in the range `[0, 1)`. The
|
|
lower bound 0 is included in the range, while the upper bound 1 is excluded.</p></div></div><div class="top"><p class="src"><a name="v:depthwiseConv2dNative" class="def">depthwiseConv2dNative</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes a 2-D depthwise convolution given 4-D <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> tensors.</p><p>Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
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and a filter / kernel tensor of shape
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`[filter_height, filter_width, in_channels, channel_multiplier]`, containing
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<code>in_channels</code> convolutional filters of depth 1, <code>depthwise_conv2d</code> applies
|
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a different filter to each input channel (expanding from 1 channel to
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<code>channel_multiplier</code> channels for each), then concatenates the results
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together. Thus, the output has `in_channels * channel_multiplier` channels.</p><p>for k in 0..in_channels-1
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for q in 0..channel_multiplier-1
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output[b, i, j, k * channel_multiplier + q] =
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sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
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filter[di, dj, k, q]</p><p>Must have `strides[0] = strides[3] = 1`. For the most common case of the same
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|
horizontal and vertices strides, `strides = [1, stride, stride, 1]`.</p></div></div><div class="top"><p class="src"><a name="v:sparseApplyAdadelta" class="def">sparseApplyAdadelta</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>accum_update</strong>: : Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>lr</strong>: Learning rate. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>rho</strong>: Decay factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>epsilon</strong>: Constant factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>var: Should be from a Variable().</p></div></div><div class="top"><p class="src"><a name="v:depthwiseConv2dNativeBackpropFilter" class="def">depthwiseConv2dNativeBackpropFilter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>filter_sizes</strong>: An integer vector representing the tensor shape of <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>,
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where <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> is a 4-D
|
|
`[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, out_channels]`.
|
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Gradients w.r.t. the output of the convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape
|
|
`[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
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|
the <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> input of the convolution.</p></td></tr></table></div><div class="doc"><p>Computes the gradients of depthwise convolution with respect to the filter.</p></div></div><div class="top"><p class="src"><a name="v:conv3D" class="def">conv3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, in_depth, in_height, in_width, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: Shape `[filter_depth, filter_height, filter_width, in_channels,
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|
out_channels]`. <code>in_channels</code> must match between <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes a 3-D convolution given 5-D <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> tensors.</p><p>In signal processing, cross-correlation is a measure of similarity of
|
|
two waveforms as a function of a time-lag applied to one of them. This
|
|
is also known as a sliding dot product or sliding inner-product.</p><p>Our Conv3D implements a form of cross-correlation.</p></div></div><div class="top"><p class="src"><a name="v:greaterEqual" class="def">greaterEqual</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x >= y) element-wise.</p><ul><li>NOTE*: <code>GreaterEqual</code> supports broadcasting. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:sparseDenseCwiseAdd" class="def">sparseDenseCwiseAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
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|
SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sp_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>sp_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>dense</strong>: <code>R</code>-D. The dense Tensor operand.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D. The <code>N</code> values that are operated on.</p></td></tr></table></div><div class="doc"><p>Adds up a SparseTensor and a dense Tensor, using these special rules:</p><ol><li>Broadcasts the dense side to have the same shape as the sparse side, if
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eligible;</li><li>Then, only the dense values pointed to by the indices of the SparseTensor
|
|
participate in the cwise addition.</li></ol><p>By these rules, the result is a logical SparseTensor with exactly the same
|
|
indices and shape, but possibly with different non-zero values. The output of
|
|
this Op is the resultant non-zero values.</p></div></div><div class="top"><p class="src"><a name="v:conv3DBackpropFilter" class="def">conv3DBackpropFilter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, depth, rows, cols, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: Shape `[depth, rows, cols, in_channels, out_channels]`.
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<code>in_channels</code> must match between <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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|
out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradients of 3-D convolution with respect to the filter.</p></div></div><div class="top"><p class="src"><a name="v:conv3DBackpropInputV2" class="def">conv3DBackpropInputV2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>input_sizes</strong>: An integer vector representing the tensor shape of <code>input</code>,
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|
where <code>input</code> is a 5-D
|
|
`[batch, depth, rows, cols, in_channels]` tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: Shape `[depth, rows, cols, in_channels, out_channels]`.
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|
<code>in_channels</code> must match between <code>input</code> and <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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|
out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradients of 3-D convolution with respect to the input.</p></div></div><div class="top"><p class="src"><a name="v:mod" class="def">mod</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns element-wise remainder of division.</p><ul><li>NOTE*: <code>Mod</code> supports broadcasting. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:refMerge" class="def">refMerge</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t]</td><td class="doc"><p><strong>inputs</strong>: The input tensors, exactly one of which will become available.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</td><td class="doc"><p>(<strong>output</strong>, <strong>value_index</strong>)</p><ul><li><strong>output</strong>: Will be set to the available input tensor.</li><li><strong>value_index</strong>: The index of the chosen input tensor in <code>inputs</code>.</li></ul></td></tr></table></div><div class="doc"><p>Forwards the value of an available tensor from <code>inputs</code> to <code>output</code>.</p><p><code>Merge</code> waits for at least one of the tensors in <code>inputs</code> to become available.
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It is usually combined with <code>Switch</code> to implement branching.</p><p><code>Merge</code> forwards the first tensor for become available to <code>output</code>, and sets
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<code>value_index</code> to its index in <code>inputs</code>.</p></div></div><div class="top"><p class="src"><a name="v:conv3DBackpropFilterV2" class="def">conv3DBackpropFilterV2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, depth, rows, cols, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>filter_sizes</strong>: An integer vector representing the tensor shape of <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>,
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where <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> is a 5-D
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`[filter_depth, filter_height, filter_width, in_channels, out_channels]`
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tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: Backprop signal of shape `[batch, out_depth, out_rows, out_cols,
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out_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradients of 3-D convolution with respect to the filter.</p></div></div><div class="top"><p class="src"><a name="v:serializeManySparse" class="def">serializeManySparse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sparse_indices</strong>: 2-D. The <code>indices</code> of the minibatch <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sparse_values</strong>: 1-D. The <code>values</code> of the minibatch <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sparse_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the minibatch <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>serialized_sparse</strong></p></td></tr></table></div><div class="doc"><p>Serialize an <code>N</code>-minibatch <code>SparseTensor</code> into an `[N, 3]` string <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code>.</p><p>The <code>SparseTensor</code> must have rank <code>R</code> greater than 1, and the first dimension
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is treated as the minibatch dimension. Elements of the <code>SparseTensor</code>
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|
must be sorted in increasing order of this first dimension. The serialized
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<code>SparseTensor</code> objects going into each row of <code>serialized_sparse</code> will have
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rank `R-1`.</p><p>The minibatch size <code>N</code> is extracted from `sparse_shape[0]`.</p></div></div><div class="top"><p class="src"><a name="v:avgPool3DGrad" class="def">avgPool3DGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>orig_input_shape</strong>: The original input dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>grad</strong>: Output backprop of shape `[batch, depth, rows, cols, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The backprop for input.</p></td></tr></table></div><div class="doc"><p>Computes gradients of average pooling function.</p></div></div><div class="top"><p class="src"><a name="v:maxPool3DGrad" class="def">maxPool3DGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>orig_input</strong>: The original input tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>orig_output</strong>: The original output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>grad</strong>: Output backprop of shape `[batch, depth, rows, cols, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes gradients of max pooling function.</p></div></div><div class="top"><p class="src"><a name="v:sparseReduceSum" class="def">sparseReduceSum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
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SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>input_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>input_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>reduction_axes</strong>: 1-D. Length-<code>K</code> vector containing the reduction axes.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: `R-K`-D. The reduced Tensor.</p></td></tr></table></div><div class="doc"><p>Computes the sum of elements across dimensions of a SparseTensor.</p><p>This Op takes a SparseTensor and is the sparse counterpart to
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`tf.reduce_sum()`. In particular, this Op also returns a dense <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code>
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instead of a sparse one.</p><p>Reduces <code>sp_input</code> along the dimensions given in <code>reduction_axes</code>. Unless
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<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
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<code>reduction_axes</code>. If <code>keep_dims</code> is true, the reduced dimensions are retained
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with length 1.</p><p>If <code>reduction_axes</code> has no entries, all dimensions are reduced, and a tensor
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with a single element is returned. Additionally, the axes can be negative,
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which are interpreted according to the indexing rules in Python.</p></div></div><div class="top"><p class="src"><a name="v:relu" class="def">relu</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>activations</strong></p></td></tr></table></div><div class="doc"><p>Computes rectified linear: `max(features, 0)`.</p></div></div><div class="top"><p class="src"><a name="v:l2Loss" class="def">l2Loss</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>t</strong>: Typically 2-D, but may have any dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 0-D.</p></td></tr></table></div><div class="doc"><p>L2 Loss.</p><p>Computes half the L2 norm of a tensor without the <code><a href="../base-4.8.2.0/Prelude.html#v:sqrt">sqrt</a></code>:</p><p>output = sum(t ** 2) / 2</p></div></div><div class="top"><p class="src"><a name="v:readerRestoreState" class="def">readerRestoreState</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: Handle to a Reader.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>state</strong>: Result of a ReaderSerializeState of a Reader with type
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matching reader_handle.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Restore a reader to a previously saved state.</p><p>Not all Readers support being restored, so this can produce an
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Unimplemented error.</p></div></div><div class="top"><p class="src"><a name="v:shape" class="def">shape</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` out_type)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the shape of a tensor.</p><p>This operation returns a 1-D integer tensor representing the shape of <code>input</code>.</p><p>For example:</p><p>```prettyprint
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# <code>t</code> is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
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shape(t) ==> [2, 2, 3]
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```</p></div></div><div class="top"><p class="src"><a name="v:softmaxCrossEntropyWithLogits" class="def">softmaxCrossEntropyWithLogits</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong>: batch_size x num_classes matrix</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>labels</strong>: batch_size x num_classes matrix
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The caller must ensure that each batch of labels represents a valid
|
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probability distribution.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>loss</strong>, <strong>backprop</strong>)</p><ul><li><strong>loss</strong>: Per example loss (batch_size vector).</li><li><strong>backprop</strong>: backpropagated gradients (batch_size x num_classes matrix).</li></ul></td></tr></table></div><div class="doc"><p>Computes softmax cross entropy cost and gradients to backpropagate.</p><p>Inputs are the logits, not probabilities.</p></div></div><div class="top"><p class="src"><a name="v:maxPool" class="def">maxPool</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D input to pool over.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The max pooled output tensor.</p></td></tr></table></div><div class="doc"><p>Performs max pooling on the input.</p></div></div><div class="top"><p class="src"><a name="v:dilation2DBackpropInput" class="def">dilation2DBackpropInput</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: 3-D with shape `[filter_height, filter_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>in_backprop</strong>: 4-D with shape `[batch, in_height, in_width, depth]`.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of morphological 2-D dilation with respect to the input.</p></div></div><div class="top"><p class="src"><a name="v:equal" class="def">equal</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x == y) element-wise.</p><ul><li>NOTE*: <code>Equal</code> supports broadcasting. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:dilation2DBackpropFilter" class="def">dilation2DBackpropFilter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>filter</strong>: 3-D with shape `[filter_height, filter_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, depth]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>filter_backprop</strong>: 3-D with shape `[filter_height, filter_width, depth]`.</p></td></tr></table></div><div class="doc"><p>Computes the gradient of morphological 2-D dilation with respect to the filter.</p></div></div><div class="top"><p class="src"><a name="v:reluGrad" class="def">reluGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>gradients</strong>: The backpropagated gradients to the corresponding Relu operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>features</strong>: The features passed as input to the corresponding Relu operation, OR
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the outputs of that operation (both work equivalently).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>backprops</strong>: `gradients * (features > 0)`.</p></td></tr></table></div><div class="doc"><p>Computes rectified linear gradients for a Relu operation.</p></div></div><div class="top"><p class="src"><a name="v:relu6" class="def">relu6</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>activations</strong></p></td></tr></table></div><div class="doc"><p>Computes rectified linear 6: `min(max(features, 0), 6)`.</p></div></div><div class="top"><p class="src"><a name="v:resizeBicubic" class="def">resizeBicubic</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
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new size for the images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>resized_images</strong>: 4-D with shape
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`[batch, new_height, new_width, channels]`.</p></td></tr></table></div><div class="doc"><p>Resize <code>images</code> to <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> using bicubic interpolation.</p><p>Input images can be of different types but output images are always float.</p></div></div><div class="top"><p class="src"><a name="v:relu6Grad" class="def">relu6Grad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>gradients</strong>: The backpropagated gradients to the corresponding Relu6 operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>features</strong>: The features passed as input to the corresponding Relu6 operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>backprops</strong>: The gradients:
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`gradients * features * (features > 0) * (features < 6)`.</p></td></tr></table></div><div class="doc"><p>Computes rectified linear 6 gradients for a Relu6 operation.</p></div></div><div class="top"><p class="src"><a name="v:sparseTensorDenseMatMul" class="def">sparseTensorDenseMatMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_indices</strong>: 2-D. The <code>indices</code> of the <code>SparseTensor</code>, size `[nnz, 2]` Matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>a_values</strong>: 1-D. The <code>values</code> of the <code>SparseTensor</code>, size `[nnz]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the <code>SparseTensor</code>, size `[2]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>b</strong>: 2-D. A dense Matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>product</strong></p></td></tr></table></div><div class="doc"><p>Multiply SparseTensor (of rank 2) <a href="A.html">A</a> by dense matrix <a href="B.html">B</a>.</p><p>No validity checking is performed on the indices of A. However, the following
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input format is recommended for optimal behavior:</p><p>if adjoint_a == false:
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A should be sorted in lexicographically increasing order. Use SparseReorder
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if you're not sure.
