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5657 lines
274 KiB
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5657 lines
274 KiB
Plaintext
-- Hoogle documentation, generated by Haddock
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-- See Hoogle, http://www.haskell.org/hoogle/
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-- | Haskell wrappers for Core Tensorflow Ops.
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--
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-- Code generated signatures for the Ops in libtensorflow_c.
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@package tensorflow-core-ops
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@version 0.1.0.0
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module TensorFlow.GenOps.Core
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-- | Receives the named tensor from send_device on recv_device.
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--
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-- _HostRecv requires its input on host memory whereas _Recv requires its
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-- input on device memory.
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_HostRecv :: (TensorType tensor_type) => Int64 -> Build (Tensor Value tensor_type)
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-- | Sends the named tensor from send_device to recv_device.
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--
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-- _HostSend requires its input on host memory whereas _Send requires its
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-- input on device memory.
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_HostSend :: (TensorType t) => Int64 -> Tensor v1 t -> Build (ControlNode)
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-- | Receives the named tensor from send_device on recv_device.
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_Recv :: (TensorType tensor_type) => Int64 -> Build (Tensor Value tensor_type)
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-- | Sends the named tensor from send_device to recv_device.
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_Send :: (TensorType t) => Int64 -> Tensor v1 t -> Build (ControlNode)
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-- | Does nothing. Only useful as a placeholder for control edges.
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noOp :: ControlNode
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-- | A graph node which represents a return value of a function.
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_Retval :: (TensorType t) => Int64 -> Tensor v1 t -> Build (ControlNode)
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-- | A graph node which represents an argument to a function.
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_Arg :: (TensorType t) => Int64 -> Build (Tensor Value t)
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-- | Quantized Batch normalization.
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--
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-- This op is deprecated and will be removed in the future. Prefer
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-- `tf.nn.batch_normalization`.
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quantizedBatchNormWithGlobalNormalization :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Bool -> Float -> Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 tinput -> Tensor v5 Float -> Tensor v6 Float -> Tensor v7 tinput -> Tensor v8 Float -> Tensor v9 Float -> Tensor v10 tinput -> Tensor v11 Float -> Tensor v12 Float -> Tensor v13 tinput -> Tensor v14 Float -> Tensor v15 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
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-- | Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
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quantizedRelu6 :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
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-- | Adds Tensor <tt>bias</tt> to Tensor <tt>input</tt> for Quantized
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-- types.
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--
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-- Broadcasts the values of bias on dimensions 0..N-2 of <tt>input</tt>.
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quantizedBiasAdd :: (TensorType t1, OneOf '[Int16, Int32, Word16, Word8] t1, TensorType t2, OneOf '[Int16, Int32, Word16, Word8] t2, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 t1 -> Tensor v2 t2 -> Tensor v3 Float -> Tensor v4 Float -> Tensor v5 Float -> Tensor v6 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
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-- | Computes gradient of the FractionalAvgPool function.
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--
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-- Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
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-- FractionalAvgPoolGrad, we just need to evenly back-propagate each
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-- element of out_backprop to those indices that form the same pooling
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-- cell. Therefore, we just need to know the shape of original input
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-- tensor, instead of the whole tensor.
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fractionalAvgPoolGrad :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor Value t
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-- | Computes gradient of the FractionalMaxPool function.
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fractionalMaxPoolGrad :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 Int64 -> Tensor v5 Int64 -> Tensor Value t
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-- | Performs fractional max pooling on the input.
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--
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-- Fractional max pooling is slightly different than regular max pooling.
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-- In regular max pooling, you downsize an input set by taking the
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-- maximum value of smaller N x N subsections of the set (often 2x2), and
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-- try to reduce the set by a factor of N, where N is an integer.
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-- Fractional max pooling, as you might expect from the word
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-- "fractional", means that the overall reduction ratio N does not have
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-- to be an integer.
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--
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-- The sizes of the pooling regions are generated randomly but are fairly
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-- uniform. For example, let's look at the height dimension, and the
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-- constraints on the list of rows that will be pool boundaries.
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--
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-- First we define the following:
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--
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-- <ol>
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-- <li>input_row_length : the number of rows from the input set</li>
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-- <li>output_row_length : which will be smaller than the input</li>
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-- <li>alpha = input_row_length / output_row_length : our reduction
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-- ratio</li>
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-- <li>K = floor(alpha)</li>
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-- <li>row_pooling_sequence : this is the result list of pool boundary
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-- rows</li>
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-- </ol>
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--
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-- Then, row_pooling_sequence should satisfy:
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--
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-- <ol>
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-- <li>a[0] = 0 : the first value of the sequence is 0</li>
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-- <li>a[end] = input_row_length : the last value of the sequence is the
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-- size</li>
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-- <li>K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1
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-- size</li>
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-- <li>length(row_pooling_sequence) = output_row_length+1</li>
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-- </ol>
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--
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-- For more details on fractional max pooling, see this paper:
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-- <a>Benjamin Graham, Fractional Max-Pooling</a>
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fractionalMaxPool :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value Int64, Tensor Value Int64)
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-- | Finds values and indices of the <tt>k</tt> largest elements for the
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-- last dimension.
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--
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-- If the input is a vector (rank-1), finds the <tt>k</tt> largest
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-- entries in the vector and outputs their values and indices as vectors.
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-- Thus `values[j]` is the <tt>j</tt>-th largest entry in <tt>input</tt>,
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-- and its index is `indices[j]`.
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--
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-- For matrices (resp. higher rank input), computes the top <tt>k</tt>
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-- entries in each row (resp. vector along the last dimension). Thus,
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--
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-- values.shape = indices.shape = input.shape[:-1] + [k]
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--
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-- If two elements are equal, the lower-index element appears first.
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--
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-- If <tt>k</tt> varies dynamically, use <tt>TopKV2</tt> below.
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topK :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Int64 -> Tensor v1 t -> (Tensor Value t, Tensor Value Int32)
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-- | Says whether the targets are in the top <tt>K</tt> predictions.
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--
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-- This outputs a <tt>batch_size</tt> bool array, an entry `out[i]` is
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-- <tt>true</tt> if the prediction for the target class is among the top
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-- <tt>k</tt> predictions among all predictions for example <tt>i</tt>.
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-- Note that the behavior of <tt>InTopK</tt> differs from the
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-- <tt>TopK</tt> op in its handling of ties; if multiple classes have the
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-- same prediction value and straddle the top-<tt>k</tt> boundary, all of
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-- those classes are considered to be in the top <tt>k</tt>.
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--
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-- More formally, let
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--
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-- \(predictions_i\) be the predictions for all classes for example
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-- <tt>i</tt>, \(targets_i\) be the target class for example <tt>i</tt>,
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-- \(out_i\) be the output for example <tt>i</tt>,
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--
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-- $$out_i = predictions_{i, targets_i} in
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-- TopKIncludingTies(predictions_i)$$
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inTopK :: (TensorType t, OneOf '[Int32, Int64] t) => Int64 -> Tensor v1 Float -> Tensor v2 t -> Tensor Value Bool
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-- | Computes softmax cross entropy cost and gradients to backpropagate.
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--
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-- Unlike <tt>SoftmaxCrossEntropyWithLogits</tt>, this operation does not
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-- accept a matrix of label probabilities, but rather a single label per
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-- row of features. This label is considered to have probability 1.0 for
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-- the given row.
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--
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-- Inputs are the logits, not probabilities.
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sparseSoftmaxCrossEntropyWithLogits :: (TensorType t, OneOf '[Word16, Double, Float] t, TensorType tlabels, OneOf '[Int32, Int64] tlabels) => Tensor v1 t -> Tensor v2 tlabels -> (Tensor Value t, Tensor Value t)
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-- | Computes softmax cross entropy cost and gradients to backpropagate.
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--
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-- Inputs are the logits, not probabilities.
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softmaxCrossEntropyWithLogits :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> (Tensor Value t, Tensor Value t)
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-- | Computes log softmax activations.
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--
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-- For each batch <tt>i</tt> and class <tt>j</tt> we have
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--
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-- logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
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logSoftmax :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
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-- | Computes softsign gradients for a softsign operation.
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softsignGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Computes softplus: `log(exp(features) + 1)`.
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softplus :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
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-- | Computes gradients for the exponential linear (Elu) operation.
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eluGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Computes exponential linear: `exp(features) - 1` if < 0,
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-- <tt>features</tt> otherwise.
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--
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-- See <a>Fast and Accurate Deep Network Learning by Exponential Linear
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-- Units (ELUs)</a>
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elu :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
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-- | Computes rectified linear 6: `min(max(features, 0), 6)`.
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relu6 :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
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-- | Computes rectified linear gradients for a Relu operation.
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reluGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Computes the gradient of morphological 2-D dilation with respect to
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-- the input.
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dilation2DBackpropInput :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
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-- | Computes gradients of the maxpooling function.
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maxPoolGrad :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
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-- | Gradients for Local Response Normalization.
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lRNGrad :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
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-- | Computes gradients of max pooling function.
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maxPool3DGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Float -> Tensor v2 Float -> Tensor v3 t -> Tensor Value t
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-- | Computes the gradients of 3-D convolution with respect to the filter.
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conv3DBackpropFilterV2 :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> Tensor Value t
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-- | Computes the gradients of 3-D convolution with respect to the filter.
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conv3DBackpropFilter :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
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-- | Computes a 3-D convolution given 5-D <tt>input</tt> and <a>filter</a>
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-- tensors.
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--
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-- In signal processing, cross-correlation is a measure of similarity of
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-- two waveforms as a function of a time-lag applied to one of them. This
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-- is also known as a sliding dot product or sliding inner-product.
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--
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-- Our Conv3D implements a form of cross-correlation.
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conv3D :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Computes the gradients of depthwise convolution with respect to the
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-- filter.
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depthwiseConv2dNativeBackpropFilter :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> Tensor Value t
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-- | Computes the gradients of convolution with respect to the filter.
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conv2DBackpropFilter :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> Tensor Value t
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-- | Computes the gradients of convolution with respect to the input.
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conv2DBackpropInput :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
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-- | Computes a 2-D convolution given 4-D <tt>input</tt> and <a>filter</a>
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-- tensors.
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--
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-- Given an input tensor of shape `[batch, in_height, in_width,
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-- in_channels]` and a filter / kernel tensor of shape `[filter_height,
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-- filter_width, in_channels, out_channels]`, this op performs the
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-- following:
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--
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-- <ol>
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-- <li>Flattens the filter to a 2-D matrix with shape `[filter_height *
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-- filter_width * in_channels, output_channels]`.</li>
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-- <li>Extracts image patches from the input tensor to form a *virtual*
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-- tensor of shape `[batch, out_height, out_width, filter_height *
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-- filter_width * in_channels]`.</li>
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-- <li>For each patch, right-multiplies the filter matrix and the image
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-- patch vector.</li>
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-- </ol>
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--
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-- In detail, with the default NHWC format,
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--
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-- output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di,
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-- strides[2] * j + dj, q] * filter[di, dj, q, k]
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--
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-- Must have `strides[0] = strides[3] = 1`. For the most common case of
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-- the same horizontal and vertices strides, `strides = [1, stride,
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-- stride, 1]`.
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conv2D :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Adds <tt>bias</tt> to <a>value</a>.
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--
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-- This is a special case of `tf.add` where <tt>bias</tt> is restricted
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-- to be 1-D. Broadcasting is supported, so <a>value</a> may have any
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-- number of dimensions.
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biasAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Batch normalization.
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--
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-- Note that the size of 4D Tensors are defined by either <a>NHWC</a> or
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-- <a>NCHW</a>. The size of 1D Tensors matches the dimension C of the 4D
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-- Tensors.
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fusedBatchNorm :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> (Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t)
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-- | Gradients for batch normalization.
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--
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-- This op is deprecated. See `tf.nn.batch_normalization`.
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batchNormWithGlobalNormalizationGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Bool -> Float -> Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> (Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t)
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batchFFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
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batchIFFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
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-- | Performs average pooling on the input.
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--
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-- Each entry in <tt>output</tt> is the mean of the corresponding size
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-- <tt>ksize</tt> window in <a>value</a>.
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avgPool :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
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batchFFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
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batchFFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
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-- | Given a quantized tensor described by (input, input_min, input_max),
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-- outputs a
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--
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-- range that covers the actual values present in that tensor. This op is
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-- typically used to produce the requested_output_min and
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-- requested_output_max for Requantize.
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requantizationRange :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value Float, Tensor Value Float)
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-- | Convert the quantized <tt>input</tt> tensor into a lower-precision
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-- <tt>output</tt>, using the
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--
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-- output range specified with <tt>requested_output_min</tt> and
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-- <tt>requested_output_max</tt>.
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--
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-- <ul>
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-- <li><i>input_min, input_max</i> are scalar floats that specify the
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-- range for the float interpretation of the <tt>input</tt> data. For
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-- example, if input_min is -1.0f and input_max is 1.0f, and we are
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-- dealing with quint16 quantized data, then a 0 value in the 16-bit data
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-- should be interpreted as -1.0f, and a 65535 means 1.0f.</li>
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-- </ul>
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requantize :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 Float -> Tensor v5 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
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-- | Convert the quantized <tt>input</tt> tensor into a lower-precision
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-- <tt>output</tt>, using the
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--
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-- actual distribution of the values to maximize the usage of the lower
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-- bit depth and adjusting the output min and max ranges accordingly.
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--
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-- <ul>
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-- <li><i>input_min, input_max</i> are scalar floats that specify the
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-- range for the float interpretation of the <tt>input</tt> data. For
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-- example, if input_min is -1.0f and input_max is 1.0f, and we are
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-- dealing with quint16 quantized data, then a 0 value in the 16-bit data
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-- should be interpreted as -1.0f, and a 65535 means 1.0f.</li>
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-- </ul>
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--
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-- This operator tries to squeeze as much precision as possible into an
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-- output with a lower bit depth by calculating the actual min and max
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-- values found in the data. For example, maybe that quint16 input has no
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-- values lower than 16,384 and none higher than 49,152. That means only
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-- half the range is actually needed, all the float interpretations are
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-- between -0.5f and 0.5f, so if we want to compress the data into a
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-- quint8 output, we can use that range rather than the theoretical -1.0f
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-- to 1.0f that is suggested by the input min and max.
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--
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-- In practice, this is most useful for taking output from operations
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-- like QuantizedMatMul that can produce higher bit-depth outputs than
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-- their inputs and may have large potential output ranges, but in
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-- practice have a distribution of input values that only uses a small
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-- fraction of the possible range. By feeding that output into this
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-- operator, we can reduce it from 32 bits down to 8 with minimal loss of
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-- accuracy.
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quantizeDownAndShrinkRange :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
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-- | Perform a quantized matrix multiplication of <tt>a</tt> by the matrix
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-- <tt>b</tt>.
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--
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-- The inputs must be two-dimensional matrices and the inner dimension of
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-- <tt>a</tt> (after being transposed if <tt>transpose_a</tt> is
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-- non-zero) must match the outer dimension of <tt>b</tt> (after being
|
|
-- transposed if <tt>transposed_b</tt> is non-zero).
|
|
quantizedMatMul :: (TensorType t1, OneOf '[Int16, Int32, Word16, Word8] t1, TensorType t2, OneOf '[Int16, Int32, Word16, Word8] t2, TensorType toutput, OneOf '[Int16, Int32, Word16, Word8] toutput) => Tensor v1 t1 -> Tensor v2 t2 -> Tensor v3 Float -> Tensor v4 Float -> Tensor v5 Float -> Tensor v6 Float -> (Tensor Value toutput, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Compute the cumulative product of the tensor <tt>x</tt> along
|
|
-- <tt>axis</tt>.
|
|
--
|
|
-- By default, this op performs an inclusive cumprod, which means that
|
|
-- the first element of the input is identical to the first element of
|
|
-- the output: ```prettyprint tf.cumprod([a, b, c]) ==> [a, a * b, a *
|
|
-- b * c] ```
|
|
--
|
|
-- By setting the <tt>exclusive</tt> kwarg to <a>True</a>, an exclusive
|
|
-- cumprod is performed instead: ```prettyprint tf.cumprod([a, b, c],
|
|
-- exclusive=True) ==> [0, a, a * b] ```
|
|
--
|
|
-- By setting the <a>reverse</a> kwarg to <a>True</a>, the cumprod is
|
|
-- performed in the opposite direction: ```prettyprint tf.cumprod([a, b,
|
|
-- c], reverse=True) ==> [a * b * c, b * c, c] ``` This is more
|
|
-- efficient than using separate `tf.reverse` ops.
|
|
--
|
|
-- The <a>reverse</a> and <tt>exclusive</tt> kwargs can also be combined:
|
|
-- ```prettyprint tf.cumprod([a, b, c], exclusive=True, reverse=True)
|
|
-- ==> [b * c, c, 0] ```
|
|
cumprod :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Compute the cumulative sum of the tensor <tt>x</tt> along
|
|
-- <tt>axis</tt>.
|
|
--
|
|
-- By default, this op performs an inclusive cumsum, which means that the
|
|
-- first element of the input is identical to the first element of the
|
|
-- output: ```prettyprint tf.cumsum([a, b, c]) ==> [a, a + b, a + b +
|
|
-- c] ```
|
|
--
|
|
-- By setting the <tt>exclusive</tt> kwarg to <a>True</a>, an exclusive
|
|
-- cumsum is performed instead: ```prettyprint tf.cumsum([a, b, c],
|
|
-- exclusive=True) ==> [0, a, a + b] ```
|
|
--
|
|
-- By setting the <a>reverse</a> kwarg to <a>True</a>, the cumsum is
|
|
-- performed in the opposite direction: ```prettyprint tf.cumsum([a, b,
|
|
-- c], reverse=True) ==> [a + b + c, b + c, c] ``` This is more
|
|
-- efficient than using separate `tf.reverse` ops.
|
|
--
|
|
-- The <a>reverse</a> and <tt>exclusive</tt> kwargs can also be combined:
|
|
-- ```prettyprint tf.cumsum([a, b, c], exclusive=True, reverse=True)
|
|
-- ==> [b + c, c, 0] ```
|
|
cumsum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Compute the pairwise cross product.
|
|
--
|
|
-- <tt>a</tt> and <tt>b</tt> must be the same shape; they can either be
|
|
-- simple 3-element vectors, or any shape where the innermost dimension
|
|
-- is 3. In the latter case, each pair of corresponding 3-element vectors
|
|
-- is cross-multiplied independently.
|
|
cross :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Compute the inverse 3-dimensional discrete Fourier Transform over the
|
|
-- inner-most
|
|
--
|
|
-- 3 dimensions of <tt>input</tt>.
|
|
iFFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Compute the 3-dimensional discrete Fourier Transform over the
|
|
-- inner-most 3
|
|
--
|
|
-- dimensions of <tt>input</tt>.
|
|
fFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Computes gradients of the maxpooling function.
|
|
maxPoolGradWithArgmax :: (TensorType targmax, OneOf '[Int32, Int64] targmax, TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 targmax -> Tensor Value t
|
|
|
|
-- | Compute the 2-dimensional discrete Fourier Transform over the
|
|
-- inner-most
|
|
--
|
|
-- 2 dimensions of <tt>input</tt>.
|
|
fFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Compute the inverse 1-dimensional discrete Fourier Transform over the
|
|
-- inner-most
|
|
--
|
|
-- dimension of <tt>input</tt>.
|
|
iFFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Compute the 1-dimensional discrete Fourier Transform over the
|
|
-- inner-most
|
|
--
|
|
-- dimension of <tt>input</tt>.
|
|
fFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Returns the complex conjugate of a complex number.
|
|
--
|
|
-- Given a tensor <tt>input</tt> of complex numbers, this operation
|
|
-- returns a tensor of complex numbers that are the complex conjugate of
|
|
-- each element in <tt>input</tt>. The complex numbers in <tt>input</tt>
|
|
-- must be of the form \(a + bj\), where *a* is the real part and *b* is
|
|
-- the imaginary part.
|
|
--
|
|
-- The complex conjugate returned by this operation is of the form \(a -
|
|
-- bj\).
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <tt>input</tt> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
-- tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] ```
|
|
conj :: (TensorType t, OneOf '[Complex Double, Complex Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns the real part of a complex number.
|
|
--
|
|
-- Given a tensor <tt>input</tt> of complex numbers, this operation
|
|
-- returns a tensor of type <tt>float</tt> that is the real part of each
|
|
-- element in <tt>input</tt>. All elements in <tt>input</tt> 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.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <tt>input</tt> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
-- tf.real(input) ==> [-2.25, 3.25] ```
|
|
real :: (TensorType t, OneOf '[Complex Double, Complex Float] t, TensorType tout, OneOf '[Double, Float] tout) => Tensor v1 t -> Tensor Value tout
|
|
|
|
-- | Converts two real numbers to a complex number.
|
|
--
|
|
-- Given a tensor <a>real</a> representing the real part of a complex
|
|
-- number, and a tensor <a>imag</a> representing the imaginary part of a
|
|
-- complex number, this operation returns complex numbers elementwise of
|
|
-- the form \(a + bj\), where *a* represents the <a>real</a> part and *b*
|
|
-- represents the <a>imag</a> part.
|
|
--
|
|
-- The input tensors <a>real</a> and <a>imag</a> must have the same
|
|
-- shape.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <a>real</a> is [2.25, 3.25] # tensor <a>imag</a> is
|
|
-- [4.75, 5.75] tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 +
|
|
-- 5.75j]] ```
|
|
complex :: (TensorType t, OneOf '[Double, Float] t, TensorType tout, OneOf '[Complex Double, Complex Float] tout) => Tensor v1 t -> Tensor v2 t -> Tensor Value tout
|
|
|
|
-- | Creates a sequence of numbers.
|
|
--
|
|
-- This operation creates a sequence of numbers that begins at
|
|
-- <tt>start</tt> and extends by increments of <tt>delta</tt> up to but
|
|
-- not including <tt>limit</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # <tt>start</tt> is 3 # <tt>limit</tt> is 18 # <tt>delta</tt> is 3
|
|
-- tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] ```
|
|
range :: (TensorType tidx, OneOf '[Int32, Int64, Double, Float] tidx) => Tensor v1 tidx -> Tensor v2 tidx -> Tensor v3 tidx -> Tensor Value tidx
|
|
|
|
-- | Computes the "logical or" of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
any :: (TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 Bool -> Tensor v2 tidx -> Tensor Value Bool
|
|
|
|
-- | Computes the mean along sparse segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Like <tt>SegmentMean</tt>, but <tt>segment_ids</tt> can have rank less
|
|
-- than `data`'s first dimension, selecting a subset of dimension 0,
|
|
-- specified by <tt>indices</tt>.
|
|
sparseSegmentMean :: (TensorType t, OneOf '[Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor v3 Int32 -> Tensor Value t
|
|
|
|
-- | Computes the sum along sparse segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Like <tt>SegmentSum</tt>, but <tt>segment_ids</tt> can have rank less
|
|
-- than `data`'s first dimension, selecting a subset of dimension 0,
|
|
-- specified by <tt>indices</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
|
|
--
|
|
-- # Select two rows, one segment. tf.sparse_segment_sum(c,
|
|
-- tf.constant([0, 1]), tf.constant([0, 0])) ==> [[0 0 0 0]]
|
|
--
|
|
-- # 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]]
|
|
--
|
|
-- # 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]]
|
|
--
|
|
-- # Which is equivalent to: tf.segment_sum(c, tf.constant([0, 0, 1]))
|
|
-- ```
|
|
sparseSegmentSum :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor v3 Int32 -> Tensor Value t
|
|
|
|
-- | Computes the sum along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- 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 <tt>SegmentSum</tt>, <tt>segment_ids</tt> need not be sorted
|
|
-- and need not cover all values in the full range of valid values.
|
|
--
|
|
-- If the sum is empty for a given segment ID <tt>i</tt>, `output[i] =
|
|
-- 0`.
|
|
--
|
|
-- <tt>num_segments</tt> should equal the number of distinct segment IDs.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/UnsortedSegmentSum.png" alt</a> <a>/div</a>
|
|
unsortedSegmentSum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor v3 Int32 -> Tensor Value t
|
|
|
|
-- | Computes the minimum along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Computes a tensor such that \(output_i = min_j(data_j)\) where
|
|
-- <a>min</a> is over <tt>j</tt> such that `segment_ids[j] == i`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/SegmentMin.png" alt</a> <a>/div</a>
|
|
segmentMin :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor Value t
|
|
|
|
-- | Computes the product along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Computes a tensor such that \(output_i = prod_j data_j\) where the
|
|
-- product is over <tt>j</tt> such that `segment_ids[j] == i`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/SegmentProd.png" alt</a> <a>/div</a>
|
|
segmentProd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor Value t
|
|
|
|
-- | Computes the mean along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Computes a tensor such that \(output_i = frac{sum_j data_j}{N}\) where
|
|
-- <a>mean</a> is over <tt>j</tt> such that `segment_ids[j] == i` and
|
|
-- <tt>N</tt> is the total number of values summed.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/SegmentMean.png" alt</a> <a>/div</a>
|
|
segmentMean :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor Value t
|
|
|
|
-- | Computes the sum along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Computes a tensor such that \(output_i = sum_j data_j\) where sum is
|
|
-- over <tt>j</tt> such that `segment_ids[j] == i`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/SegmentSum.png" alt</a> <a>/div</a>
|
|
segmentSum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor Value t
|
|
|
|
-- | Returns the index with the smallest value across dimensions of a
|
|
-- tensor.
|
|
argMin :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value Int64
|
|
|
|
-- | Computes the maximum of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
max :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Computes the minimum of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
min :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Computes the product of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
prod :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Computes the sum of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
sum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Multiply matrix "a" by matrix "b".
|
|
--
|
|
-- The inputs must be two-dimensional matrices and the inner dimension of
|
|
-- "a" must match the outer dimension of "b". This op is optimized for
|
|
-- the case where at least one of "a" or "b" is sparse. The breakeven for
|
|
-- using this versus a dense matrix multiply on one platform was 30% zero
|
|
-- values in the sparse matrix.
|
|
sparseMatMul :: (TensorType ta, OneOf '[Word16, Float] ta, TensorType tb, OneOf '[Word16, Float] tb) => Tensor v1 ta -> Tensor v2 tb -> Tensor Value Float
|
|
|
|
-- | Multiply the matrix "a" by the matrix "b".
|
|
--
|
|
-- 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).
|
|
--
|
|
-- <ul>
|
|
-- <li>Note*: The default kernel implementation for MatMul on GPUs uses
|
|
-- cublas.</li>
|
|
-- </ul>
|
|
matMul :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the truth value of x AND y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>LogicalAnd</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
logicalAnd :: Tensor v1 Bool -> Tensor v2 Bool -> Tensor Value Bool
|
|
|
|
-- | Returns the truth value of (x == y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Equal</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
equal :: (TensorType t, OneOf '[Complex Double, Complex Float, Bool, ByteString, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Returns the truth value of (x >= y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>GreaterEqual</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
greaterEqual :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Returns the truth value of (x <= y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>LessEqual</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
lessEqual :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Returns the truth value of (x < y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Less</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
less :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Compute the polygamma function \(psi^{(n)}(x)\).
|
|
--
|
|
-- The polygamma function is defined as:
|
|
--
|
|
-- ``` psi^{(n)}(x) = frac{d^n}{dx^n} psi(x) ``` where \(psi(x)\) is the
|
|
-- digamma function.
|
|
polygamma :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Compute the lower regularized incomplete Gamma function `Q(a, x)`.
|
|
--
|
|
-- The lower regularized incomplete Gamma function is defined as:
|
|
--
|
|
-- ``` 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.
|
|
--
|
|
-- Note, above `Q(a, x)` (<tt>Igammac</tt>) is the upper regularized
|
|
-- complete Gamma function.
|
|
igamma :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Compute the upper regularized incomplete Gamma function `Q(a, x)`.
|
|
--
|
|
-- The upper regularized incomplete Gamma function is defined as:
|
|
--
|
|
-- ``` Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x) ``` where ```
|
|
-- Gamma(a, x) = int_{x}^{infty} t^{a-1} exp(-t) dt ``` is the upper
|
|
-- incomplete Gama function.
|
|
--
|
|
-- Note, above `P(a, x)` (<tt>Igamma</tt>) is the lower regularized
|
|
-- complete Gamma function.
|
|
igammac :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns element-wise remainder of division.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Mod</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
mod :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the min of x and y (i.e. x < y ? x : y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Minimum</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
minimum :: (TensorType t, OneOf '[Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the max of x and y (i.e. x > y ? x : y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Maximum</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
maximum :: (TensorType t, OneOf '[Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns (x - y)(x - y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>SquaredDifference</tt> supports broadcasting. More
|
|
-- about broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
squaredDifference :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes softplus gradients for a softplus operation.
|
|
softplusGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | BatchToSpace for 4-D tensors of type T.
|
|
--
|
|
-- This is a legacy version of the more general BatchToSpaceND.
|
|
--
|
|
-- 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 <tt>batch</tt> dimension are moved in
|
|
-- spatial blocks to the <tt>height</tt> and <tt>width</tt> dimensions,
|
|
-- followed by cropping along the <tt>height</tt> and <tt>width</tt>
|
|
-- dimensions.
|
|
batchToSpace :: (TensorType t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Int64 -> Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Returns x * y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Mul</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
mul :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns element-wise integer closest to x.
|
|
--
|
|
-- If the result is midway between two representable values, the even
|
|
-- representable is chosen. For example:
|
|
--
|
|
-- ``` rint(-1.5) ==> -2.0 rint(0.5000001) ==> 1.0 rint([-1.7,
|
|
-- -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2.,
|
|
-- 2.] ```
|
|
rint :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns element-wise smallest integer in not less than x.
|
|
ceil :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns element-wise largest integer not greater than x.
|
|
floor :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Performs 3D max pooling on the input.
|
|
maxPool3D :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns which elements of x are Inf.
|
|
--
|
|
-- <tt>compatibility(numpy) Equivalent to np.isinf </tt>end_compatibility
|
|
isInf :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
|
|
|
|
-- | Computes the gradients of depthwise convolution with respect to the
|
|
-- input.
