mirror of
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352 lines
23 KiB
Text
352 lines
23 KiB
Text
-- Hoogle documentation, generated by Haddock
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-- See Hoogle, http://www.haskell.org/hoogle/
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-- | Friendly layer around TensorFlow bindings.
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--
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-- Please see README.md
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@package tensorflow-ops
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@version 0.2.0.0
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-- | This module contains definitions for some built-in TensorFlow
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-- operations.
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--
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-- Note that certain, "stateful" ops like <tt>variable</tt> and
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-- <tt>assign</tt> return a <a>Build</a> action (e.g., <tt>Build (Tensor
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-- Ref a)</tt> instead of a pure value; the returned <a>Tensor</a>s are
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-- always rendered in the current <a>Build</a> context. This approach
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-- helps us avoid problems with inlining or common subexpression
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-- elimination, by writing
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--
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-- <pre>
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-- do
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-- v <- variable []
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-- w <- assign v 3
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-- render $ w * w
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-- </pre>
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--
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-- instead of
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--
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-- <pre>
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-- let
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-- v = variable []
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-- w = assign v 3
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-- in w * w
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-- </pre>
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--
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-- since the latter could be reasonably transformed by the compiler into
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-- (or vice versa)
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--
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-- <pre>
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-- let
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-- v = variable []
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-- w = assign v 3
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-- w' = assign v 3
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-- in w * w'
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-- </pre>
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--
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-- Ops should return a <a>Build</a> action if their original
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-- <tt>OpDef</tt> marks them as stateful, or if they take any Refs as
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-- input. (This mirrors the rules that TensorFlow uses to avoid common
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-- subexpression elimination.)
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module TensorFlow.Ops
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add :: OneOf (:) * Complex Double (:) * Complex Float (:) * ByteString (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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add' :: OneOf (:) * Complex Double (:) * Complex Float (:) * ByteString (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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abs :: OneOf (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor Build t
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abs' :: OneOf (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor Build t
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addN :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float (:) * Variant [] * t => [Tensor v'1 t] -> Tensor Build t
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addN' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float (:) * Variant [] * t => OpParams -> [Tensor v'1 t] -> Tensor Build t
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argMax :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx, OneOf (:) * Int32 (:) * Int64 [] * output_type) => Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build output_type
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argMax' :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx, OneOf (:) * Int32 (:) * Int64 [] * output_type) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build output_type
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assign :: (MonadBuild m', TensorType t) => Tensor Ref t -> Tensor v'2 t -> m' Tensor Ref t
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assign' :: (MonadBuild m', TensorType t) => OpParams -> Tensor Ref t -> Tensor v'2 t -> m' Tensor Ref t
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broadcastGradientArgs :: OneOf (:) * Int32 (:) * Int64 [] * t => Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build t)
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broadcastGradientArgs' :: OneOf (:) * Int32 (:) * Int64 [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build t)
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cast :: (TensorType srcT, TensorType dstT) => Tensor v'1 srcT -> Tensor Build dstT
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cast' :: (TensorType srcT, TensorType dstT) => OpParams -> Tensor v'1 srcT -> Tensor Build dstT
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concat :: TensorType t => Tensor v'1 Int32 -> [Tensor v'2 t] -> Tensor Build t
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concat' :: TensorType t => OpParams -> Tensor v'1 Int32 -> [Tensor v'2 t] -> Tensor Build t
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-- | Create a constant tensor.
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--
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-- The values should be in row major order, e.g.,
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--
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-- element 0: index (0, ..., 0) element 1: index (0, ..., 1) ...
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constant :: TensorType a => Shape -> [a] -> Tensor Build a
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constant' :: forall a. TensorType a => OpParams -> Shape -> [a] -> Tensor Build a
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equal :: OneOf (:) * Complex Double (:) * Complex Float (:) * Bool (:) * ByteString (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build Bool
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equal' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Bool (:) * ByteString (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build Bool
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expandDims :: TensorType t => Tensor v1 t -> Tensor v2 Int32 -> Tensor Build t
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expandDims' :: TensorType t => OpParams -> Tensor v1 t -> Tensor v2 Int32 -> Tensor Build t
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-- | Creates a variable initialized to the given value. Initialization
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-- happens next time session runs.