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if adjoint_a == true:
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A should be sorted in order of increasing dimension 1 (i.e., "column major"
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order instead of "row major" order).</p></div></div><div class="top"><p class="src"><a name="v:softplus" class="def">softplus</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>features</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>activations</strong></p></td></tr></table></div><div class="doc"><p>Computes softplus: `log(exp(features) + 1)`.</p></div></div><div class="top"><p class="src"><a name="v:batchMatMul" class="def">batchMatMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong>: 3-D or higher with shape `[..., r_x, c_x]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong>: 3-D or higher with shape `[..., r_y, c_y]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 3-D or higher with shape `[..., r_o, c_o]`</p></td></tr></table></div><div class="doc"><p>Multiplies slices of two tensors in batches.</p><p>Multiplies all slices of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> <code>x</code> and <code>y</code> (each slice can be
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viewed as an element of a batch), and arranges the individual results
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in a single output tensor of the same batch size. Each of the
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individual slices can optionally be adjointed (to adjoint a matrix
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means to transpose and conjugate it) before multiplication by setting
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the <code>adj_x</code> or <code>adj_y</code> flag to <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, which are by default <code><a href="../base-4.8.2.0/Data-Bool.html#v:False">False</a></code>.</p><p>The input tensors <code>x</code> and <code>y</code> are 3-D or higher with shape `[..., r_x, c_x]`
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and `[..., r_y, c_y]`.</p><p>The output tensor is 3-D or higher with shape `[..., r_o, c_o]`, where:</p><p>r_o = c_x if adj_x else r_x
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c_o = r_y if adj_y else c_y</p><p>It is computed as:</p><p>output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])</p></div></div><div class="top"><p class="src"><a name="v:softsignGrad" class="def">softsignGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>gradients</strong>: The backpropagated gradients to the corresponding softsign operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>features</strong>: The features passed as input to the corresponding softsign operation.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>backprops</strong>: The gradients: `gradients / (1 + abs(-features)) ** 2`.</p></td></tr></table></div><div class="doc"><p>Computes softsign gradients for a softsign operation.</p></div></div><div class="top"><p class="src"><a name="v:lessEqual" class="def">lessEqual</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x <= y) element-wise.</p><ul><li>NOTE*: <code>LessEqual</code> supports broadcasting. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:logSoftmax" class="def">logSoftmax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>logits</strong>: 2-D with shape `[batch_size, num_classes]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>logsoftmax</strong>: Same shape as <code>logits</code>.</p></td></tr></table></div><div class="doc"><p>Computes log softmax activations.</p><p>For each batch <code>i</code> and class <code>j</code> we have</p><p>logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))</p></div></div><div class="top"><p class="src"><a name="v:inTopK" class="def">inTopK</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>k</strong>: Number of top elements to look at for computing precision.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>predictions</strong>: A <code>batch_size</code> x <code>classes</code> tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>targets</strong>: A <code>batch_size</code> vector of class ids.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>precision</strong>: Computed Precision at <code>k</code> as a `bool Tensor`.</p></td></tr></table></div><div class="doc"><p>Says whether the targets are in the top <code>K</code> predictions.</p><p>This outputs a <code>batch_size</code> bool array, an entry `out[i]` is <code>true</code> if the
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prediction for the target class is among the top <code>k</code> predictions among
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all predictions for example <code>i</code>. Note that the behavior of <code>InTopK</code> differs
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from the <code>TopK</code> op in its handling of ties; if multiple classes have the
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same prediction value and straddle the top-<code>k</code> boundary, all of those
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classes are considered to be in the top <code>k</code>.</p><p>More formally, let</p><p>\(predictions_i\) be the predictions for all classes for example <code>i</code>,
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\(targets_i\) be the target class for example <code>i</code>,
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\(out_i\) be the output for example <code>i</code>,</p><p>$$out_i = predictions_{i, targets_i} in TopKIncludingTies(predictions_i)$$</p></div></div><div class="top"><p class="src"><a name="v:matrixDiag" class="def">matrixDiag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>diagonal</strong>: Rank <code>k</code>, where `k >= 1`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.</p></td></tr></table></div><div class="doc"><p>Returns a batched diagonal tensor with a given batched diagonal values.</p><p>Given a <code>diagonal</code>, this operation returns a tensor with the <code>diagonal</code> and
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everything else padded with zeros. The diagonal is computed as follows:</p><p>Assume <code>diagonal</code> has <code>k</code> dimensions `[I, J, K, ..., N]`, then the output is a
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tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:</p><p>`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.</p><p>For example:</p><p>```prettyprint
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# <code>diagonal</code> is [[1, 2, 3, 4], [5, 6, 7, 8]]</p><p>and diagonal.shape = (2, 4)</p><p>tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
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[0, 2, 0, 0]
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[0, 0, 3, 0]
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[0, 0, 0, 4]],
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[[5, 0, 0, 0]
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[0, 6, 0, 0]
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[0, 0, 7, 0]
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[0, 0, 0, 8]]]</p><p>which has shape (2, 4, 4)
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```</p></div></div><div class="top"><p class="src"><a name="v:maxPool3D" class="def">maxPool3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape `[batch, depth, rows, cols, channels]` tensor to pool over.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The max pooled output tensor.</p></td></tr></table></div><div class="doc"><p>Performs 3D max pooling on the input.</p></div></div><div class="top"><p class="src"><a name="v:topK" class="def">topK</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>k</strong>: Number of top elements to look for along the last dimension (along each
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row for matrices).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 1-D or higher with last dimension at least <code>k</code>.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</td><td class="doc"><p>(<strong>values</strong>, <strong>indices</strong>)</p><ul><li><strong>values</strong>: The <code>k</code> largest elements along each last dimensional slice.</li><li><strong>indices</strong>: The indices of <code>values</code> within the last dimension of <code>input</code>.</li></ul></td></tr></table></div><div class="doc"><p>Finds values and indices of the <code>k</code> largest elements for the last dimension.</p><p>If the input is a vector (rank-1), finds the <code>k</code> largest entries in the vector
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and outputs their values and indices as vectors. Thus `values[j]` is the
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|
<code>j</code>-th largest entry in <code>input</code>, and its index is `indices[j]`.</p><p>For matrices (resp. higher rank input), computes the top <code>k</code> entries in each
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|
row (resp. vector along the last dimension). Thus,</p><p>values.shape = indices.shape = input.shape[:-1] + [k]</p><p>If two elements are equal, the lower-index element appears first.</p><p>If <code>k</code> varies dynamically, use <code>TopKV2</code> below.</p></div></div><div class="top"><p class="src"><a name="v:topKV2" class="def">topKV2</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 1-D or higher with last dimension at least <code>k</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>k</strong>: 0-D. Number of top elements to look for along the last dimension (along each
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row for matrices).</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>)</td><td class="doc"><p>(<strong>values</strong>, <strong>indices</strong>)</p><ul><li><strong>values</strong>: The <code>k</code> largest elements along each last dimensional slice.</li><li><strong>indices</strong>: The indices of <code>values</code> within the last dimension of <code>input</code>.</li></ul></td></tr></table></div><div class="doc"><p>Finds values and indices of the <code>k</code> largest elements for the last dimension.</p><p>If the input is a vector (rank-1), finds the <code>k</code> largest entries in the vector
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|
and outputs their values and indices as vectors. Thus `values[j]` is the
|
|
<code>j</code>-th largest entry in <code>input</code>, and its index is `indices[j]`.</p><p>For matrices (resp. higher rank input), computes the top <code>k</code> entries in each
|
|
row (resp. vector along the last dimension). Thus,</p><p>values.shape = indices.shape = input.shape[:-1] + [k]</p><p>If two elements are equal, the lower-index element appears first.</p><p>This is the same as <code>TopK</code>, but takes <code>k</code> as in input rather than an attr.</p></div></div><div class="top"><p class="src"><a name="v:fractionalMaxPool" class="def">fractionalMaxPool</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>output</strong>, <strong>row_pooling_sequence</strong>, <strong>col_pooling_sequence</strong>)</p><ul><li><strong>output</strong>: output tensor after fractional max pooling.</li><li><strong>row_pooling_sequence</strong>: row pooling sequence, needed to calculate gradient.</li><li><strong>col_pooling_sequence</strong>: column pooling sequence, needed to calculate gradient.</li></ul></td></tr></table></div><div class="doc"><p>Performs fractional max pooling on the input.</p><p>Fractional max pooling is slightly different than regular max pooling. In
|
|
regular max pooling, you downsize an input set by taking the maximum value of
|
|
smaller N x N subsections of the set (often 2x2), and try to reduce the set by
|
|
a factor of N, where N is an integer. Fractional max pooling, as you might
|
|
expect from the word "fractional", means that the overall reduction ratio N
|
|
does not have to be an integer.</p><p>The sizes of the pooling regions are generated randomly but are fairly uniform.