|
|
depthwiseConv2dNativeBackpropInput :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | Returns which elements of x are NaN.
|
|
--
|
|
-- <tt>compatibility(numpy) Equivalent to np.isnan </tt>end_compatibility
|
|
isNan :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
|
|
|
|
-- | Computes natural logarithm of (1 + x) element-wise.
|
|
--
|
|
-- I.e., \(y = log_e (1 + x)\).
|
|
log1p :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes asin of x element-wise.
|
|
asin :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Finds values and indices of the <tt>k</tt> largest elements for the
|
|
-- last dimension.
|
|
--
|
|
-- If the input is a vector (rank-1), finds the <tt>k</tt> largest
|
|
-- entries in the vector and outputs their values and indices as vectors.
|
|
-- Thus `values[j]` is the <tt>j</tt>-th largest entry in <tt>input</tt>,
|
|
-- and its index is `indices[j]`.
|
|
--
|
|
-- For matrices (resp. higher rank input), computes the top <tt>k</tt>
|
|
-- entries in each row (resp. vector along the last dimension). Thus,
|
|
--
|
|
-- values.shape = indices.shape = input.shape[:-1] + [k]
|
|
--
|
|
-- If two elements are equal, the lower-index element appears first.
|
|
--
|
|
-- This is the same as <tt>TopK</tt>, but takes <tt>k</tt> as in input
|
|
-- rather than an attr.
|
|
topKV2 :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> (Tensor Value t, Tensor Value Int32)
|
|
|
|
-- | Computes cos of x element-wise.
|
|
cos :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes sin of x element-wise.
|
|
sin :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Outputs random integers from a uniform distribution.
|
|
--
|
|
-- The generated values are uniform integers in the range `[minval,
|
|
-- maxval)`. The lower bound <tt>minval</tt> is included in the range,
|
|
-- while the upper bound <tt>maxval</tt> is excluded.
|
|
--
|
|
-- 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`).
|
|
randomUniformInt :: (TensorType tout, OneOf '[Int32, Int64] tout, TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Tensor v2 tout -> Tensor v3 tout -> Build (Tensor Value tout)
|
|
|
|
-- | Computes the complementary error function of <tt>x</tt> element-wise.
|
|
erfc :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes Psi, the derivative of Lgamma (the log of the absolute value
|
|
-- of
|
|
--
|
|
-- `Gamma(x)`), element-wise.
|
|
digamma :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Performs a resize and padding as a preprocess during a convolution.
|
|
--
|
|
-- 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 <tt>NHWC</tt>
|
|
-- 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.
|
|
fusedResizeAndPadConv2D :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 Int32 -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Returns x - y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Sub</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
sub :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns an element-wise indication of the sign of a number.
|
|
--
|
|
-- `y = sign(x) = -1` if `x <a>0 if `x == 0`; 1 if `x</a> 0`.
|
|
--
|
|
-- For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y
|
|
-- = 0`.
|
|
sign :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the log of the absolute value of `Gamma(x)` element-wise.
|
|
lgamma :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes natural logarithm of x element-wise.
|
|
--
|
|
-- I.e., \(y = log_e x\).
|
|
log :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes exponential of x element-wise. \(y = e^x\).
|
|
exp :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the grayscale dilation of 4-D <tt>input</tt> and 3-D
|
|
-- <a>filter</a> tensors.
|
|
--
|
|
-- The <tt>input</tt> tensor has shape `[batch, in_height, in_width,
|
|
-- depth]` and the <a>filter</a> 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
|
|
-- <tt>output</tt> tensor has shape `[batch, out_height, out_width,
|
|
-- depth]`. The spatial dimensions of the output tensor depend on the
|
|
-- <tt>padding</tt> algorithm. We currently only support the default
|
|
-- <a>NHWC</a> <tt>data_format</tt>.
|
|
--
|
|
-- In detail, the grayscale morphological 2-D dilation is the max-sum
|
|
-- correlation (for consistency with <tt>conv2d</tt>, we use unmirrored
|
|
-- filters):
|
|
--
|
|
-- 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]
|
|
--
|
|
-- Max-pooling is a special case when the filter has size equal to the
|
|
-- pooling kernel size and contains all zeros.
|
|
--
|
|
-- Note on duality: The dilation of <tt>input</tt> by the <a>filter</a>
|
|
-- is equal to the negation of the erosion of `-input` by the reflected
|
|
-- <a>filter</a>.
|
|
dilation2D :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes the gradient for the rsqrt of <tt>x</tt> wrt its input.
|
|
--
|
|
-- Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and
|
|
-- <tt>dy</tt> is the corresponding input gradient.
|
|
rsqrtGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes reciprocal of square root of x element-wise.
|
|
--
|
|
-- I.e., \(y = 1 / sqrt{x}\).
|
|
rsqrt :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Produces the max pool of the input tensor for quantized types.
|
|
quantizedMaxPool :: (TensorType t, OneOf '[Int16, Int32, Word16, Word8] t) => Tensor v1 t -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value t, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes square root of x element-wise.
|
|
--
|
|
-- I.e., \(y = sqrt{x} = x^{1/2}\).
|
|
sqrt :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | A Reader that outputs the queued work as both the key and value.
|
|
--
|
|
-- To use, enqueue strings in a Queue. ReaderRead will take the front
|
|
-- work string and output (work, work).
|
|
identityReader :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Computes square of x element-wise.
|
|
--
|
|
-- I.e., \(y = x * x = x^2\).
|
|
square :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Reshapes a quantized tensor as per the Reshape op.
|
|
--
|
|
-- ```
|
|
quantizedReshape :: (TensorType t, TensorType tshape, OneOf '[Int32, Int64] tshape) => Tensor v1 t -> Tensor v2 tshape -> Tensor v3 Float -> Tensor v4 Float -> (Tensor Value t, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes the gradient for the inverse of <tt>x</tt> wrt its input.
|
|
--
|
|
-- Specifically, `grad = -dy * y*y`, where `y = 1/x`, and <tt>dy</tt> is
|
|
-- the corresponding input gradient.
|
|
reciprocalGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes the gradient for the inverse of <tt>x</tt> wrt its input.
|
|
--
|
|
-- Specifically, `grad = -dy * y*y`, where `y = 1/x`, and <tt>dy</tt> is
|
|
-- the corresponding input gradient.
|
|
invGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes the reciprocal of x element-wise.
|
|
--
|
|
-- I.e., \(y = 1 / x\).
|
|
inv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Concat the elements from the TensorArray into value <a>value</a>.
|
|
--
|
|
-- Takes <tt>T</tt> elements of shapes
|
|
--
|
|
-- ``` (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1
|
|
-- x ...) ```
|
|
--
|
|
-- and concatenates them into a Tensor of shape:
|
|
--
|
|
-- ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```
|
|
--
|
|
-- All elements must have the same shape (excepting the first dimension).
|
|
tensorArrayConcatV2 :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Float -> (Tensor Value dtype, Tensor Value Int64)
|
|
|
|
-- | Computes the complex absolute value of a tensor.
|
|
--
|
|
-- Given a tensor <tt>x</tt> of complex numbers, this operation returns a
|
|
-- tensor of type <tt>float</tt> or <tt>double</tt> that is the absolute
|
|
-- value of each element in <tt>x</tt>. All elements in <tt>x</tt> must
|
|
-- be complex numbers of the form \(a + bj\). The absolute value is
|
|
-- computed as \( sqrt{a^2 + b^2}\).
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <tt>x</tt> is [[-2.25 + 4.75j], [-3.25 + 5.75j]]
|
|
-- tf.complex_abs(x) ==> [5.25594902, 6.60492229] ```
|
|
complexAbs :: (TensorType t, OneOf '[Complex Double, Complex Float] t, TensorType tout, OneOf '[Double, Float] tout) => Tensor v1 t -> Tensor Value tout
|
|
|
|
-- | Cast x of type SrcT to y of DstT.
|
|
--
|
|
-- _HostCast requires its input and produces its output in host memory.
|
|
_HostCast :: (TensorType srcT, TensorType dstT) => Tensor v1 srcT -> Tensor Value dstT
|
|
|
|
-- | Resize <tt>images</tt> to <a>size</a> using nearest neighbor
|
|
-- interpolation.
|
|
resizeNearestNeighbor :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value t
|
|
|
|
-- | Deprecated. Disallowed in GraphDef version >= 2.
|
|
adjustContrast :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 Float -> Tensor Value Float
|
|
batchMatrixDiagPart :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
batchMatrixSetDiag :: (TensorType t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
batchMatrixDiag :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
|
|
fakeQuantWithMinMaxVarsPerChannelGradient :: Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 Float -> (Tensor Value Float, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes gradients for SparseSegmentSqrtN.
|
|
--
|
|
-- Returns tensor "output" with same shape as grad, except for dimension
|
|
-- 0 whose value is output_dim0.
|
|
sparseSegmentSqrtNGrad :: (TensorType t, OneOf '[Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor Value t
|
|
|
|
-- | Fake-quantize the <tt>inputs</tt> tensor of type float and one of the
|
|
-- shapes: `[d]`,
|
|
--
|
|
-- `[b, d]` `[b, h, w, d]` via per-channel floats <a>min</a> and
|
|
-- <a>max</a> of shape `[d]` to <tt>outputs</tt> tensor of same shape as
|
|
-- <tt>inputs</tt>.
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min; max</i> is the clamping range for the <tt>inputs</tt> data
|
|
-- in the corresponding depth channel. Op divides this range into 255
|
|
-- steps (total of 256 values), then replaces each <tt>inputs</tt> value
|
|
-- with the closest of the quantized step values.</li>
|
|
-- </ul>
|
|
--
|
|
-- This operation has a gradient and thus allows for training <a>min</a>
|
|
-- and <a>max</a> values.
|
|
fakeQuantWithMinMaxVarsPerChannel :: Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Float -> Tensor Value Float
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with scalar values.
|
|
--
|
|
-- The input <tt>tags</tt> and <tt>values</tt> must have the same shape.
|
|
-- The generated summary has a summary value for each tag-value pair in
|
|
-- <tt>tags</tt> and <tt>values</tt>.
|
|
scalarSummary :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor Value ByteString
|
|
|
|
-- | Computes numerical negative value element-wise.
|
|
--
|
|
-- I.e., \(y = -x\).
|
|
neg :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Compute gradients for a FakeQuantWithMinMaxArgs operation.
|
|
fakeQuantWithMinMaxArgsGradient :: Tensor v1 Float -> Tensor v2 Float -> Tensor Value Float
|
|
|
|
-- | Debug NaN Value Counter Op
|
|
--
|
|
-- Counts number of NaNs in the input tensor, for debugging.
|
|
debugNanCount :: (TensorType t) => Tensor v1 t -> Tensor Value Int64
|
|
|
|
-- | Debug Identity Op.
|
|
--
|
|
-- Provides an identity mapping of the non-Ref type input tensor for
|
|
-- debugging.
|
|
debugIdentity :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Bitcasts a tensor from one type to another without copying data.
|
|
--
|
|
-- Given a tensor <tt>input</tt>, this operation returns a tensor that
|
|
-- has the same buffer data as <tt>input</tt> with datatype `type`.
|
|
--
|
|
-- If the input datatype <tt>T</tt> is larger than the output datatype
|
|
-- `type` then the shape changes from [...] to [...,
|
|
-- sizeof(<tt>T</tt>)/sizeof(`type`)].
|
|
--
|
|
-- If <tt>T</tt> is smaller than `type`, the operator requires that the
|
|
-- rightmost dimension be equal to sizeof(`type`)/sizeof(<tt>T</tt>). The
|
|
-- shape then goes from [..., sizeof(`type`)/sizeof(<tt>T</tt>)] to
|
|
-- [...].
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: Bitcast is implemented as a low-level cast, so machines
|
|
-- with different endian orderings will give different results.</li>
|
|
-- </ul>
|
|
bitcast :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType type', OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] type') => Tensor v1 t -> Tensor Value type'
|
|
|
|
-- | Computes sigmoid of <tt>x</tt> element-wise.
|
|
--
|
|
-- Specifically, `y = 1 / (1 + exp(-x))`.
|
|
sigmoid :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Copy Op.
|
|
--
|
|
-- Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on
|
|
-- the device on which the tensor is allocated.
|
|
--
|
|
-- Unlike the CopyHost Op, this op does not have HostMemory constraint on
|
|
-- its input or output.
|
|
copy :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Generates labels for candidate sampling with a learned unigram
|
|
-- distribution.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- The vocabulary file should be in CSV-like format, with the last field
|
|
-- being the weight associated with the word.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
fixedUnigramCandidateSampler :: Int64 -> Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes the difference between two lists of numbers or strings.
|
|
--
|
|
-- Given a list <tt>x</tt> and a list <tt>y</tt>, this operation returns
|
|
-- a list <tt>out</tt> that represents all values that are in <tt>x</tt>
|
|
-- but not in <tt>y</tt>. The returned list <tt>out</tt> is sorted in the
|
|
-- same order that the numbers appear in <tt>x</tt> (duplicates are
|
|
-- preserved). This operation also returns a list <tt>idx</tt> that
|
|
-- represents the position of each <tt>out</tt> element in <tt>x</tt>. In
|
|
-- other words:
|
|
--
|
|
-- `out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`
|
|
--
|
|
-- For example, given this input:
|
|
--
|
|
-- ```prettyprint x = [1, 2, 3, 4, 5, 6] y = [1, 3, 5] ```
|
|
--
|
|
-- This operation would return:
|
|
--
|
|
-- ```prettyprint out ==> [2, 4, 6] idx ==> [1, 3, 5] ```
|
|
listDiff :: (TensorType t, TensorType out_idx, OneOf '[Int32, Int64] out_idx) => Tensor v1 t -> Tensor v2 t -> (Tensor Value t, Tensor Value out_idx)
|
|
|
|
-- | Extract <tt>patches</tt> from <tt>images</tt> and put them in the
|
|
-- "depth" output dimension.
|
|
extractImagePatches :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | SpaceToDepth for tensors of type T.
|
|
--
|
|
-- Rearranges blocks of spatial data, into depth. More specifically, this
|
|
-- op outputs a copy of the input tensor where values from the
|
|
-- <tt>height</tt> and <tt>width</tt> dimensions are moved to the
|
|
-- <tt>depth</tt> dimension. The attr <tt>block_size</tt> indicates the
|
|
-- input block size and how the data is moved.
|
|
--
|
|
-- <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>
|
|
--
|
|
-- That is, assuming the input is in the shape: `[batch, height, width,
|
|
-- depth]`, the shape of the output will be: `[batch,
|
|
-- height<i>block_size, width</i>block_size,
|
|
-- depth*block_size*block_size]`
|
|
--
|
|
-- This operation requires that the input tensor be of rank 4, and that
|
|
-- <tt>block_size</tt> be >=1 and a divisor of both the input
|
|
-- <tt>height</tt> and <tt>width</tt>.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- For example, given this input of shape `[1, 2, 2, 1]`, and block_size
|
|
-- of 2:
|
|
--
|
|
-- ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ```
|
|
--
|
|
-- This operation will output a tensor of shape `[1, 1, 1, 4]`:
|
|
--
|
|
-- ```prettyprint [[[[1, 2, 3, 4]]]] ```
|
|
--
|
|
-- 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]`.
|
|
--
|
|
-- For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`,
|
|
-- e.g.
|
|
--
|
|
-- ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11,
|
|
-- 12]]]] ```
|
|
--
|
|
-- This operation, for block_size of 2, will return the following tensor
|
|
-- of shape `[1, 1, 1, 12]`
|
|
--
|
|
-- ```prettyprint [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ```
|
|
--
|
|
-- Similarly, for the following input of shape `[1 4 4 1]`, and a block
|
|
-- size of 2:
|
|
--
|
|
-- ```prettyprint x = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9],
|
|
-- [10], [13], [14]], [[11], [12], [15], [16]]]] ```
|
|
--
|
|
-- the operator will return the following tensor of shape `[1 2 2 4]`:
|
|
--
|
|
-- ```prettyprint x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12],
|
|
-- [13, 14, 15, 16]]]] ```
|
|
spaceToDepth :: (TensorType t) => Int64 -> Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the gradient of the crop_and_resize op wrt the input boxes
|
|
-- tensor.
|
|
cropAndResizeGradBoxes :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Float -> Tensor v2 t -> Tensor v3 Float -> Tensor v4 Int32 -> Tensor Value Float
|
|
|
|
-- | BatchToSpace for N-D tensors of type T.
|
|
--
|
|
-- 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 <tt>crops</tt> to produce the output. This is the reverse
|
|
-- of SpaceToBatch. See below for a precise description.
|
|
batchToSpaceND :: (TensorType t, TensorType tblock_shape, OneOf '[Int32, Int64] tblock_shape, TensorType tcrops, OneOf '[Int32, Int64] tcrops) => Tensor v1 t -> Tensor v2 tblock_shape -> Tensor v3 tcrops -> Tensor Value t
|
|
|
|
-- | SpaceToBatch for 4-D tensors of type T.
|
|
--
|
|
-- This is a legacy version of the more general SpaceToBatchND.
|
|
--
|
|
-- 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 <tt>height</tt> and <tt>width</tt> dimensions
|
|
-- are moved to the <tt>batch</tt> dimension. After the zero-padding,
|
|
-- both <tt>height</tt> and <tt>width</tt> of the input must be divisible
|
|
-- by the block size.
|
|
spaceToBatch :: (TensorType t, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Int64 -> Tensor v1 t -> Tensor v2 tpaddings -> Tensor Value t
|
|
|
|
-- | Adjust the hue of one or more images.
|
|
--
|
|
-- <tt>images</tt> is a tensor of at least 3 dimensions. The last
|
|
-- dimension is interpretted as channels, and must be three.
|
|
--
|
|
-- The input image is considered in the RGB colorspace. Conceptually, the
|
|
-- RGB colors are first mapped into HSV. A delta is then applied all the
|
|
-- hue values, and then remapped back to RGB colorspace.
|
|
adjustHue :: Tensor v1 Float -> Tensor v2 Float -> Tensor Value Float
|
|
|
|
-- | SpaceToBatch for N-D tensors of type T.
|
|
--
|
|
-- This operation divides "spatial" dimensions `[1, ..., M]` of the input
|
|
-- into a grid of blocks of shape <tt>block_shape</tt>, 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 <tt>paddings</tt>. See below for a precise description.
|
|
spaceToBatchND :: (TensorType t, TensorType tblock_shape, OneOf '[Int32, Int64] tblock_shape, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Tensor v1 t -> Tensor v2 tblock_shape -> Tensor v3 tpaddings -> Tensor Value t
|
|
|
|
-- | Returns the diagonal part of the tensor.
|
|
--
|
|
-- This operation returns a tensor with the <tt>diagonal</tt> part of the
|
|
-- <tt>input</tt>. The <tt>diagonal</tt> part is computed as follows:
|
|
--
|
|
-- Assume <tt>input</tt> has dimensions `[D1,..., Dk, D1,..., Dk]`, then
|
|
-- the output is a tensor of rank <tt>k</tt> with dimensions `[D1,...,
|
|
-- Dk]` where:
|
|
--
|
|
-- `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>input</tt> is [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0,
|
|
-- 3, 0] [0, 0, 0, 4]]
|
|
--
|
|
-- tf.diag_part(input) ==> [1, 2, 3, 4] ```
|
|
diagPart :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | A placeholder op for a value that will be fed into the computation.
|
|
--
|
|
-- 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.
|
|
placeholderV2 :: (TensorType dtype) => Shape -> Tensor Value dtype
|
|
|
|
-- | Computes acos of x element-wise.
|
|
acos :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | A placeholder op for a value that will be fed into the computation.
|
|
--
|
|
-- 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.
|
|
placeholder :: (TensorType dtype) => Tensor Value dtype
|
|
|
|
-- | Does nothing. Serves as a control trigger for scheduling.
|
|
--
|
|
-- Only useful as a placeholder for control edges.
|
|
controlTrigger :: ControlNode
|
|
|
|
-- | Computes atan of x element-wise.
|
|
atan :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Pads a tensor with mirrored values.
|
|
--
|
|
-- This operation pads a <tt>input</tt> with mirrored values according to
|
|
-- the <tt>paddings</tt> you specify. <tt>paddings</tt> is an integer
|
|
-- tensor with shape `[n, 2]`, where n is the rank of <tt>input</tt>. For
|
|
-- each dimension D of <tt>input</tt>, `paddings[D, 0]` indicates how
|
|
-- many values to add before the contents of <tt>input</tt> in that
|
|
-- dimension, and `paddings[D, 1]` indicates how many values to add after
|
|
-- the contents of <tt>input</tt> 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 <tt>copy_border</tt> is true (if
|
|
-- false, respectively).
|
|
--
|
|
-- The padded size of each dimension D of the output is:
|
|
--
|
|
-- `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[1, 2, 3], [4, 5, 6]]. #
|
|
-- <tt>paddings</tt> is [[1, 1]], [2, 2]]. # <tt>mode</tt> is SYMMETRIC.
|
|
-- # rank of <tt>t</tt> 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]]
|
|
-- ```
|
|
mirrorPad :: (TensorType t, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Tensor v1 t -> Tensor v2 tpaddings -> Tensor Value t
|
|
|
|
-- | Returns locations of true values in a boolean tensor.
|
|
--
|
|
-- This operation returns the coordinates of true elements in
|
|
-- <tt>input</tt>. 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 <tt>input</tt>. Indices
|
|
-- are output in row-major order.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>input</tt> tensor is [[True, False] # [True,
|
|
-- False]] # <tt>input</tt> has two true values, so output has two
|
|
-- coordinates. # <tt>input</tt> has rank of 2, so coordinates have two
|
|
-- indices. where(input) ==> [[0, 0], [1, 0]]
|
|
--
|
|
-- # <tt>input</tt> tensor is [[[True, False] # [True, False]] # [[False,
|
|
-- True] # [False, True]] # [[False, False] # [False, True]]] #
|
|
-- <tt>input</tt> has 5 true values, so output has 5 coordinates. #
|
|
-- <tt>input</tt> 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]] ```
|
|
where' :: Tensor v1 Bool -> Tensor Value Int64
|
|
|
|
-- | Computes gradients of average pooling function.
|
|
avgPool3DGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Restore a Reader to its initial clean state.
|
|
readerReset :: Tensor Ref ByteString -> Build (ControlNode)
|
|
|
|
-- | Returns the gradient of <tt>Tile</tt>.
|
|
--
|
|
-- Since <tt>Tile</tt> takes an input and repeats the input
|
|
-- <tt>multiples</tt> times along each dimension, <tt>TileGrad</tt> takes
|
|
-- in <tt>multiples</tt> and aggregates each repeated tile of
|
|
-- <tt>input</tt> into <tt>output</tt>.
|
|
tileGrad :: (TensorType t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value t
|
|
|
|
-- | Inserts a dimension of 1 into a tensor's shape.
|
|
--
|
|
-- Given a tensor <tt>input</tt>, this operation inserts a dimension of 1
|
|
-- at the dimension index <tt>dim</tt> of <tt>input</tt>'s shape. The
|
|
-- dimension index <tt>dim</tt> starts at zero; if you specify a negative
|
|
-- number for <tt>dim</tt> it is counted backward from the end.
|
|
--
|
|
-- This operation is useful if you want to add a batch dimension to a
|
|
-- single element. For example, if you have a single image of shape
|
|
-- `[height, width, channels]`, you can make it a batch of 1 image with
|
|
-- `expand_dims(image, 0)`, which will make the shape `[1, height, width,
|
|
-- channels]`.
|
|
--
|
|
-- Other examples:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is a tensor of shape [2]
|
|
-- shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==>
|
|
-- [2, 1] shape(expand_dims(t, -1)) ==> [2, 1]
|
|
--
|
|
-- # <tt>t2</tt> is a tensor of shape [2, 3, 5] 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] ```
|
|
--
|
|
-- This operation requires that:
|
|
--
|
|
-- `-1-input.dims() <= dim <= input.dims()`
|
|
--
|
|
-- This operation is related to `squeeze()`, which removes dimensions of
|
|
-- size 1.
|
|
expandDims :: (TensorType t, TensorType tdim, OneOf '[Int32, Int64] tdim) => Tensor v1 t -> Tensor v2 tdim -> Tensor Value t
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with a tensor.
|
|
tensorSummary :: (TensorType t) => Tensor v1 t -> Tensor Value ByteString
|
|
|
|
-- | Constructs a tensor by tiling a given tensor.
|
|
--
|
|
-- This operation creates a new tensor by replicating <tt>input</tt>
|
|
-- <tt>multiples</tt> times. The output tensor's i'th dimension has
|
|
-- `input.dims(i) * multiples[i]` elements, and the values of
|
|
-- <tt>input</tt> are replicated `multiples[i]` times along the
|
|
-- <tt>i</tt>th dimension. For example, tiling `[a b c d]` by `[2]`
|
|
-- produces `[a b c d a b c d]`.
|
|
tile :: (TensorType t, TensorType tmultiples, OneOf '[Int32, Int64] tmultiples) => Tensor v1 t -> Tensor v2 tmultiples -> Tensor Value t
|
|
|
|
-- | Return a strided slice from <tt>input</tt>.
|
|
--
|
|
-- Note, most python users will want to use the Python <a>__getitem__</a>
|
|
-- or <a>__getitem__</a> rather than this op directly.
|
|
--
|
|
-- The goal of this op is to produce a new tensor with a subset of the
|
|
-- elements from the <tt>n</tt> dimensional <tt>input</tt> tensor. The
|
|
-- subset is chosen using a sequence of <tt>m</tt> sparse range
|
|
-- specifications encoded into the arguments of this function. Note, in
|
|
-- some cases <tt>m</tt> could be equal to <tt>n</tt>, but this need not
|
|
-- be the case. Each range specification entry can be one of the
|
|
-- following:
|
|
--
|
|
-- <ul>
|
|
-- <li>An ellipsis (...). Ellipses are used to imply zero or more
|
|
-- dimensions of full-dimension selection and are produced using
|
|
-- <tt>ellipsis_mask</tt>. For example, `foo[...]` is the identity
|
|
-- slice.</li>
|
|
-- <li>A new axis. This is used to insert a new shape=1 dimension and is
|
|
-- produced using <tt>new_axis_mask</tt>. For example, `foo[:, ...]`
|
|
-- where <tt>foo</tt> is shape `(3, 4)` produces a `(1, 3, 4)`
|
|
-- tensor.</li>
|
|
-- <li>A range `begin:end:stride`. This is used to specify how much to
|
|
-- choose from a given dimension. <tt>stride</tt> can be any integer but
|
|
-- 0. <tt>begin</tt> is an integer which represents the index of the
|
|
-- first value to select while <tt>end</tt> represents the index of the
|
|
-- last value to select. The number of values selected in each dimension
|
|
-- is `end - begin` if `stride > 0` and `begin - end` if `stride <
|
|
-- 0`. <tt>begin</tt> and <tt>end</tt> can be negative where `-1` is the
|
|
-- last element, `-2` is the second to last. <tt>begin_mask</tt> controls
|
|
-- whether to replace the explicitly given <tt>begin</tt> with an
|
|
-- implicit effective value of `0` if `stride > 0` and `-1` if `stride
|
|
-- < 0`. <tt>end_mask</tt> is analogous but produces the number
|
|
-- required to create the largest open interval. For example, given a
|
|
-- shape `(3,)` tensor `foo[:]`, the effective <tt>begin</tt> and
|
|
-- <tt>end</tt> are `0` and `3`. Do not assume this is equivalent to
|
|
-- `foo[0:-1]` which has an effective <tt>begin</tt> and <tt>end</tt> of
|
|
-- `0` and `2`. Another example is `foo[-2::-1]` which reverses the first
|
|
-- dimension of a tensor while dropping the last two (in the original
|
|
-- order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is
|
|
-- `[4,3]`.</li>
|
|
-- <li>A single index. This is used to keep only elements that have a
|
|
-- given index. For example (`foo[2, :]` on a shape `(5,6)` tensor
|
|
-- produces a shape `(6,)` tensor. This is encoded in <tt>begin</tt> and
|
|
-- <tt>end</tt> and <tt>shrink_axis_mask</tt>.</li>
|
|
-- </ul>
|
|
--
|
|
-- Each conceptual range specification is encoded in the op's argument.
|
|
-- This encoding is best understand by considering a non-trivial example.
|
|
-- In particular, `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as
|
|
--
|
|
-- ```prettyprint begin = [1, 2, x, x, 0, x] # x denotes don't care
|
|
-- (usually 0) end = [2, 4, x, x, -3, x] strides = [1, 1, x, x, -1, 1]
|
|
-- begin_mask = 1<<4 | 1 << 5 = 48 end_mask = 1<<5 = 32
|
|
-- ellipsis_mask = 1<<3 = 8 new_axis_mask = 1<<2 4
|
|
-- shrink_axis_mask = 1<<0 ```
|
|
--
|
|
-- In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
|
|
-- the slice becomes (2, 1, 5, 5, 2, 5). Let us walk step by step through
|
|
-- each argument specification.
|
|
--
|
|
-- <ol>
|
|
-- <li>The first argument in the example slice is turned into `begin = 1`
|
|
-- and `end = begin + 1 = 2`. To disambiguate from the original spec
|
|
-- `2:4` we also set the appropriate bit in
|
|
-- <tt>shrink_axis_mask</tt>.</li>
|
|
-- <li>`2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks
|
|
-- have zero bits contributed.</li>
|
|
-- <li>None is a synonym for `tf.newaxis`. This means insert a dimension
|
|
-- of size 1 dimension in the final shape. Dummy values are contributed
|
|
-- to begin, end and stride, while the new_axis_mask bit is set.</li>
|
|
-- <li><tt>...</tt> grab the full ranges from as many dimensions as
|
|
-- needed to fully specify a slice for every dimension of the input
|
|
-- shape.</li>
|
|
-- <li>`:-3:-1` shows the use of negative indices. A negative index
|
|
-- <tt>i</tt> associated with a dimension that has shape <tt>s</tt> is
|
|
-- converted to a positive index `s + i`. So `-1` becomes `s-1` (i.e. the
|
|
-- last element). This conversion is done internally so begin, end and
|
|
-- strides receive x, -3, and -1. The appropriate begin_mask bit is set
|
|
-- to indicate the start range is the full range (ignoring the x).</li>
|
|
-- <li><tt>:</tt> indicates that the entire contents of the corresponding
|
|
-- dimension is selected. This is equivalent to `::` or `0::1`. begin,
|
|
-- end, and strides receive 0, 0, and 1, respectively. The appropriate
|
|
-- bits in <tt>begin_mask</tt> and <tt>end_mask</tt> are also set.</li>
|
|
-- </ol>
|
|
--
|
|
-- <ul>
|
|
-- <li>Requirements*: `0 != strides[i] for i in [0, m)` `ellipsis_mask
|
|
-- must be a power of two (only one ellipsis)`</li>
|
|
-- </ul>
|
|
stridedSlice :: (TensorType t, TensorType index, OneOf '[Int32, Int64] index) => Tensor v1 t -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor Value t
|
|
|
|
-- | Return a slice from <tt>input</tt>.