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initializedVariable :: (MonadBuild m, TensorType a) => Tensor v a -> m (Tensor Ref a)
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initializedVariable' :: (MonadBuild m, TensorType a) => OpParams -> Tensor v a -> m (Tensor Ref a)
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-- | Creates a zero-initialized variable with the given shape.
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zeroInitializedVariable :: (MonadBuild m, TensorType a, Num a) => Shape -> m (Tensor Ref a)
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zeroInitializedVariable' :: (MonadBuild m, TensorType a, Num a) => OpParams -> Shape -> m (Tensor Ref a)
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fill :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * index_type) => Tensor v'1 index_type -> Tensor v'2 t -> Tensor Build t
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fill' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * index_type) => OpParams -> Tensor v'1 index_type -> Tensor v'2 t -> Tensor Build t
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identity :: TensorType t => Tensor v'1 t -> Tensor Build t
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identity' :: TensorType t => OpParams -> Tensor v'1 t -> Tensor Build t
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matMul :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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matMul' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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matTranspose :: TensorType a => Tensor e a -> Tensor Build a
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matTranspose' :: TensorType a => OpParams -> Tensor v a -> Tensor Build a
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mean :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx) => Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
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mean' :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
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mul :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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mul' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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neg :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor Build t
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neg' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor Build t
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oneHot :: (TensorType t, OneOf (:) * Int32 (:) * Int64 (:) * Word8 [] * tI) => Tensor v'1 tI -> Tensor v'2 Int32 -> Tensor v'3 t -> Tensor v'4 t -> Tensor Build t
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oneHot' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 (:) * Word8 [] * tI) => OpParams -> Tensor v'1 tI -> Tensor v'2 Int32 -> Tensor v'3 t -> Tensor v'4 t -> Tensor Build t
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pack :: TensorType t => [Tensor v'1 t] -> Tensor Build t
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pack' :: TensorType t => OpParams -> [Tensor v'1 t] -> Tensor Build t
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placeholder :: (MonadBuild m, TensorType a) => Shape -> m (Tensor Value a)
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placeholder' :: forall m a. (MonadBuild m, TensorType a) => OpParams -> Shape -> m (Tensor Value a)
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range :: OneOf (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * tidx => Tensor v'1 tidx -> Tensor v'2 tidx -> Tensor v'3 tidx -> Tensor Build tidx
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range' :: OneOf (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * tidx => OpParams -> Tensor v'1 tidx -> Tensor v'2 tidx -> Tensor v'3 tidx -> Tensor Build tidx
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-- | Helper function for reduction ops (translation of
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-- math_ops.reduced_shape).
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reducedShape :: (OneOf '[Int32, Int64] t1, OneOf '[Int32, Int64] t2) => Tensor v1 t1 -> Tensor v2 t2 -> Tensor Build Int32
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-- | Computes the mean of elements across dimensions of a tensor. See
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-- <a>mean</a>
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reduceMean :: (TensorType a, OneOf '[Double, Float, Complex Float, Complex Double] a) => Tensor v a -> Tensor Build a
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reduceMean' :: (TensorType a, OneOf '[Double, Float, Complex Float, Complex Double] a) => OpParams -> Tensor v a -> Tensor Build a
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relu :: OneOf (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor Build t
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relu' :: OneOf (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor Build t
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reluGrad :: OneOf (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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reluGrad' :: OneOf (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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reshape :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tshape) => Tensor v'1 t -> Tensor v'2 tshape -> Tensor Build t
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reshape' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tshape) => OpParams -> Tensor v'1 t -> Tensor v'2 tshape -> Tensor Build t
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-- | Restore a tensor's value from a checkpoint file.
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restore :: forall a m. (MonadBuild m, TensorType a) => ByteString -> Tensor Ref a -> m ControlNode
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-- | Restore a tensor's value from a checkpoint file.
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--
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-- This version allows restoring from a checkpoint file that uses a
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-- different tensor name than the variable.
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restoreFromName :: forall a m. (MonadBuild m, TensorType a) => ByteString -> ByteString -> Tensor Ref a -> m ControlNode
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save :: forall a m v. (Rendered (Tensor v), MonadBuild m, TensorType a) => ByteString -> [Tensor v a] -> m ControlNode
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-- | Create a constant scalar.