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|
For example, let's look at the height dimension, and the constraints on the
|
|
list of rows that will be pool boundaries.</p><p>First we define the following:</p><ol><li>input_row_length : the number of rows from the input set</li><li>output_row_length : which will be smaller than the input</li><li>alpha = input_row_length / output_row_length : our reduction ratio</li><li>K = floor(alpha)</li><li>row_pooling_sequence : this is the result list of pool boundary rows</li></ol><p>Then, row_pooling_sequence should satisfy:</p><ol><li>a[0] = 0 : the first value of the sequence is 0</li><li>a[end] = input_row_length : the last value of the sequence is the size</li><li>K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size</li><li>length(row_pooling_sequence) = output_row_length+1</li></ol><p>For more details on fractional max pooling, see this paper:
|
|
<a href="http://arxiv.org/abs/1412.6071">Benjamin Graham, Fractional Max-Pooling</a></p></div></div><div class="top"><p class="src"><a name="v:matrixBandPart" class="def">matrixBandPart</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Rank <code>k</code> tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_lower</strong>: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire
|
|
lower triangle.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_upper</strong>: 0-D tensor. Number of superdiagonals to keep. If negative, keep
|
|
entire upper triangle.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>band</strong>: Rank <code>k</code> tensor of the same shape as input. The extracted banded tensor.</p></td></tr></table></div><div class="doc"><p>Copy a tensor setting everything outside a central band in each innermost matrix</p><p>to zero.</p><p>The <code>band</code> part is computed as follows:
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|
Assume <code>input</code> has <code>k</code> dimensions `[I, J, K, ..., M, N]`, then the output is a
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|
tensor with the same shape where</p><p>`band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.</p><p>The indicator function 'in_band(m, n)` is one if
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|
`(num_lower < 0 || (m-n) <= num_lower)) &&
|
|
(num_upper < 0 || (n-m) <= num_upper)`, and zero otherwise.</p><p>For example:</p><p>```prettyprint
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|
# if <code>input</code> is [[ 0, 1, 2, 3]
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|
[-1, 0, 1, 2]
|
|
[-2, -1, 0, 1]
|
|
[-3, -2, -1, 0]],</p><p>tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
|
|
[-1, 0, 1, 2]
|
|
[ 0, -1, 0, 1]
|
|
[ 0, 0, -1, 0]],</p><p>tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
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|
[-1, 0, 1, 0]
|
|
[-2, -1, 0, 1]
|
|
[ 0, -2, -1, 0]]
|
|
```</p><p>Useful special cases:</p><p>```prettyprint
|
|
tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
|
|
tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
|
|
tf.matrix_band_part(input, 0, 0) ==> Diagonal.
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:decodeRaw" class="def">decodeRaw</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> out_type, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` out_type)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>bytes</strong>: All the elements must have the same length.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> out_type</td><td class="doc"><p><strong>output</strong>: A Tensor with one more dimension than the input <code>bytes</code>. The
|
|
added dimension will have size equal to the length of the elements
|
|
of <code>bytes</code> divided by the number of bytes to represent <code>out_type</code>.</p></td></tr></table></div><div class="doc"><p>Reinterpret the bytes of a string as a vector of numbers.</p></div></div><div class="top"><p class="src"><a name="v:decodeJSONExample" class="def">decodeJSONExample</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>json_examples</strong>: Each string is a JSON object serialized according to the JSON
|
|
mapping of the Example proto.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>binary_examples</strong>: Each string is a binary Example protocol buffer corresponding
|
|
to the respective element of <code>json_examples</code>.</p></td></tr></table></div><div class="doc"><p>Convert JSON-encoded Example records to binary protocol buffer strings.</p><p>This op translates a tensor containing Example records, encoded using
|
|
the <a href="https://developers.google.com/protocol-buffers/docs/proto3#json">standard JSON
|
|
mapping</a>,
|
|
into a tensor containing the same records encoded as binary protocol
|
|
buffers. The resulting tensor can then be fed to any of the other
|
|
Example-parsing ops.</p></div></div><div class="top"><p class="src"><a name="v:truncatedNormal" class="def">truncatedNormal</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` dtype)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>shape</strong>: The shape of the output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>output</strong>: A tensor of the specified shape filled with random truncated normal
|
|
values.</p></td></tr></table></div><div class="doc"><p>Outputs random values from a truncated normal distribution.</p><p>The generated values follow a normal distribution with mean 0 and standard
|
|
deviation 1, except that values whose magnitude is more than 2 standard
|
|
deviations from the mean are dropped and re-picked.</p></div></div><div class="top"><p class="src"><a name="v:randomShuffle" class="def">randomShuffle</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: The tensor to be shuffled.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: A tensor of same shape and type as <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>, shuffled along its first
|
|
dimension.</p></td></tr></table></div><div class="doc"><p>Randomly shuffles a tensor along its first dimension.</p><p>The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
|
|
to one and only one `output[i]`. For example, a mapping that might occur for a
|
|
3x2 tensor is:</p><p>```prettyprint
|
|
[[1, 2], [[5, 6],
|
|
[3, 4], ==> [1, 2],
|
|
[5, 6]] [3, 4]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:multinomial" class="def">multinomial</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>logits</strong>: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`
|
|
represents the unnormalized log probabilities for all classes.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>num_samples</strong>: 0-D. Number of independent samples to draw for each row slice.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong>: 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]`
|
|
contains the drawn class labels with range `[0, num_classes)`.</p></td></tr></table></div><div class="doc"><p>Draws samples from a multinomial distribution.</p></div></div><div class="top"><p class="src"><a name="v:randomGamma" class="def">randomGamma</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> s, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` s, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 s</td><td class="doc"><p><strong>shape</strong>: 1-D integer tensor. Shape of independent samples to draw from each
|
|
distribution described by the shape parameters given in alpha.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>alpha</strong>: A tensor in which each scalar is a "shape" parameter describing the
|
|
associated gamma distribution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: A tensor with shape `shape + shape(alpha)`. Each slice
|
|
`[:, ..., :, i0, i1, ...iN]` contains the samples drawn for
|
|
`alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.</p></td></tr></table></div><div class="doc"><p>Outputs random values from the Gamma distribution(s) described by alpha.</p><p>This op uses the algorithm by Marsaglia et al. to acquire samples via
|
|
transformation-rejection from pairs of uniform and normal random variables.
|
|
See <a href="http://dl.acm.org/citation.cfm?id=358414">http://dl.acm.org/citation.cfm?id=358414</a></p></div></div><div class="top"><p class="src"><a name="v:addN" class="def">addN</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t]</td><td class="doc"><p><strong>inputs</strong>: Must all be the same size and shape.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>sum</strong></p></td></tr></table></div><div class="doc"><p>Add all input tensors element wise.</p></div></div><div class="top"><p class="src"><a name="v:max" class="def">max</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the maximum of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
|
|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:_Retval" class="def">_Retval</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>index</strong>: This return value is the index-th return value of the function.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The return value.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>A graph node which represents a return value of a function.</p></div></div><div class="top"><p class="src"><a name="v:destroyTemporaryVariable" class="def">destroyTemporaryVariable</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: A reference to the temporary variable tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>value</strong></p></td></tr></table></div><div class="doc"><p>Destroys the temporary variable and returns its final value.</p><p>Sets output to the value of the Tensor pointed to by <code>ref</code>, then destroys
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the temporary variable called <code>var_name</code>.
|
|
All other uses of <code>ref</code> *must* have executed before this op.