|
|
--
|
|
-- The output tensor is a tensor with dimensions described by <a>size</a>
|
|
-- whose values are extracted from <tt>input</tt> starting at the offsets
|
|
-- in <tt>begin</tt>.
|
|
--
|
|
-- <ul>
|
|
-- <li>Requirements*: 0 <= begin[i] <= begin[i] + size[i] <= Di
|
|
-- for i in [0, n)</li>
|
|
-- </ul>
|
|
slice :: (TensorType t, TensorType index, OneOf '[Int32, Int64] index) => Tensor v1 t -> Tensor v2 index -> Tensor v3 index -> Tensor Value t
|
|
|
|
-- | Computes a 2D convolution given quantized 4D input and filter tensors.
|
|
--
|
|
-- The inputs are quantized tensors where the lowest value represents the
|
|
-- real number of the associated minimum, and the highest represents the
|
|
-- maximum. This means that you can only interpret the quantized output
|
|
-- in the same way, by taking the returned minimum and maximum values
|
|
-- into account.
|
|
quantizedConv2D :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType tfilter, OneOf '[Int16, Int32, Word16, Word8] tfilter, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 tfilter -> Tensor v3 Float -> Tensor v4 Float -> Tensor v5 Float -> Tensor v6 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes rectified linear 6 gradients for a Relu6 operation.
|
|
relu6Grad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes gradients of the average pooling function.
|
|
avgPoolGrad :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Split elements of <tt>input</tt> based on <tt>delimiter</tt> into a
|
|
-- <tt>SparseTensor</tt>.
|
|
--
|
|
-- Let N be the size of source (typically N will be the batch size).
|
|
-- Split each element of <tt>input</tt> based on <tt>delimiter</tt> and
|
|
-- return a <tt>SparseTensor</tt> containing the splitted tokens. Empty
|
|
-- tokens are ignored.
|
|
--
|
|
-- <tt>delimiter</tt> can be empty or a single-byte character. If
|
|
-- <tt>delimiter</tt> is an empty string, each element of <tt>input</tt>
|
|
-- is split into individual single-byte character strings, including
|
|
-- splitting of UTF-8 multibyte sequences.
|
|
--
|
|
-- For example: N = 2, input[0] is 'hello world' and input[1] is 'a b c',
|
|
-- then the output will be
|
|
--
|
|
-- indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] shape = [2, 3] values =
|
|
-- [<tt>hello</tt>, <tt>world</tt>, <tt>a</tt>, <tt>b</tt>, <tt>c</tt>]
|
|
stringSplit :: Tensor v1 ByteString -> Tensor v2 ByteString -> (Tensor Value Int64, Tensor Value ByteString, Tensor Value Int64)
|
|
|
|
-- | Returns the rank of a tensor.
|
|
--
|
|
-- This operation returns an integer representing the rank of
|
|
-- <tt>input</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3],
|
|
-- [4, 4, 4]]] # shape of tensor <tt>t</tt> is [2, 2, 3] rank(t) ==> 3
|
|
-- ```
|
|
--
|
|
-- <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>
|
|
rank :: (TensorType t) => Tensor v1 t -> Tensor Value Int32
|
|
|
|
-- | Computes the reciprocal of x element-wise.
|
|
--
|
|
-- I.e., \(y = 1 / x\).
|
|
reciprocal :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Reverses variable length slices.
|
|
--
|
|
-- This op first slices <tt>input</tt> along the dimension
|
|
-- <tt>batch_dim</tt>, and for each slice <tt>i</tt>, reverses the first
|
|
-- `seq_lengths[i]` elements along the dimension <tt>seq_dim</tt>.
|
|
--
|
|
-- The elements of <tt>seq_lengths</tt> must obey `seq_lengths[i] <
|
|
-- input.dims[seq_dim]`, and <tt>seq_lengths</tt> must be a vector of
|
|
-- length `input.dims[batch_dim]`.
|
|
--
|
|
-- The output slice <tt>i</tt> along dimension <tt>batch_dim</tt> is then
|
|
-- given by input slice <tt>i</tt>, with the first `seq_lengths[i]`
|
|
-- slices along dimension <tt>seq_dim</tt> reversed.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # Given this: batch_dim = 0 seq_dim = 1 input.dims =
|
|
-- (4, 8, ...) seq_lengths = [7, 2, 3, 5]
|
|
--
|
|
-- # 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, :, ...]
|
|
--
|
|
-- # 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:, :, ...] ```
|
|
--
|
|
-- In contrast, if:
|
|
--
|
|
-- ```prettyprint # Given this: batch_dim = 2 seq_dim = 0 input.dims =
|
|
-- (8, ?, 4, ...) seq_lengths = [7, 2, 3, 5]
|
|
--
|
|
-- # 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, :, ...]
|
|
--
|
|
-- # 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, :, ...] ```
|
|
reverseSequence :: (TensorType t, TensorType tlen, OneOf '[Int32, Int64] tlen) => Int64 -> Tensor v1 t -> Tensor v2 tlen -> Tensor Value t
|
|
|
|
-- | The backward operation for <a>BiasAdd</a> on the "bias" tensor.
|
|
--
|
|
-- It accumulates all the values from out_backprop into the feature
|
|
-- dimension. For NHWC data format, the feature dimension is the last.
|
|
-- For NCHW data format, the feature dimension is the third-to-last.
|
|
biasAddGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Add a <tt>SparseTensor</tt> to a <tt>SparseTensorsMap</tt> return its
|
|
-- handle.
|
|
--
|
|
-- A <tt>SparseTensor</tt> is represented by three tensors:
|
|
-- <tt>sparse_indices</tt>, <tt>sparse_values</tt>, and
|
|
-- <tt>sparse_shape</tt>.
|
|
--
|
|
-- This operator takes the given <tt>SparseTensor</tt> and adds it to a
|
|
-- container object (a <tt>SparseTensorsMap</tt>). A unique key within
|
|
-- this container is generated in the form of an <tt>int64</tt>, and this
|
|
-- is the value that is returned.
|
|
--
|
|
-- The <tt>SparseTensor</tt> can then be read out as part of a minibatch
|
|
-- by passing the key as a vector element to
|
|
-- <tt>TakeManySparseFromTensorsMap</tt>. To ensure the correct
|
|
-- <tt>SparseTensorsMap</tt> is accessed, ensure that the same
|
|
-- <tt>container</tt> and <tt>shared_name</tt> are passed to that Op. If
|
|
-- no <tt>shared_name</tt> is provided here, instead use the *name* of
|
|
-- the Operation created by calling <tt>AddSparseToTensorsMap</tt> as the
|
|
-- <tt>shared_name</tt> passed to <tt>TakeManySparseFromTensorsMap</tt>.
|
|
-- Ensure the Operations are colocated.
|
|
addSparseToTensorsMap :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Build (Tensor Value Int64)
|
|
|
|
-- | Computes tan of x element-wise.
|
|
tan :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the sum of elements across dimensions of a SparseTensor.
|
|
--
|
|
-- This Op takes a SparseTensor and is the sparse counterpart to
|
|
-- `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a
|
|
-- SparseTensor.
|
|
--
|
|
-- Reduces <tt>sp_input</tt> along the dimensions given in
|
|
-- <tt>reduction_axes</tt>. Unless <tt>keep_dims</tt> is true, the rank
|
|
-- of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_axes</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
--
|
|
-- If <tt>reduction_axes</tt> has no entries, all dimensions are reduced,
|
|
-- and a tensor with a single element is returned. Additionally, the axes
|
|
-- can be negative, which are interpreted according to the indexing rules
|
|
-- in Python.
|
|
sparseReduceSumSparse :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int32 -> (Tensor Value Int64, Tensor Value t, Tensor Value Int64)
|
|
|
|
-- | Returns shape of tensors.
|
|
--
|
|
-- This operation returns N 1-D integer tensors representing shape of
|
|
-- `input[i]s`.
|
|
shapeN :: (TensorType t, TensorType out_type, OneOf '[Int32, Int64] out_type) => [Tensor v1 t] -> [Tensor Value out_type]
|
|
|
|
-- | Returns the shape of a tensor.
|
|
--
|
|
-- This operation returns a 1-D integer tensor representing the shape of
|
|
-- <tt>input</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3],
|
|
-- [4, 4, 4]]] shape(t) ==> [2, 2, 3] ```
|
|
shape :: (TensorType t, TensorType out_type, OneOf '[Int32, Int64] out_type) => Tensor v1 t -> Tensor Value out_type
|
|
|
|
-- | Finds unique elements in a 1-D tensor.
|
|
--
|
|
-- This operation returns a tensor <tt>y</tt> containing all of the
|
|
-- unique elements of <tt>x</tt> sorted in the same order that they occur
|
|
-- in <tt>x</tt>. This operation also returns a tensor <tt>idx</tt> the
|
|
-- same size as <tt>x</tt> that contains the index of each value of
|
|
-- <tt>x</tt> in the unique output <tt>y</tt>. In other words:
|
|
--
|
|
-- `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>x</tt> 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] ```
|
|
unique :: (TensorType t, TensorType out_idx, OneOf '[Int32, Int64] out_idx) => Tensor v1 t -> (Tensor Value t, Tensor Value out_idx)
|
|
|
|
-- | Outputs random values from a truncated normal distribution.
|
|
--
|
|
-- 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.
|
|
truncatedNormal :: (TensorType dtype, OneOf '[Word16, Double, Float] dtype, TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Build (Tensor Value dtype)
|
|
|
|
-- | Computes the inverse permutation of a tensor.
|
|
--
|
|
-- This operation computes the inverse of an index permutation. It takes
|
|
-- a 1-D integer tensor <tt>x</tt>, which represents the indices of a
|
|
-- zero-based array, and swaps each value with its index position. In
|
|
-- other words, for an output tensor <tt>y</tt> and an input tensor
|
|
-- <tt>x</tt>, this operation computes the following:
|
|
--
|
|
-- `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
|
|
--
|
|
-- The values must include 0. There can be no duplicate values or
|
|
-- negative values.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>x</tt> is [3, 4, 0, 2, 1]
|
|
-- invert_permutation(x) ==> [2, 4, 3, 0, 1] ```
|
|
invertPermutation :: (TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Checks a tensor for NaN and Inf values.
|
|
--
|
|
-- When run, reports an <tt>InvalidArgument</tt> error if <tt>tensor</tt>
|
|
-- has any values that are not a number (NaN) or infinity (Inf).
|
|
-- Otherwise, passes <tt>tensor</tt> as-is.
|
|
checkNumerics :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Generates labels for candidate sampling with a uniform distribution.
|
|
--
|
|
-- See explanations of candidate sampling and the data formats at
|
|
-- go/candidate-sampling.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
uniformCandidateSampler :: Int64 -> Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Gather slices from <tt>params</tt> according to <tt>indices</tt>.
|
|
--
|
|
-- <tt>indices</tt> 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:
|
|
--
|
|
-- ```python # Scalar indices output[:, ..., :] = params[indices, :, ...
|
|
-- :]
|
|
--
|
|
-- # Vector indices output[i, :, ..., :] = params[indices[i], :, ... :]
|
|
--
|
|
-- # Higher rank indices output[i, ..., j, :, ... :] = params[indices[i,
|
|
-- ..., j], :, ..., :] ```
|
|
--
|
|
-- If <tt>indices</tt> is a permutation and `len(indices) ==
|
|
-- params.shape[0]` then this operation will permute <tt>params</tt>
|
|
-- accordingly.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/Gather.png" alt</a> <a>/div</a>
|
|
gather :: (TensorType tparams, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 tparams -> Tensor v2 tindices -> Tensor Value tparams
|
|
|
|
-- | Returns a constant tensor.
|
|
const :: (TensorType dtype) => Tensor Value dtype
|
|
|
|
-- | Creates a tensor filled with a scalar value.
|
|
--
|
|
-- This operation creates a tensor of shape <tt>dims</tt> and fills it
|
|
-- with <a>value</a>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # Output tensor has shape [2, 3]. fill([2, 3], 9)
|
|
-- ==> [[9, 9, 9] [9, 9, 9]] ```
|
|
fill :: (TensorType t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes the (possibly normalized) Levenshtein Edit Distance.
|
|
--
|
|
-- The inputs are variable-length sequences provided by SparseTensors
|
|
-- (hypothesis_indices, hypothesis_values, hypothesis_shape) and
|
|
-- (truth_indices, truth_values, truth_shape).
|
|
--
|
|
-- The inputs are:
|
|
editDistance :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor v5 t -> Tensor v6 Int64 -> Tensor Value Float
|
|
|
|
-- | Reverses specific dimensions of a tensor.
|
|
--
|
|
-- Given a <tt>tensor</tt>, and a <tt>bool</tt> tensor <tt>dims</tt>
|
|
-- representing the dimensions of <tt>tensor</tt>, this operation
|
|
-- reverses each dimension i of <tt>tensor</tt> where `dims[i]` is
|
|
-- <a>True</a>.
|
|
--
|
|
-- <tt>tensor</tt> can have up to 8 dimensions. The number of dimensions
|
|
-- of <tt>tensor</tt> must equal the number of elements in <tt>dims</tt>.
|
|
-- In other words:
|
|
--
|
|
-- `rank(tensor) = size(dims)`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>t</tt> 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 <tt>t</tt> shape is [1, 2, 3, 4]
|
|
--
|
|
-- # <tt>dims</tt> 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]]]]
|
|
--
|
|
-- # <tt>dims</tt> 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]]]]
|
|
--
|
|
-- # <tt>dims</tt> 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]]]] ```
|
|
reverse :: (TensorType t, OneOf '[Complex Double, Complex Float, Bool, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Bool -> Tensor Value t
|
|
|
|
-- | Returns a batched matrix tensor with new batched diagonal values.
|
|
--
|
|
-- Given <tt>input</tt> and <tt>diagonal</tt>, this operation returns a
|
|
-- tensor with the same shape and values as <tt>input</tt>, except for
|
|
-- the main diagonal of the innermost matrices. These will be overwritten
|
|
-- by the values in <tt>diagonal</tt>.
|
|
--
|
|
-- The output is computed as follows:
|
|
--
|
|
-- Assume <tt>input</tt> has `k+1` dimensions `[I, J, K, ..., M, N]` and
|
|
-- <tt>diagonal</tt> has <tt>k</tt> dimensions `[I, J, K, ..., min(M,
|
|
-- N)]`. Then the output is a tensor of rank `k+1` with dimensions `[I,
|
|
-- J, K, ..., M, N]` where:
|
|
--
|
|
-- <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>
|
|
matrixSetDiag :: (TensorType t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns a batched diagonal tensor with a given batched diagonal
|
|
-- values.
|
|
--
|
|
-- Given a <tt>diagonal</tt>, this operation returns a tensor with the
|
|
-- <tt>diagonal</tt> and everything else padded with zeros. The diagonal
|
|
-- is computed as follows:
|
|
--
|
|
-- Assume <tt>diagonal</tt> has <tt>k</tt> dimensions `[I, J, K, ...,
|
|
-- N]`, then the output is a tensor of rank `k+1` with dimensions [I, J,
|
|
-- K, ..., N, N]` where:
|
|
--
|
|
-- `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>diagonal</tt> is [[1, 2, 3, 4], [5, 6, 7, 8]]
|
|
--
|
|
-- and diagonal.shape = (2, 4)
|
|
--
|
|
-- tf.matrix_diag(diagonal) ==> [[[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]]]
|
|
--
|
|
-- which has shape (2, 4, 4) ```
|
|
matrixDiag :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns a diagonal tensor with a given diagonal values.
|
|
--
|
|
-- Given a <tt>diagonal</tt>, this operation returns a tensor with the
|
|
-- <tt>diagonal</tt> and everything else padded with zeros. The diagonal
|
|
-- is computed as follows:
|
|
--
|
|
-- Assume <tt>diagonal</tt> has dimensions [D1,..., Dk], then the output
|
|
-- is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
|
|
--
|
|
-- `output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0
|
|
-- everywhere else.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>diagonal</tt> is [1, 2, 3, 4] tf.diag(diagonal)
|
|
-- ==> [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]] ```
|
|
diag :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns immutable tensor from memory region.
|
|
--
|
|
-- The current implementation memmaps the tensor from a file.
|
|
immutableConst :: (TensorType dtype) => Shape -> Tensor Value dtype
|
|
|
|
-- | Concatenates tensors along one dimension.
|
|
concat :: (TensorType t) => Tensor v1 Int32 -> [Tensor v2 t] -> Tensor Value t
|
|
|
|
-- | Unpacks a given dimension of a rank-<tt>R</tt> tensor into
|
|
-- <tt>num</tt> rank-`(R-1)` tensors.
|
|
--
|
|
-- Unpacks <tt>num</tt> tensors from <a>value</a> by chipping it along
|
|
-- the <tt>axis</tt> dimension. For example, given a tensor of shape `(A,
|
|
-- B, C, D)`;
|
|
--
|
|
-- If `axis == 0` then the i'th tensor in <tt>output</tt> is the slice
|
|
-- `value[i, :, :, :]` and each tensor in <tt>output</tt> will have shape
|
|
-- `(B, C, D)`. (Note that the dimension unpacked along is gone, unlike
|
|
-- <a>split</a>).
|
|
--
|
|
-- If `axis == 1` then the i'th tensor in <tt>output</tt> is the slice
|
|
-- `value[:, i, :, :]` and each tensor in <tt>output</tt> will have shape
|
|
-- `(A, C, D)`. Etc.
|
|
--
|
|
-- This is the opposite of <a>pack</a>.
|
|
unpack :: (TensorType t) => Int64 -> Tensor v1 t -> [Tensor Value t]
|
|
|
|
-- | Output a fact about factorials.
|
|
fact :: Tensor Value ByteString
|
|
|
|
-- | Computes the absolute value of a tensor.
|
|
--
|
|
-- Given a tensor <tt>x</tt>, this operation returns a tensor containing
|
|
-- the absolute value of each element in <tt>x</tt>. For example, if x is
|
|
-- an input element and y is an output element, this operation computes
|
|
-- \(y = |x|\).
|
|
abs :: (TensorType t, OneOf '[Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes softmax activations.
|
|
--
|
|
-- For each batch <tt>i</tt> and class <tt>j</tt> we have
|
|
--
|
|
-- softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))
|
|
softmax :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Reverses specific dimensions of a tensor.
|
|
--
|
|
-- Given a <tt>tensor</tt>, and a <tt>int32</tt> tensor <tt>axis</tt>
|
|
-- representing the set of dimensions of <tt>tensor</tt> to reverse. This
|
|
-- operation reverses each dimension <tt>i</tt> for which there exists
|
|
-- <tt>j</tt> s.t. `axis[j] == i`.
|
|
--
|
|
-- <tt>tensor</tt> can have up to 8 dimensions. The number of dimensions
|
|
-- specified in <tt>axis</tt> may be 0 or more entries. If an index is
|
|
-- specified more than once, a InvalidArgument error is raised.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>t</tt> 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 <tt>t</tt> shape is [1, 2, 3, 4]
|
|
--
|
|
-- # <tt>dims</tt> is [3] or <tt>dims</tt> is -1 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]]]]
|
|
--
|
|
-- # <tt>dims</tt> is '[1]' (or <tt>dims</tt> is '[-3]') 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]]]]
|
|
--
|
|
-- # <tt>dims</tt> is '[2]' (or <tt>dims</tt> is '[-2]') 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]]]] ```
|
|
reverseV2 :: (TensorType tidx, OneOf '[Int32, Int64] tidx, TensorType t, OneOf '[Complex Double, Complex Float, Bool, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Return a tensor with the same shape and contents as the input tensor
|
|
-- or value.
|
|
identity :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Adds two <tt>SparseTensor</tt> objects to produce another
|
|
-- <tt>SparseTensor</tt>.
|
|
--
|
|
-- The input <tt>SparseTensor</tt> objects' indices are assumed ordered
|
|
-- in standard lexicographic order. If this is not the case, before this
|
|
-- step run <tt>SparseReorder</tt> to restore index ordering.
|
|
--
|
|
-- By default, if two values sum to zero at some index, the output
|
|
-- <tt>SparseTensor</tt> would still include that particular location in
|
|
-- its index, storing a zero in the corresponding value slot. To override
|
|
-- this, callers can specify <tt>thresh</tt>, indicating that if the sum
|
|
-- has a magnitude strictly smaller than <tt>thresh</tt>, 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.
|
|
--
|
|
-- In the following shapes, <tt>nnz</tt> is the count after taking
|
|
-- <tt>thresh</tt> into account.
|
|
sparseAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType treal, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] treal) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor v5 t -> Tensor v6 Int64 -> Tensor v7 treal -> (Tensor Value Int64, Tensor Value t, Tensor Value Int64)
|
|
|
|
-- | Update '*var' according to the centered RMSProp algorithm.
|
|
--
|
|
-- The centered RMSProp algorithm uses an estimate of the centered second
|
|
-- moment (i.e., the variance) for normalization, as opposed to regular
|
|
-- RMSProp, which uses the (uncentered) second moment. This often helps
|
|
-- with training, but is slightly more expensive in terms of computation
|
|
-- and memory.
|
|
--
|
|
-- Note that in dense implementation of this algorithm, mg, ms, and mom
|
|
-- will update even if the grad is zero, but in this sparse
|
|
-- implementation, mg, ms, and mom will not update in iterations during
|
|
-- which the grad is zero.
|
|
--
|
|
-- mean_square = decay * mean_square + (1-decay) * gradient ** 2
|
|
-- mean_grad = decay * mean_grad + (1-decay) * gradient Delta =
|
|
-- learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad **
|
|
-- 2)
|
|
--
|
|
-- ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum *
|
|
-- mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
|
|
sparseApplyCenteredRMSProp :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 t -> Tensor v10 tindices -> Build (Tensor Ref t)
|
|
|
|
-- | Add all input tensors element wise.
|
|
addN :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => [Tensor v1 t] -> Tensor Value t
|
|
|
|
-- | Computes offsets of concat inputs within its output.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>x</tt> is [2, 2, 7] # <tt>y</tt> is [2, 3, 7] #
|
|
-- <tt>z</tt> is [2, 5, 7] concat_offset(2, [x, y, z]) => [0, 0, 0],
|
|
-- [0, 2, 0], [0, 5, 0] ```
|
|
concatOffset :: Tensor v1 Int32 -> [Tensor v2 Int32] -> [Tensor Value Int32]
|
|
|
|
-- | Concatenates tensors along one dimension.
|
|
concatV2 :: (TensorType t, TensorType tidx, OneOf '[Int32, Int64] tidx) => [Tensor v1 t] -> Tensor v2 tidx -> Tensor Value t
|
|
|
|
-- | Returns a tensor of zeros with the same shape and type as x.
|
|
zerosLike :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Update '*var' according to the centered RMSProp algorithm.
|
|
--
|
|
-- The centered RMSProp algorithm uses an estimate of the centered second
|
|
-- moment (i.e., the variance) for normalization, as opposed to regular
|
|
-- RMSProp, which uses the (uncentered) second moment. This often helps
|
|
-- with training, but is slightly more expensive in terms of computation
|
|
-- and memory.
|
|
--
|
|
-- Note that in dense implementation of this algorithm, mg, ms, and mom
|
|
-- will update even if the grad is zero, but in this sparse
|
|
-- implementation, mg, ms, and mom will not update in iterations during
|
|
-- which the grad is zero.
|
|
--
|
|
-- mean_square = decay * mean_square + (1-decay) * gradient ** 2
|
|
-- mean_grad = decay * mean_grad + (1-decay) * gradient
|
|
--
|
|
-- Delta = learning_rate * gradient / sqrt(mean_square + epsilon -
|
|
-- mean_grad ** 2)
|
|
--
|
|
-- mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} +
|
|
-- (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad /
|
|
-- sqrt(ms - mg * mg + epsilon) var <- var - mom
|
|
applyCenteredRMSProp :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 t -> Build (Tensor Ref t)
|
|
|
|
-- | Update '*var' according to the RMSProp algorithm.
|
|
--
|
|
-- Note that in dense implementation of this algorithm, ms and mom will
|
|
-- update even if the grad is zero, but in this sparse implementation, ms
|
|
-- and mom will not update in iterations during which the grad is zero.
|
|
--
|
|
-- mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta =
|
|
-- learning_rate * gradient / sqrt(mean_square + epsilon)
|
|
--
|
|
-- ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum *
|
|
-- mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
|
|
applyRMSProp :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Build (Tensor Ref t)
|
|
|
|
-- | Adds a value to the current value of a variable.
|
|
--
|
|
-- Any ReadVariableOp which depends directly or indirectly on this assign
|
|
-- is guaranteed to see the incremented value or a subsequent newer one.
|
|
--
|
|
-- Outputs the incremented value, which can be used to totally order the
|
|
-- increments to this variable.
|
|
assignAddVariableOp :: (TensorType dtype) => ResourceHandle dtype -> Tensor v2 dtype -> Build (ControlNode)
|
|
|
|
-- | Update '*var' according to the Adam algorithm.
|
|
--
|
|
-- lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) m_t <-
|
|
-- beta1 * m_{t-1} + (1 - beta1) * g_t v_t <- beta2 * v_{t-1} + (1 -
|
|
-- beta2) * g_t * g_t variable <- variable - lr_t * m_t / (sqrt(v_t) +
|
|
-- epsilon)
|
|
applyAdam :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 t -> Tensor v10 t -> Build (Tensor Ref t)
|
|
|
|
-- | Extracts a glimpse from the input tensor.
|
|
--
|
|
-- Returns a set of windows called glimpses extracted at location
|
|
-- <tt>offsets</tt> from the input tensor. If the windows only partially
|
|
-- overlaps the inputs, the non overlapping areas will be filled with
|
|
-- random noise.
|
|
--
|
|
-- 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 <a>size</a> parameter.
|
|
--
|
|
-- The argument <tt>normalized</tt> and <tt>centered</tt> controls how
|
|
-- the windows are built:
|
|
--
|
|
-- <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>
|
|
extractGlimpse :: Tensor v1 Float -> Tensor v2 Int32 -> Tensor v3 Float -> Tensor Value Float
|
|
|
|
-- | Update relevant entries in '*var' and '*accum' according to the
|
|
-- momentum scheme.
|
|
--
|
|
-- Set use_nesterov = True if you want to use Nesterov momentum.
|
|
--
|
|
-- That is for rows we have grad for, we update var and accum as follows:
|
|
--
|
|
-- accum = accum * momentum + grad var -= lr * accum
|
|
sparseApplyMomentum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Build (Tensor Ref t)
|
|
|
|
-- | Update '*var' according to the momentum scheme. Set use_nesterov =
|
|
-- True if you
|
|
--
|
|
-- want to use Nesterov momentum.
|
|
--
|
|
-- accum = accum * momentum + grad var -= lr * accum
|
|
applyMomentum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Build (Tensor Ref t)
|
|
|
|
-- | A queue that produces elements in first-in first-out order.
|
|
fIFOQueue :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Update relevant entries in '*var' according to the Ftrl-proximal
|
|
-- scheme.
|
|
--
|
|
-- That is for rows we have grad for, we update var, accum and linear as
|
|
-- follows: accum_new = accum + grad * grad linear += grad +
|
|
-- (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0
|
|
-- / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 -
|
|
-- linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new
|
|
sparseApplyFtrl :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 t -> Build (Tensor Ref t)
|
|
|
|
-- | Update entries in '*var' and '*accum' according to the proximal
|
|
-- adagrad scheme.
|
|
sparseApplyAdagradDA :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 Int64 -> Build (Tensor Ref t)
|
|
|
|
-- | Returns x // y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>FloorDiv</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
floorDiv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Update '*var' according to the proximal adagrad scheme.
|
|
applyAdagradDA :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 Int64 -> Build (Tensor Ref t)
|
|
|
|
-- | Update '*var' according to the adagrad scheme.
|
|
--
|
|
-- accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
|
|
applyAdagrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Build (Tensor Ref t)
|
|
|
|
-- | Computes the gradient of the sigmoid of <tt>x</tt> wrt its input.
|
|
--
|
|
-- Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
|
|
-- <tt>dy</tt> is the corresponding input gradient.
|
|
sigmoidGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Update '*var' according to the adadelta scheme.
|
|
--
|
|
-- 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;
|
|
applyAdadelta :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Build (Tensor Ref t)
|
|
|
|
-- | Sparse update '*var' as FOBOS algorithm with fixed learning rate.
|
|
--
|
|
-- 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}
|
|
sparseApplyProximalGradientDescent :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 tindices -> Build (Tensor Ref t)
|
|
|
|
-- | Update '*var' as FOBOS algorithm with fixed learning rate.
|
|
--
|
|
-- prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) *
|
|
-- max{|prox_v|-alpha*l1,0}
|
|
applyProximalGradientDescent :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Build (Tensor Ref t)
|
|
|
|
-- | Solves systems of linear equations.
|
|
--
|
|
-- <tt>Matrix</tt> is a tensor of shape `[..., M, M]` whose inner-most 2
|
|
-- dimensions form square matrices. <tt>Rhs</tt> is a tensor of shape
|
|
-- `[..., M, K]`. The <tt>output</tt> is a tensor shape `[..., M, K]`. If
|
|
-- <tt>adjoint</tt> is <a>False</a> then each output matrix satisfies
|
|
-- `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. If
|
|
-- <tt>adjoint</tt> is <a>True</a> then each output matrix satisfies
|
|
-- `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`.