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scalar :: TensorType a => a -> Tensor Build a
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scalar' :: TensorType a => OpParams -> a -> Tensor Build a
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shape :: TensorType t => Tensor v t -> Tensor Build Int32
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shape' :: TensorType t => OpParams -> Tensor v t -> Tensor Build Int32
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sign :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor Build t
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sign' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int32 (:) * Int64 (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor Build t
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size :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * out_type) => Tensor v'1 t -> Tensor Build out_type
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size' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * out_type) => OpParams -> Tensor v'1 t -> Tensor Build out_type
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softmax :: OneOf (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor Build t
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softmax' :: OneOf (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor Build t
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softmaxCrossEntropyWithLogits :: OneOf (:) * Word16 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build t)
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softmaxCrossEntropyWithLogits' :: OneOf (:) * Word16 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build t)
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sparseToDense :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tindices) => Tensor v'1 tindices -> Tensor v'2 tindices -> Tensor v'3 t -> Tensor v'4 t -> Tensor Build t
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sparseToDense' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tindices) => OpParams -> Tensor v'1 tindices -> Tensor v'2 tindices -> Tensor v'3 t -> Tensor v'4 t -> Tensor Build t
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sub :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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sub' :: OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word8 (:) * Double (:) * Float [] * t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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sum :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx) => Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
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sum' :: (OneOf (:) * Complex Double (:) * Complex Float (:) * Int16 (:) * Int32 (:) * Int64 (:) * Int8 (:) * Word16 (:) * Word32 (:) * Word64 (:) * Word8 (:) * Double (:) * Float [] * t, OneOf (:) * Int32 (:) * Int64 [] * tidx) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
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-- | Sum a tensor down to a scalar Seee <a>sum</a>
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reduceSum :: (OneOf '[Double, Float, Int32, Int64, Complex Float, Complex Double] a) => Tensor v a -> Tensor Build a
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reduceSum' :: (OneOf '[Double, Float, Int32, Int64, Complex Float, Complex Double] a) => OpParams -> Tensor v a -> Tensor Build a
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transpose :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tperm) => Tensor v'1 t -> Tensor v'2 tperm -> Tensor Build t
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transpose' :: (TensorType t, OneOf (:) * Int32 (:) * Int64 [] * tperm) => OpParams -> Tensor v'1 t -> Tensor v'2 tperm -> Tensor Build t
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-- | Random tensor from the unit normal distribution with bounded values.
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--
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-- This is a type-restricted version of <a>truncatedNormal</a>.
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truncatedNormal :: (MonadBuild m, OneOf '[Word16, Double, Float] a) => Tensor v Int64 -> m (Tensor Value a)
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truncatedNormal' :: (MonadBuild m, OneOf '[Word16, Double, Float] a) => OpParams -> Tensor v Int64 -> m (Tensor Value a)
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variable :: (MonadBuild m', TensorType dtype) => Shape -> m' Tensor Ref dtype
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variable' :: (MonadBuild m', TensorType dtype) => OpParams -> Shape -> m' Tensor Ref dtype
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-- | Create a constant vector.
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vector :: TensorType a => [a] -> Tensor Build a
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vector' :: TensorType a => OpParams -> [a] -> Tensor Build a
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zeros :: forall a. (Num a, TensorType a) => Shape -> Tensor Build a
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zerosLike :: TensorType t => Tensor v'1 t -> Tensor Build t
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zerosLike' :: TensorType t => OpParams -> Tensor v'1 t -> Tensor Build t
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-- | Reshape a N-D tensor down to a scalar.
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--
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-- See <a>reshape</a>.
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scalarize :: TensorType a => Tensor v a -> Tensor Build a
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instance (TensorFlow.Types.TensorType a, GHC.Num.Num a, v ~ TensorFlow.Build.Build, TensorFlow.Types.OneOf '[GHC.Types.Double, GHC.Types.Float, GHC.Int.Int32, GHC.Int.Int64, Data.Complex.Complex GHC.Types.Float, Data.Complex.Complex GHC.Types.Double] a) => GHC.Num.Num (TensorFlow.Tensor.Tensor v a)
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module TensorFlow.NN
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-- | Computes sigmoid cross entropy given <tt>logits</tt>.
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--
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-- Measures the probability error in discrete classification tasks in
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-- which each class is independent and not mutually exclusive. For
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-- instance, one could perform multilabel classification where a picture
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-- can contain both an elephant and a dog at the same time.