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|
This is typically achieved by chaining the ref through each assign op, or by
|
|
using control dependencies.</p><p>Outputs the final value of the tensor pointed to by <code>ref</code>.</p></div></div><div class="top"><p class="src"><a name="v:cast" class="def">cast</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dstT, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> srcT)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 srcT</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dstT</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Cast x of type SrcT to y of DstT.</p></div></div><div class="top"><p class="src"><a name="v:countUpTo" class="def">countUpTo</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>limit</strong>: If incrementing ref would bring it above limit, instead generates an
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|
<code>OutOfRange</code> error.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>ref</strong>: Should be from a scalar <code>Variable</code> node.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: A copy of the input before increment. If nothing else modifies the
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input, the values produced will all be distinct.</p></td></tr></table></div><div class="doc"><p>Increments <code>ref</code> until it reaches <code>limit</code>.</p><p>This operation outputs "ref" after the update is done. This makes it
|
|
easier to chain operations that need to use the updated value.</p></div></div><div class="top"><p class="src"><a name="v:abs" class="def">abs</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the absolute value of a tensor.</p><p>Given a tensor <code>x</code>, this operation returns a tensor containing the absolute
|
|
value of each element in <code>x</code>. For example, if x is an input element and y is
|
|
an output element, this operation computes \(y = |x|\).</p></div></div><div class="top"><p class="src"><a name="v:neg" class="def">neg</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes numerical negative value element-wise.</p><p>I.e., \(y = -x\).</p></div></div><div class="top"><p class="src"><a name="v:sparseSparseMaximum" class="def">sparseSparseMaximum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
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SparseTensor, in the canonical lexicographic ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>a_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>a_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_indices</strong>: counterpart to <code>a_indices</code> for the other operand.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>b_values</strong>: counterpart to <code>a_values</code> for the other operand; must be of the same dtype.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_shape</strong>: counterpart to <code>a_shape</code> for the other operand; the two shapes must be equal.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>)</p><ul><li><strong>output_indices</strong>: 2-D. The indices of the output SparseTensor.</li><li><strong>output_values</strong>: 1-D. The values of the output SparseTensor.</li></ul></td></tr></table></div><div class="doc"><p>Returns the element-wise max of two SparseTensors.</p><p>Assumes the two SparseTensors have the same shape, i.e., no broadcasting.</p></div></div><div class="top"><p class="src"><a name="v:invGrad" class="def">invGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradient for the inverse of <code>x</code> wrt its input.</p><p>Specifically, `grad = -dy * y*y`, where `y = 1/x`, and <code>dy</code>
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|
is the corresponding input gradient.</p></div></div><div class="top"><p class="src"><a name="v:sqrt" class="def">sqrt</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes square root of x element-wise.</p><p>I.e., \(y = sqrt{x} = x^{1/2}\).</p></div></div><div class="top"><p class="src"><a name="v:matrixInverse" class="def">matrixInverse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[..., M, M]`.</p></td></tr></table></div><div class="doc"><p>Computes the inverse of one or more square invertible matrices or their</p><p>adjoints (conjugate transposes).</p><p>The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
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form square matrices. The output is a tensor of the same shape as the input
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|
containing the inverse for all input submatrices `[..., :, :]`.</p><p>The op uses LU decomposition with partial pivoting to compute the inverses.</p><p>If a matrix is not invertible there is no guarantee what the op does. It
|
|
may detect the condition and raise an exception or it may simply return a
|
|
garbage result.</p></div></div><div class="top"><p class="src"><a name="v:sqrtGrad" class="def">sqrtGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradient for the sqrt of <code>x</code> wrt its input.</p><p>Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and <code>dy</code>
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|
is the corresponding input gradient.</p></div></div><div class="top"><p class="src"><a name="v:expandDims" class="def">expandDims</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tdim, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tdim)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tdim</td><td class="doc"><p><strong>dim</strong>: 0-D (scalar). Specifies the dimension index at which to
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|
expand the shape of <code>input</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Contains the same data as <code>input</code>, but its shape has an additional
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|
dimension of size 1 added.</p></td></tr></table></div><div class="doc"><p>Inserts a dimension of 1 into a tensor's shape.</p><p>Given a tensor <code>input</code>, this operation inserts a dimension of 1 at the
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|
dimension index <code>dim</code> of <code>input</code>'s shape. The dimension index <code>dim</code> starts at
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zero; if you specify a negative number for <code>dim</code> it is counted backward from
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the end.</p><p>This operation is useful if you want to add a batch dimension to a single
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|
element. For example, if you have a single image of shape `[height, width,
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|
channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
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|
which will make the shape `[1, height, width, channels]`.</p><p>Other examples:</p><p>```prettyprint
|
|
# <code>t</code> is a tensor of shape [2]
|
|
shape(expand_dims(t, 0)) ==> [1, 2]
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|
shape(expand_dims(t, 1)) ==> [2, 1]
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|
shape(expand_dims(t, -1)) ==> [2, 1]</p><p># <code>t2</code> is a tensor of shape [2, 3, 5]
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|
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
|
|
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
|
|
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
|
|
```</p><p>This operation requires that:</p><p>`-1-input.dims() <= dim <= input.dims()`</p><p>This operation is related to `squeeze()`, which removes dimensions of
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|
size 1.</p></div></div><div class="top"><p class="src"><a name="v:all" class="def">all</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the "logical and" of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
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|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:cTCBeamSearchDecoder" class="def">cTCBeamSearchDecoder</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>beam_width</strong>: A scalar >= 0 (beam search beam width).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>top_paths</strong>: A scalar >= 0, <= beam_width (controls output size).</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>inputs</strong>: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>sequence_length</strong>: A vector containing sequence lengths, size `(batch)`.</p></td></tr><tr><td class="src">-> ([<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>], <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>decoded_indices</strong>, <strong>decoded_values</strong>, <strong>decoded_shape</strong>, <strong>log_probability</strong>)</p><ul><li><strong>decoded_indices</strong>: A list (length: top_paths) of indices matrices. Matrix j,
|
|
size `(total_decoded_outputs[j] x 2)`, has indices of a
|
|
`SparseTensor<a href="int64,">2</a>`. The rows store: [batch, time].</li><li><strong>decoded_values</strong>: A list (length: top_paths) of values vectors. Vector j,
|
|
size `(length total_decoded_outputs[j])`, has the values of a
|
|
`SparseTensor<a href="int64,">2</a>`. The vector stores the decoded classes for beam j.</li><li><strong>decoded_shape</strong>: A list (length: top_paths) of shape vector. Vector j,
|
|
size `(2)`, stores the shape of the decoded `SparseTensor[j]`.
|
|
Its values are: `[batch_size, max_decoded_length[j]]`.</li><li><strong>log_probability</strong>: A matrix, shaped: `(batch_size x top_paths)`. The
|
|
sequence log-probabilities.</li></ul></td></tr></table></div><div class="doc"><p>Performs beam search decoding on the logits given in input.</p><p>A note about the attribute merge_repeated: For the beam search decoder,
|
|
this means that if consecutive entries in a beam are the same, only
|
|
the first of these is emitted. That is, when the top path is "A B B B B",
|
|
"A B" is returned if merge_repeated = True but "A B B B B" is
|
|
returned if merge_repeated = False.</p></div></div><div class="top"><p class="src"><a name="v:rsqrt" class="def">rsqrt</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes reciprocal of square root of x element-wise.</p><p>I.e., \(y = 1 / sqrt{x}\).</p></div></div><div class="top"><p class="src"><a name="v:tanhGrad" class="def">tanhGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the gradient for the tanh of <code>x</code> wrt its input.</p><p>Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and <code>dy</code>
|
|
is the corresponding input gradient.</p></div></div><div class="top"><p class="src"><a name="v:sin" class="def">sin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes sin of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:matrixDeterminant" class="def">matrixDeterminant</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Shape is `[..., M, M]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Shape is `[...]`.</p></td></tr></table></div><div class="doc"><p>Computes the determinant of one ore more square matrices.</p><p>The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
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form square matrices. The output is a tensor containing the determinants
|
|
for all input submatrices `[..., :, :]`.</p></div></div><div class="top"><p class="src"><a name="v:cos" class="def">cos</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes cos of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:batchToSpace" class="def">batchToSpace</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>block_size</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D tensor with shape
|
|
`[batch*block_size*block_size, height_pad<em>block_size, width_pad</em>block_size,
|
|
depth]`. Note that the batch size of the input tensor must be divisible by
|
|
`block_size * block_size`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>crops</strong>: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
|
|
how many elements to crop from the intermediate result across the spatial
|
|
dimensions as follows:</p><p>crops = [[crop_top, crop_bottom], [crop_left, crop_right]]</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape `[batch, height, width, depth]`, where:</p><p>height = height_pad - crop_top - crop_bottom
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|
width = width_pad - crop_left - crop_right</p><p>The attr <code>block_size</code> must be greater than one. It indicates the block size.</p><p>Some examples:</p><ol><li>For the following input of shape `[4, 1, 1, 1]` and block_size of 2:</li></ol><p>```prettyprint
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|
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
|
|
```</p><p>The output tensor has shape `[1, 2, 2, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [2]], [[3], [4]]]]
|
|
```</p><ol><li>For the following input of shape `[4, 1, 1, 3]` and block_size of 2:</li></ol><p>```prettyprint
|
|
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
|
|
```</p><p>The output tensor has shape `[1, 2, 2, 3]` and value:</p><p>```prettyprint
|
|
x = [[[[1, 2, 3], [4, 5, 6]],
|
|
[[7, 8, 9], [10, 11, 12]]]]
|
|
```</p><ol><li>For the following input of shape `[4, 2, 2, 1]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1], [3]], [[5], [7]]],
|
|
[[[2], [4]], [[10], [12]]],
|
|
[[[5], [7]], [[13], [15]]],
|
|
[[[6], [8]], [[14], [16]]]]
|
|
```</p><p>The output tensor has shape `[1, 4, 4, 1]` and value:</p><p>```prettyprint
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|
x = [[[1], [2], [3], [4]],
|
|
[[5], [6], [7], [8]],
|
|
[[9], [10], [11], [12]],
|
|
[[13], [14], [15], [16]]]
|
|
```</p><ol><li>For the following input of shape `[8, 1, 2, 1]` and block_size of 2:</li></ol><p>```prettyprint
|
|
x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
|
|
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
|
|
```</p><p>The output tensor has shape `[2, 2, 4, 1]` and value:</p><p>```prettyprint
|
|
x = [[[[1], [3]], [[5], [7]]],
|
|
[[[2], [4]], [[10], [12]]],
|
|
[[[5], [7]], [[13], [15]]],
|
|
[[[6], [8]], [[14], [16]]]]
|
|
```</p></td></tr></table></div><div class="doc"><p>BatchToSpace for 4-D tensors of type T.</p><p>This is a legacy version of the more general BatchToSpaceND.</p><p>Rearranges (permutes) data from batch into blocks of spatial data, followed by
|
|
cropping. This is the reverse transformation of SpaceToBatch. More specifically,
|
|
this op outputs a copy of the input tensor where values from the <code>batch</code>
|
|
dimension are moved in spatial blocks to the <code>height</code> and <code>width</code> dimensions,
|
|
followed by cropping along the <code>height</code> and <code>width</code> dimensions.</p></div></div><div class="top"><p class="src"><a name="v:sparseToDense" class="def">sparseToDense</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tindices</td><td class="doc"><p><strong>sparse_indices</strong>: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete
|
|
index where `sparse_values[i]` will be placed.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>output_shape</strong>: 1-D. Shape of the dense output tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>sparse_values</strong>: 1-D. Values corresponding to each row of <code>sparse_indices</code>,
|
|
or a scalar value to be used for all sparse indices.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>default_value</strong>: Scalar value to set for indices not specified in
|
|
<code>sparse_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>dense</strong>: Dense output tensor of shape <code>output_shape</code>.</p></td></tr></table></div><div class="doc"><p>Converts a sparse representation into a dense tensor.</p><p>Builds an array <code>dense</code> with shape <code>output_shape</code> such that</p><p>```prettyprint
|
|
# If sparse_indices is scalar
|
|
dense[i] = (i == sparse_indices ? sparse_values : default_value)</p><p># If sparse_indices is a vector, then for each i
|
|
dense[sparse_indices[i]] = sparse_values[i]</p><p># If sparse_indices is an n by d matrix, then for each i in [0, n)
|
|
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
|
|
```</p><p>All other values in <code>dense</code> are set to <code>default_value</code>. If <code>sparse_values</code> is a
|
|
scalar, all sparse indices are set to this single value.</p><p>Indices should be sorted in lexicographic order, and indices must not
|
|
contain any repeats. If <code>validate_indices</code> is true, these properties
|
|