|
|
matrixSolve :: (TensorType t, OneOf '[Complex Double, Complex Float, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Sparse update entries in '*var' and '*accum' according to FOBOS
|
|
-- algorithm.
|
|
--
|
|
-- 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}
|
|
sparseApplyProximalAdagrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 tindices -> Build (Tensor Ref t)
|
|
|
|
-- | Update '*var' by subtracting <tt>alpha</tt> * <tt>delta</tt> from it.
|
|
applyGradientDescent :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor v2 t -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Batch normalization.
|
|
--
|
|
-- This op is deprecated. Prefer `tf.nn.batch_normalization`.
|
|
batchNormWithGlobalNormalization :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Bool -> Float -> Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor Value t
|
|
|
|
-- | Encode strings into web-safe base64 format.
|
|
--
|
|
-- Refer to the following article for more information on base64 format:
|
|
-- en.wikipedia.org<i>wiki</i>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.
|
|
--
|
|
-- Web-safe means that the encoder uses - and _ instead of + and /.
|
|
encodeBase64 :: Tensor v1 ByteString -> Tensor Value ByteString
|
|
|
|
-- | Joins the strings in the given list of string tensors into one tensor;
|
|
--
|
|
-- with the given separator (default is an empty separator).
|
|
stringJoin :: [Tensor v1 ByteString] -> Tensor Value ByteString
|
|
|
|
-- | Computes the gradient of the crop_and_resize op wrt the input image
|
|
-- tensor.
|
|
cropAndResizeGradImage :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor Value t
|
|
|
|
-- | Computes hyperbolic tangent of <tt>x</tt> element-wise.
|
|
tanh :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Converts each entry in the given tensor to strings. Supports many
|
|
-- numeric
|
|
--
|
|
-- types and boolean.
|
|
asString :: (TensorType t, OneOf '[Complex Float, Bool, Int32, Int64, Int8, Double, Float] t) => Tensor v1 t -> Tensor Value ByteString
|
|
|
|
-- | Compute the inverse 2-dimensional discrete Fourier Transform over the
|
|
-- inner-most
|
|
--
|
|
-- 2 dimensions of <tt>input</tt>.
|
|
iFFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Concatenates a list of <tt>SparseTensor</tt> along the specified
|
|
-- dimension.
|
|
--
|
|
-- Concatenation is with respect to the dense versions of these sparse
|
|
-- tensors. It is assumed that each input is a <tt>SparseTensor</tt>
|
|
-- whose elements are ordered along increasing dimension number.
|
|
--
|
|
-- All inputs' shapes must match, except for the concat dimension. The
|
|
-- <tt>indices</tt>, <tt>values</tt>, and <tt>shapes</tt> lists must have
|
|
-- the same length.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- The output elements will be resorted to preserve the sort order along
|
|
-- increasing dimension number.
|
|
--
|
|
-- This op runs in `O(M log M)` time, where <tt>M</tt> 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.
|
|
--
|
|
-- For example, if `concat_dim = 1` and the inputs are
|
|
--
|
|
-- sp_inputs[0]: shape = [2, 3] [0, 2]: "a" [1, 0]: "b" [1, 1]: "c"
|
|
--
|
|
-- sp_inputs[1]: shape = [2, 4] [0, 1]: "d" [0, 2]: "e"
|
|
--
|
|
-- then the output will be
|
|
--
|
|
-- shape = [2, 7] [0, 2]: "a" [0, 4]: "d" [0, 5]: "e" [1, 0]: "b" [1, 1]:
|
|
-- "c"
|
|
--
|
|
-- Graphically this is equivalent to doing
|
|
--
|
|
-- <ul>
|
|
-- <li><i> a</i> concat [ d e ] = [ a d e ]</li>
|
|
-- <li><i>b c </i> [ ] [b c ]</li>
|
|
-- </ul>
|
|
sparseConcat :: (TensorType t) => Int64 -> [Tensor v1 Int64] -> [Tensor v2 t] -> [Tensor v3 Int64] -> (Tensor Value Int64, Tensor Value t, Tensor Value Int64)
|
|
|
|
-- | Generate a glob pattern matching all sharded file names.
|
|
shardedFilespec :: Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor Value ByteString
|
|
|
|
-- | Shuffle dimensions of x according to a permutation.
|
|
--
|
|
-- The output <tt>y</tt> has the same rank as <tt>x</tt>. The shapes of
|
|
-- <tt>x</tt> and <tt>y</tt> satisfy: `y.shape[i] == x.shape[perm[i]] for
|
|
-- i in [0, 1, ..., rank(x) - 1]`
|
|
transpose :: (TensorType t, TensorType tperm, OneOf '[Int32, Int64] tperm) => Tensor v1 t -> Tensor v2 tperm -> Tensor Value t
|
|
|
|
-- | Joins a string Tensor across the given dimensions.
|
|
--
|
|
-- 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
|
|
-- <tt>reduction_indices</tt> joins all strings in linear index order and
|
|
-- outputs a scalar string.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <tt>a</tt> 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"] ```
|
|
reduceJoin :: Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor Value ByteString
|
|
|
|
-- | Converts each string in the input Tensor to its hash mod by a number
|
|
-- of buckets.
|
|
--
|
|
-- The hash function is deterministic on the content of the string within
|
|
-- the process.
|
|
--
|
|
-- Note that the hash function may change from time to time. This
|
|
-- functionality will be deprecated and it's recommended to use
|
|
-- `tf.string_to_hash_bucket_fast()` or
|
|
-- `tf.string_to_hash_bucket_strong()`.
|
|
stringToHashBucket :: Int64 -> Tensor v1 ByteString -> Tensor Value Int64
|
|
|
|
-- | Draws samples from a multinomial distribution.
|
|
multinomial :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Build (Tensor Value Int64)
|
|
|
|
-- | Converts each string in the input Tensor to its hash mod by a number
|
|
-- of buckets.
|
|
--
|
|
-- The hash function is deterministic on the content of the string within
|
|
-- the process. The hash function is a keyed hash function, where
|
|
-- attribute <tt>key</tt> defines the key of the hash function.
|
|
-- <tt>key</tt> is an array of 2 elements.
|
|
--
|
|
-- 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`.
|
|
stringToHashBucketStrong :: Int64 -> Tensor v1 ByteString -> Tensor Value Int64
|
|
|
|
-- | Applies sparse <tt>updates</tt> to individual values or slices within
|
|
-- a given
|
|
--
|
|
-- variable according to <tt>indices</tt>.
|
|
--
|
|
-- <tt>ref</tt> is a <a>Tensor</a> with rank <tt>P</tt> and
|
|
-- <tt>indices</tt> is a <a>Tensor</a> of rank <tt>Q</tt>.
|
|
--
|
|
-- <tt>indices</tt> must be integer tensor, containing indices into
|
|
-- <tt>ref</tt>. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 <
|
|
-- K <= P`.
|
|
--
|
|
-- The innermost dimension of <tt>indices</tt> (with length <tt>K</tt>)
|
|
-- corresponds to indices into elements (if `K = P`) or slices (if `K
|
|
-- < P`) along the <tt>K</tt>th dimension of <tt>ref</tt>.
|
|
--
|
|
-- <tt>updates</tt> is <a>Tensor</a> of rank `Q-1+P-K` with shape:
|
|
--
|
|
-- ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ```
|
|
--
|
|
-- For example, say we want to update 4 scattered elements to a rank-1
|
|
-- tensor to 8 elements. In Python, that update would look like this:
|
|
--
|
|
-- ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices =
|
|
-- tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11,
|
|
-- 12]) update = tf.scatter_nd_update(ref, indices, updates) with
|
|
-- tf.Session() as sess: print sess.run(update)
|
|
--
|
|
-- The resulting update to ref would look like this:
|
|
--
|
|
-- <ul>
|
|
-- <li><i>1, 11, 3, 10, 9, 6, 7, 12</i></li>
|
|
-- </ul>
|
|
--
|
|
-- See <a>tf.scatter_nd</a> for more details about how to make updates to
|
|
-- slices.
|
|
scatterNdUpdate :: (TensorType t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Compute gradients for a FakeQuantWithMinMaxVars operation.
|
|
fakeQuantWithMinMaxVarsGradient :: Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 Float -> (Tensor Value Float, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Returns the size of a tensor.
|
|
--
|
|
-- This operation returns an integer representing the number of elements
|
|
-- in <tt>input</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3],
|
|
-- [4, 4, 4]]]] size(t) ==> 12 ```
|
|
size :: (TensorType t, TensorType out_type, OneOf '[Int32, Int64] out_type) => Tensor v1 t -> Tensor Value out_type
|
|
|
|
-- | Divides a variable reference by sparse updates.
|
|
--
|
|
-- This operation computes
|
|
--
|
|
-- # Scalar indices ref[indices, ...] /= updates[...]
|
|
--
|
|
-- # Vector indices (for each i) ref[indices[i], ...] /= updates[i, ...]
|
|
--
|
|
-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
|
|
-- /= updates[i, ..., j, ...]
|
|
--
|
|
-- This operation outputs <tt>ref</tt> after the update is done. This
|
|
-- makes it easier to chain operations that need to use the reset value.
|
|
--
|
|
-- Duplicate entries are handled correctly: if multiple <tt>indices</tt>
|
|
-- reference the same location, their contributions divide.
|
|
--
|
|
-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
|
|
scatterDiv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Multiplies sparse updates into a variable reference.
|
|
--
|
|
-- This operation computes
|
|
--
|
|
-- # Scalar indices ref[indices, ...] *= updates[...]
|
|
--
|
|
-- # Vector indices (for each i) ref[indices[i], ...] *= updates[i, ...]
|
|
--
|
|
-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
|
|
-- *= updates[i, ..., j, ...]
|
|
--
|
|
-- This operation outputs <tt>ref</tt> after the update is done. This
|
|
-- makes it easier to chain operations that need to use the reset value.
|
|
--
|
|
-- Duplicate entries are handled correctly: if multiple <tt>indices</tt>
|
|
-- reference the same location, their contributions multiply.
|
|
--
|
|
-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
|
|
scatterMul :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Copy Host Op.
|
|
--
|
|
-- Performs CPU-to-CPU deep-copying of tensor.
|
|
--
|
|
-- Unlike the Copy Op, this op has HostMemory constraint on its input or
|
|
-- output.
|
|
copyHost :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | A Reader that outputs the entire contents of a file as a value.
|
|
--
|
|
-- To use, enqueue filenames in a Queue. The output of ReaderRead will be
|
|
-- a filename (key) and the contents of that file (value).
|
|
wholeFileReader :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Read <tt>SparseTensors</tt> from a <tt>SparseTensorsMap</tt> and
|
|
-- concatenate them.
|
|
--
|
|
-- The input <tt>sparse_handles</tt> must be an <tt>int64</tt> matrix of
|
|
-- shape `[N, 1]` where <tt>N</tt> is the minibatch size and the rows
|
|
-- correspond to the output handles of <tt>AddSparseToTensorsMap</tt> or
|
|
-- <tt>AddManySparseToTensorsMap</tt>. The ranks of the original
|
|
-- <tt>SparseTensor</tt> objects that went into the given input ops must
|
|
-- all match. When the final <tt>SparseTensor</tt> is created, it has
|
|
-- rank one higher than the ranks of the incoming <tt>SparseTensor</tt>
|
|
-- objects (they have been concatenated along a new row dimension on the
|
|
-- left).
|
|
--
|
|
-- The output <tt>SparseTensor</tt> object's shape values for all
|
|
-- dimensions but the first are the max across the input
|
|
-- <tt>SparseTensor</tt> objects' shape values for the corresponding
|
|
-- dimensions. Its first shape value is <tt>N</tt>, the minibatch size.
|
|
--
|
|
-- The input <tt>SparseTensor</tt> objects' indices are assumed ordered
|
|
-- in standard lexicographic order. If this is not the case, after this
|
|
-- step run <tt>SparseReorder</tt> to restore index ordering.
|
|
--
|
|
-- For example, if the handles represent an input, which is a `[2, 3]`
|
|
-- matrix representing two original <tt>SparseTensor</tt> objects:
|
|
--
|
|
-- ``` index = [ 0] [10] [20] values = [1, 2, 3] shape = [50] ```
|
|
--
|
|
-- and
|
|
--
|
|
-- ``` index = [ 2] [10] values = [4, 5] shape = [30] ```
|
|
--
|
|
-- then the final <tt>SparseTensor</tt> will be:
|
|
--
|
|
-- ``` index = [0 0] [0 10] [0 20] [1 2] [1 10] values = [1, 2, 3, 4, 5]
|
|
-- shape = [2 50] ```
|
|
takeManySparseFromTensorsMap :: (TensorType dtype) => Tensor v1 Int64 -> Build ((Tensor Value Int64, Tensor Value dtype, Tensor Value Int64))
|
|
|
|
-- | Destroys the temporary variable and returns its final value.
|
|
--
|
|
-- Sets output to the value of the Tensor pointed to by <tt>ref</tt>,
|
|
-- then destroys the temporary variable called <tt>var_name</tt>. All
|
|
-- other uses of <tt>ref</tt> *must* have executed before this op. This
|
|
-- is typically achieved by chaining the ref through each assign op, or
|
|
-- by using control dependencies.
|
|
--
|
|
-- Outputs the final value of the tensor pointed to by <tt>ref</tt>.
|
|
destroyTemporaryVariable :: (TensorType t) => Tensor Ref t -> Build (Tensor Value t)
|
|
|
|
-- | Update <tt>ref</tt> by subtracting <a>value</a> from it.
|
|
--
|
|
-- This operation outputs "ref" after the update is done. This makes it
|
|
-- easier to chain operations that need to use the reset value.
|
|
assignSub :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor v2 t -> Build (Tensor Ref t)
|
|
|
|
-- | JPEG-encode an image.
|
|
--
|
|
-- <tt>image</tt> is a 3-D uint8 Tensor of shape `[height, width,
|
|
-- channels]`.
|
|
--
|
|
-- The attr <tt>format</tt> can be used to override the color format of
|
|
-- the encoded output. Values can be:
|
|
--
|
|
-- <ul>
|
|
-- <li>`''`: Use a default format based on the number of channels in the
|
|
-- image.</li>
|
|
-- <li><tt>grayscale</tt>: Output a grayscale JPEG image. The
|
|
-- <tt>channels</tt> dimension of <tt>image</tt> must be 1.</li>
|
|
-- <li><tt>rgb</tt>: Output an RGB JPEG image. The <tt>channels</tt>
|
|
-- dimension of <tt>image</tt> must be 3.</li>
|
|
-- </ul>
|
|
--
|
|
-- If <tt>format</tt> is not specified or is the empty string, a default
|
|
-- format is picked in function of the number of channels in
|
|
-- <tt>image</tt>:
|
|
--
|
|
-- <ul>
|
|
-- <li>1: Output a grayscale image.</li>
|
|
-- <li>3: Output an RGB image.</li>
|
|
-- </ul>
|
|
encodeJpeg :: Tensor v1 Word8 -> Tensor Value ByteString
|
|
|
|
-- | Returns a tensor that may be mutated, but only persists within a
|
|
-- single step.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- It is the caller's responsibility to ensure that <tt>ref</tt> is
|
|
-- eventually passed to a matching <tt>DestroyTemporaryVariable</tt> op
|
|
-- after all other uses have completed.
|
|
--
|
|
-- Outputs a ref to the tensor state so it may be read or modified.
|
|
--
|
|
-- 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)
|
|
temporaryVariable :: (TensorType dtype) => Shape -> Build (Tensor Ref dtype)
|
|
|
|
-- | Checks whether a tensor has been initialized.
|
|
--
|
|
-- Outputs boolean scalar indicating whether the tensor has been
|
|
-- initialized.
|
|
isVariableInitialized :: (TensorType dtype) => Tensor Ref dtype -> Build (Tensor Value Bool)
|
|
|
|
-- | Holds state in the form of a tensor that persists across steps.
|
|
--
|
|
-- Outputs a ref to the tensor state so it may be read or modified.
|
|
-- TODO(zhifengc/mrry): Adds a pointer to a more detail document about
|
|
-- sharing states in tensorflow.
|
|
variable :: (TensorType dtype) => Shape -> Build (Tensor Ref dtype)
|
|
|
|
-- | Returns the element-wise min of two SparseTensors.
|
|
--
|
|
-- Assumes the two SparseTensors have the same shape, i.e., no
|
|
-- broadcasting.
|
|
sparseSparseMinimum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor v5 t -> Tensor v6 Int64 -> (Tensor Value Int64, Tensor Value t)
|
|
|
|
-- | Compute the regularized incomplete beta integral \(I_x(a, b)\).
|
|
--
|
|
-- The regularized incomplete beta integral is defined as:
|
|
--
|
|
-- ``` I_x(a, b) = frac{B(x; a, b)}{B(a, b)} ``` where
|
|
--
|
|
-- ``` B(x; a, b) = int_0^x t^{a-1} (1 - t)^{b-1} dt ```
|
|
--
|
|
-- is the incomplete beta function and \(B(a, b)\) is the *complete* beta
|
|
-- function.
|
|
betainc :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | Update <tt>ref</tt> by assigning <a>value</a> to it.
|
|
--
|
|
-- This operation outputs "ref" after the assignment is done. This makes
|
|
-- it easier to chain operations that need to use the reset value.
|
|
assign :: (TensorType t) => Tensor Ref t -> Tensor v2 t -> Build (Tensor Ref t)
|
|
|
|
-- | Applies softmax to a batched N-D <tt>SparseTensor</tt>.
|
|
--
|
|
-- The inputs represent an N-D SparseTensor with logical shape `[..., B,
|
|
-- C]` (where `N >= 2`), and with indices sorted in the canonical
|
|
-- lexicographic order.
|
|
--
|
|
-- 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:
|
|
--
|
|
-- <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>
|
|
--
|
|
-- Hence, the <tt>SparseTensor</tt> result has exactly the same non-zero
|
|
-- indices and shape.
|
|
sparseSoftmax :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor Value t
|
|
|
|
-- | Adds up a SparseTensor and a dense Tensor, using these special rules:
|
|
--
|
|
-- <ol>
|
|
-- <li>Broadcasts the dense side to have the same shape as the sparse
|
|
-- side, if eligible;</li>
|
|
-- <li>Then, only the dense values pointed to by the indices of the
|
|
-- SparseTensor participate in the cwise addition.</li>
|
|
-- </ol>
|
|
--
|
|
-- 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.
|
|
sparseDenseCwiseAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Returns the truth value of NOT x element-wise.
|
|
logicalNot :: Tensor v1 Bool -> Tensor Value Bool
|
|
|
|
-- | Computes the number of elements in the given queue.
|
|
queueSize :: Tensor Ref ByteString -> Build (Tensor Value Int32)
|
|
|
|
-- | Update relevant entries in '*var' and '*accum' according to the
|
|
-- adagrad scheme.
|
|
--
|
|
-- That is for rows we have grad for, we update var and accum as follows:
|
|
-- accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
|
|
sparseApplyAdagrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Build (Tensor Ref t)
|
|
|
|
-- | Store the input tensor in the state of the current session.
|
|
getSessionHandle :: (TensorType t) => Tensor v1 t -> Tensor Value ByteString
|
|
|
|
-- | Component-wise multiplies a SparseTensor by a dense Tensor.
|
|
--
|
|
-- 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).
|
|
--
|
|
-- <ul>
|
|
-- <li>Limitation*: this Op only broadcasts the dense side to the sparse
|
|
-- side, but not the other direction.</li>
|
|
-- </ul>
|
|
sparseDenseCwiseMul :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Adds up a <tt>SparseTensor</tt> and a dense <a>Tensor</a>, producing a
|
|
-- dense <a>Tensor</a>.
|
|
--
|
|
-- This Op does not require <tt>a_indices</tt> be sorted in standard
|
|
-- lexicographic order.
|
|
sparseTensorDenseAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 tindices -> Tensor v2 t -> Tensor v3 tindices -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Get the value of the tensor specified by its handle.
|
|
getSessionTensor :: (TensorType dtype) => Tensor v1 ByteString -> Tensor Value dtype
|
|
|
|
-- | Reorders a SparseTensor into the canonical, row-major ordering.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- Reordering does not affect the shape of the SparseTensor.
|
|
--
|
|
-- If the tensor has rank <tt>R</tt> and <tt>N</tt> non-empty values,
|
|
-- <tt>input_indices</tt> has shape `[N, R]`, input_values has length
|
|
-- <tt>N</tt>, and input_shape has length <tt>R</tt>.
|
|
sparseReorder :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> (Tensor Value Int64, Tensor Value t)
|
|
|
|
-- | Split a <tt>SparseTensor</tt> into <tt>num_split</tt> tensors along
|
|
-- one dimension.
|
|
--
|
|
-- If the `shape[split_dim]` is not an integer multiple of
|
|
-- <tt>num_split</tt>. 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
|
|
--
|
|
-- input_tensor = shape = [2, 7] [ a d e ] [b c ]
|
|
--
|
|
-- Graphically the output tensors are:
|
|
--
|
|
-- output_tensor[0] = shape = [2, 4] [ a ] [b c ]
|
|
--
|
|
-- output_tensor[1] = shape = [2, 3] [ d e ] [ ]
|
|
sparseSplit :: (TensorType t) => Int64 -> Tensor v1 Int64 -> Tensor v2 Int64 -> Tensor v3 t -> Tensor v4 Int64 -> ([Tensor Value Int64], [Tensor Value t], [Tensor Value Int64])
|
|
|
|
-- | Pads a tensor with zeros.
|
|
--
|
|
-- This operation pads a <tt>input</tt> with zeros according to the
|
|
-- <tt>paddings</tt> you specify. <tt>paddings</tt> is an integer tensor
|
|
-- with shape `[Dn, 2]`, where n is the rank of <tt>input</tt>. For each
|
|
-- dimension D of <tt>input</tt>, `paddings[D, 0]` indicates how many
|
|
-- zeros to add before the contents of <tt>input</tt> in that dimension,
|
|
-- and `paddings[D, 1]` indicates how many zeros to add after the
|
|
-- contents of <tt>input</tt> in that dimension.
|
|
--
|
|
-- The padded size of each dimension D of the output is:
|
|
--
|
|
-- `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[1, 1], [2, 2]] # <tt>paddings</tt> is
|
|
-- [[1, 1], [2, 2]] # rank of <tt>t</tt> is 2 pad(t, paddings) ==>
|
|
-- [[0, 0, 0, 0, 0, 0] [0, 0, 1, 1, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0,
|
|
-- 0, 0]] ```
|
|
pad :: (TensorType t, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Tensor v1 t -> Tensor v2 tpaddings -> Tensor Value t
|
|
|
|
-- | Converts a sparse representation into a dense tensor.
|
|
--
|
|
-- Builds an array <tt>dense</tt> with shape <tt>output_shape</tt> such
|
|
-- that
|
|
--
|
|
-- ```prettyprint # If sparse_indices is scalar dense[i] = (i ==
|
|
-- sparse_indices ? sparse_values : default_value)
|
|
--
|
|
-- # If sparse_indices is a vector, then for each i
|
|
-- dense[sparse_indices[i]] = sparse_values[i]
|
|
--
|
|
-- # 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] ```
|
|
--
|
|
-- All other values in <tt>dense</tt> are set to <tt>default_value</tt>.
|
|
-- If <tt>sparse_values</tt> is a scalar, all sparse indices are set to
|
|
-- this single value.
|
|
--
|
|
-- Indices should be sorted in lexicographic order, and indices must not
|
|
-- contain any repeats. If <tt>validate_indices</tt> is true, these
|
|
-- properties are checked during execution.
|
|
sparseToDense :: (TensorType t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 tindices -> Tensor v2 tindices -> Tensor v3 t -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Multiply SparseTensor (of rank 2) <a>A</a> by dense matrix <a>B</a>.
|
|
--
|
|
-- No validity checking is performed on the indices of A. However, the
|
|
-- following input format is recommended for optimal behavior:
|
|
--
|
|
-- if adjoint_a == false: A should be sorted in lexicographically
|
|
-- increasing order. Use SparseReorder if you're not sure. if adjoint_a
|
|
-- == true: A should be sorted in order of increasing dimension 1 (i.e.,
|
|
-- "column major" order instead of "row major" order).
|
|
sparseTensorDenseMatMul :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Gradient op for <tt>MirrorPad</tt> op. This op folds a mirror-padded
|
|
-- tensor.
|
|
--
|
|
-- This operation folds the padded areas of <tt>input</tt> by
|
|
-- <tt>MirrorPad</tt> according to the <tt>paddings</tt> you specify.
|
|
-- <tt>paddings</tt> must be the same as <tt>paddings</tt> argument given
|
|
-- to the corresponding <tt>MirrorPad</tt> op.
|
|
--
|
|
-- The folded size of each dimension D of the output is:
|
|
--
|
|
-- `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. #
|
|
-- <tt>paddings</tt> is [[0, 1]], [0, 1]]. # <tt>mode</tt> is SYMMETRIC.
|
|
-- # rank of <tt>t</tt> is 2. pad(t, paddings) ==> [[ 1, 5] [11, 28]]
|
|
-- ```
|
|
mirrorPadGrad :: (TensorType t, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Tensor v1 t -> Tensor v2 tpaddings -> Tensor Value t
|
|
|
|
-- | Randomly shuffles a tensor along its first dimension.
|
|
--
|
|
-- 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:
|
|
--
|
|
-- ```prettyprint [[1, 2], [[5, 6], [3, 4], ==> [1, 2], [5, 6]] [3,
|
|
-- 4]] ```
|
|
randomShuffle :: (TensorType t) => Tensor v1 t -> Build (Tensor Value t)
|
|
|
|
-- | Selects elements from <tt>t</tt> or <tt>e</tt>, depending on
|
|
-- <tt>condition</tt>.
|
|
--
|
|
-- The <tt>t</tt>, and <tt>e</tt> tensors must all have the same shape,
|
|
-- and the output will also have that shape.
|
|
--
|
|
-- The <tt>condition</tt> tensor must be a scalar if <tt>t</tt> and
|
|
-- <tt>e</tt> are scalars. If <tt>t</tt> and <tt>e</tt> are vectors or
|
|
-- higher rank, then <tt>condition</tt> must be either a scalar, a vector
|
|
-- with size matching the first dimension of <tt>t</tt>, or must have the
|
|
-- same shape as <tt>t</tt>.
|
|
--
|
|
-- The <tt>condition</tt> 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 <tt>t</tt> (if true) or <tt>e</tt> (if
|
|
-- false).
|
|
--
|
|
-- If <tt>condition</tt> is a vector and <tt>t</tt> and <tt>e</tt> are
|
|
-- higher rank matrices, then it chooses which row (outer dimension) to
|
|
-- copy from <tt>t</tt> and <tt>e</tt>. If <tt>condition</tt> has the
|
|
-- same shape as <tt>t</tt> and <tt>e</tt>, then it chooses which element
|
|
-- to copy from <tt>t</tt> and <tt>e</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>condition</tt> tensor is [[True, False] # [False,
|
|
-- True]] # <tt>t</tt> is [[1, 2], # [3, 4]] # <tt>e</tt> is [[5, 6], #
|
|
-- [7, 8]] select(condition, t, e) ==> [[1, 6], [7, 4]]
|
|
--
|
|
-- # <tt>condition</tt> tensor is [True, False] # <tt>t</tt> is [[1, 2],
|
|
-- # [3, 4]] # <tt>e</tt> is [[5, 6], # [7, 8]] select(condition, t, e)
|
|
-- ==> [[1, 2], [7, 8]]
|
|
--
|
|
-- ```
|
|
select :: (TensorType t) => Tensor v1 Bool -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | The gradient operator for the SparseAdd op.
|
|
--
|
|
-- The SparseAdd op calculates A + B, where A, B, and the sum are all
|
|
-- represented as <tt>SparseTensor</tt> 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.
|
|
sparseAddGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor v4 Int64 -> (Tensor Value t, Tensor Value t)
|
|
|
|
-- | Computes fingerprints of the input strings.
|
|
sdcaFprint :: Tensor v1 ByteString -> Tensor Value Int64
|
|
tensorArrayUnpack :: (TensorType t) => Tensor Ref ByteString -> Tensor v2 t -> Tensor v3 Float -> Build (Tensor Value Float)
|
|
|
|
-- | Produces the average pool of the input tensor for quantized types.
|
|
quantizedAvgPool :: (TensorType t, OneOf '[Int16, Int32, Word16, Word8] t) => Tensor v1 t -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value t, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Adjust the contrast of one or more images.
|
|
--
|
|
-- <tt>images</tt> 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].`
|
|
--
|
|
-- Contrast is adjusted independently for each channel of each image.
|
|
--
|
|
-- 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`.
|
|
adjustContrastv2 :: Tensor v1 Float -> Tensor v2 Float -> Tensor Value Float
|
|
|
|
-- | Gather slices from the variable pointed to by <tt>resource</tt>
|
|
-- according to <tt>indices</tt>.
|
|
--
|
|
-- <tt>indices</tt> 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:
|
|
--
|
|
-- ```python # Scalar indices output[:, ..., :] = params[indices, :, ...
|
|
-- :]
|
|
--
|
|
-- # Vector indices output[i, :, ..., :] = params[indices[i], :, ... :]
|
|
--
|
|
-- # Higher rank indices output[i, ..., j, :, ... :] = params[indices[i,
|
|
-- ..., j], :, ..., :] ```
|
|
resourceGather :: (TensorType dtype, TensorType tindices, OneOf '[Int32, Int64] tindices) => ResourceHandle dtype -> Tensor v2 tindices -> Build (Tensor Value dtype)
|
|
|
|
-- | Merges summaries.
|
|
--
|
|
-- This op creates a <a>`Summary`</a> protocol buffer that contains the
|
|
-- union of all the values in the input summaries.
|
|
--
|
|
-- When the Op is run, it reports an <tt>InvalidArgument</tt> error if
|
|
-- multiple values in the summaries to merge use the same tag.