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--
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-- For brevity, let `x = logits`, `z = targets`. The logistic loss is
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--
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-- z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = z * -log(1 <i>
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-- (1 + exp(-x))) + (1 - z) * -log(exp(-x) </i> (1 + exp(-x))) = z *
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-- log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x))) = z *
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-- log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x)) = (1 - z) * x +
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-- log(1 + exp(-x)) = x - x * z + log(1 + exp(-x))
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--
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-- For x < 0, to avoid overflow in exp(-x), we reformulate the above
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--
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-- x - x * z + log(1 + exp(-x)) = log(exp(x)) - x * z + log(1 + exp(-x))
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-- = - x * z + log(1 + exp(x))
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--
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-- Hence, to ensure stability and avoid overflow, the implementation uses
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-- this equivalent formulation
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--
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-- max(x, 0) - x * z + log(1 + exp(-abs(x)))
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--
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-- <tt>logits</tt> and <tt>targets</tt> must have the same type and
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-- shape.
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sigmoidCrossEntropyWithLogits :: (MonadBuild m, OneOf '[Float, Double] a, TensorType a, Num a) => Tensor Value a -> Tensor Value a -> m (Tensor Value a)
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module TensorFlow.Gradient
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type GradientCompatible a = (Num a, OneOf '[Float, Complex Float, Complex Double] a)
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-- | Gradient of <tt>y</tt> w.r.t. each element of <tt>xs</tt>.
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gradients :: forall a v1 t m. (MonadBuild m, Rendered t, ToTensor t, GradientCompatible a) => Tensor v1 a -> [t a] -> m [Tensor Value a]
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-- | Parallel lookups on the list of tensors.
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module TensorFlow.EmbeddingOps
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-- | Looks up <tt>ids</tt> in a list of embedding tensors.
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--
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-- This function is used to perform parallel lookups on the list of
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-- tensors in <tt>params</tt>. It is a generalization of <a>gather</a>,
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-- where <tt>params</tt> is interpreted as a partition of a larger
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-- embedding tensor.
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--
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-- The partition_strategy is "mod", we assign each id to partition `p =
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-- id % len(params)`. For instance, 13 ids are split across 5 partitions
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-- as: `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
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--
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-- The results of the lookup are concatenated into a dense tensor. The
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-- returned tensor has shape `shape(ids) + shape(params)[1:]`.
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embeddingLookup :: forall a b v1 v2 m. (MonadBuild m, Rendered (Tensor v1), TensorType a, OneOf '[Int64, Int32] b, Num b) => [Tensor v1 a] -> Tensor v2 b -> m (Tensor Value a)
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-- | Queues in TensorFlow graph. Very limited support for now.
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module TensorFlow.Queue
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-- | A queue carrying tuples.
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data Queue (as :: [*])
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-- | Creates a new queue with the given capacity and shared name.
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makeQueue :: forall as m. (MonadBuild m, TensorTypes as) => Int64 -> ByteString -> m (Queue as)
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-- | Adds the given values to the queue.
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enqueue :: forall as v m. (MonadBuild m, TensorTypes as) => Queue as -> TensorList v as -> m ControlNode
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-- | Retrieves the values from the queue.
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dequeue :: forall as m. (MonadBuild m, TensorTypes as) => Queue as -> m (TensorList Value as)
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-- | An implementation of ResourceHandle-based variables.
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--
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-- The main difference between this and <a>Ref</a>-based variables is
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-- that reads are explicit, via the <a>readValue</a> op.
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--
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-- TODO: given that distinction, figure out a good story around gradients
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-- and save/restore. Then, merge this module into TensorFlow.Ops.
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module TensorFlow.Variable
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data Variable a
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-- | Creates a new, uninitialized variable.
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variable :: (MonadBuild m, TensorType a) => Shape -> m (Variable a)
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variable' :: forall m a. (MonadBuild m, TensorType a) => OpParams -> Shape -> m (Variable a)
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-- | Gets the value stored in a variable.