are checked during execution.</p></div></div><div class="top"><p class="src"><a name="v:asin" class="def">asin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes asin of x element-wise.</p></div></div><div class="top"><p class="src"><a name="v:argMin" class="def">argMin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>dimension</strong>: int32, 0 <= dimension < rank(input). Describes which dimension
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of the input Tensor to reduce across. For vectors, use dimension = 0.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the index with the smallest value across dimensions of a tensor.</p></div></div><div class="top"><p class="src"><a name="v:isInf" class="def">isInf</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns which elements of x are Inf.</p></div></div><div class="top"><p class="src"><a name="v:sign" class="def">sign</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns an element-wise indication of the sign of a number.</p><p>`y = sign(x) = -1` if `x <a href="0`;">0 if `x == 0`; 1 if `x</a> 0`.</p><p>For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.</p></div></div><div class="top"><p class="src"><a name="v:add" class="def">add</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns x + y element-wise.</p><ul><li>NOTE*: <code>Add</code> supports broadcasting. <code>AddN</code> does not. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:sparseApplyFtrl" class="def">sparseApplyFtrl</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>var</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>accum</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>linear</strong>: Should be from a Variable().</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>grad</strong>: The gradient.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 tindices</td><td class="doc"><p><strong>indices</strong>: A vector of indices into the first dimension of var and accum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 t</td><td class="doc"><p><strong>lr</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 t</td><td class="doc"><p><strong>l1</strong>: L1 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v8 t</td><td class="doc"><p><strong>l2</strong>: L2 regularization. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v9 t</td><td class="doc"><p><strong>lr_power</strong>: Scaling factor. Must be a scalar.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong>: Same as "var".</p></td></tr></table></div><div class="doc"><p>Update relevant entries in '*var' according to the Ftrl-proximal scheme.</p><p>That is for rows we have grad for, we update var, accum and linear as follows:
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accum_new = accum + grad * grad
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linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
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quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
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var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
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accum = accum_new</p></div></div><div class="top"><p class="src"><a name="v:sub" class="def">sub</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns x - y element-wise.</p><ul><li>NOTE*: <code>Sub</code> supports broadcasting. More about broadcasting
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<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:batchFFT3D" class="def">batchFFT3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:sparseReduceSumSparse" class="def">sparseReduceSumSparse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
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SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>input_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>input_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>reduction_axes</strong>: 1-D. Length-<code>K</code> vector containing the reduction axes.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>, <strong>output_shape</strong>)</p><ul><li><strong>output_indices</strong></li><li><strong>output_values</strong></li><li><strong>output_shape</strong></li></ul></td></tr></table></div><div class="doc"><p>Computes the sum of elements across dimensions of a SparseTensor.</p><p>This Op takes a SparseTensor and is the sparse counterpart to
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`tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a
|
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SparseTensor.</p><p>Reduces <code>sp_input</code> along the dimensions given in <code>reduction_axes</code>. Unless
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<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
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<code>reduction_axes</code>. If <code>keep_dims</code> is true, the reduced dimensions are retained
|
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with length 1.</p><p>If <code>reduction_axes</code> has no entries, all dimensions are reduced, and a tensor
|
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with a single element is returned. Additionally, the axes can be negative,
|
|
which are interpreted according to the indexing rules in Python.</p></div></div><div class="top"><p class="src"><a name="v:biasAdd" class="def">biasAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>value</strong>: Any number of dimensions.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>bias</strong>: 1-D with size the last dimension of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Broadcasted sum of <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> and <code>bias</code>.</p></td></tr></table></div><div class="doc"><p>Adds <code>bias</code> to <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code>.</p><p>This is a special case of `tf.add` where <code>bias</code> is restricted to be 1-D.
|
|
Broadcasting is supported, so <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#v:value">value</a></code> may have any number of dimensions.</p></div></div><div class="top"><p class="src"><a name="v:mul" class="def">mul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns x * y element-wise.</p><ul><li>NOTE*: <code>Mul</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:div" class="def">div</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns x / y element-wise.</p><ul><li>NOTE*: <code>Div</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:loopCond" class="def">loopCond</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>input</strong>: A boolean scalar, representing the branch predicate of the Switch op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>output</strong>: The same tensor as <code>input</code>.</p></td></tr></table></div><div class="doc"><p>Forwards the input to the output.</p><p>This operator represents the loop termination condition used by the
|
|
"pivot" switches of a loop.</p></div></div><div class="top"><p class="src"><a name="v:squaredDifference" class="def">squaredDifference</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns (x - y)(x - y) element-wise.</p><ul><li>NOTE*: <code>SquaredDifference</code> supports broadcasting. More about broadcasting
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|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:maximum" class="def">maximum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the max of x and y (i.e. x > y ? x : y) element-wise.</p><ul><li>NOTE*: <code>Maximum</code> supports broadcasting. More about broadcasting
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|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:logUniformCandidateSampler" class="def">logUniformCandidateSampler</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_sampled</strong>: Number of candidates to randomly sample per batch.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_true</strong>: Number of true labels per context.</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>range_max</strong>: The sampler will sample integers from the interval [0, range_max).</p></td></tr><tr><td class="src">-> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>unique</strong>: If unique is true, we sample with rejection, so that all sampled
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|
candidates in a batch are unique. This requires some approximation to
|
|
estimate the post-rejection sampling probabilities.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>true_classes</strong>: A batch_size * num_true matrix, in which each row contains the
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|
IDs of the num_true target_classes in the corresponding original label.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p>(<strong>sampled_candidates</strong>, <strong>true_expected_count</strong>, <strong>sampled_expected_count</strong>)</p><ul><li><strong>sampled_candidates</strong>: A vector of length num_sampled, in which each element is
|
|
the ID of a sampled candidate.</li><li><strong>true_expected_count</strong>: A batch_size * num_true matrix, representing
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|
the number of times each candidate is expected to occur in a batch
|
|
of sampled candidates. If unique=true, then this is a probability.</li><li><strong>sampled_expected_count</strong>: A vector of length num_sampled, for each sampled
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|
candidate representing the number of times the candidate is expected
|
|
to occur in a batch of sampled candidates. If unique=true, then this is a
|
|
probability.</li></ul></td></tr></table></div><div class="doc"><p>Generates labels for candidate sampling with a log-uniform distribution.</p><p>See explanations of candidate sampling and the data formats at
|
|
go/candidate-sampling.</p><p>For each batch, this op picks a single set of sampled candidate labels.</p><p>The advantages of sampling candidates per-batch are simplicity and the
|
|
possibility of efficient dense matrix multiplication. The disadvantage is that
|
|
the sampled candidates must be chosen independently of the context and of the
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|
true labels.</p></div></div><div class="top"><p class="src"><a name="v:less" class="def">less</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x < y) element-wise.</p><ul><li>NOTE*: <code>Less</code> supports broadcasting. More about broadcasting
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|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:pow" class="def">pow</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Computes the power of one value to another.</p><p>Given a tensor <code>x</code> and a tensor <code>y</code>, this operation computes \(x^y\) for
|
|
corresponding elements in <code>x</code> and <code>y</code>. For example:</p><p>```
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|
# tensor <code>x</code> is [[2, 2]], [3, 3]]
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|
# tensor <code>y</code> is [[8, 16], [2, 3]]
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|
tf.pow(x, y) ==> [[256, 65536], [9, 27]]
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|
```</p></div></div><div class="top"><p class="src"><a name="v:igammac" class="def">igammac</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Compute the upper regularized incomplete Gamma function `Q(a, x)`.</p><p>The upper regularized incomplete Gamma function is defined as:</p><p>```
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|
Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)
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|
```
|
|
where
|
|
```
|
|
Gamma(a, x) = int_{x}^{infty} t^{a-1} exp(-t) dt
|
|
```
|
|
is the upper incomplete Gama function.</p><p>Note, above `P(a, x)` (<code>Igamma</code>) is the lower regularized complete
|
|
Gamma function.</p></div></div><div class="top"><p class="src"><a name="v:igamma" class="def">igamma</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Compute the lower regularized incomplete Gamma function `Q(a, x)`.</p><p>The lower regularized incomplete Gamma function is defined as:</p><p>```
|
|
P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)
|
|
```
|
|
where
|
|
```
|
|
gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt
|
|
```
|
|
is the lower incomplete Gamma function.</p><p>Note, above `Q(a, x)` (<code>Igammac</code>) is the upper regularized complete
|
|
Gamma function.</p></div></div><div class="top"><p class="src"><a name="v:zeta" class="def">zeta</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>q</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Compute the Hurwitz zeta function \(zeta(x, q)\).</p><p>The Hurwitz zeta function is defined as:</p><p>```
|
|
zeta(x, q) = sum_{n=0}^{infty} (q + n)^{-x}
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:imag" class="def">imag</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the imaginary part of a complex number.</p><p>Given a tensor <code>input</code> of complex numbers, this operation returns a tensor of
|
|
type <code>float</code> that is the imaginary part of each element in <code>input</code>. All
|
|
elements in <code>input</code> must be complex numbers of the form \(a + bj\), where *a*
|
|
is the real part and *b* is the imaginary part returned by this operation.</p><p>For example:</p><p>```
|
|
# tensor <code>input</code> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
tf.imag(input) ==> [4.75, 5.75]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:complex" class="def">complex</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>real</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>imag</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>out</strong></p></td></tr></table></div><div class="doc"><p>Converts two real numbers to a complex number.</p><p>Given a tensor <code><a href="TensorFlow-GenOps-Core.html#v:real">real</a></code> representing the real part of a complex number, and a
|
|
tensor <code><a href="TensorFlow-GenOps-Core.html#v:imag">imag</a></code> representing the imaginary part of a complex number, this
|
|
operation returns complex numbers elementwise of the form \(a + bj\), where
|
|
*a* represents the <code><a href="TensorFlow-GenOps-Core.html#v:real">real</a></code> part and *b* represents the <code><a href="TensorFlow-GenOps-Core.html#v:imag">imag</a></code> part.</p><p>The input tensors <code><a href="TensorFlow-GenOps-Core.html#v:real">real</a></code> and <code><a href="TensorFlow-GenOps-Core.html#v:imag">imag</a></code> must have the same shape.</p><p>For example:</p><p>```
|
|
# tensor <code><a href="TensorFlow-GenOps-Core.html#v:real">real</a></code> is [2.25, 3.25]
|
|
# tensor <code><a href="TensorFlow-GenOps-Core.html#v:imag">imag</a></code> is [4.75, 5.75]
|
|
tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:notEqual" class="def">notEqual</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a>, <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of (x != y) element-wise.</p><ul><li>NOTE*: <code>NotEqual</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:complexAbs" class="def">complexAbs</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the complex absolute value of a tensor.</p><p>Given a tensor <code>x</code> of complex numbers, this operation returns a tensor of type
|
|
<code>float</code> or <code>double</code> that is the absolute value of each element in <code>x</code>. All
|
|
elements in <code>x</code> must be complex numbers of the form \(a + bj\). The absolute
|
|
value is computed as \( sqrt{a^2 + b^2}\).</p><p>For example:</p><p>```
|
|
# tensor <code>x</code> is [[-2.25 + 4.75j], [-3.25 + 5.75j]]
|
|
tf.complex_abs(x) ==> [5.25594902, 6.60492229]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:logicalAnd" class="def">logicalAnd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>z</strong></p></td></tr></table></div><div class="doc"><p>Returns the truth value of x AND y element-wise.</p><ul><li>NOTE*: <code>LogicalAnd</code> supports broadcasting. More about broadcasting
|
|
<a href="http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html">here</a></li></ul></div></div><div class="top"><p class="src"><a name="v:batchFFT" class="def">batchFFT</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:select" class="def">select</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>condition</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>t</strong>: = A <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> which may have the same shape as <code>condition</code>.