|
|
mergeSummary :: [Tensor v1 ByteString] -> Tensor Value ByteString
|
|
|
|
-- | Serialize a <tt>SparseTensor</tt> into a string 3-vector (1-D
|
|
-- <a>Tensor</a>) object.
|
|
serializeSparse :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor Value ByteString
|
|
|
|
-- | Training via negative sampling.
|
|
negTrain :: Int64 -> Tensor Ref Float -> Tensor Ref Float -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor v5 Float -> Build (ControlNode)
|
|
|
|
-- | Delete the TensorArray from its resource container. This enables
|
|
--
|
|
-- the user to close and release the resource in the middle of a
|
|
-- step/run.
|
|
tensorArrayCloseV2 :: Tensor v1 ByteString -> ControlNode
|
|
|
|
-- | Generates labels for candidate sampling with a learned unigram
|
|
-- distribution.
|
|
--
|
|
-- See explanations of candidate sampling and the data formats at
|
|
-- go/candidate-sampling.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
threadUnsafeUnigramCandidateSampler :: Int64 -> Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Converts each string in the input Tensor to the specified numeric
|
|
-- type.
|
|
--
|
|
-- (Note that int32 overflow results in an error while float overflow
|
|
-- results in a rounded value.)
|
|
stringToNumber :: (TensorType out_type, OneOf '[Int32, Float] out_type) => Tensor v1 ByteString -> Tensor Value out_type
|
|
|
|
-- | Performs beam search decoding on the logits given in input.
|
|
--
|
|
-- 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.
|
|
cTCBeamSearchDecoder :: Int64 -> Int64 -> Tensor v1 Float -> Tensor v2 Int32 -> ([Tensor Value Int64], [Tensor Value Int64], [Tensor Value Int64], Tensor Value Float)
|
|
|
|
-- | Transforms a serialized tensorflow.TensorProto proto into a Tensor.
|
|
parseTensor :: (TensorType out_type) => Tensor v1 ByteString -> Tensor Value out_type
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with images.
|
|
--
|
|
-- The summary has up to <tt>max_images</tt> summary values containing
|
|
-- images. The images are built from <tt>tensor</tt> which must be 4-D
|
|
-- with shape `[batch_size, height, width, channels]` and where
|
|
-- <tt>channels</tt> can be:
|
|
--
|
|
-- <ul>
|
|
-- <li>1: <tt>tensor</tt> is interpreted as Grayscale.</li>
|
|
-- <li>3: <tt>tensor</tt> is interpreted as RGB.</li>
|
|
-- <li>4: <tt>tensor</tt> is interpreted as RGBA.</li>
|
|
-- </ul>
|
|
--
|
|
-- 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]`. <tt>uint8</tt> values are unchanged. The op uses
|
|
-- two different normalization algorithms:
|
|
--
|
|
-- <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>
|
|
--
|
|
-- The <tt>tag</tt> argument is a scalar <a>Tensor</a> of type
|
|
-- <tt>string</tt>. It is used to build the <tt>tag</tt> of the summary
|
|
-- values:
|
|
--
|
|
-- <ul>
|
|
-- <li>If <tt>max_images</tt> is 1, the summary value tag is
|
|
-- '*tag*/image'.</li>
|
|
-- <li>If <tt>max_images</tt> is greater than 1, the summary value tags
|
|
-- are generated sequentially as '*tag*/image/0', '*tag*/image/1',
|
|
-- etc.</li>
|
|
-- </ul>
|
|
--
|
|
-- The <tt>bad_color</tt> argument is the color to use in the generated
|
|
-- images for non-finite input values. It is a <tt>unit8</tt> 1-D tensor
|
|
-- of length <tt>channels</tt>. 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.
|
|
imageSummary :: (TensorType t, OneOf '[Word16, Word8, Float] t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor Value ByteString
|
|
|
|
-- | Returns x / y element-wise for integer types.
|
|
--
|
|
-- Truncation designates that negative numbers will round fractional
|
|
-- quantities toward zero. I.e. -7 / 5 = 1. This matches C semantics but
|
|
-- it is different than Python semantics. See <tt>FloorDiv</tt> for a
|
|
-- division function that matches Python Semantics.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>TruncateDiv</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
truncateDiv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes the Cholesky decomposition of one or more square matrices.
|
|
--
|
|
-- 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 `[..., :, :]`.
|
|
cholesky :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
batchMatrixSolveLs :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 Double -> Tensor Value t
|
|
|
|
-- | Outputs all keys and values in the table.
|
|
lookupTableExport :: (TensorType tkeys, TensorType tvalues) => Tensor Ref ByteString -> Build ((Tensor Value tkeys, Tensor Value tvalues))
|
|
batchSvd :: (TensorType t, OneOf '[Complex Double, Complex Float, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t, Tensor Value t)
|
|
|
|
-- | Resize <tt>images</tt> to <a>size</a> using bicubic interpolation.
|
|
--
|
|
-- Input images can be of different types but output images are always
|
|
-- float.
|
|
resizeBicubic :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value Float
|
|
|
|
-- | Convert one or more images from HSV to RGB.
|
|
--
|
|
-- Outputs a tensor of the same shape as the <tt>images</tt> tensor,
|
|
-- containing the RGB value of the pixels. The output is only well
|
|
-- defined if the value in <tt>images</tt> are in `[0,1]`.
|
|
--
|
|
-- See <tt>rgb_to_hsv</tt> for a description of the HSV encoding.
|
|
hSVToRGB :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Performs 3D average pooling on the input.
|
|
avgPool3D :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Delete the stack from its resource container.
|
|
stackClose :: Tensor Ref ByteString -> Build (ControlNode)
|
|
|
|
-- | Assigns a new value to a variable.
|
|
--
|
|
-- Any ReadVariableOp with a control dependency on this op is guaranteed
|
|
-- to return this value or a subsequent newer value of the variable.
|
|
assignVariableOp :: (TensorType dtype) => ResourceHandle dtype -> Tensor v2 dtype -> Build (ControlNode)
|
|
|
|
-- | Local Response Normalization.
|
|
--
|
|
-- The 4-D <tt>input</tt> tensor is treated as a 3-D array of 1-D vectors
|
|
-- (along the last dimension), and each vector is normalized
|
|
-- independently. Within a given vector, each component is divided by the
|
|
-- weighted, squared sum of inputs within <tt>depth_radius</tt>. In
|
|
-- detail,
|
|
--
|
|
-- sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d +
|
|
-- depth_radius + 1] ** 2) output = input / (bias + alpha * sqr_sum) **
|
|
-- beta
|
|
--
|
|
-- For details, see <a>Krizhevsky et al., ImageNet classification with
|
|
-- deep convolutional neural networks (NIPS 2012)</a>.
|
|
lRN :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Compute the Hurwitz zeta function \(zeta(x, q)\).
|
|
--
|
|
-- The Hurwitz zeta function is defined as:
|
|
--
|
|
-- ``` zeta(x, q) = sum_{n=0}^{infty} (q + n)^{-x} ```
|
|
zeta :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Creates a TensorArray for storing the gradients of values in the given
|
|
-- handle.
|
|
--
|
|
-- If the given TensorArray gradient already exists, returns a reference
|
|
-- to it.
|
|
--
|
|
-- Locks the size of the original TensorArray by disabling its dynamic
|
|
-- size flag.
|
|
--
|
|
-- <ul>
|
|
-- <li>*A note about the input flow_in:**</li>
|
|
-- </ul>
|
|
--
|
|
-- The handle flow_in forces the execution of the gradient lookup to
|
|
-- occur only after certain other operations have occurred. For example,
|
|
-- when the forward TensorArray is dynamically sized, writes to this
|
|
-- TensorArray may resize the object. The gradient TensorArray is
|
|
-- statically sized based on the size of the forward TensorArray when
|
|
-- this operation executes. Furthermore, the size of the forward
|
|
-- TensorArray is frozen by this call. As a result, the flow is used to
|
|
-- ensure that the call to generate the gradient TensorArray only happens
|
|
-- after all writes are executed.
|
|
--
|
|
-- In the case of dynamically sized TensorArrays, gradient computation
|
|
-- should only be performed on read operations that have themselves been
|
|
-- chained via flow to occur only after all writes have executed. That
|
|
-- way the final size of the forward TensorArray is known when this
|
|
-- operation is called.
|
|
--
|
|
-- <ul>
|
|
-- <li>*A note about the source attribute:**</li>
|
|
-- </ul>
|
|
--
|
|
-- TensorArray gradient calls use an accumulator TensorArray object. If
|
|
-- multiple gradients are calculated and run in the same session, the
|
|
-- multiple gradient nodes may accidentally flow throuth the same
|
|
-- accumulator TensorArray. This double counts and generally breaks the
|
|
-- TensorArray gradient flow.
|
|
--
|
|
-- The solution is to identify which gradient call this particular
|
|
-- TensorArray gradient is being called in. This is performed by
|
|
-- identifying a unique string (e.g. "gradients", "gradients_1", ...)
|
|
-- from the input gradient Tensor's name. This string is used as a suffix
|
|
-- when creating the TensorArray gradient object here (the attribute
|
|
-- <tt>source</tt>).
|
|
--
|
|
-- The attribute <tt>source</tt> is added as a suffix to the forward
|
|
-- TensorArray's name when performing the creation / lookup, so that each
|
|
-- separate gradient calculation gets its own TensorArray accumulator.
|
|
tensorArrayGradV2 :: Tensor v1 ByteString -> Tensor v2 Float -> Build (Tensor Value ByteString)
|
|
|
|
-- | Cast x of type SrcT to y of DstT.
|
|
cast :: (TensorType srcT, TensorType dstT) => Tensor v1 srcT -> Tensor Value dstT
|
|
|
|
-- | Computes the Gauss error function of <tt>x</tt> element-wise.
|
|
erf :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
batchMatrixTriangularSolve :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Adds sparse updates to the variable referenced by <tt>resource</tt>.
|
|
--
|
|
-- This operation computes
|
|
--
|
|
-- # Scalar indices ref[indices, ...] += updates[...]
|
|
--
|
|
-- # Vector indices (for each i) ref[indices[i], ...] += updates[i, ...]
|
|
--
|
|
-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
|
|
-- += updates[i, ..., j, ...]
|
|
--
|
|
-- Duplicate entries are handled correctly: if multiple <tt>indices</tt>
|
|
-- reference the same location, their contributions add.
|
|
--
|
|
-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/ScatterAdd.png" alt</a> <a>/div</a>
|
|
resourceScatterAdd :: (TensorType dtype, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] dtype, TensorType tindices, OneOf '[Int32, Int64] tindices) => ResourceHandle dtype -> Tensor v2 tindices -> Tensor v3 dtype -> Build (ControlNode)
|
|
batchCholeskyGrad :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
batchMatrixInverse :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Return the same ref tensor as the input ref tensor.
|
|
refIdentity :: (TensorType t) => Tensor Ref t -> Build (Tensor Ref t)
|
|
|
|
-- | Computes the singular value decompositions of one or more matrices.
|
|
--
|
|
-- Computes the SVD of each inner matrix in <tt>input</tt> such that
|
|
-- `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) *
|
|
-- transpose(v[..., :, :])`
|
|
--
|
|
-- ```prettyprint # a is a tensor containing a batch of matrices. # s is
|
|
-- a tensor of singular values for each matrix. # u is the tensor
|
|
-- containing of left singular vectors for each matrix. # v is the tensor
|
|
-- containing of right singular vectors for each matrix. s, u, v = svd(a)
|
|
-- s, _, _ = svd(a, compute_uv=False) ```
|
|
svd :: (TensorType t, OneOf '[Complex Double, Complex Float, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t, Tensor Value t)
|
|
|
|
-- | Solves one or more linear least-squares problems.
|
|
--
|
|
-- <tt>matrix</tt> 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.
|
|
--
|
|
-- matrix and right-hand sides in the batch:
|
|
--
|
|
-- <tt>matrix</tt>=\(A in Re^{m times n}\), <tt>rhs</tt>=\(B in Re^{m
|
|
-- times k}\), <tt>output</tt>=\(X in Re^{n times k}\),
|
|
-- <tt>l2_regularizer</tt>=\(lambda\).
|
|
--
|
|
-- If <tt>fast</tt> is <a>True</a>, 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 <tt>output</tt> 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.
|
|
--
|
|
-- If <tt>fast</tt> is <a>False</a> 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 <tt>fast</tt> is <a>False</a> then <tt>l2_regularizer</tt> is
|
|
-- ignored.
|
|
matrixSolveLs :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 Double -> Tensor Value t
|
|
|
|
-- | Packs a list of <tt>N</tt> rank-<tt>R</tt> tensors into one
|
|
-- rank-`(R+1)` tensor.
|
|
--
|
|
-- Packs the <tt>N</tt> tensors in <tt>values</tt> into a tensor with
|
|
-- rank one higher than each tensor in <tt>values</tt>, by packing them
|
|
-- along the <tt>axis</tt> dimension. Given a list of tensors of shape
|
|
-- `(A, B, C)`;
|
|
--
|
|
-- if `axis == 0` then the <tt>output</tt> tensor will have the shape
|
|
-- `(N, A, B, C)`. if `axis == 1` then the <tt>output</tt> tensor will
|
|
-- have the shape `(A, N, B, C)`. Etc.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>x</tt> is [1, 4] # <tt>y</tt> is [2, 5] #
|
|
-- <tt>z</tt> 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]] ```
|
|
--
|
|
-- This is the opposite of <a>unpack</a>.
|
|
pack :: (TensorType t) => [Tensor v1 t] -> Tensor Value t
|
|
|
|
-- | Closes the given barrier.
|
|
--
|
|
-- 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.
|
|
barrierClose :: Tensor Ref ByteString -> Build (ControlNode)
|
|
|
|
-- | Computes the eigen decomposition of one or more square self-adjoint
|
|
-- matrices.
|
|
--
|
|
-- Computes the eigenvalues and (optionally) eigenvectors of each inner
|
|
-- matrix in <tt>input</tt> such that `input[..., :, :] = v[..., :, :] *
|
|
-- diag(e[..., :])`.
|
|
--
|
|
-- ```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) ```
|
|
selfAdjointEigV2 :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t)
|
|
|
|
-- | Subtracts sparse updates to a variable reference.
|
|
--
|
|
-- # Scalar indices ref[indices, ...] -= updates[...]
|
|
--
|
|
-- # Vector indices (for each i) ref[indices[i], ...] -= updates[i, ...]
|
|
--
|
|
-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
|
|
-- -= updates[i, ..., j, ...]
|
|
--
|
|
-- This operation outputs <tt>ref</tt> after the update is done. This
|
|
-- makes it easier to chain operations that need to use the reset value.
|
|
--
|
|
-- Duplicate entries are handled correctly: if multiple <tt>indices</tt>
|
|
-- reference the same location, their (negated) contributions add.
|
|
--
|
|
-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/ScatterSub.png" alt</a> <a>/div</a>
|
|
scatterSub :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Computes the Eigen Decomposition of a batch of square self-adjoint
|
|
-- matrices.
|
|
--
|
|
-- 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 SelfAdjointEig.
|
|
--
|
|
-- The result is a [..., M+1, M] matrix with [..., 0,:] containing the
|
|
-- eigenvalues, and subsequent [...,1:, :] containing the eigenvectors.
|
|
selfAdjointEig :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Stops gradient computation.
|
|
--
|
|
-- When executed in a graph, this op outputs its input tensor as-is.
|
|
--
|
|
-- 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 <tt>loss</tt> 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.
|
|
--
|
|
-- 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:
|
|
--
|
|
-- <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>
|
|
stopGradient :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns the index with the largest value across dimensions of a
|
|
-- tensor.
|
|
argMax :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value Int64
|
|
|
|
-- | Computes the reverse mode backpropagated gradient of the Cholesky
|
|
-- algorithm.
|
|
--
|
|
-- For an explanation see "Differentiation of the Cholesky algorithm" by
|
|
-- Iain Murray <a>http://arxiv.org/abs/1602.07527</a>.
|
|
choleskyGrad :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Reshapes a SparseTensor to represent values in a new dense shape.
|
|
--
|
|
-- This operation has the same semantics as reshape on the represented
|
|
-- dense tensor. The <tt>input_indices</tt> are recomputed based on the
|
|
-- requested <tt>new_shape</tt>.
|
|
--
|
|
-- If one component of <tt>new_shape</tt> 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 <tt>new_shape</tt> can be
|
|
-- -1. The number of dense elements implied by <tt>new_shape</tt> must be
|
|
-- the same as the number of dense elements originally implied by
|
|
-- <tt>input_shape</tt>.
|
|
--
|
|
-- Reshaping does not affect the order of values in the SparseTensor.
|
|
--
|
|
-- If the input tensor has rank <tt>R_in</tt> and <tt>N</tt> non-empty
|
|
-- values, and <tt>new_shape</tt> has length <tt>R_out</tt>, then
|
|
-- <tt>input_indices</tt> has shape `[N, R_in]`, <tt>input_shape</tt> has
|
|
-- length <tt>R_in</tt>, <tt>output_indices</tt> has shape `[N, R_out]`,
|
|
-- and <tt>output_shape</tt> has length <tt>R_out</tt>.
|
|
sparseReshape :: Tensor v1 Int64 -> Tensor v2 Int64 -> Tensor v3 Int64 -> (Tensor Value Int64, Tensor Value Int64)
|
|
|
|
-- | var: Should be from a Variable().
|
|
sparseApplyAdadelta :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 tindices -> Build (Tensor Ref t)
|
|
|
|
-- | Computes the gradient of morphological 2-D dilation with respect to
|
|
-- the filter.
|
|
dilation2DBackpropFilter :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
batchSelfAdjointEigV2 :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t)
|
|
|
|
-- | Computes the number of incomplete elements in the given barrier.
|
|
barrierIncompleteSize :: Tensor Ref ByteString -> Build (Tensor Value Int32)
|
|
|
|
-- | Fake-quantize the <tt>inputs</tt> tensor of type float and shape `[b,
|
|
-- h, w, d]` via
|
|
--
|
|
-- global float scalars <a>min</a> and <a>max</a> to <tt>outputs</tt>
|
|
-- tensor of same shape as <tt>inputs</tt>.
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min; max</i> is the clamping range for the <tt>inputs</tt>
|
|
-- data. Op divides this range into 255 steps (total of 256 values), then
|
|
-- replaces each <tt>inputs</tt> value with the closest of the quantized
|
|
-- step values.</li>
|
|
-- </ul>
|
|
--
|
|
-- This operation has a gradient and thus allows for training <a>min</a>
|
|
-- and <a>max</a> values.
|
|
fakeQuantWithMinMaxVars :: Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Float -> Tensor Value Float
|
|
|
|
-- | Reads the value of a variable.
|
|
--
|
|
-- The tensor returned by this operation is immutable.
|
|
--
|
|
-- The value returned by this operation is guaranteed to be influenced by
|
|
-- all the writes on which this operation depends directly or indirectly,
|
|
-- and to not be influenced by any of the writes which depend directly or
|
|
-- indirectly on this operation.
|
|
readVariableOp :: (TensorType dtype) => ResourceHandle dtype -> Build (Tensor Value dtype)
|
|
|
|
-- | Gradient for batch normalization.
|
|
--
|
|
-- Note that the size of 4D Tensors are defined by either <a>NHWC</a> or
|
|
-- <a>NCHW</a>. The size of 1D Tensors matches the dimension C of the 4D
|
|
-- Tensors.
|
|
fusedBatchNormGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> (Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t, Tensor Value t)
|
|
|
|
-- | A queue that produces elements in first-in first-out order.
|
|
--
|
|
-- Variable-size shapes are allowed by setting the corresponding shape
|
|
-- dimensions to 0 in the shape attr. In this case DequeueMany will pad
|
|
-- up to the maximum size of any given element in the minibatch. See
|
|
-- below for details.
|
|
paddingFIFOQueue :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Computes the inverse of one or more square invertible matrices or
|
|
-- their
|
|
--
|
|
-- adjoints (conjugate transposes).
|
|
--
|
|
-- The input is a tensor of shape `[..., M, M]` whose inner-most 2
|
|
-- dimensions form square matrices. The output is a tensor of the same
|
|
-- shape as the input containing the inverse for all input submatrices
|
|
-- `[..., :, :]`.
|
|
--
|
|
-- The op uses LU decomposition with partial pivoting to compute the
|
|
-- inverses.
|
|
--
|
|
-- 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.
|
|
matrixInverse :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with audio.
|
|
--
|
|
-- The summary has up to <tt>max_outputs</tt> summary values containing
|
|
-- audio. The audio is built from <tt>tensor</tt> which must be 3-D with
|
|
-- shape `[batch_size, 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 <tt>sample_rate</tt>.
|
|
--
|
|
-- The <tt>tag</tt> argument is a scalar <a>Tensor</a> of type
|
|
-- <tt>string</tt>. It is used to build the <tt>tag</tt> of the summary
|
|
-- values:
|
|
--
|
|
-- <ul>
|
|
-- <li>If <tt>max_outputs</tt> is 1, the summary value tag is
|
|
-- '*tag*/audio'.</li>
|
|
-- <li>If <tt>max_outputs</tt> is greater than 1, the summary value tags
|
|
-- are generated sequentially as '*tag*/audio/0', '*tag*/audio/1',
|
|
-- etc.</li>
|
|
-- </ul>
|
|
audioSummaryV2 :: Tensor v1 ByteString -> Tensor v2 Float -> Tensor v3 Float -> Tensor Value ByteString
|
|
|
|
-- | Computes the determinant of one ore more square matrices.
|
|
--
|
|
-- The input is a tensor of shape `[..., M, M]` whose inner-most 2
|
|
-- dimensions form square matrices. The output is a tensor containing the
|
|
-- determinants for all input submatrices `[..., :, :]`.
|
|
matrixDeterminant :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Writes contents to the file at input filename. Creates file if not
|
|
-- existing.
|
|
writeFile :: Tensor v1 ByteString -> Tensor v2 ByteString -> ControlNode
|
|
|
|
-- | Concatenates quantized tensors along one dimension.
|
|
quantizedConcat :: (TensorType t) => Tensor v1 Int32 -> [Tensor v2 t] -> [Tensor v3 Float] -> [Tensor v4 Float] -> (Tensor Value t, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Creates a handle to a Variable resource.
|
|
varHandleOp :: (TensorType dtype) => Shape -> Build (ResourceHandle dtype)
|
|
|
|
-- | Assign <a>value</a> to the sliced l-value reference of <tt>ref</tt>.
|
|
--
|
|
-- The values of <a>value</a> are assigned to the positions in the
|
|
-- variable <tt>ref</tt> that are selected by the slice parameters. The
|
|
-- slice parameters `begin, <tt>end</tt>, <tt>strides</tt>, etc. work
|
|
-- exactly as in <tt>StridedSlice</tt>.
|
|
--
|
|
-- NOTE this op currently does not support broadcasting and so
|
|
-- <a>value</a>'s shape must be exactly the shape produced by the slice
|
|
-- of <tt>ref</tt>.
|
|
stridedSliceAssign :: (TensorType t, TensorType index, OneOf '[Int32, Int64] index) => Tensor Ref t -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor v5 t -> Build (Tensor Ref t)
|
|
|
|
-- | Checks whether a resource handle-based variable has been initialized.
|
|
varIsInitializedOp :: ResourceHandle dtype -> Build (Tensor Value Bool)
|
|
|
|
-- | Update '*var' according to the RMSProp algorithm.
|
|
--
|
|
-- Note that in dense implementation of this algorithm, ms and mom will
|
|
-- update even if the grad is zero, but in this sparse implementation, ms
|
|
-- and mom will not update in iterations during which the grad is zero.
|
|
--
|
|
-- mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta =
|
|
-- learning_rate * gradient / sqrt(mean_square + epsilon)
|
|
--
|
|
-- ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum *
|
|
-- mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom
|
|
sparseApplyRMSProp :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 tindices -> Build (Tensor Ref t)
|
|
batchCholesky :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
tensorArrayGather :: (TensorType dtype) => Tensor Ref ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> Build (Tensor Value dtype)
|
|
|
|
-- | Restore a reader to a previously saved state.
|
|
--
|
|
-- Not all Readers support being restored, so this can produce an
|
|
-- Unimplemented error.
|
|
readerRestoreState :: Tensor Ref ByteString -> Tensor v2 ByteString -> Build (ControlNode)
|
|
|
|
-- | Computes the gradient for the sqrt of <tt>x</tt> wrt its input.
|
|
--
|
|
-- Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and
|
|
-- <tt>dy</tt> is the corresponding input gradient.
|
|
sqrtGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Splits a tensor into <tt>num_split</tt> tensors along one dimension.
|
|
split :: (TensorType t) => Int64 -> Tensor v1 Int32 -> Tensor v2 t -> [Tensor Value t]
|
|
|
|
-- | A Reader that outputs the lines of a file delimited by '\n'.
|
|
textLineReader :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Copy a tensor setting everything outside a central band in each
|
|
-- innermost matrix
|
|
--
|
|
-- to zero.
|
|
--
|
|
-- The <tt>band</tt> part is computed as follows: Assume <tt>input</tt>
|
|
-- has <tt>k</tt> dimensions `[I, J, K, ..., M, N]`, then the output is a
|
|
-- tensor with the same shape where
|
|
--
|
|
-- `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m,
|
|
-- n]`.
|
|
--
|
|
-- The indicator function
|
|
--
|
|
-- `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))
|
|
-- && (num_upper < 0 || (n-m) <= num_upper)`.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # if <tt>input</tt> is [[ 0, 1, 2, 3] [-1, 0, 1, 2]
|
|
-- [-2, -1, 0, 1] [-3, -2, -1, 0]],
|
|
--
|
|
-- tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] [-1, 0, 1, 2]
|
|
-- [ 0, -1, 0, 1] [ 0, 0, -1, 0]],
|
|
--
|
|
-- tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] [-1, 0, 1, 0]
|
|
-- [-2, -1, 0, 1] [ 0, -2, -1, 0]] ```
|
|
--
|
|
-- Useful special cases:
|
|
--
|
|
-- ```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. ```
|
|
matrixBandPart :: (TensorType t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor Value t
|
|
|
|
-- | Closes the given queue.
|
|
--
|
|
-- 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.
|
|
queueClose :: Tensor Ref ByteString -> Build (ControlNode)
|
|
|
|
-- | V2 format specific: merges the metadata files of sharded checkpoints.
|
|
-- The
|
|
--
|
|
-- result is one logical checkpoint, with one physical metadata file and
|
|
-- renamed data files.
|
|
--
|
|
-- Intended for "grouping" multiple checkpoints in a sharded checkpoint
|
|
-- setup.
|
|
--
|
|
-- If delete_old_dirs is true, attempts to delete recursively the dirname
|
|
-- of each path in the input checkpoint_prefixes. This is useful when
|
|
-- those paths are non user-facing temporary locations.
|
|
mergeV2Checkpoints :: Tensor v1 ByteString -> Tensor v2 ByteString -> ControlNode
|
|
|
|
-- | Computes the number of complete elements in the given barrier.
|
|
barrierReadySize :: Tensor Ref ByteString -> Build (Tensor Value Int32)
|
|
|
|
-- | A queue that randomizes the order of elements.
|
|
randomShuffleQueue :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Returns the truth value of (x != y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>NotEqual</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
notEqual :: (TensorType t, OneOf '[Complex Double, Complex Float, Bool, ByteString, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Greedily selects a subset of bounding boxes in descending order of
|
|
-- score,
|
|
--
|
|
-- 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.
|
|
--
|
|
-- 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:
|
|
--
|
|
-- selected_indices = tf.image.non_max_suppression( boxes, scores,
|
|
-- max_output_size, iou_threshold) selected_boxes = tf.gather(boxes,
|
|
-- selected_indices)
|
|
nonMaxSuppression :: Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Int32 -> Tensor Value Int32
|
|
tensorArrayWrite :: (TensorType t) => Tensor Ref ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Build (Tensor Value Float)
|
|
|
|
-- | Quantizes then dequantizes a tensor.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- 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
|
|
--
|
|
-- <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>
|
|
--
|
|
-- Our input tensor range is then [-m, m].
|
|
--
|
|
-- Next, we choose our fixed-point quantization buckets, [min_fixed,
|
|
-- max_fixed]. If signed_input is true, this is
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min_fixed, max_fixed </i> =</li>
|
|
-- <li><i>-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) -
|
|
-- 1</i> .</li>
|
|
-- </ul>
|
|
--
|
|
-- Otherwise, if signed_input is false, the fixed-point range is
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min_fixed, max_fixed</i> = [0, (1 << num_bits) - 1].</li>
|
|
-- </ul>
|
|
--
|
|
-- From this we compute our scaling factor, s:
|
|
--
|
|
-- s = (max_fixed - min_fixed) / (2 * m).
|
|
--
|
|
-- Now we can quantize and dequantize the elements of our tensor. An
|
|
-- element e is transformed into e':
|
|
--
|
|
-- e' = (e * s).round_to_nearest() / s.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- For example, suppose num_bits = 8 and m = 1. Then
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min_fixed, max_fixed</i> = [-127, 127], and s = (127 + 127) / 2
|
|
-- = 127.</li>
|
|
-- </ul>
|
|
--
|
|
-- Given the vector {-1, -0.5, 0, 0.3}, this is quantized to {-127, -63,
|
|
-- 0, 38}, and dequantized to {-1, -63.0<i>127, 0, 38.0</i>127}.
|
|
quantizeAndDequantize :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Returns the next record (key, value pair) produced by a Reader.
|
|
--
|
|
-- 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).
|
|
readerRead :: Tensor Ref ByteString -> Tensor Ref ByteString -> Build ((Tensor Value ByteString, Tensor Value ByteString))
|
|
|
|
-- | Solves systems of linear equations with upper or lower triangular
|
|
-- matrices by
|
|
--
|
|
-- backsubstitution.
|
|
--
|
|
-- <tt>matrix</tt> is a tensor of shape `[..., M, M]` whose inner-most 2
|
|
-- dimensions form square matrices. If <tt>lower</tt> is <a>True</a> then
|
|
-- the strictly upper triangular part of each inner-most matrix is
|
|
-- assumed to be zero and not accessed. If <tt>lower</tt> is False then
|
|
-- the strictly lower triangular part of each inner-most matrix is
|
|
-- assumed to be zero and not accessed. <tt>rhs</tt> is a tensor of shape
|
|
-- `[..., M, K]`.