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--
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-- Note that this op is stateful since it depends on the value of the
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-- variable; however, it may be CSE'd with other reads in the same
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-- context. The context can be fixed by using <a>render</a> along with
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-- (for example) <a>withControlDependencies</a>. For example:
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--
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-- <pre>
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-- runSession $ do
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-- v <- variable []
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-- a <- assign v 24
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-- r <- withControlDependencies a $ render $ readValue v + 18
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-- result <- run r
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-- liftIO $ (42 :: Float) @=? unScalar result
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-- </pre>
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readValue :: TensorType a => Variable a -> Tensor Build a
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-- | The initial value of a <a>Variable</a> created with
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-- <a>initializedVariable</a>.
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initializedValue :: Variable a -> Maybe (Tensor Value a)
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-- | Creates a variable initialized to the given value. Initialization
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-- happens next time session runs.
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initializedVariable :: (MonadBuild m, TensorType a) => Tensor v a -> m (Variable a)
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initializedVariable' :: forall a m v. (MonadBuild m, TensorType a) => OpParams -> Tensor v a -> m (Variable a)
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-- | Creates a zero-initialized variable with the given shape.
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zeroInitializedVariable :: (MonadBuild m, TensorType a, Num a) => Shape -> m (Variable a)
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zeroInitializedVariable' :: (MonadBuild m, TensorType a, Num a) => OpParams -> Shape -> m (Variable a)
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-- | Sets the value of a variable.
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assign :: (MonadBuild m, TensorType a) => Variable a -> Tensor v a -> m ControlNode
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assign' :: (MonadBuild m, TensorType a) => OpParams -> Variable a -> Tensor v a -> m ControlNode
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-- | Increments the value of a variable.
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assignAdd :: (MonadBuild m, TensorType a) => Variable a -> Tensor v a -> m ControlNode
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assignAdd' :: (MonadBuild m, TensorType a) => OpParams -> Variable a -> Tensor v a -> m ControlNode
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|
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-- | Update '*var' according to the Adam algorithm.
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--
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-- lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) m_t <-
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-- beta1 * m_{t-1} + (1 - beta1) * g_t v_t <- beta2 * v_{t-1} + (1 -
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-- beta2) * g_t * g_t variable <- variable - lr_t * m_t / (sqrt(v_t) +
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-- epsilon)
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resourceApplyAdam :: (MonadBuild m, OneOf '[(Complex Double), (Complex Float), Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Variable t -> Variable t -> Variable t -> Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> m (ControlNode)
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resourceApplyAdam' :: (MonadBuild m, OneOf '[(Complex Double), (Complex Float), Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => OpParams -> Variable t -> Variable t -> Variable t -> Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> m (ControlNode)
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instance TensorFlow.Tensor.Rendered TensorFlow.Variable.Variable
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instance TensorFlow.Tensor.ToTensor TensorFlow.Variable.Variable
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module TensorFlow.Minimize
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-- | Functions that minimize a loss w.r.t. a set of <a>Variable</a>s.
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--
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-- Generally only performs one step of an iterative algorithm.
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--
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-- <a>Minimizer</a>s are defined as a function of the gradients instead
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-- of the loss so that users can apply transformations to the gradients.
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|
type Minimizer a = forall m. MonadBuild m => [Variable a] -> [Tensor Value a] -> m ControlNode
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|
|
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-- | Convenience wrapper around <a>gradients</a> and a <a>Minimizer</a>.
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|
minimizeWith :: (MonadBuild m, GradientCompatible a) => Minimizer a -> Tensor v a -> [Variable a] -> m ControlNode
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|
|
|
-- | Perform one step of the gradient descent algorithm.
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|
gradientDescent :: GradientCompatible a => a -> Minimizer a
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|
data AdamConfig
|
|
AdamConfig :: Float -> Float -> Float -> Float -> AdamConfig
|
|
[adamLearningRate] :: AdamConfig -> Float
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|
[adamBeta1] :: AdamConfig -> Float
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|
[adamBeta2] :: AdamConfig -> Float
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|
[adamEpsilon] :: AdamConfig -> Float
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|
|
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-- | Perform one step of the adam algorithm.
|
|
--
|
|
-- See <a>https://arxiv.org/abs/1412.6980</a>.
|
|
--
|
|
-- NOTE: Currently requires all <a>Variable</a>s to have an
|
|
-- <a>initializedValue</a>.
|
|
adam :: Minimizer Float
|
|
adam' :: AdamConfig -> Minimizer Float
|
|
instance Data.Default.Class.Default TensorFlow.Minimize.AdamConfig
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