|
|
If <code>condition</code> is rank 1, <code>t</code> may have higher rank,
|
|
but its first dimension must match the size of <code>condition</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>e</strong>: = A <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> with the same type and shape as <code>t</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: = A <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code> with the same type and shape as <code>t</code> and <code>e</code>.</p></td></tr></table></div><div class="doc"><p>Selects elements from <code>t</code> or <code>e</code>, depending on <code>condition</code>.</p><p>The <code>t</code>, and <code>e</code> tensors must all have the same shape,
|
|
and the output will also have that shape. The <code>condition</code> tensor
|
|
must be a scalar if <code>t</code> and <code>e</code> are scalars. If <code>t</code> and <code>e</code> are vectors
|
|
or higher rank, then <code>condition</code> must be either a vector with size
|
|
matching the first dimension of <code>t</code>, or must have the same shape as <code>t</code>.</p><p>The <code>condition</code> tensor acts as a mask that chooses, based on the value at each
|
|
element, whether the corresponding element / row in the output should be
|
|
taken from <code>t</code> (if true) or <code>e</code> (if false).</p><p>If <code>condition</code> is a vector and <code>t</code> and <code>e</code> are higher rank matrices, then
|
|
it chooses which row (outer dimension) to copy from <code>t</code> and <code>e</code>.
|
|
If <code>condition</code> has the same shape as <code>t</code> and <code>e</code>, then it chooses which
|
|
element to copy from <code>t</code> and <code>e</code>.</p><p>For example:</p><p>```prettyprint
|
|
# <code>condition</code> tensor is [[True, False]
|
|
# [False, True]]
|
|
# <code>t</code> is [[1, 2],
|
|
# [3, 4]]
|
|
# <code>e</code> is [[5, 6],
|
|
# [7, 8]]
|
|
select(condition, t, e) ==> [[1, 6],
|
|
[7, 4]]</p><p># <code>condition</code> tensor is [True, False]
|
|
# <code>t</code> is [[1, 2],
|
|
# [3, 4]]
|
|
# <code>e</code> is [[5, 6],
|
|
# [7, 8]]
|
|
select(condition, t, e) ==> [[1, 2],
|
|
[7, 8]]</p><p>```</p></div></div><div class="top"><p class="src"><a name="v:matMul" class="def">matMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>b</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>product</strong></p></td></tr></table></div><div class="doc"><p>Multiply the matrix "a" by the matrix "b".</p><p>The inputs must be two-dimensional matrices and the inner dimension of
|
|
"a" (after being transposed if transpose_a is true) must match the
|
|
outer dimension of "b" (after being transposed if transposed_b is
|
|
true).</p><ul><li>Note*: The default kernel implementation for MatMul on GPUs uses
|
|
cublas.</li></ul></div></div><div class="top"><p class="src"><a name="v:digamma" class="def">digamma</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes Psi, the derivative of Lgamma (the log of the absolute value of</p><p>`Gamma(x)`), element-wise.</p></div></div><div class="top"><p class="src"><a name="v:conv2DBackpropFilter" class="def">conv2DBackpropFilter</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: 4-D with shape `[batch, in_height, in_width, in_channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>filter_sizes</strong>: An integer vector representing the tensor shape of <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code>,
|
|
where <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> is a 4-D
|
|
`[filter_height, filter_width, in_channels, out_channels]` tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>out_backprop</strong>: 4-D with shape `[batch, out_height, out_width, out_channels]`.
|
|
Gradients w.r.t. the output of the convolution.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 4-D with shape
|
|
`[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t.
|
|
the <code><a href="../base-4.8.2.0/GHC-OldList.html#v:filter">filter</a></code> input of the convolution.</p></td></tr></table></div><div class="doc"><p>Computes the gradients of convolution with respect to the filter.</p></div></div><div class="top"><p class="src"><a name="v:min" class="def">min</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the minimum of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
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|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:isFinite" class="def">isFinite</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns which elements of x are finite.</p></div></div><div class="top"><p class="src"><a name="v:argMax" class="def">argMax</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>dimension</strong>: int32, 0 <= dimension < rank(input). Describes which dimension
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|
of the input Tensor to reduce across. For vectors, use dimension = 0.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the index with the largest value across dimensions of a tensor.</p></div></div><div class="top"><p class="src"><a name="v:segmentMean" class="def">segmentMean</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor whose rank is equal to the rank of `data`'s
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|
first dimension. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
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|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the mean along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Computes a tensor such that
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|
\(output_i = frac{sum_j data_j}{N}\) where <code><a href="TensorFlow-GenOps-Core.html#v:mean">mean</a></code> is
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|
over <code>j</code> such that `segment_ids[j] == i` and <code>N</code> is the total number of
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|
values summed.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
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<a href="img">style="width:100%" src="../../images/SegmentMean.png" alt</a>
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<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:cumprod" class="def">cumprod</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>axis</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong></p></td></tr></table></div><div class="doc"><p>Compute the cumulative product of the tensor <code>x</code> along <code>axis</code>.</p><p>By default, this op performs an inclusive cumprod, which means that the first
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element of the input is identical to the first element of the output:
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|
```prettyprint
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|
tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c]
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|
```</p><p>By setting the <code>exclusive</code> kwarg to <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, an exclusive cumprod is
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|
performed instead:
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|
```prettyprint
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|
tf.cumprod([a, b, c], exclusive=True) ==> [0, a, a * b]
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```</p><p>By setting the <code><a href="../base-4.8.2.0/GHC-OldList.html#v:reverse">reverse</a></code> kwarg to <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, the cumprod is performed in the
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|
opposite direction:
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|
```prettyprint
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|
tf.cumprod([a, b, c], reverse=True) ==> [a * b * c, b * c, c]
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|
```
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This is more efficient than using separate `tf.reverse` ops.</p><p>The <code><a href="../base-4.8.2.0/GHC-OldList.html#v:reverse">reverse</a></code> and <code>exclusive</code> kwargs can also be combined:
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|
```prettyprint
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|
tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 0]
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```</p></div></div><div class="top"><p class="src"><a name="v:segmentMin" class="def">segmentMin</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor whose rank is equal to the rank of `data`'s
|
|
first dimension. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the minimum along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Computes a tensor such that
|
|
\(output_i = min_j(data_j)\) where <code><a href="../base-4.8.2.0/Data-Ord.html#v:min">min</a></code> is over <code>j</code> such
|
|
that `segment_ids[j] == i`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
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|
<a href="img">style="width:100%" src="../../images/SegmentMin.png" alt</a>
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<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:unsortedSegmentSum" class="def">unsortedSegmentSum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A tensor whose shape is a prefix of `data.shape`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>num_segments</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for the first `segment_ids.rank`
|
|
dimensions, which are replaced with a single dimension which has size
|
|
<code>num_segments</code>.</p></td></tr></table></div><div class="doc"><p>Computes the sum along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Computes a tensor such that
|
|
`(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such
|
|
that `segment_ids[j...] == i`. Unlike <code>SegmentSum</code>, <code>segment_ids</code>
|
|
need not be sorted and need not cover all values in the full
|
|
range of valid values.</p><p>If the sum is empty for a given segment ID <code>i</code>, `output[i] = 0`.</p><p><code>num_segments</code> should equal the number of distinct segment IDs.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/UnsortedSegmentSum.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:tFRecordReader" class="def">tFRecordReader</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>reader_handle</strong>: The handle to reference the Reader.</p></td></tr></table></div><div class="doc"><p>A Reader that outputs the records from a TensorFlow Records file.</p></div></div><div class="top"><p class="src"><a name="v:sparseSegmentSum" class="def">sparseSegmentSum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>indices</strong>: A 1-D tensor. Has same rank as <code>segment_ids</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the sum along sparse segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Like <code>SegmentSum</code>, but <code>segment_ids</code> can have rank less than `data`'s first
|
|
dimension, selecting a subset of dimension 0, specified by <code>indices</code>.</p><p>For example:</p><p>```prettyprint
|
|
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])</p><p># Select two rows, one segment.
|
|
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
|
|
==> [[0 0 0 0]]</p><p># Select two rows, two segment.
|
|
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
|
|
==> [[ 1 2 3 4]
|
|
[-1 -2 -3 -4]]</p><p># Select all rows, two segments.
|
|
tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
|
|
==> [[0 0 0 0]
|
|
[5 6 7 8]]</p><p># Which is equivalent to:
|
|
tf.segment_sum(c, tf.constant([0, 0, 1]))
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:sparseSegmentSqrtN" class="def">sparseSegmentSqrtN</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>indices</strong>: A 1-D tensor. Has same rank as <code>segment_ids</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the sum along sparse segments of a tensor divided by the sqrt of N.</p><p>N is the size of the segment being reduced.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p></div></div><div class="top"><p class="src"><a name="v:copyHost" class="def">copyHost</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong>: Input tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Output tensor, deep-copied from input.</p></td></tr></table></div><div class="doc"><p>Copy Host Op.</p><p>Performs CPU-to-CPU deep-copying of tensor.</p><p>Unlike the Copy Op, this op has HostMemory constraint on its input or output.</p></div></div><div class="top"><p class="src"><a name="v:variable" class="def">variable</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> dtype</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> dtype</td><td class="doc"><p><strong>ref</strong>: A reference to the variable tensor.</p></td></tr></table></div><div class="doc"><p>Holds state in the form of a tensor that persists across steps.</p><p>Outputs a ref to the tensor state so it may be read or modified.