|
|
--
|
|
-- The output is a tensor of shape `[..., M, K]`. If <tt>adjoint</tt> is
|
|
-- <a>True</a> then the innermost matrices in output` satisfy matrix
|
|
-- equations `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. If
|
|
-- <tt>adjoint</tt> is <a>False</a> then the strictly then the innermost
|
|
-- matrices in <tt>output</tt> satisfy matrix equations
|
|
-- `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`.
|
|
matrixTriangularSolve :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Split the data from the input value into TensorArray elements.
|
|
--
|
|
-- Assuming that <tt>lengths</tt> takes on values
|
|
--
|
|
-- ```(n0, n1, ..., n(T-1))```
|
|
--
|
|
-- and that <a>value</a> has shape
|
|
--
|
|
-- ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```,
|
|
--
|
|
-- this splits values into a TensorArray with T tensors.
|
|
--
|
|
-- TensorArray index t will be the subtensor of values with starting
|
|
-- position
|
|
--
|
|
-- ```(n0 + n1 + ... + n(t-1), 0, 0, ...)```
|
|
--
|
|
-- and having size
|
|
--
|
|
-- ```nt x d0 x d1 x ...```
|
|
tensorArraySplitV2 :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Float -> Tensor Value Float
|
|
|
|
-- | Restores a tensor from checkpoint files.
|
|
--
|
|
-- Reads a tensor stored in one or several files. If there are several
|
|
-- files (for instance because a tensor was saved as slices),
|
|
-- <tt>file_pattern</tt> may contain wildcard symbols (<a>*</a> and
|
|
-- <tt>?</tt>) in the filename portion only, not in the directory
|
|
-- portion.
|
|
--
|
|
-- If a <tt>file_pattern</tt> matches several files,
|
|
-- <tt>preferred_shard</tt> 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 <tt>preferred_shard</tt> 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 <tt>preferred_shard</tt> to match the value passed
|
|
-- as the <tt>shard</tt> input of a matching <tt>Save</tt> Op may speed
|
|
-- up Restore. This attribute only affects performance, not correctness.
|
|
-- The default value -1 means files are processed in order.
|
|
--
|
|
-- See also <tt>RestoreSlice</tt>.
|
|
restore :: (TensorType dt) => Tensor v1 ByteString -> Tensor v2 ByteString -> Tensor Value dt
|
|
|
|
-- | Computes Quantized Rectified Linear X: `min(max(features, 0),
|
|
-- max_value)`
|
|
quantizedReluX :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> Tensor v4 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Extracts the average gradient in the given ConditionalAccumulator,
|
|
-- provided
|
|
--
|
|
-- that sufficient (i.e., more than num_required) gradients have been
|
|
-- accumulated. The op blocks until sufficient gradients have been
|
|
-- accumulated. If the accumulator has already aggregated more than
|
|
-- num_required gradients, it returns the average of the accumulated
|
|
-- gradients. Also automatically increments the recorded global_step in
|
|
-- the accumulator by 1, and resets the aggregate to 0.
|
|
accumulatorTakeGradient :: (TensorType dtype, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] dtype) => Tensor Ref ByteString -> Tensor v2 Int32 -> Build (Tensor Value dtype)
|
|
|
|
-- | Returns element-wise remainder of division. When `x < 0` xor `y
|
|
-- < 0` is
|
|
--
|
|
-- true, this follows Python semantics in that the result here is
|
|
-- consistent with a flooring divide. E.g. `floor(x / y) * y + mod(x, y)
|
|
-- = x`.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>FloorMod</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
floorMod :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the set of files matching a pattern.
|
|
--
|
|
-- Note that this routine only supports wildcard characters in the
|
|
-- basename portion of the pattern, not in the directory portion.
|
|
matchingFiles :: Tensor v1 ByteString -> Tensor Value ByteString
|
|
|
|
-- | Performs max pooling on the input.
|
|
maxPool :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the ids of the positions in sampled_candidates that match
|
|
-- true_labels.
|
|
--
|
|
-- 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 <tt>removing</tt> the sampled
|
|
-- labels that match the true labels by making the classifier sure that
|
|
-- they are sampled labels.
|
|
computeAccidentalHits :: Int64 -> Tensor v1 Int64 -> Tensor v2 Int64 -> (Tensor Value Int32, Tensor Value Int64, Tensor Value Float)
|
|
|
|
-- | Deserialize and concatenate <tt>SparseTensors</tt> from a serialized
|
|
-- minibatch.
|
|
--
|
|
-- The input <tt>serialized_sparse</tt> must be a string matrix of shape
|
|
-- `[N x 3]` where <tt>N</tt> is the minibatch size and the rows
|
|
-- correspond to packed outputs of <tt>SerializeSparse</tt>. The ranks of
|
|
-- the original <tt>SparseTensor</tt> objects must all match. When the
|
|
-- final <tt>SparseTensor</tt> is created, it has rank one higher than
|
|
-- the ranks of the incoming <tt>SparseTensor</tt> objects (they have
|
|
-- been concatenated along a new row dimension).
|
|
--
|
|
-- The output <tt>SparseTensor</tt> object's shape values for all
|
|
-- dimensions but the first are the max across the input
|
|
-- <tt>SparseTensor</tt> objects' shape values for the corresponding
|
|
-- dimensions. Its first shape value is <tt>N</tt>, the minibatch size.
|
|
--
|
|
-- The input <tt>SparseTensor</tt> objects' indices are assumed ordered
|
|
-- in standard lexicographic order. If this is not the case, after this
|
|
-- step run <tt>SparseReorder</tt> to restore index ordering.
|
|
--
|
|
-- For example, if the serialized input is a `[2 x 3]` matrix
|
|
-- representing two original <tt>SparseTensor</tt> objects:
|
|
--
|
|
-- index = [ 0] [10] [20] values = [1, 2, 3] shape = [50]
|
|
--
|
|
-- and
|
|
--
|
|
-- index = [ 2] [10] values = [4, 5] shape = [30]
|
|
--
|
|
-- then the final deserialized <tt>SparseTensor</tt> will be:
|
|
--
|
|
-- index = [0 0] [0 10] [0 20] [1 2] [1 10] values = [1, 2, 3, 4, 5]
|
|
-- shape = [2 50]
|
|
deserializeManySparse :: (TensorType dtype) => Tensor v1 ByteString -> (Tensor Value Int64, Tensor Value dtype, Tensor Value Int64)
|
|
|
|
-- | Extracts crops from the input image tensor and bilinearly resizes them
|
|
-- (possibly
|
|
--
|
|
-- with aspect ratio change) to a common output size specified by
|
|
-- <tt>crop_size</tt>. This is more general than the
|
|
-- <tt>crop_to_bounding_box</tt> op which extracts a fixed size slice
|
|
-- from the input image and does not allow resizing or aspect ratio
|
|
-- change.
|
|
--
|
|
-- Returns a tensor with <tt>crops</tt> from the input <tt>image</tt> at
|
|
-- positions defined at the bounding box locations in <tt>boxes</tt>. 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]`.
|
|
cropAndResize :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Float -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor Value Float
|
|
|
|
-- | Applies sparse updates to a variable reference.
|
|
--
|
|
-- This operation computes
|
|
--
|
|
-- # Scalar indices ref[indices, ...] = updates[...]
|
|
--
|
|
-- # Vector indices (for each i) ref[indices[i], ...] = updates[i, ...]
|
|
--
|
|
-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
|
|
-- = updates[i, ..., j, ...]
|
|
--
|
|
-- This operation outputs <tt>ref</tt> after the update is done. This
|
|
-- makes it easier to chain operations that need to use the reset value.
|
|
--
|
|
-- If values in <tt>ref</tt> is to be updated more than once, because
|
|
-- there are duplicate entires in <tt>indices</tt>, the order at which
|
|
-- the updates happen for each value is undefined.
|
|
--
|
|
-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/ScatterUpdate.png" alt</a> <a>/div</a>
|
|
scatterUpdate :: (TensorType t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Outputs random values from the Gamma distribution(s) described by
|
|
-- alpha.
|
|
--
|
|
-- 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>http://dl.acm.org/citation.cfm?id=358414</a>
|
|
randomGamma :: (TensorType s, OneOf '[Int32, Int64] s, TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 s -> Tensor v2 t -> Build (Tensor Value t)
|
|
batchMatrixSolve :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
batchMatrixBandPart :: (TensorType t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor Value t
|
|
tensorArrayClose :: Tensor Ref ByteString -> Build (ControlNode)
|
|
|
|
-- | Computes the "logical and" of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
all :: (TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 Bool -> Tensor v2 tidx -> Tensor Value Bool
|
|
|
|
-- | Returns the number of records this Reader has produced.
|
|
--
|
|
-- This is the same as the number of ReaderRead executions that have
|
|
-- succeeded.
|
|
readerNumRecordsProduced :: Tensor Ref ByteString -> Build (Tensor Value Int64)
|
|
|
|
-- | Pop the element at the top of the stack.
|
|
stackPop :: (TensorType elem_type) => Tensor Ref ByteString -> Build (Tensor Value elem_type)
|
|
|
|
-- | Scatter the data from the input value into specific TensorArray
|
|
-- elements.
|
|
--
|
|
-- <tt>indices</tt> must be a vector, its length must match the first dim
|
|
-- of <a>value</a>.
|
|
tensorArrayScatterV2 :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Tensor Value Float
|
|
|
|
-- | Converts one or more images from RGB to HSV.
|
|
--
|
|
-- Outputs a tensor of the same shape as the <tt>images</tt> tensor,
|
|
-- containing the HSV value of the pixels. The output is only well
|
|
-- defined if the value in <tt>images</tt> are in `[0,1]`.
|
|
--
|
|
-- `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<i>3 is pure green, and 2</i>3
|
|
-- is pure blue.
|
|
rGBToHSV :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Serialize an <tt>N</tt>-minibatch <tt>SparseTensor</tt> into an `[N,
|
|
-- 3]` string <a>Tensor</a>.
|
|
--
|
|
-- The <tt>SparseTensor</tt> must have rank <tt>R</tt> greater than 1,
|
|
-- and the first dimension is treated as the minibatch dimension.
|
|
-- Elements of the <tt>SparseTensor</tt> must be sorted in increasing
|
|
-- order of this first dimension. The serialized <tt>SparseTensor</tt>
|
|
-- objects going into each row of <tt>serialized_sparse</tt> will have
|
|
-- rank `R-1`.
|
|
--
|
|
-- The minibatch size <tt>N</tt> is extracted from `sparse_shape[0]`.
|
|
serializeManySparse :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor Value ByteString
|
|
|
|
-- | Initializes a table from a text file.
|
|
--
|
|
-- 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 <tt>delimiter</tt> or the line
|
|
-- number (starting from zero). Where to extract the key and value from a
|
|
-- line is specified by <tt>key_index</tt> and <tt>value_index</tt>.
|
|
--
|
|
-- <ul>
|
|
-- <li>A value of -1 means use the line number(starting from zero),
|
|
-- expects <tt>int64</tt>.</li>
|
|
-- <li>A value of -2 means use the whole line content, expects
|
|
-- <tt>string</tt>.</li>
|
|
-- <li>A value >= 0 means use the index (starting at zero) of the
|
|
-- split line based on <tt>delimiter</tt>.</li>
|
|
-- </ul>
|
|
initializeTableFromTextFile :: Int64 -> Int64 -> Tensor Ref ByteString -> Tensor v2 ByteString -> Build (ControlNode)
|
|
|
|
-- | Decode a PNG-encoded image to a uint8 or uint16 tensor.
|
|
--
|
|
-- The attr <tt>channels</tt> indicates the desired number of color
|
|
-- channels for the decoded image.
|
|
--
|
|
-- Accepted values are:
|
|
--
|
|
-- <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>
|
|
--
|
|
-- If needed, the PNG-encoded image is transformed to match the requested
|
|
-- number of color channels.
|
|
decodePng :: (TensorType dtype, OneOf '[Word16, Word8] dtype) => Tensor v1 ByteString -> Tensor Value dtype
|
|
|
|
-- | Get the current size of the TensorArray.
|
|
tensorArraySizeV2 :: Tensor v1 ByteString -> Tensor v2 Float -> Tensor Value Int32
|
|
|
|
-- | Returns x / y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Div</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
div :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Generates labels for candidate sampling with a log-uniform
|
|
-- distribution.
|
|
--
|
|
-- See explanations of candidate sampling and the data formats at
|
|
-- go/candidate-sampling.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
logUniformCandidateSampler :: Int64 -> Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Defines a barrier that persists across different graph executions.
|
|
--
|
|
-- A barrier represents a key-value map, where each key is a string, and
|
|
-- each value is a tuple of tensors.
|
|
--
|
|
-- At runtime, the barrier contains <tt>complete</tt> and
|
|
-- <tt>incomplete</tt> 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.
|
|
barrier :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Creates a variable resource.
|
|
createVariableOp :: (TensorType dtype) => ResourceHandle dtype -> Tensor v2 dtype -> Build (ControlNode)
|
|
|
|
-- | Applies a gradient to a given accumulator. Does not add if local_step
|
|
-- is lesser
|
|
--
|
|
-- than the accumulator's global_step.
|
|
accumulatorApplyGradient :: (TensorType dtype, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] dtype) => Tensor Ref ByteString -> Tensor v2 Int64 -> Tensor v3 dtype -> Build (ControlNode)
|
|
|
|
-- | Outputs random values from a normal distribution.
|
|
--
|
|
-- The generated values will have mean 0 and standard deviation 1.
|
|
randomStandardNormal :: (TensorType dtype, OneOf '[Word16, Double, Float] dtype, TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Build (Tensor Value dtype)
|
|
|
|
-- | Outputs random values from a normal distribution. The parameters may
|
|
-- each be a
|
|
--
|
|
-- scalar which applies to the entire output, or a vector of length
|
|
-- shape[0] which stores the parameters for each batch.
|
|
parameterizedTruncatedNormal :: (TensorType dtype, OneOf '[Word16, Double, Float] dtype, TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Tensor v2 dtype -> Tensor v3 dtype -> Tensor v4 dtype -> Tensor v5 dtype -> Build (Tensor Value dtype)
|
|
|
|
-- | Updates the accumulator with a new value for global_step. Logs warning
|
|
-- if the
|
|
--
|
|
-- accumulator's value is already higher than new_global_step.
|
|
accumulatorSetGlobalStep :: Tensor Ref ByteString -> Tensor v2 Int64 -> Build (ControlNode)
|
|
|
|
-- | Resize <tt>images</tt> to <a>size</a> using bilinear interpolation.
|
|
--
|
|
-- Input images can be of different types but output images are always
|
|
-- float.
|
|
resizeBilinear :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value Float
|
|
|
|
-- | Quantize the <tt>input</tt> tensor of type float to <tt>output</tt>
|
|
-- tensor of type <tt>T</tt>.
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min_range, max_range</i> are scalar floats that specify the
|
|
-- range for the <tt>input</tt> data. The <tt>mode</tt> attribute
|
|
-- controls exactly which calculations are used to convert the float
|
|
-- values to their quantized equivalents.</li>
|
|
-- </ul>
|
|
--
|
|
-- In <tt>MIN_COMBINED</tt> mode, each value of the tensor will undergo
|
|
-- the following:
|
|
--
|
|
-- ``` out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
|
|
-- if T == qint8, out[i] -= (range(T) + 1) / 2.0 ``` here `range(T) =
|
|
-- numeric_limits<a>T</a>::max() - numeric_limits<a>T</a>::min()`
|
|
--
|
|
-- <ul>
|
|
-- <li>MIN_COMBINED Mode Example*</li>
|
|
-- </ul>
|
|
--
|
|
-- Assume the input is type float and has a possible range of [0.0, 6.0]
|
|
-- and the output type is quint8 ([0, 255]). The min_range and max_range
|
|
-- values should be specified as 0.0 and 6.0. Quantizing from float to
|
|
-- quint8 will multiply each value of the input by 255/6 and cast to
|
|
-- quint8.
|
|
--
|
|
-- If the output type was qint8 ([-128, 127]), the operation will
|
|
-- additionally subtract each value by 128 prior to casting, so that the
|
|
-- range of values aligns with the range of qint8.
|
|
--
|
|
-- If the mode is <tt>MIN_FIRST</tt>, then this approach is used:
|
|
--
|
|
-- ``` number_of_steps = 1 << (# of bits in T) range_adjust =
|
|
-- number_of_steps / (number_of_steps - 1) range = (range_max -
|
|
-- range_min) * range_adjust range_scale = number_of_steps / range
|
|
-- quantized = round(input * range_scale) - round(range_min *
|
|
-- range_scale) + numeric_limits<a>T</a>::min() quantized =
|
|
-- max(quantized, numeric_limits<a>T</a>::min()) quantized =
|
|
-- min(quantized, numeric_limits<a>T</a>::max()) ```
|
|
--
|
|
-- The biggest difference between this and MIN_COMBINED is that the
|
|
-- minimum range is rounded first, before it's subtracted from the
|
|
-- rounded value. With MIN_COMBINED, a small bias is introduced where
|
|
-- repeated iterations of quantizing and dequantizing will introduce a
|
|
-- larger and larger error.
|
|
--
|
|
-- One thing to watch out for is that the operator may choose to adjust
|
|
-- the requested minimum and maximum values slightly during the
|
|
-- quantization process, so you should always use the output ports as the
|
|
-- range for further calculations. For example, if the requested minimum
|
|
-- and maximum values are close to equal, they will be separated by a
|
|
-- small epsilon value to prevent ill-formed quantized buffers from being
|
|
-- created. Otherwise, you can end up with buffers where all the
|
|
-- quantized values map to the same float value, which causes problems
|
|
-- for operations that have to perform further calculations on them.
|
|
quantizeV2 :: (TensorType t, OneOf '[Int16, Int32, Word16, Word8] t) => Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value t, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Decode a JPEG-encoded image to a uint8 tensor.
|
|
--
|
|
-- The attr <tt>channels</tt> indicates the desired number of color
|
|
-- channels for the decoded image.
|
|
--
|
|
-- Accepted values are:
|
|
--
|
|
-- <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>
|
|
--
|
|
-- If needed, the JPEG-encoded image is transformed to match the
|
|
-- requested number of color channels.
|
|
--
|
|
-- The attr <tt>ratio</tt> 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.
|
|
decodeJpeg :: Tensor v1 ByteString -> Tensor Value Word8
|
|
|
|
-- | Computes the power of one value to another.
|
|
--
|
|
-- Given a tensor <tt>x</tt> and a tensor <tt>y</tt>, this operation
|
|
-- computes \(x^y\) for corresponding elements in <tt>x</tt> and
|
|
-- <tt>y</tt>. For example:
|
|
--
|
|
-- ``` # tensor <tt>x</tt> is [[2, 2]], [3, 3]] # tensor <tt>y</tt> is
|
|
-- [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]] ```
|
|
pow :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Forwards the input to the output.
|
|
--
|
|
-- This operator represents the loop termination condition used by the
|
|
-- "pivot" switches of a loop.
|
|
loopCond :: Tensor v1 Bool -> Tensor Value Bool
|
|
|
|
-- | Reads and outputs the entire contents of the input filename.
|
|
readFile :: Tensor v1 ByteString -> Tensor Value ByteString
|
|
|
|
-- | Returns the imaginary part of a complex number.
|
|
--
|
|
-- Given a tensor <tt>input</tt> of complex numbers, this operation
|
|
-- returns a tensor of type <tt>float</tt> that is the imaginary part of
|
|
-- each element in <tt>input</tt>. All elements in <tt>input</tt> 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.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` # tensor <tt>input</tt> is [-2.25 + 4.75j, 3.25 + 5.75j]
|
|
-- tf.imag(input) ==> [4.75, 5.75] ```
|
|
imag :: (TensorType t, OneOf '[Complex Double, Complex Float] t, TensorType tout, OneOf '[Double, Float] tout) => Tensor v1 t -> Tensor Value tout
|
|
tensorArrayGrad :: Tensor v1 ByteString -> Tensor v2 Float -> Build (Tensor Ref ByteString)
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with a histogram.
|
|
--
|
|
-- The generated <a>`Summary`</a> has one summary value containing a
|
|
-- histogram for <tt>values</tt>.
|
|
--
|
|
-- This op reports an <tt>InvalidArgument</tt> error if any value is not
|
|
-- finite.
|
|
histogramSummary :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor Value ByteString
|
|
|
|
-- | Computes the gradients of 3-D convolution with respect to the input.
|
|
conv3DBackpropInputV2 :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | Computes the gradient of bilinear interpolation.
|
|
resizeBilinearGrad :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Float -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Add an <tt>N</tt>-minibatch <tt>SparseTensor</tt> to a
|
|
-- <tt>SparseTensorsMap</tt>, return <tt>N</tt> handles.
|
|
--
|
|
-- A <tt>SparseTensor</tt> of rank <tt>R</tt> is represented by three
|
|
-- tensors: <tt>sparse_indices</tt>, <tt>sparse_values</tt>, and
|
|
-- <tt>sparse_shape</tt>, where
|
|
--
|
|
-- ```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
|
|
--
|
|
-- An <tt>N</tt>-minibatch of <tt>SparseTensor</tt> objects is
|
|
-- represented as a <tt>SparseTensor</tt> having a first
|
|
-- <tt>sparse_indices</tt> column taking values between `[0, N)`, where
|
|
-- the minibatch size `N == sparse_shape[0]`.
|
|
--
|
|
-- The input <tt>SparseTensor</tt> must have rank <tt>R</tt> greater than
|
|
-- 1, and the first dimension is treated as the minibatch dimension.
|
|
-- Elements of the <tt>SparseTensor</tt> must be sorted in increasing
|
|
-- order of this first dimension. The stored <tt>SparseTensor</tt>
|
|
-- objects pointed to by each row of the output <tt>sparse_handles</tt>
|
|
-- will have rank `R-1`.
|
|
--
|
|
-- The <tt>SparseTensor</tt> values can then be read out as part of a
|
|
-- minibatch by passing the given keys as vector elements to
|
|
-- <tt>TakeManySparseFromTensorsMap</tt>. To ensure the correct
|
|
-- <tt>SparseTensorsMap</tt> is accessed, ensure that the same
|
|
-- <tt>container</tt> and <tt>shared_name</tt> are passed to that Op. If
|
|
-- no <tt>shared_name</tt> is provided here, instead use the *name* of
|
|
-- the Operation created by calling <tt>AddManySparseToTensorsMap</tt> as
|
|
-- the <tt>shared_name</tt> passed to
|
|
-- <tt>TakeManySparseFromTensorsMap</tt>. Ensure the Operations are
|
|
-- colocated.
|
|
addManySparseToTensorsMap :: (TensorType t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Build (Tensor Value Int64)
|
|
batchIFFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
batchMatrixDeterminant :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Delete the tensor specified by its handle in the session.
|
|
deleteSessionTensor :: Tensor v1 ByteString -> ControlNode
|
|
|
|
-- | Computes the number of elements in the given table.
|
|
lookupTableSize :: Tensor Ref ByteString -> Build (Tensor Value Int64)
|
|
|
|
-- | Computes rectified linear: `max(features, 0)`.
|
|
relu :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Interleave the values from the `data` tensors into a single tensor.
|
|
--
|
|
-- Builds a merged tensor such that
|
|
--
|
|
-- ```python merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
|
|
-- ```
|
|
--
|
|
-- For example, if each `indices[m]` is scalar or vector, we have
|
|
--
|
|
-- ```python # Scalar indices: merged[indices[m], ...] = data[m][...]
|
|
--
|
|
-- # Vector indices: merged[indices[m][i], ...] = data[m][i, ...] ```
|
|
--
|
|
-- 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. <tt>i</tt>. That is, we must have `data[i].shape =
|
|
-- indices[i].shape + constant`. In terms of this <tt>constant</tt>, the
|
|
-- output shape is
|
|
--
|
|
-- merged.shape = [max(indices)] + constant
|
|
--
|
|
-- 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.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```python 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]] ```
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/DynamicStitch.png" alt</a> <a>/div</a>
|
|
dynamicStitch :: (TensorType t) => [Tensor v1 Int32] -> [Tensor v2 t] -> Tensor Value t
|
|
|
|
-- | Looks up keys in a table, outputs the corresponding values.
|
|
--
|
|
-- The tensor <tt>keys</tt> must of the same type as the keys of the
|
|
-- table. The output <tt>values</tt> is of the type of the table values.
|
|
--
|
|
-- The scalar <tt>default_value</tt> is the value output for keys not
|
|
-- present in the table. It must also be of the same type as the table
|
|
-- values.
|
|
lookupTableFind :: (TensorType tin, TensorType tout) => Tensor Ref ByteString -> Tensor v2 tin -> Tensor v3 tout -> Build (Tensor Value tout)
|
|
|
|
-- | Generate a single randomly distorted bounding box for an image.
|
|
--
|
|
-- 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 <tt>image_size</tt>, <tt>bounding_boxes</tt>
|
|
-- and a series of constraints.
|
|
--
|
|
-- 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:
|
|
-- <tt>begin</tt>, <a>size</a> and <tt>bboxes</tt>. 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_boxes` to visualize what the
|
|
-- bounding box looks like.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- For example,
|
|
--
|
|
-- ```python # Generate a single distorted bounding box. begin, size,
|
|
-- bbox_for_draw = tf.image.sample_distorted_bounding_box(
|
|
-- tf.shape(image), bounding_boxes=bounding_boxes)
|
|
--
|
|
-- # 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(<tt>images_with_box</tt>, image_with_box)
|
|
--
|
|
-- # Employ the bounding box to distort the image. distorted_image =
|
|
-- tf.slice(image, begin, size) ```
|
|
--
|
|
-- 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
|
|
-- <tt>use_image_if_no_bounding_boxes</tt> is false and no bounding boxes
|
|
-- are supplied, an error is raised.
|
|
sampleDistortedBoundingBox :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word8] t) => Tensor v1 t -> Tensor v2 Float -> Build ((Tensor Value t, Tensor Value t, Tensor Value Float))
|
|
|
|
-- | Splits a tensor into <tt>num_split</tt> tensors along one dimension.
|
|
splitV :: (TensorType t, TensorType tlen, OneOf '[Int32, Int64] tlen) => Int64 -> Tensor v1 t -> Tensor v2 tlen -> Tensor v3 Int32 -> [Tensor Value t]
|
|
|
|
-- | Performs a padding as a preprocess during a convolution.
|
|
--
|
|
-- Similar to FusedResizeAndPadConv2d, this op allows for an optimized
|
|
-- implementation where the spatial padding transformation stage is fused
|
|
-- with the im2col lookup, but in this case without the bilinear
|
|
-- filtering required for resizing. Fusing the padding 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 <tt>NHWC</tt> order is used instead.
|
|
-- 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.
|
|
fusedPadConv2D :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | For each key, assigns the respective value to the specified component.
|
|
--
|
|
-- If a key is not found in the barrier, this operation will create a new
|
|
-- 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
|
|
-- INVALID_ARGUMENT, and leave the barrier in an undefined state.
|
|
barrierInsertMany :: (TensorType t) => Int64 -> Tensor Ref ByteString -> Tensor v2 ByteString -> Tensor v3 t -> Build (ControlNode)
|
|
|
|
-- | Raise a exception to abort the process when called.
|
|
--
|
|
-- Returns nothing but an exception.
|
|
abort :: ControlNode
|
|
|
|
-- | Performs max pooling on the input and outputs both max values and
|
|
-- indices.
|
|
--
|
|
-- The indices in <tt>argmax</tt> are flattened, so that a maximum value
|
|
-- at position `[b, y, x, c]` becomes flattened index `((b * height + y)
|
|
-- * width + x) * channels + c`.
|
|
maxPoolWithArgmax :: (TensorType targmax, OneOf '[Int32, Int64] targmax, TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value targmax)
|
|
|
|
-- | Creates or finds a child frame, and makes `data` available to the
|
|
-- child frame.
|
|
--
|
|
-- The unique <tt>frame_name</tt> is used by the <tt>Executor</tt> to
|
|
-- identify frames. If <tt>is_constant</tt> is true, <tt>output</tt> is a
|
|
-- constant in the child frame; otherwise it may be changed in the child
|
|
-- frame. At most <tt>parallel_iterations</tt> iterations are run in
|
|
-- parallel in the child frame.
|
|
refEnter :: (TensorType t) => Tensor Ref t -> Build (Tensor Ref t)
|
|
|
|
-- | Dequantize the <tt>input</tt> tensor into a float Tensor.
|
|
--
|
|
-- <ul>
|
|
-- <li><i>min_range, max_range</i> are scalar floats that specify the
|
|
-- range for the <tt>input</tt> data. The <tt>mode</tt> attribute
|
|
-- controls exactly which calculations are used to convert the float
|
|
-- values to their quantized equivalents.</li>
|
|
-- </ul>
|
|
--
|
|
-- In <tt>MIN_COMBINED</tt> mode, each value of the tensor will undergo
|
|
-- the following:
|
|
--
|
|
-- ``` if T == qint8, in[i] += (range(T) + 1)/ 2.0 out[i] = min_range +
|
|
-- (in[i]* (max_range - min_range) / range(T)) ``` here `range(T) =
|
|
-- numeric_limits<a>T</a>::max() - numeric_limits<a>T</a>::min()`
|
|
--
|
|
-- <ul>
|
|
-- <li>MIN_COMBINED Mode Example*</li>
|
|
-- </ul>
|
|
--
|
|
-- If the input comes from a QuantizedRelu6, the output type is quint8
|
|
-- (range of 0-255) but the possible range of QuantizedRelu6 is 0-6. The
|
|
-- min_range and max_range values are therefore 0.0 and 6.0. Dequantize
|
|
-- on quint8 will take each value, cast to float, and multiply by 6 /
|
|
-- 255. Note that if quantizedtype is qint8, the operation will
|
|
-- additionally add each value by 128 prior to casting.