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|
TODO(zhifengc/mrry): Adds a pointer to a more detail document
|
|
about sharing states in tensorflow.</p></div></div><div class="top"><p class="src"><a name="v:sparseSegmentSqrtNGrad" class="def">sparseSegmentSqrtNGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>grad</strong>: gradient propagated to the SparseSegmentSqrtN op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>indices</strong>: indices passed to the corresponding SparseSegmentSqrtN op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>segment_ids</strong>: segment_ids passed to the corresponding SparseSegmentSqrtN op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>output_dim0</strong>: dimension 0 of "data" passed to SparseSegmentSqrtN op.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Computes gradients for SparseSegmentSqrtN.</p><p>Returns tensor "output" with same shape as grad, except for dimension 0 whose
|
|
value is output_dim0.</p></div></div><div class="top"><p class="src"><a name="v:range" class="def">range</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 tidx</td><td class="doc"><p><strong>start</strong>: 0-D (scalar). First entry in the sequence.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>limit</strong>: 0-D (scalar). Upper limit of sequence, exclusive.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tidx</td><td class="doc"><p><strong>delta</strong>: 0-D (scalar). Optional. Default is 1. Number that increments <code>start</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tidx</td><td class="doc"><p><strong>output</strong>: 1-D.</p></td></tr></table></div><div class="doc"><p>Creates a sequence of integers.</p><p>This operation creates a sequence of integers that begins at <code>start</code> and
|
|
extends by increments of <code>delta</code> up to but not including <code>limit</code>.</p><p>For example:</p><p>```
|
|
# <code>start</code> is 3
|
|
# <code>limit</code> is 18
|
|
# <code>delta</code> is 3
|
|
tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:any" class="def">any</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>input</strong>: The tensor to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>reduction_indices</strong>: The dimensions to reduce.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Bool.html#t:Bool">Bool</a></td><td class="doc"><p><strong>output</strong>: The reduced tensor.</p></td></tr></table></div><div class="doc"><p>Computes the "logical or" of elements across dimensions of a tensor.</p><p>Reduces <code>input</code> along the dimensions given in <code>reduction_indices</code>. Unless
|
|
<code>keep_dims</code> is true, the rank of the tensor is reduced by 1 for each entry in
|
|
<code>reduction_indices</code>. If <code>keep_dims</code> is true, the reduced dimensions are
|
|
retained with length 1.</p></div></div><div class="top"><p class="src"><a name="v:linSpace" class="def">linSpace</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>start</strong>: First entry in the range.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>stop</strong>: Last entry in the range.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 tidx</td><td class="doc"><p><strong>num</strong>: Number of values to generate.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D. The generated values.</p></td></tr></table></div><div class="doc"><p>Generates values in an interval.</p><p>A sequence of <code>num</code> evenly-spaced values are generated beginning at <code>start</code>.
|
|
If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
|
|
so that the last one is exactly <code>stop</code>.</p><p>For example:</p><p>```
|
|
tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:resizeArea" class="def">resizeArea</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>images</strong>: 4-D with shape `[batch, height, width, channels]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>size</strong>: = A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
|
|
new size for the images.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a></td><td class="doc"><p><strong>resized_images</strong>: 4-D with shape
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|
`[batch, new_height, new_width, channels]`.</p></td></tr></table></div><div class="doc"><p>Resize <code>images</code> to <code><a href="TensorFlow-GenOps-Core.html#v:size">size</a></code> using area interpolation.</p><p>Input images can be of different types but output images are always float.</p></div></div><div class="top"><p class="src"><a name="v:real" class="def">real</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tout, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` tout)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> tout</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div><div class="doc"><p>Returns the real part of a complex number.</p><p>Given a tensor <code>input</code> of complex numbers, this operation returns a tensor of
|
|
type <code>float</code> that is the real part of each element in <code>input</code>. All elements in
|
|
<code>input</code> must be complex numbers of the form \(a + bj\), where *a* is the real
|
|
part returned by this operation and *b* is the imaginary part.</p><p>For example:</p><p>```
|
|
# tensor <code>input</code> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
tf.real(input) ==> [-2.25, 3.25]
|
|
```</p></div></div><div class="top"><p class="src"><a name="v:iFFT" class="def">iFFT</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most
|
|
dimension of <code>input</code> is replaced with its inverse 1D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the inverse 1-dimensional discrete Fourier Transform over the inner-most</p><p>dimension of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:iFFT3D" class="def">iFFT3D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong>: A complex64 tensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong>: A complex64 tensor of the same shape as <code>input</code>. The inner-most 3
|
|
dimensions of <code>input</code> are replaced with their inverse 3D Fourier Transform.</p></td></tr></table></div><div class="doc"><p>Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most</p><p>3 dimensions of <code>input</code>.</p></div></div><div class="top"><p class="src"><a name="v:cross" class="def">cross</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>a</strong>: A tensor containing 3-element vectors.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>b</strong>: Another tensor, of same type and shape as <code>a</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>product</strong>: Pairwise cross product of the vectors in <code>a</code> and <code>b</code>.</p></td></tr></table></div><div class="doc"><p>Compute the pairwise cross product.</p><p><code>a</code> and <code>b</code> must be the same shape; they can either be simple 3-element vectors,
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or any shape where the innermost dimension is 3. In the latter case, each pair
|
|
of corresponding 3-element vectors is cross-multiplied independently.</p></div></div><div class="top"><p class="src"><a name="v:cumsum" class="def">cumsum</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tidx, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tidx)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tidx</td><td class="doc"><p><strong>axis</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>out</strong></p></td></tr></table></div><div class="doc"><p>Compute the cumulative sum of the tensor <code>x</code> along <code>axis</code>.</p><p>By default, this op performs an inclusive cumsum, which means that the first
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element of the input is identical to the first element of the output:
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|
```prettyprint
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|
tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
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|
```</p><p>By setting the <code>exclusive</code> kwarg to <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, an exclusive cumsum is
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performed instead:
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|
```prettyprint
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tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
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```</p><p>By setting the <code><a href="../base-4.8.2.0/GHC-OldList.html#v:reverse">reverse</a></code> kwarg to <code><a href="../base-4.8.2.0/Data-Bool.html#v:True">True</a></code>, the cumsum is performed in the
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opposite direction:
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```prettyprint
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tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
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```
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This is more efficient than using separate `tf.reverse` ops.</p><p>The <code><a href="../base-4.8.2.0/GHC-OldList.html#v:reverse">reverse</a></code> and <code>exclusive</code> kwargs can also be combined:
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```prettyprint
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|
tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]
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```</p></div></div><div class="top"><p class="src"><a name="v:batchIFFT" class="def">batchIFFT</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:erf" class="def">erf</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Computes the Gauss error function of <code>x</code> element-wise.</p></div></div><div class="top"><p class="src"><a name="v:barrierInsertMany" class="def">barrierInsertMany</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>component_index</strong>: The component of the barrier elements that is being assigned.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>handle</strong>: The handle to a barrier.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>keys</strong>: A one-dimensional tensor of keys, with length n.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 t</td><td class="doc"><p><strong>values</strong>: An any-dimensional tensor of values, which are associated with the
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respective keys. The 0th dimension must have length n.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Output.html#t:ControlNode">ControlNode</a></td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>For each key, assigns the respective value to the specified component.</p><p>If a key is not found in the barrier, this operation will create a new
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incomplete element. If a key is found in the barrier, and the element
|
|
already has a value at component_index, this operation will fail with
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|
INVALID_ARGUMENT, and leave the barrier in an undefined state.</p></div></div><div class="top"><p class="src"><a name="v:floor" class="def">floor</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>x</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>y</strong></p></td></tr></table></div><div class="doc"><p>Returns element-wise largest integer not greater than x.</p></div></div><div class="top"><p class="src"><a name="v:batchFFT2D" class="def">batchFFT2D</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> (<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>)</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:sparseAddGrad" class="def">sparseAddGrad</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>backprop_val_grad</strong>: 1-D with shape `[nnz(sum)]`. The gradient with respect to
|
|
the non-empty values of the sum.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_indices</strong>: 2-D. The <code>indices</code> of the <code>SparseTensor</code> A, size `[nnz(A), ndims]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_indices</strong>: 2-D. The <code>indices</code> of the <code>SparseTensor</code> B, size `[nnz(B), ndims]`.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sum_indices</strong>: 2-D. The <code>indices</code> of the sum <code>SparseTensor</code>, size
|
|
`[nnz(sum), ndims]`.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t)</td><td class="doc"><p>(<strong>a_val_grad</strong>, <strong>b_val_grad</strong>)</p><ul><li><strong>a_val_grad</strong>: 1-D with shape `[nnz(A)]`. The gradient with respect to the
|
|
non-empty values of A.</li><li><strong>b_val_grad</strong>: 1-D with shape `[nnz(B)]`. The gradient with respect to the
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|
non-empty values of B.</li></ul></td></tr></table></div><div class="doc"><p>The gradient operator for the SparseAdd op.</p><p>The SparseAdd op calculates A + B, where A, B, and the sum are all represented
|
|
as <code>SparseTensor</code> objects. This op takes in the upstream gradient w.r.t.
|
|
non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
|
|
values of A and B.</p></div></div><div class="top"><p class="src"><a name="v:sparseAdd" class="def">sparseAdd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> treal, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` treal)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_indices</strong>: 2-D. The <code>indices</code> of the first <code>SparseTensor</code>, size `[nnz, ndims]` Matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>a_values</strong>: 1-D. The <code>values</code> of the first <code>SparseTensor</code>, size `[nnz]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>a_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the first <code>SparseTensor</code>, size `[ndims]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_indices</strong>: 2-D. The <code>indices</code> of the second <code>SparseTensor</code>, size `[nnz, ndims]` Matrix.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v5 t</td><td class="doc"><p><strong>b_values</strong>: 1-D. The <code>values</code> of the second <code>SparseTensor</code>, size `[nnz]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v6 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>b_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the second <code>SparseTensor</code>, size `[ndims]` Vector.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v7 treal</td><td class="doc"><p><strong>thresh</strong>: 0-D. The magnitude threshold that determines if an output value/index
|
|
pair takes space.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>sum_indices</strong>, <strong>sum_values</strong>, <strong>sum_shape</strong>)</p><ul><li><strong>sum_indices</strong></li><li><strong>sum_values</strong></li><li><strong>sum_shape</strong></li></ul></td></tr></table></div><div class="doc"><p>Adds two <code>SparseTensor</code> objects to produce another <code>SparseTensor</code>.</p><p>The input <code>SparseTensor</code> objects' indices are assumed ordered in standard
|
|
lexicographic order. If this is not the case, before this step run
|
|
<code>SparseReorder</code> to restore index ordering.</p><p>By default, if two values sum to zero at some index, the output <code>SparseTensor</code>
|
|
would still include that particular location in its index, storing a zero in the
|
|
corresponding value slot. To override this, callers can specify <code>thresh</code>,
|
|
indicating that if the sum has a magnitude strictly smaller than <code>thresh</code>, its
|
|
corresponding value and index would then not be included. In particular,
|
|
`thresh == 0` (default) means everything is kept and actual thresholding happens
|
|
only for a positive value.</p><p>In the following shapes, <code>nnz</code> is the count after taking <code>thresh</code> into account.</p></div></div><div class="top"><p class="src"><a name="v:batchCholesky" class="def">batchCholesky</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>input</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong></p></td></tr></table></div></div><div class="top"><p class="src"><a name="v:dynamicPartition" class="def">dynamicPartition</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>num_partitions</strong>: The number of partitions to output.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a></td><td class="doc"><p><strong>partitions</strong>: Any shape. Indices in the range `[0, num_partitions)`.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t]</td><td class="doc"><p><strong>outputs</strong></p></td></tr></table></div><div class="doc"><p>Partitions `data` into <code>num_partitions</code> tensors using indices from <code>partitions</code>.</p><p>For each index tuple <code>js</code> of size `partitions.ndim`, the slice `data[js, ...]`
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|
becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i`
|
|
are placed in `outputs[i]` in lexicographic order of <code>js</code>, and the first
|
|
dimension of `outputs[i]` is the number of entries in <code>partitions</code> equal to <code>i</code>.
|
|
In detail,</p><p>outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:]</p><p>outputs[i] = pack([data[js, ...] for js if partitions[js] == i])</p><p>`data.shape` must start with `partitions.shape`.</p><p>For example:</p><p># Scalar partitions
|
|
partitions = 1
|
|
num_partitions = 2
|
|
data = [10, 20]
|
|
outputs[0] = [] # Empty with shape [0, 2]
|
|
outputs[1] = [[10, 20]]</p><p># Vector partitions
|
|
partitions = [0, 0, 1, 1, 0]
|
|
num_partitions = 2
|
|
data = [10, 20, 30, 40, 50]
|
|
outputs[0] = [10, 20, 50]
|
|
outputs[1] = [30, 40]</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/DynamicPartition.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:serializeSparse" class="def">serializeSparse</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sparse_indices</strong>: 2-D. The <code>indices</code> of the <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sparse_values</strong>: 1-D. The <code>values</code> of the <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sparse_shape</strong>: 1-D. The <code><a href="TensorFlow-GenOps-Core.html#v:shape">shape</a></code> of the <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../bytestring-0.10.6.0/Data-ByteString.html#t:ByteString">ByteString</a></td><td class="doc"><p><strong>serialized_sparse</strong></p></td></tr></table></div><div class="doc"><p>Serialize a <code>SparseTensor</code> into a string 3-vector (1-D <code><a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a></code>) object.</p></div></div><div class="top"><p class="src"><a name="v:sparseConcat" class="def">sparseConcat</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>concat_dim</strong>: Dimension to concatenate along. Must be in range [-rank, rank),
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|
where rank is the number of dimensions in each input <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]</td><td class="doc"><p><strong>indices</strong>: 2-D. Indices of each input <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t]</td><td class="doc"><p><strong>values</strong>: 1-D. Non-empty values of each <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> [<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]</td><td class="doc"><p><strong>shapes</strong>: 1-D. Shapes of each <code>SparseTensor</code>.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_values</strong>, <strong>output_shape</strong>)</p><ul><li><strong>output_indices</strong>: 2-D. Indices of the concatenated <code>SparseTensor</code>.</li><li><strong>output_values</strong>: 1-D. Non-empty values of the concatenated <code>SparseTensor</code>.</li><li><strong>output_shape</strong>: 1-D. Shape of the concatenated <code>SparseTensor</code>.</li></ul></td></tr></table></div><div class="doc"><p>Concatenates a list of <code>SparseTensor</code> along the specified dimension.</p><p>Concatenation is with respect to the dense versions of these sparse tensors.
|
|
It is assumed that each input is a <code>SparseTensor</code> whose elements are ordered
|
|
along increasing dimension number.</p><p>All inputs' shapes must match, except for the concat dimension. The
|
|
<code>indices</code>, <code>values</code>, and <code>shapes</code> lists must have the same length.</p><p>The output shape is identical to the inputs', except along the concat
|
|
dimension, where it is the sum of the inputs' sizes along that dimension.</p><p>The output elements will be resorted to preserve the sort order along
|
|
increasing dimension number.</p><p>This op runs in `O(M log M)` time, where <code>M</code> is the total number of non-empty
|
|
values across all inputs. This is due to the need for an internal sort in
|
|
order to concatenate efficiently across an arbitrary dimension.</p><p>For example, if `concat_dim = 1` and the inputs are</p><p>sp_inputs[0]: shape = [2, 3]
|
|
[0, 2]: "a"
|
|
[1, 0]: "b"
|
|
[1, 1]: "c"</p><p>sp_inputs[1]: shape = [2, 4]
|
|
[0, 1]: "d"
|
|
[0, 2]: "e"</p><p>then the output will be</p><p>shape = [2, 7]
|
|
[0, 2]: "a"
|
|
[0, 4]: "d"
|
|
[0, 5]: "e"
|
|
[1, 0]: "b"
|
|
[1, 1]: "c"</p><p>Graphically this is equivalent to doing</p><dl><dt> a</dt><dd>concat [ d e ] = [ a d e ]</dd><dt>b c </dt><dd>[ ] [b c ]</dd></dl></div></div><div class="top"><p class="src"><a name="v:segmentProd" class="def">segmentProd</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> tindices, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>]` tindices)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 t</td><td class="doc"><p><strong>data</strong></p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 tindices</td><td class="doc"><p><strong>segment_ids</strong>: A 1-D tensor whose rank is equal to the rank of `data`'s
|
|
first dimension. Values should be sorted and can be repeated.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: Has same shape as data, except for dimension 0 which
|
|
has size <code>k</code>, the number of segments.</p></td></tr></table></div><div class="doc"><p>Computes the product along segments of a tensor.</p><p>Read <a href="../../api_docs/python/math_ops.md#segmentation">the section on
|
|
Segmentation</a> for an explanation
|
|
of segments.</p><p>Computes a tensor such that
|
|
\(output_i = prod_j data_j\) where the product is over <code>j</code> such
|
|
that `segment_ids[j] == i`.</p><p><a href="div">style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"</a>
|
|
<a href="img">style="width:100%" src="../../images/SegmentProd.png" alt</a>
|
|
<a href="/div">/div</a></p></div></div><div class="top"><p class="src"><a name="v:sparseReshape" class="def">sparseReshape</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_indices</strong>: 2-D. `N x R_in` matrix with the indices of non-empty values in a
|
|
SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>input_shape</strong>: 1-D. <code>R_in</code> vector with the input SparseTensor's dense shape.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>new_shape</strong>: 1-D. <code>R_out</code> vector with the requested new dense shape.</p></td></tr><tr><td class="src">-> (<a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>)</td><td class="doc"><p>(<strong>output_indices</strong>, <strong>output_shape</strong>)</p><ul><li><strong>output_indices</strong>: 2-D. `N x R_out` matrix with the updated indices of non-empty
|
|
values in the output SparseTensor.</li><li><strong>output_shape</strong>: 1-D. <code>R_out</code> vector with the full dense shape of the output
|
|
SparseTensor. This is the same as <code>new_shape</code> but with any -1 dimensions
|
|
filled in.</li></ul></td></tr></table></div><div class="doc"><p>Reshapes a SparseTensor to represent values in a new dense shape.</p><p>This operation has the same semantics as reshape on the represented dense
|
|
tensor. The <code>input_indices</code> are recomputed based on the requested <code>new_shape</code>.</p><p>If one component of <code>new_shape</code> is the special value -1, the size of that
|
|
dimension is computed so that the total dense size remains constant. At
|
|
most one component of <code>new_shape</code> can be -1. The number of dense elements
|
|
implied by <code>new_shape</code> must be the same as the number of dense elements
|
|
originally implied by <code>input_shape</code>.</p><p>Reshaping does not affect the order of values in the SparseTensor.</p><p>If the input tensor has rank <code>R_in</code> and <code>N</code> non-empty values, and <code>new_shape</code>
|
|
has length <code>R_out</code>, then <code>input_indices</code> has shape `[N, R_in]`,
|
|
<code>input_shape</code> has length <code>R_in</code>, <code>output_indices</code> has shape `[N, R_out]`, and
|
|
<code>output_shape</code> has length <code>R_out</code>.</p></div></div><div class="top"><p class="src"><a name="v:sparseDenseCwiseMul" class="def">sparseDenseCwiseMul</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
|
|
SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sp_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>sp_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>dense</strong>: <code>R</code>-D. The dense Tensor operand.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D. The <code>N</code> values that are operated on.</p></td></tr></table></div><div class="doc"><p>Component-wise multiplies a SparseTensor by a dense Tensor.</p><p>The output locations corresponding to the implicitly zero elements in the sparse
|
|
tensor will be zero (i.e., will not take up storage space), regardless of the
|
|
contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).</p><ul><li>Limitation*: this Op only broadcasts the dense side to the sparse side, but not
|
|
the other direction.</li></ul></div></div><div class="top"><p class="src"><a name="v:sparseDenseCwiseDiv" class="def">sparseDenseCwiseDiv</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: (<a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:TensorType">TensorType</a> t, <a href="../tensorflow-0.1.0.0/TensorFlow-Types.html#t:OneOf">OneOf</a> `[<a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Data-Complex.html#t:Complex">Complex</a> <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int16">Int16</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int32">Int32</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a>, <a href="../base-4.8.2.0/Data-Int.html#t:Int8">Int8</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word16">Word16</a>, <a href="../base-4.8.2.0/Data-Word.html#t:Word8">Word8</a>, <a href="../base-4.8.2.0/Prelude.html#t:Double">Double</a>, <a href="../base-4.8.2.0/Prelude.html#t:Float">Float</a>]` t)</td><td class="doc empty"> </td></tr><tr><td class="src">=> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v1 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_indices</strong>: 2-D. `N x R` matrix with the indices of non-empty values in a
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SparseTensor, possibly not in canonical ordering.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v2 t</td><td class="doc"><p><strong>sp_values</strong>: 1-D. <code>N</code> non-empty values corresponding to <code>sp_indices</code>.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v3 <a href="../base-4.8.2.0/Data-Int.html#t:Int64">Int64</a></td><td class="doc"><p><strong>sp_shape</strong>: 1-D. Shape of the input SparseTensor.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> v4 t</td><td class="doc"><p><strong>dense</strong>: <code>R</code>-D. The dense Tensor operand.</p></td></tr><tr><td class="src">-> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Tensor">Tensor</a> <a href="../tensorflow-0.1.0.0/TensorFlow-Tensor.html#t:Value">Value</a> t</td><td class="doc"><p><strong>output</strong>: 1-D. The <code>N</code> values that are operated on.</p></td></tr></table></div><div class="doc"><p>Component-wise divides a SparseTensor by a dense Tensor.</p><ul><li>Limitation*: this Op only broadcasts the dense side to the sparse side, but not
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the other direction.</li></ul></div></div></div></div><div id="footer"><p>Produced by <a href="http://www.haskell.org/haddock/">Haddock</a> version 2.16.1</p></div></body></html> |