|
|
--
|
|
-- If the mode is <tt>MIN_FIRST</tt>, then this approach is used:
|
|
--
|
|
-- ``` number_of_steps = 1 << (# of bits in T) range_adjust =
|
|
-- number_of_steps / (number_of_steps - 1) range = (range_max -
|
|
-- range_min) * range_adjust range_scale = range / number_of_steps const
|
|
-- double offset_input = static_cast<a>double</a>(input) -
|
|
-- lowest_quantized; result = range_min + ((input -
|
|
-- numeric_limits<a>T</a>::min()) * range_scale) ```
|
|
dequantize :: (TensorType t, OneOf '[Int16, Int32, Word16, Word8] t) => Tensor v1 t -> Tensor v2 Float -> Tensor v3 Float -> Tensor Value Float
|
|
|
|
-- | Draw bounding boxes on a batch of images.
|
|
--
|
|
-- Outputs a copy of <tt>images</tt> but draws on top of the pixels zero
|
|
-- or more bounding boxes specified by the locations in <tt>boxes</tt>.
|
|
-- The coordinates of the each bounding box in <tt>boxes</tt> 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.
|
|
--
|
|
-- 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)`.
|
|
--
|
|
-- Parts of the bounding box may fall outside the image.
|
|
drawBoundingBoxes :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 Float -> Tensor Value t
|
|
tensorArraySplit :: (TensorType t) => Tensor Ref ByteString -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Float -> Build (Tensor Value Float)
|
|
|
|
-- | Converts each string in the input Tensor to its hash mod by a number
|
|
-- of buckets.
|
|
--
|
|
-- The hash function is deterministic on the content of the string within
|
|
-- the 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`.
|
|
stringToHashBucketFast :: Int64 -> Tensor v1 ByteString -> Tensor Value Int64
|
|
tensorArrayScatter :: (TensorType t) => Tensor Ref ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Build (Tensor Value Float)
|
|
|
|
-- | Returns a one-hot tensor.
|
|
--
|
|
-- The locations represented by indices in <tt>indices</tt> take value
|
|
-- <tt>on_value</tt>, while all other locations take value
|
|
-- <tt>off_value</tt>.
|
|
--
|
|
-- If the input <tt>indices</tt> is rank <tt>N</tt>, the output will have
|
|
-- rank `N+1`, The new axis is created at dimension <tt>axis</tt>
|
|
-- (default: the new axis is appended at the end).
|
|
--
|
|
-- If <tt>indices</tt> is a scalar the output shape will be a vector of
|
|
-- length <tt>depth</tt>.
|
|
--
|
|
-- If <tt>indices</tt> is a vector of length <tt>features</tt>, the
|
|
-- output shape will be: ``` features x depth if axis == -1 depth x
|
|
-- features if axis == 0 ```
|
|
--
|
|
-- If <tt>indices</tt> 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 ```
|
|
--
|
|
-- Examples =========
|
|
--
|
|
-- Suppose that
|
|
--
|
|
-- ``` indices = [0, 2, -1, 1] depth = 3 on_value = 5.0 off_value = 0.0
|
|
-- axis = -1 ```
|
|
--
|
|
-- Then output is `[4 x 3]`:
|
|
--
|
|
-- ```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) ```
|
|
--
|
|
-- Suppose that
|
|
--
|
|
-- ``` indices = [0, 2, -1, 1] depth = 3 on_value = 0.0 off_value = 3.0
|
|
-- axis = 0 ```
|
|
--
|
|
-- Then output is `[3 x 4]`:
|
|
--
|
|
-- ```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
|
|
--
|
|
-- ``` indices = [[0, 2], [1, -1]] depth = 3 on_value = 1.0 off_value =
|
|
-- 0.0 axis = -1 ```
|
|
--
|
|
-- Then output is `[2 x 2 x 3]`:
|
|
--
|
|
-- ```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) ]```
|
|
oneHot :: (TensorType t, TensorType tI, OneOf '[Int32, Int64, Word8] tI) => Tensor v1 tI -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 t -> Tensor Value t
|
|
batchIFFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
|
|
|
|
-- | Reinterpret the bytes of a string as a vector of numbers.
|
|
decodeRaw :: (TensorType out_type, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] out_type) => Tensor v1 ByteString -> Tensor Value out_type
|
|
tensorArrayPack :: (TensorType dtype) => Tensor Ref ByteString -> Tensor v2 Float -> Build (Tensor Value dtype)
|
|
|
|
-- | Update '*var' and '*accum' according to FOBOS with Adagrad learning
|
|
-- rate.
|
|
--
|
|
-- accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var
|
|
-- = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
|
|
applyProximalAdagrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Build (Tensor Ref t)
|
|
|
|
-- | Applies a sparse gradient to a given accumulator. Does not add if
|
|
-- local_step is
|
|
--
|
|
-- lesser than the accumulator's global_step.
|
|
sparseAccumulatorApplyGradient :: (TensorType dtype, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] dtype) => Bool -> Tensor Ref ByteString -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor v4 dtype -> Tensor v5 Int64 -> Build (ControlNode)
|
|
|
|
-- | Returns x + y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Add</tt> supports broadcasting. <tt>AddN</tt> does not.
|
|
-- More about broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
add :: (TensorType t, OneOf '[Complex Double, Complex Float, ByteString, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Computes softsign: `features / (abs(features) + 1)`.
|
|
softsign :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
tensorArrayRead :: (TensorType dtype) => Tensor Ref ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> Build (Tensor Value dtype)
|
|
|
|
-- | Applies sparse subtraction between <tt>updates</tt> and individual
|
|
-- values or slices
|
|
--
|
|
-- within a given variable according to <tt>indices</tt>.
|
|
--
|
|
-- <tt>ref</tt> is a <a>Tensor</a> with rank <tt>P</tt> and
|
|
-- <tt>indices</tt> is a <a>Tensor</a> of rank <tt>Q</tt>.
|
|
--
|
|
-- <tt>indices</tt> must be integer tensor, containing indices into
|
|
-- <tt>ref</tt>. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 <
|
|
-- K <= P`.
|
|
--
|
|
-- The innermost dimension of <tt>indices</tt> (with length <tt>K</tt>)
|
|
-- corresponds to indices into elements (if `K = P`) or slices (if `K
|
|
-- < P`) along the <tt>K</tt>th dimension of <tt>ref</tt>.
|
|
--
|
|
-- <tt>updates</tt> is <a>Tensor</a> of rank `Q-1+P-K` with shape:
|
|
--
|
|
-- ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ```
|
|
--
|
|
-- For example, say we want to subtract 4 scattered elements from a
|
|
-- rank-1 tensor with 8 elements. In Python, that subtraction would look
|
|
-- like this:
|
|
--
|
|
-- ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices =
|
|
-- tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11,
|
|
-- 12]) sub = tf.scatter_nd_sub(ref, indices, updates) with tf.Session()
|
|
-- as sess: print sess.run(sub)
|
|
--
|
|
-- The resulting update to ref would look like this:
|
|
--
|
|
-- <ul>
|
|
-- <li><i>1, -9, 3, -6, -4, 6, 7, -4</i></li>
|
|
-- </ul>
|
|
--
|
|
-- See <a>tf.scatter_nd</a> for more details about how to make updates to
|
|
-- slices.
|
|
scatterNdSub :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Restores a tensor from checkpoint files.
|
|
--
|
|
-- This is like <tt>Restore</tt> except that restored tensor can be
|
|
-- listed as filling only a slice of a larger tensor.
|
|
-- <tt>shape_and_slice</tt> specifies the shape of the larger tensor and
|
|
-- the slice that the restored tensor covers.
|
|
--
|
|
-- The <tt>shape_and_slice</tt> input has the same format as the elements
|
|
-- of the <tt>shapes_and_slices</tt> input of the <tt>SaveSlices</tt> op.
|
|
restoreSlice :: (TensorType dt) => Tensor v1 ByteString -> Tensor v2 ByteString -> Tensor v3 ByteString -> Tensor Value dt
|
|
|
|
-- | Update <tt>ref</tt> by adding <a>value</a> to it.
|
|
--
|
|
-- This operation outputs "ref" after the update is done. This makes it
|
|
-- easier to chain operations that need to use the reset value.
|
|
assignAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor v2 t -> Build (Tensor Ref t)
|
|
|
|
-- | Returns the truth value of (x > y) element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Greater</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
greater :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value Bool
|
|
|
|
-- | Returns the number of work units this Reader has finished processing.
|
|
readerNumWorkUnitsCompleted :: Tensor Ref ByteString -> Build (Tensor Value Int64)
|
|
|
|
-- | Gather specific elements from the TensorArray into output
|
|
-- <a>value</a>.
|
|
--
|
|
-- All elements selected by <tt>indices</tt> must have the same shape.
|
|
tensorArrayGatherV2 :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> Tensor Value dtype
|
|
|
|
-- | Read an element from the TensorArray into output <a>value</a>.
|
|
tensorArrayReadV2 :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> Tensor Value dtype
|
|
|
|
-- | Decode web-safe base64-encoded strings.
|
|
--
|
|
-- Input may or may not have padding at the end. See EncodeBase64 for
|
|
-- padding. Web-safe means that input must use - and _ instead of + and
|
|
-- /.
|
|
decodeBase64 :: Tensor v1 ByteString -> Tensor Value ByteString
|
|
|
|
-- | Push an element onto the tensor_array.
|
|
tensorArrayWriteV2 :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Tensor Value Float
|
|
|
|
-- | Outputs a <tt>Summary</tt> protocol buffer with audio.
|
|
--
|
|
-- The summary has up to <tt>max_outputs</tt> summary values containing
|
|
-- audio. The audio is built from <tt>tensor</tt> which must be 3-D with
|
|
-- shape `[batch_size, 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 <tt>sample_rate</tt>.
|
|
--
|
|
-- The <tt>tag</tt> argument is a scalar <a>Tensor</a> of type
|
|
-- <tt>string</tt>. It is used to build the <tt>tag</tt> of the summary
|
|
-- values:
|
|
--
|
|
-- <ul>
|
|
-- <li>If <tt>max_outputs</tt> is 1, the summary value tag is
|
|
-- '*tag*/audio'.</li>
|
|
-- <li>If <tt>max_outputs</tt> is greater than 1, the summary value tags
|
|
-- are generated sequentially as '*tag*/audio/0', '*tag*/audio/1',
|
|
-- etc.</li>
|
|
-- </ul>
|
|
audioSummary :: Float -> Tensor v1 ByteString -> Tensor v2 Float -> Tensor Value ByteString
|
|
|
|
-- | Returns which elements of x are finite.
|
|
--
|
|
-- <tt>compatibility(numpy) Equivalent to np.isfinite
|
|
-- </tt>end_compatibility
|
|
isFinite :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
|
|
tensorArrayConcat :: (TensorType dtype) => Tensor Ref ByteString -> Tensor v2 Float -> Build ((Tensor Value dtype, Tensor Value Int64))
|
|
|
|
-- | Computes the sum of elements across dimensions of a SparseTensor.
|
|
--
|
|
-- This Op takes a SparseTensor and is the sparse counterpart to
|
|
-- `tf.reduce_sum()`. In particular, this Op also returns a dense
|
|
-- <a>Tensor</a> instead of a sparse one.
|
|
--
|
|
-- Reduces <tt>sp_input</tt> along the dimensions given in
|
|
-- <tt>reduction_axes</tt>. Unless <tt>keep_dims</tt> is true, the rank
|
|
-- of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_axes</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
--
|
|
-- If <tt>reduction_axes</tt> has no entries, all dimensions are reduced,
|
|
-- and a tensor with a single element is returned. Additionally, the axes
|
|
-- can be negative, which are interpreted according to the indexing rules
|
|
-- in Python.
|
|
sparseReduceSum :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int32 -> Tensor Value t
|
|
|
|
-- | Returns x / y element-wise for real types.
|
|
--
|
|
-- If <tt>x</tt> and <tt>y</tt> are reals, this will return the
|
|
-- floating-point division.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Div</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
realDiv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
tensorArraySize :: Tensor Ref ByteString -> Tensor v2 Float -> Build (Tensor Value Int32)
|
|
|
|
-- | Adds <tt>bias</tt> to <a>value</a>.
|
|
--
|
|
-- This is a deprecated version of BiasAdd and will be soon removed.
|
|
--
|
|
-- This is a special case of `tf.add` where <tt>bias</tt> is restricted
|
|
-- to be 1-D. Broadcasting is supported, so <a>value</a> may have any
|
|
-- number of dimensions.
|
|
biasAddV1 :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the truth value of x OR y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>LogicalOr</tt> supports broadcasting. More about
|
|
-- broadcasting <a>here</a></li>
|
|
-- </ul>
|
|
logicalOr :: Tensor v1 Bool -> Tensor v2 Bool -> Tensor Value Bool
|
|
|
|
-- | Push an element onto the stack.
|
|
stackPush :: (TensorType t) => Tensor Ref ByteString -> Tensor v2 t -> Build (Tensor Value t)
|
|
|
|
-- | Computes Quantized Rectified Linear: `max(features, 0)`
|
|
quantizedRelu :: (TensorType tinput, OneOf '[Int16, Int32, Word16, Word8] tinput, TensorType out_type, OneOf '[Int16, Int32, Word16, Word8] out_type) => Tensor v1 tinput -> Tensor v2 Float -> Tensor v3 Float -> (Tensor Value out_type, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Return the reduction indices for computing gradients of s0 op s1 with
|
|
-- broadcast.
|
|
--
|
|
-- This is typically used by gradient computations for a broadcasting
|
|
-- operation.
|
|
broadcastGradientArgs :: (TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Tensor v2 t -> (Tensor Value t, Tensor Value t)
|
|
|
|
-- | Finds unique elements in a 1-D tensor.
|
|
--
|
|
-- This operation returns a tensor <tt>y</tt> containing all of the
|
|
-- unique elements of <tt>x</tt> sorted in the same order that they occur
|
|
-- in <tt>x</tt>. This operation also returns a tensor <tt>idx</tt> the
|
|
-- same size as <tt>x</tt> that contains the index of each value of
|
|
-- <tt>x</tt> in the unique output <tt>y</tt>. Finally, it returns a
|
|
-- third tensor <tt>count</tt> that contains the count of each element of
|
|
-- <tt>y</tt> in <tt>x</tt>. In other words:
|
|
--
|
|
-- `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>x</tt> 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] ```
|
|
uniqueWithCounts :: (TensorType t, TensorType out_idx, OneOf '[Int32, Int64] out_idx) => Tensor v1 t -> (Tensor Value t, Tensor Value out_idx, Tensor Value out_idx)
|
|
|
|
-- | Returns element-wise remainder of division. This emulates C semantics
|
|
-- where
|
|
--
|
|
-- true, this follows C semantics in that the result here is consistent
|
|
-- with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Mod</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
truncateMod :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Returns the gradient of <tt>StridedSlice</tt>.
|
|
--
|
|
-- Since <tt>StridedSlice</tt> cuts out pieces of its <tt>input</tt>
|
|
-- which is size <a>shape</a>, its gradient will have the same shape
|
|
-- (which is passed here as <a>shape</a>). The gradient will be zero in
|
|
-- any element that the slice does not select.
|
|
--
|
|
-- Arguments are the same as StridedSliceGrad with the exception that
|
|
-- <tt>dy</tt> is the input gradient to be propagated and <a>shape</a> is
|
|
-- the shape of <tt>StridedSlice</tt>'s <tt>input</tt>.
|
|
stridedSliceGrad :: (TensorType t, TensorType index, OneOf '[Int32, Int64] index) => Tensor v1 index -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor v5 t -> Tensor Value t
|
|
|
|
-- | Performs fractional average pooling on the input.
|
|
--
|
|
-- 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.
|
|
fractionalAvgPool :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value Int64, Tensor Value Int64)
|
|
|
|
-- | Extracts the average sparse gradient in the given
|
|
-- SparseConditionalAccumulator,
|
|
--
|
|
-- provided that sufficient (i.e., more than num_required) gradients have
|
|
-- been accumulated. The op will blocks until sufficient gradients have
|
|
-- been accumulated. If the accumulator has already aggregated more than
|
|
-- num_required gradients, it will return its average of the accumulated
|
|
-- gradients. Also automatically increments the recorded global_step in
|
|
-- the accumulator by 1, and resets the aggregate to 0.
|
|
sparseAccumulatorTakeGradient :: (TensorType dtype, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] dtype) => Tensor Ref ByteString -> Tensor v2 Int32 -> Build ((Tensor Value Int64, Tensor Value dtype, Tensor Value Int64))
|
|
|
|
-- | Convert JSON-encoded Example records to binary protocol buffer
|
|
-- strings.
|
|
--
|
|
-- This op translates a tensor containing Example records, encoded using
|
|
-- the <a>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.
|
|
decodeJSONExample :: Tensor v1 ByteString -> Tensor Value ByteString
|
|
|
|
-- | A placeholder op that passes though <tt>input</tt> when its output is
|
|
-- not fed.
|
|
placeholderWithDefault :: (TensorType dtype) => Shape -> Tensor v1 dtype -> Tensor Value dtype
|
|
|
|
-- | Update '*var' according to the Ftrl-proximal scheme.
|
|
--
|
|
-- accum_new = accum + grad * grad linear += grad +
|
|
-- (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0
|
|
-- / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 -
|
|
-- linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new
|
|
applyFtrl :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor Ref t -> Tensor Ref t -> Tensor Ref t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Build (Tensor Ref t)
|
|
|
|
-- | Applies L1 regularization shrink step on the parameters.
|
|
sdcaShrinkL1 :: Float -> Float -> [Tensor Ref Float] -> Build (ControlNode)
|
|
|
|
-- | Generate a sharded filename. The filename is printf formatted as
|
|
--
|
|
-- %s-%05d-of-%05d, basename, shard, num_shards.
|
|
shardedFilename :: Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 Int32 -> Tensor Value ByteString
|
|
|
|
-- | Fake-quantize the <tt>inputs</tt> tensor, type float to
|
|
-- <tt>outputs</tt> tensor of same type.
|
|
--
|
|
-- Attributes [min; max] define the clamping range for the
|
|
-- <tt>inputs</tt> data. Op divides this range into 255 steps (total of
|
|
-- 256 values), then replaces each <tt>inputs</tt> value with the closest
|
|
-- of the quantized step values.
|
|
--
|
|
-- Quantization is called fake since the output is still in floating
|
|
-- point.
|
|
fakeQuantWithMinMaxArgs :: Tensor v1 Float -> Tensor Value Float
|
|
|
|
-- | Applies sparse addition between <tt>updates</tt> and individual values
|
|
-- or slices
|
|
--
|
|
-- within a given variable according to <tt>indices</tt>.
|
|
--
|
|
-- <tt>ref</tt> is a <a>Tensor</a> with rank <tt>P</tt> and
|
|
-- <tt>indices</tt> is a <a>Tensor</a> of rank <tt>Q</tt>.
|
|
--
|
|
-- <tt>indices</tt> must be integer tensor, containing indices into
|
|
-- <tt>ref</tt>. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 <
|
|
-- K <= P`.
|
|
--
|
|
-- The innermost dimension of <tt>indices</tt> (with length <tt>K</tt>)
|
|
-- corresponds to indices into elements (if `K = P`) or slices (if `K
|
|
-- < P`) along the <tt>K</tt>th dimension of <tt>ref</tt>.
|
|
--
|
|
-- <tt>updates</tt> is <a>Tensor</a> of rank `Q-1+P-K` with shape:
|
|
--
|
|
-- ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ```
|
|
--
|
|
-- For example, say we want to add 4 scattered elements to a rank-1
|
|
-- tensor to 8 elements. In Python, that addition would look like this:
|
|
--
|
|
-- ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices =
|
|
-- tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11,
|
|
-- 12]) add = tf.scatter_nd_add(ref, indices, updates) with tf.Session()
|
|
-- as sess: print sess.run(add)
|
|
--
|
|
-- The resulting update to ref would look like this:
|
|
--
|
|
-- <ul>
|
|
-- <li><i>1, 13, 3, 14, 14, 6, 7, 20</i></li>
|
|
-- </ul>
|
|
--
|
|
-- See <a>tf.scatter_nd</a> for more details about how to make updates to
|
|
-- slices.
|
|
scatterNdAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
|
|
|
|
-- | Returns the number of gradients aggregated in the given accumulators.
|
|
accumulatorNumAccumulated :: Tensor Ref ByteString -> Build (Tensor Value Int32)
|
|
|
|
-- | Computes the sum along sparse segments of a tensor divided by the sqrt
|
|
-- of N.
|
|
--
|
|
-- N is the size of the segment being reduced.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
sparseSegmentSqrtN :: (TensorType t, OneOf '[Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor v3 Int32 -> Tensor Value t
|
|
|
|
-- | DepthToSpace for tensors of type T.
|
|
--
|
|
-- 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
|
|
-- <tt>depth</tt> dimension are moved in spatial blocks to the
|
|
-- <tt>height</tt> and <tt>width</tt> dimensions. The attr
|
|
-- <tt>block_size</tt> indicates the input block size and how the data is
|
|
-- moved.
|
|
--
|
|
-- <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>
|
|
--
|
|
-- 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)]`
|
|
--
|
|
-- This operation requires that the input tensor be of rank 4, and that
|
|
-- <tt>block_size</tt> be >=1 and that `block_size * block_size` be a
|
|
-- divisor of the input depth.
|
|
--
|
|
-- 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.
|
|
--
|
|
-- For example, given this input of shape `[1, 1, 1, 4]`, and a block
|
|
-- size of 2:
|
|
--
|
|
-- ```prettyprint x = [[[[1, 2, 3, 4]]]]
|
|
--
|
|
-- ```
|
|
--
|
|
-- This operation will output a tensor of shape `[1, 2, 2, 1]`:
|
|
--
|
|
-- ```prettyprint [[[[1], [2]], [[3], [4]]]] ```
|
|
--
|
|
-- 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]`.
|
|
--
|
|
-- For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`,
|
|
-- e.g.
|
|
--
|
|
-- ```prettyprint x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ```
|
|
--
|
|
-- This operation, for block size of 2, will return the following tensor
|
|
-- of shape `[1, 2, 2, 3]`
|
|
--
|
|
-- ```prettyprint [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]]
|
|
--
|
|
-- ```
|
|
--
|
|
-- Similarly, for the following input of shape `[1 2 2 4]`, and a block
|
|
-- size of 2:
|
|
--
|
|
-- ```prettyprint x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12],
|
|
-- [13, 14, 15, 16]]]] ```
|
|
--
|
|
-- the operator will return the following tensor of shape `[1 4 4 1]`:
|
|
--
|
|
-- ```prettyprint x = [[ [1], [2], [5], [6]], [ [3], [4], [7], [8]], [
|
|
-- [9], [10], [13], [14]], [ [11], [12], [15], [16]]]
|
|
--
|
|
-- ```
|
|
depthToSpace :: (TensorType t) => Int64 -> Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Generates labels for candidate sampling with a learned unigram
|
|
-- distribution.
|
|
--
|
|
-- See explanations of candidate sampling and the data formats at
|
|
-- go/candidate-sampling.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
allCandidateSampler :: Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Computes the gradient of nearest neighbor interpolation.
|
|
resizeNearestNeighborGrad :: (TensorType t, OneOf '[Int32, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value t
|
|
|
|
-- | Performs greedy decoding on the logits given in inputs.
|
|
--
|
|
-- 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 <a>*</a>, the sequence "A B B * B
|
|
-- B" becomes "A B" if merge_repeated = True and "A B B B B" if
|
|
-- merge_repeated = False.
|
|
--
|
|
-- 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.
|
|
cTCGreedyDecoder :: Tensor v1 Float -> Tensor v2 Int32 -> (Tensor Value Int64, Tensor Value Int64, Tensor Value Int64, Tensor Value Float)
|
|
|
|
-- | L2 Loss.
|
|
--
|
|
-- Computes half the L2 norm of a tensor without the <a>sqrt</a>:
|
|
--
|
|
-- output = sum(t ** 2) / 2
|
|
l2Loss :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Computes the maximum along segments of a tensor.
|
|
--
|
|
-- Read <a>the section on Segmentation</a> for an explanation of
|
|
-- segments.
|
|
--
|
|
-- Computes a tensor such that \(output_i = max_j(data_j)\) where
|
|
-- <a>max</a> is over <tt>j</tt> such that `segment_ids[j] == i`.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/SegmentMax.png" alt</a> <a>/div</a>
|
|
segmentMax :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 t -> Tensor v2 tindices -> Tensor Value t
|
|
|
|
-- | Increments <tt>ref</tt> until it reaches <tt>limit</tt>.
|
|
countUpTo :: (TensorType t, OneOf '[Int32, Int64] t) => Int64 -> Tensor Ref t -> Build (Tensor Value t)
|
|
|
|
-- | A Reader that outputs the records from a TensorFlow Records file.
|
|
tFRecordReader :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Forwards `data` to the output port determined by <a>pred</a>.
|
|
--
|
|
-- If <a>pred</a> is true, the `data` input is forwarded to
|
|
-- <tt>output_true</tt>. Otherwise, the data goes to
|
|
-- <tt>output_false</tt>.
|
|
--
|
|
-- See also <tt>RefSwitch</tt> and <tt>Merge</tt>.
|
|
switch :: (TensorType t) => Tensor v1 t -> Tensor v2 Bool -> (Tensor Value t, Tensor Value t)
|
|
|
|
-- | Computes gradients for SparseSegmentMean.
|
|
--
|
|
-- Returns tensor "output" with same shape as grad, except for dimension
|
|
-- 0 whose value is output_dim0.
|
|
sparseSegmentMeanGrad :: (TensorType t, OneOf '[Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor Value t
|
|
|
|
-- | Gather values or slices from <tt>params</tt> according to
|
|
-- <tt>indices</tt>.
|
|
--
|
|
-- <tt>params</tt> is a Tensor of rank <tt>P</tt> and <tt>indices</tt> is
|
|
-- a Tensor of rank <tt>Q</tt>.
|
|
--
|
|
-- <tt>indices</tt> must be integer tensor, containing indices into
|
|
-- <tt>params</tt>. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0
|
|
-- < K <= P`.
|
|
--
|
|
-- The innermost dimension of <tt>indices</tt> (with length <tt>K</tt>)
|
|
-- corresponds to indices into elements (if `K = P`) or slices (if `K
|
|
-- < P`) along the <tt>K</tt>th dimension of <tt>params</tt>.
|
|
--
|
|
-- Produces an output tensor with shape
|
|
--
|
|
-- ``` [d_0, ..., d_{Q-2}, params.shape[K], ..., params.shape[P-1]]. ```
|
|
--
|
|
-- Some examples below.
|
|
--
|
|
-- Simple indexing into a matrix:
|
|
--
|
|
-- ```python indices = [[0, 0], [1, 1]] params = [[<tt>a</tt>,
|
|
-- <tt>b</tt>], [<tt>c</tt>, <tt>d</tt>]] output = [<tt>a</tt>,
|
|
-- <tt>d</tt>] ```
|
|
--
|
|
-- Slice indexing into a matrix:
|
|
--
|
|
-- ```python indices = [[1], [0]] params = [[<tt>a</tt>, <tt>b</tt>],
|
|
-- [<tt>c</tt>, <tt>d</tt>]] output = [[<tt>c</tt>, <tt>d</tt>],
|
|
-- [<tt>a</tt>, <tt>b</tt>]] ```
|
|
--
|
|
-- Indexing into a 3-tensor:
|
|
--
|
|
-- ```python indices = [[1]] params = [[[<tt>a0</tt>, <tt>b0</tt>],
|
|
-- [<tt>c0</tt>, <tt>d0</tt>]], [[<tt>a1</tt>, <tt>b1</tt>],
|
|
-- [<tt>c1</tt>, <tt>d1</tt>]]] output = [[[<tt>a1</tt>, <tt>b1</tt>],
|
|
-- [<tt>c1</tt>, <tt>d1</tt>]]]
|
|
--
|
|
-- indices = [[0, 1], [1, 0]] params = [[[<tt>a0</tt>, <tt>b0</tt>],
|
|
-- [<tt>c0</tt>, <tt>d0</tt>]], [[<tt>a1</tt>, <tt>b1</tt>],
|
|
-- [<tt>c1</tt>, <tt>d1</tt>]]] output = [[<tt>c0</tt>, <tt>d0</tt>],
|
|
-- [<tt>a1</tt>, <tt>b1</tt>]]
|
|
--
|
|
-- indices = [[0, 0, 1], [1, 0, 1]] params = [[[<tt>a0</tt>,
|
|
-- <tt>b0</tt>], [<tt>c0</tt>, <tt>d0</tt>]], [[<tt>a1</tt>,
|
|
-- <tt>b1</tt>], [<tt>c1</tt>, <tt>d1</tt>]]] output = [<tt>b0</tt>,
|
|
-- <tt>b1</tt>] ```
|
|
--
|
|
-- Batched indexing into a matrix:
|
|
--
|
|
-- ```python indices = [[[0, 0]], [[0, 1]]] params = [[<tt>a</tt>,
|
|
-- <tt>b</tt>], [<tt>c</tt>, <tt>d</tt>]] output = [[<tt>a</tt>],
|
|
-- [<tt>b</tt>]] ```
|
|
--
|
|
-- Batched slice indexing into a matrix:
|
|
--
|
|
-- ```python indices = [[[1]], [[0]]] params = [[<tt>a</tt>, <tt>b</tt>],
|
|
-- [<tt>c</tt>, <tt>d</tt>]] output = [[[<tt>c</tt>, <tt>d</tt>]],
|
|
-- [[<tt>a</tt>, <tt>b</tt>]]] ```
|
|
--
|
|
-- Batched indexing into a 3-tensor:
|
|
--
|
|
-- ```python indices = [[[1]], [[0]]] params = [[[<tt>a0</tt>,
|
|
-- <tt>b0</tt>], [<tt>c0</tt>, <tt>d0</tt>]], [[<tt>a1</tt>,
|
|
-- <tt>b1</tt>], [<tt>c1</tt>, <tt>d1</tt>]]] output = [[[[<tt>a1</tt>,
|
|
-- <tt>b1</tt>], [<tt>c1</tt>, <tt>d1</tt>]]], [[[<tt>a0</tt>,
|
|
-- <tt>b0</tt>], [<tt>c0</tt>, <tt>d0</tt>]]]]
|
|
--
|
|
-- indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]] params =
|
|
-- [[[<tt>a0</tt>, <tt>b0</tt>], [<tt>c0</tt>, <tt>d0</tt>]],
|
|
-- [[<tt>a1</tt>, <tt>b1</tt>], [<tt>c1</tt>, <tt>d1</tt>]]] output =
|
|
-- [[[<tt>c0</tt>, <tt>d0</tt>], [<tt>a1</tt>, <tt>b1</tt>]],
|
|
-- [[<tt>a0</tt>, <tt>b0</tt>], [<tt>c1</tt>, <tt>d1</tt>]]]
|
|
--
|
|
-- indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]] params =
|
|
-- [[[<tt>a0</tt>, <tt>b0</tt>], [<tt>c0</tt>, <tt>d0</tt>]],
|
|
-- [[<tt>a1</tt>, <tt>b1</tt>], [<tt>c1</tt>, <tt>d1</tt>]]] output =
|
|
-- [[<tt>b0</tt>, <tt>b1</tt>], [<tt>d0</tt>, <tt>c1</tt>]] ```
|
|
gatherNd :: (TensorType tparams, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 tparams -> Tensor v2 tindices -> Tensor Value tparams
|
|
|
|
-- | Removes dimensions of size 1 from the shape of a tensor.
|
|
--
|
|
-- Given a tensor <tt>input</tt>, 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 <tt>squeeze_dims</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is a tensor of shape [1, 2, 1, 3, 1, 1]
|
|
-- shape(squeeze(t)) ==> [2, 3] ```
|
|
--
|
|
-- Or, to remove specific size 1 dimensions:
|
|
--
|
|
-- ```prettyprint # <tt>t</tt> is a tensor of shape [1, 2, 1, 3, 1, 1]
|
|
-- shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] ```
|
|
squeeze :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Outputs random values from a uniform distribution.
|
|
--
|
|
-- 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.
|
|
randomUniform :: (TensorType dtype, OneOf '[Word16, Double, Float] dtype, TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 t -> Build (Tensor Value dtype)
|
|
|
|
-- | Returns up to <tt>num_records</tt> (key, value) pairs produced by a
|
|
-- Reader.
|
|
--
|
|
-- 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 <tt>num_records</tt> even
|
|
-- before the last batch.
|
|
readerReadUpTo :: Tensor Ref ByteString -> Tensor Ref ByteString -> Tensor v3 Int64 -> Build ((Tensor Value ByteString, Tensor Value ByteString))
|
|
|
|
-- | Computes the gradients of 3-D convolution with respect to the input.
|
|
conv3DBackpropInput :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
|
|
|
|
-- | Computes a 2-D depthwise convolution given 4-D <tt>input</tt> and
|
|
-- <a>filter</a> tensors.
|
|
--
|
|
-- 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, channel_multiplier]`, containing
|
|
-- <tt>in_channels</tt> convolutional filters of depth 1,
|
|
-- <tt>depthwise_conv2d</tt> applies a different filter to each input
|
|
-- channel (expanding from 1 channel to <tt>channel_multiplier</tt>
|
|
-- channels for each), then concatenates the results together. Thus, the
|
|
-- output has `in_channels * channel_multiplier` channels.
|
|
--
|
|
-- for k in 0..in_channels-1 for q in 0..channel_multiplier-1 output[b,
|
|
-- i, j, k * channel_multiplier + q] = sum_{di, dj} input[b, strides[1] *
|
|
-- i + di, strides[2] * j + dj, k] * filter[di, dj, k, q]
|
|
--
|
|
-- Must have `strides[0] = strides[3] = 1`. For the most common case of
|
|
-- the same horizontal and vertices strides, `strides = [1, stride,
|
|
-- stride, 1]`.
|
|
depthwiseConv2dNative :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Generates labels for candidate sampling with a learned unigram
|
|
-- distribution.
|
|
--
|
|
-- See explanations of candidate sampling and the data formats at
|
|
-- go/candidate-sampling.
|
|
--
|
|
-- For each batch, this op picks a single set of sampled candidate
|
|
-- labels.
|
|
--
|
|
-- 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.
|
|
learnedUnigramCandidateSampler :: Int64 -> Int64 -> Int64 -> Bool -> Tensor v1 Int64 -> (Tensor Value Int64, Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Table initializer that takes two tensors for keys and values
|
|
-- respectively.
|
|
initializeTable :: (TensorType tkey, TensorType tval) => Tensor Ref ByteString -> Tensor v2 tkey -> Tensor v3 tval -> Build (ControlNode)
|
|
|
|
-- | Forwards the value of an available tensor from <tt>inputs</tt> to
|
|
-- <tt>output</tt>.
|
|
--
|
|
-- <tt>Merge</tt> waits for at least one of the tensors in
|
|
-- <tt>inputs</tt> to become available. It is usually combined with
|
|
-- <tt>Switch</tt> to implement branching.
|
|
--
|
|
-- <tt>Merge</tt> forwards the first tensor for become available to
|
|
-- <tt>output</tt>, and sets <tt>value_index</tt> to its index in
|
|
-- <tt>inputs</tt>.
|
|
merge :: (TensorType t) => [Tensor v1 t] -> (Tensor Value t, Tensor Value Int32)
|
|
|
|
-- | Forwards the value of an available tensor from <tt>inputs</tt> to
|
|
-- <tt>output</tt>.
|
|
--
|
|
-- <tt>Merge</tt> waits for at least one of the tensors in
|
|
-- <tt>inputs</tt> to become available. It is usually combined with
|
|
-- <tt>Switch</tt> to implement branching.
|
|
--
|
|
-- <tt>Merge</tt> forwards the first tensor for become available to
|
|
-- <tt>output</tt>, and sets <tt>value_index</tt> to its index in
|
|
-- <tt>inputs</tt>.
|
|
refMerge :: (TensorType t) => [Tensor Ref t] -> Build ((Tensor Ref t, Tensor Value Int32))
|
|
|
|
-- | Rounds the values of a tensor to the nearest integer, element-wise.
|
|
--
|
|
-- Rounds half to even. Also known as bankers rounding. If you want to
|
|
-- round according to the current system rounding mode use std::cint.
|
|
round :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
batchSelfAdjointEig :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Partitions `data` into <tt>num_partitions</tt> tensors using indices
|
|
-- from <tt>partitions</tt>.
|
|
--
|
|
-- For each index tuple <tt>js</tt> of size `partitions.ndim`, the slice
|
|
-- `data[js, ...]` becomes part of `outputs[partitions[js]]`. The slices
|
|
-- with `partitions[js] = i` are placed in `outputs[i]` in lexicographic
|
|
-- order of <tt>js</tt>, and the first dimension of `outputs[i]` is the
|
|
-- number of entries in <tt>partitions</tt> equal to <tt>i</tt>. In
|
|
-- detail,
|
|
--
|
|
-- ```python outputs[i].shape = [sum(partitions == i)] +
|
|
-- data.shape[partitions.ndim:]
|
|
--
|
|
-- outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) ```
|
|
--
|
|
-- `data.shape` must start with `partitions.shape`.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```python # Scalar partitions. partitions = 1 num_partitions = 2 data
|
|
-- = [10, 20] outputs[0] = [] # Empty with shape [0, 2] outputs[1] =
|
|
-- [[10, 20]]
|
|
--
|
|
-- # 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] ```
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/DynamicPartition.png" alt</a> <a>/div</a>
|
|
dynamicPartition :: (TensorType t) => Int64 -> Tensor v1 t -> Tensor v2 Int32 -> [Tensor Value t]
|
|
|
|
-- | Reshapes a tensor.
|
|
--
|
|
-- Given <tt>tensor</tt>, this operation returns a tensor that has the
|
|
-- same values as <tt>tensor</tt> with shape <a>shape</a>.
|
|
--
|
|
-- If one component of <a>shape</a> is the special value -1, the size of
|
|
-- that dimension is computed so that the total size remains constant. In
|
|
-- particular, a <a>shape</a> of `[-1]` flattens into 1-D. At most one
|
|
-- component of <a>shape</a> can be -1.
|
|
--
|
|
-- If <a>shape</a> is 1-D or higher, then the operation returns a tensor
|
|
-- with shape <a>shape</a> filled with the values of <tt>tensor</tt>. In
|
|
-- this case, the number of elements implied by <a>shape</a> must be the
|
|
-- same as the number of elements in <tt>tensor</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # tensor <tt>t</tt> is [1, 2, 3, 4, 5, 6, 7, 8, 9] #
|
|
-- tensor <tt>t</tt> has shape [9] reshape(t, [3, 3]) ==> [[1, 2, 3],
|
|
-- [4, 5, 6], [7, 8, 9]]
|
|
--
|
|
-- # tensor <tt>t</tt> is [[[1, 1], [2, 2]], # [[3, 3], [4, 4]]] # tensor
|
|
-- <tt>t</tt> has shape [2, 2, 2] reshape(t, [2, 4]) ==> [[1, 1, 2,
|
|
-- 2], [3, 3, 4, 4]]
|
|
--
|
|
-- # tensor <tt>t</tt> is [[[1, 1, 1], # [2, 2, 2]], # [[3, 3, 3], # [4,
|
|
-- 4, 4]], # [[5, 5, 5], # [6, 6, 6]]] # tensor <tt>t</tt> has shape [3,
|
|
-- 2, 3] # pass '[-1]' to flatten <tt>t</tt> reshape(t, [-1]) ==> [1,
|
|
-- 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
|
|
--
|
|
-- # -1 can also be used to infer the shape
|
|
--
|
|
-- # -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]]]
|
|
--
|
|
-- # tensor <tt>t</tt> is [7] # shape `[]` reshapes to a scalar
|
|
-- reshape(t, []) ==> 7 ```
|
|
reshape :: (TensorType t, TensorType tshape, OneOf '[Int32, Int64] tshape) => Tensor v1 t -> Tensor v2 tshape -> Tensor Value t
|
|
|
|
-- | A Reader that outputs fixed-length records from a file.
|
|
fixedLengthRecordReader :: Int64 -> Build (Tensor Ref ByteString)
|
|
|
|
-- | Distributed version of Stochastic Dual Coordinate Ascent (SDCA)
|
|
-- optimizer for
|
|
--
|
|
-- linear models with L1 + L2 regularization. As global optimization
|
|
-- objective is strongly-convex, the optimizer optimizes the dual
|
|
-- objective at each step. The optimizer applies each update one example
|
|
-- at a time. Examples are sampled uniformly, and the optimizer is
|
|
-- learning rate free and enjoys linear convergence rate.
|
|
--
|
|
-- Proximal Stochastic Dual Coordinate Ascent, Shalev-Shwartz, Shai;
|
|
-- Zhang, Tong. 2012 arXiv1211.2717S:
|
|
-- <a>http://arxiv.org/pdf/1211.2717v1.pdf</a>
|
|
--
|
|
-- Loss objective = sum f_{i}(wx_{i}) + (l2 / 2) * |w|^2 + l1 * |w|
|
|
--
|
|
-- Adding vs. Averaging in Distributed Primal-Dual Optimization. Chenxin
|
|
-- Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtarik,
|
|
-- Martin Takac <a>http://arxiv.org/abs/1502.03508</a>
|
|
--
|
|
-- Stochastic Dual Coordinate Ascent with Adaptive Probabilities Dominik
|
|
-- Csiba, Zheng Qu, Peter Richtarik
|
|
-- <a>https://arxiv.org/abs/1502.08053</a>
|
|
sdcaOptimizer :: Float -> Float -> Int64 -> Int64 -> [Tensor v1 Int64] -> [Tensor v2 Int64] -> [Tensor v3 Float] -> [Tensor v4 Float] -> Tensor v5 Float -> Tensor v6 Float -> [Tensor v7 Int64] -> [Tensor v8 Float] -> [Tensor v9 Float] -> Tensor v10 Float -> (Tensor Value Float, [Tensor Value Float], [Tensor Value Float])
|
|
|
|
-- | Resize <tt>images</tt> to <a>size</a> using area interpolation.
|
|
--
|
|
-- Input images can be of different types but output images are always
|
|
-- float.
|
|
resizeArea :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor Value Float
|
|
|
|
-- | Generates values in an interval.
|
|
--
|
|
-- A sequence of <tt>num</tt> evenly-spaced values are generated
|
|
-- beginning at <tt>start</tt>. If `num > 1`, the values in the
|
|
-- sequence increase by `stop - start / num - 1`, so that the last one is
|
|
-- exactly <tt>stop</tt>.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ``` tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0
|
|
-- 12.0] ```
|
|
linSpace :: (TensorType t, OneOf '[Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 t -> Tensor v3 tidx -> Tensor Value t
|
|
|
|
-- | Calculates the CTC Loss (log probability) for each batch entry. Also
|
|
-- calculates
|
|
--
|
|
-- the gradient. This class performs the softmax operation for you, so
|
|
-- inputs should be e.g. linear projections of outputs by an LSTM.
|
|
cTCLoss :: Tensor v1 Float -> Tensor v2 Int64 -> Tensor v3 Int32 -> Tensor v4 Int32 -> (Tensor Value Float, Tensor Value Float)
|
|
|
|
-- | Returns the batched diagonal part of a batched tensor.
|
|
--
|
|
-- This operation returns a tensor with the <tt>diagonal</tt> part of the
|
|
-- batched <tt>input</tt>. The <tt>diagonal</tt> part is computed as
|
|
-- follows:
|
|
--
|
|
-- Assume <tt>input</tt> has <tt>k</tt> dimensions `[I, J, K, ..., M,
|
|
-- N]`, then the output is a tensor of rank `k - 1` with dimensions `[I,
|
|
-- J, K, ..., min(M, N)]` where:
|
|
--
|
|
-- `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
|
|
--
|
|
-- The input must be at least a matrix.
|
|
--
|
|
-- For example:
|
|
--
|
|
-- ```prettyprint # <tt>input</tt> 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]]]
|
|
--
|
|
-- and input.shape = (2, 4, 4)
|
|
--
|
|
-- tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
|
|
--
|
|
-- which has shape (2, 4) ```
|
|
matrixDiagPart :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Creates or finds a child frame, and makes `data` available to the
|
|
-- child frame.
|
|
--
|
|
-- This op is used together with <tt>Exit</tt> to create loops in the
|
|
-- graph. The unique <tt>frame_name</tt> is used by the <tt>Executor</tt>
|
|
-- to identify frames. If <tt>is_constant</tt> is true, <tt>output</tt>
|
|
-- is a constant in the child frame; otherwise it may be changed in the
|
|
-- child frame. At most <tt>parallel_iterations</tt> iterations are run
|
|
-- in parallel in the child frame.
|
|
enter :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | PNG-encode an image.
|
|
--
|
|
-- <tt>image</tt> is a 3-D uint8 or uint16 Tensor of shape `[height,
|
|
-- width, channels]` where <tt>channels</tt> is:
|
|
--
|
|
-- <ul>
|
|
-- <li>1: for grayscale.</li>
|
|
-- <li>2: for grayscale + alpha.</li>
|
|
-- <li>3: for RGB.</li>
|
|
-- <li>4: for RGBA.</li>
|
|
-- </ul>
|
|
--
|
|
-- The ZLIB compression level, <tt>compression</tt>, 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.
|
|
encodePng :: (TensorType t, OneOf '[Word16, Word8] t) => Tensor v1 t -> Tensor Value ByteString
|
|
|
|
-- | Exits the current frame to its parent frame.
|
|
--
|
|
-- Exit makes its input `data` available to the parent frame.
|
|
exit :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Creates a new tensor by applying sparse <tt>updates</tt> to individual
|
|
--
|
|
-- values or slices within a zero tensor of the given <a>shape</a> tensor
|
|
-- according to indices. This operator is the inverse of the
|
|
-- <a>tf.gather_nd</a> operator which extracts values or slices from a
|
|
-- given tensor.
|
|
--
|
|
-- TODO(simister): Add a link to Variable.<b>getitem</b> documentation on
|
|
-- slice syntax.
|
|
--
|
|
-- <a>shape</a> is a <tt>TensorShape</tt> with rank <tt>P</tt> and
|
|
-- <tt>indices</tt> is a <a>Tensor</a> of rank <tt>Q</tt>.
|
|
--
|
|
-- <tt>indices</tt> must be integer tensor, containing indices into
|
|
-- <a>shape</a>. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 <
|
|
-- K <= P`.
|
|
--
|
|
-- The innermost dimension of <tt>indices</tt> (with length <tt>K</tt>)
|
|
-- corresponds to indices into elements (if `K = P`) or slices (if `K
|
|
-- < P`) along the <tt>K</tt>th dimension of <a>shape</a>.
|
|
--
|
|
-- <tt>updates</tt> is Tensor of rank `Q-1+P-K` with shape:
|
|
--
|
|
-- ``` [d_0, ..., d_{Q-2}, shape[K], ..., shape[P-1]]. ```
|
|
--
|
|
-- The simplest form of scatter is to insert individual elements in a
|
|
-- tensor by index. For example, say we want to insert 4 scattered
|
|
-- elements in a rank-1 tensor with 8 elements.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/ScatterNd1.png" alt</a> <a>/div</a>
|
|
--
|
|
-- In Python, this scatter operation would look like this:
|
|
--
|
|
-- indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9,
|
|
-- 10, 11, 12]) shape = tf.constant([8]) scatter = tf.scatter_nd(indices,
|
|
-- updates, shape) with tf.Session() as sess: print sess.run(scatter)
|
|
--
|
|
-- The resulting tensor would look like this:
|
|
--
|
|
-- <ul>
|
|
-- <li><i>0, 11, 0, 10, 9, 0, 0, 12</i></li>
|
|
-- </ul>
|
|
--
|
|
-- We can also, insert entire slices of a higher rank tensor all at once.
|
|
-- For example, if we wanted to insert two slices in the first dimension
|
|
-- of a rank-3 tensor with two matrices of new values.
|
|
--
|
|
-- <a>style="width:70%; margin:auto; margin-bottom:10px;
|
|
-- margin-top:20px;"</a> <a>style="width:100%"
|
|
-- src="../../images/ScatterNd2.png" alt</a> <a>/div</a>
|
|
--
|
|
-- In Python, this scatter operation would look like this:
|
|
--
|
|
-- indices = tf.constant([[0], [2]]) updates = tf.constant([[[5, 5, 5,
|
|
-- 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[5, 5, 5, 5], [6, 6,
|
|
-- 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]) shape = tf.constant([4, 4, 4])
|
|
-- scatter = tf.scatter_nd(indices, updates, shape) with tf.Session() as
|
|
-- sess: print sess.run(scatter)
|
|
--
|
|
-- The resulting tensor would look like this:
|
|
--
|
|
-- <ul>
|
|
-- <li><i>[[5, 5, 5, 5</i> , [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8,
|
|
-- 8]],</li>
|
|
-- <li><i>[0, 0, 0, 0</i> , [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0,
|
|
-- 0]],</li>
|
|
-- <li><i>[5, 5, 5, 5</i> , [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8,
|
|
-- 8]],</li>
|
|
-- <li><i>[0, 0, 0, 0</i> , [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0,
|
|
-- 0]]]</li>
|
|
-- </ul>
|
|
scatterNd :: (TensorType t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor v1 tindices -> Tensor v2 t -> Tensor v3 tindices -> Tensor Value t
|
|
|
|
-- | A queue that produces elements sorted by the first component value.
|
|
--
|
|
-- 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.
|
|
priorityQueue :: Build (Tensor Ref ByteString)
|
|
|
|
-- | Forwards the ref tensor `data` to the output port determined by
|
|
-- <a>pred</a>.
|
|
--
|
|
-- If <a>pred</a> is true, the `data` input is forwarded to
|
|
-- <tt>output_true</tt>. Otherwise, the data goes to
|
|
-- <tt>output_false</tt>.
|
|
--
|
|
-- See also <tt>Switch</tt> and <tt>Merge</tt>.
|
|
refSwitch :: (TensorType t) => Tensor Ref t -> Tensor v2 Bool -> Build ((Tensor Ref t, Tensor Ref t))
|
|
|
|
-- | Makes its input available to the next iteration.
|
|
nextIteration :: (TensorType t) => Tensor v1 t -> Tensor Value t
|
|
|
|
-- | Makes its input available to the next iteration.
|
|
refNextIteration :: (TensorType t) => Tensor Ref t -> Build (Tensor Ref t)
|
|
|
|
-- | Multiplies slices of two tensors in batches.
|
|
--
|
|
-- Multiplies all slices of <a>Tensor</a> <tt>x</tt> and <tt>y</tt> (each
|
|
-- slice can be viewed as an element of a batch), and arranges the
|
|
-- individual results in a single output tensor of the same batch size.
|
|
-- Each of the individual slices can optionally be adjointed (to adjoint
|
|
-- a matrix means to transpose and conjugate it) before multiplication by
|
|
-- setting the <tt>adj_x</tt> or <tt>adj_y</tt> flag to <a>True</a>,
|
|
-- which are by default <a>False</a>.
|
|
--
|
|
-- The input tensors <tt>x</tt> and <tt>y</tt> are 3-D or higher with
|
|
-- shape `[..., r_x, c_x]` and `[..., r_y, c_y]`.
|
|
--
|
|
-- The output tensor is 3-D or higher with shape `[..., r_o, c_o]`,
|
|
-- where:
|
|
--
|
|
-- r_o = c_x if adj_x else r_x c_o = r_y if adj_y else c_y
|
|
--
|
|
-- It is computed as:
|
|
--
|
|
-- output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
|
|
batchMatMul :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
|
|
|
|
-- | Forwards the <tt>index</tt>th element of <tt>inputs</tt> to
|
|
-- <tt>output</tt>.
|
|
refSelect :: (TensorType t) => Tensor v1 Int32 -> [Tensor Ref t] -> Build (Tensor Ref t)
|
|
|
|
-- | Computes the mean of elements across dimensions of a tensor.
|
|
--
|
|
-- Reduces <tt>input</tt> along the dimensions given in
|
|
-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
|
|
-- rank of the tensor is reduced by 1 for each entry in
|
|
-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
|
|
-- dimensions are retained with length 1.
|
|
mean :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tidx, OneOf '[Int32, Int64] tidx) => Tensor v1 t -> Tensor v2 tidx -> Tensor Value t
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-- | Adds sparse updates to a variable reference.
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--
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-- This operation computes
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--
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-- # Scalar indices ref[indices, ...] += updates[...]
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--
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-- # Vector indices (for each i) ref[indices[i], ...] += updates[i, ...]
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--
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-- # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...]
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-- += updates[i, ..., j, ...]
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--
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-- This operation outputs <tt>ref</tt> after the update is done. This
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-- makes it easier to chain operations that need to use the reset value.
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--
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-- Duplicate entries are handled correctly: if multiple <tt>indices</tt>
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-- reference the same location, their contributions add.
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--
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-- Requires `updates.shape = indices.shape + ref.shape[1:]`.
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--
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-- <a>style="width:70%; margin:auto; margin-bottom:10px;
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-- margin-top:20px;"</a> <a>style="width:100%"
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-- src="../../images/ScatterAdd.png" alt</a> <a>/div</a>
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scatterAdd :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t, TensorType tindices, OneOf '[Int32, Int64] tindices) => Tensor Ref t -> Tensor v2 tindices -> Tensor v3 t -> Build (Tensor Ref t)
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-- | Randomly crop <tt>image</tt>.
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--
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-- <a>size</a> is a 1-D int64 tensor with 2 elements representing the
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-- crop height and width. The values must be non negative.
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--
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-- This Op picks a random location in <tt>image</tt> and crops a
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-- <tt>height</tt> by <tt>width</tt> rectangle from that location. The
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-- random location is picked so the cropped area will fit inside the
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-- original image.
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randomCrop :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int64 -> Build (Tensor Value t)
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-- | Exits the current frame to its parent frame.
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--
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-- Exit makes its input `data` available to the parent frame.
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refExit :: (TensorType t) => Tensor Ref t -> Build (Tensor Ref t)
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-- | Produce a string tensor that encodes the state of a Reader.
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--
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-- Not all Readers support being serialized, so this can produce an
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-- Unimplemented error.
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readerSerializeState :: Tensor Ref ByteString -> Build (Tensor Value ByteString)
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-- | Computes the gradient for the tanh of <tt>x</tt> wrt its input.
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--
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-- Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and
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-- <tt>dy</tt> is the corresponding input gradient.
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tanhGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
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-- | Returns the element-wise max of two SparseTensors.
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--
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-- Assumes the two SparseTensors have the same shape, i.e., no
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-- broadcasting.
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sparseSparseMaximum :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor v5 t -> Tensor v6 Int64 -> (Tensor Value Int64, Tensor Value t)
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-- | Decode the first frame of a GIF-encoded image to a uint8 tensor.
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|
--
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-- GIF with frame or transparency compression are not supported convert
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|
-- animated GIF from compressed to uncompressed by:
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--
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-- convert $src.gif -coalesce $dst.gif
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decodeGif :: Tensor v1 ByteString -> Tensor Value Word8
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-- | Return substrings from <a>Tensor</a> of strings.
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--
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-- For each string in the input <a>Tensor</a>, creates a substring
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-- starting at index <tt>pos</tt> with a total length of <tt>len</tt>.
|
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--
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-- If <tt>len</tt> defines a substring that would extend beyond the
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-- length of the input string, then as many characters as possible are
|
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-- used.
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--
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-- If <tt>pos</tt> is negative or specifies a character index larger than
|
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-- any of the input strings, then an <tt>InvalidArgumentError</tt> is
|
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-- thrown.
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|
--
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-- <tt>pos</tt> and <tt>len</tt> must have the same shape, otherwise a
|
|
-- <tt>ValueError</tt> is thrown on Op creation.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Substr</tt> supports broadcasting up to two dimensions.
|
|
-- More about broadcasting <a>here</a></li>
|
|
-- <li>--</li>
|
|
-- </ul>
|
|
--
|
|
-- Examples
|
|
--
|
|
-- Using scalar <tt>pos</tt> and <tt>len</tt>:
|
|
--
|
|
-- ``` input = [b<tt>Hello</tt>, b<tt>World</tt>] position = 1 length = 3
|
|
--
|
|
-- output = [b<tt>ell</tt>, b<tt>orl</tt>] ```
|
|
--
|
|
-- Using <tt>pos</tt> and <tt>len</tt> with same shape as <tt>input</tt>:
|
|
--
|
|
-- ``` input = [[b<tt>ten</tt>, b<tt>eleven</tt>, b<tt>twelve</tt>],
|
|
-- [b<tt>thirteen</tt>, b<tt>fourteen</tt>, b<tt>fifteen</tt>],
|
|
-- [b<tt>sixteen</tt>, b<tt>seventeen</tt>, b<tt>eighteen</tt>]] position
|
|
-- = [[1, 2, 3], [1, 2, 3], [1, 2, 3]] length = [[2, 3, 4], [4, 3, 2],
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|
-- [5, 5, 5]]
|
|
--
|
|
-- output = [[b<tt>en</tt>, b<tt>eve</tt>, b<tt>lve</tt>],
|
|
-- [b<tt>hirt</tt>, b<tt>urt</tt>, b<tt>te</tt>], [b<tt>ixtee</tt>,
|
|
-- b<tt>vente</tt>, b<tt>hteen</tt>]] ```
|
|
--
|
|
-- Broadcasting <tt>pos</tt> and <tt>len</tt> onto <tt>input</tt>:
|
|
--
|
|
-- ``` input = [[b<tt>ten</tt>, b<tt>eleven</tt>, b<tt>twelve</tt>],
|
|
-- [b<tt>thirteen</tt>, b<tt>fourteen</tt>, b<tt>fifteen</tt>],
|
|
-- [b<tt>sixteen</tt>, b<tt>seventeen</tt>, b<tt>eighteen</tt>],
|
|
-- [b<tt>nineteen</tt>, b<tt>twenty</tt>, b<tt>twentyone</tt>]] position
|
|
-- = [1, 2, 3] length = [1, 2, 3]
|
|
--
|
|
-- output = [[b<tt>e</tt>, b<tt>ev</tt>, b<tt>lve</tt>], [b<tt>h</tt>,
|
|
-- b<tt>ur</tt>, b<tt>tee</tt>], [b<tt>i</tt>, b<tt>ve</tt>,
|
|
-- b<tt>hte</tt>], [b<tt>i</tt>, b<tt>en</tt>, b<tt>nty</tt>]] ```
|
|
--
|
|
-- Broadcasting <tt>input</tt> onto <tt>pos</tt> and <tt>len</tt>:
|
|
--
|
|
-- ``` input = b<tt>thirteen</tt> position = [1, 5, 7] length = [3, 2, 1]
|
|
--
|
|
-- output = [b<tt>hir</tt>, b<tt>ee</tt>, b'n"] ```
|
|
substr :: (TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor v3 t -> Tensor Value ByteString
|
|
|
|
-- | Updates the table to associates keys with values.
|
|
--
|
|
-- The tensor <tt>keys</tt> must be of the same type as the keys of the
|
|
-- table. The tensor <tt>values</tt> must be of the type of the table
|
|
-- values.
|
|
lookupTableInsert :: (TensorType tin, TensorType tout) => Tensor Ref ByteString -> Tensor v2 tin -> Tensor v3 tout -> Build (ControlNode)
|
|
|
|
-- | Component-wise divides a SparseTensor by a dense Tensor.
|
|
--
|
|
-- <ul>
|
|
-- <li>Limitation*: this Op only broadcasts the dense side to the sparse
|
|
-- side, but not the other direction.</li>
|
|
-- </ul>
|
|
sparseDenseCwiseDiv :: (TensorType t, OneOf '[Complex Double, Complex Float, Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 t -> Tensor Value t
|
|
|
|
-- | Replaces the contents of the table with the specified keys and values.
|
|
--
|
|
-- The tensor <tt>keys</tt> must be of the same type as the keys of the
|
|
-- table. The tensor <tt>values</tt> must be of the type of the table
|
|
-- values.
|
|
lookupTableImport :: (TensorType tin, TensorType tout) => Tensor Ref ByteString -> Tensor v2 tin -> Tensor v3 tout -> Build (ControlNode)
|