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492 lines
26 KiB
Plaintext
492 lines
26 KiB
Plaintext
-- 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 0.1.0.0
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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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-- | Returns x + y element-wise.
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--
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-- <ul>
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-- <li>NOTE*: <tt>Add</tt> supports broadcasting. <tt>AddN</tt> does not.
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-- More about broadcasting <a>here</a></li>
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-- </ul>
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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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-- | Computes the absolute value of a tensor.
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--
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-- Given a tensor <tt>x</tt>, this operation returns a tensor containing
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-- the absolute value of each element in <tt>x</tt>. For example, if x is
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-- an input element and y is an output element, this operation computes
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-- \(y = |x|\).
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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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-- | Add all input tensors element wise.
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addN :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t => [Tensor v'1 t] -> Tensor Build t
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addN' :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t => OpParams -> [Tensor v'1 t] -> Tensor Build t
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-- | Returns the index with the largest value across dimensions of a
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-- tensor.
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argMax :: (OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tidx) => Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build Int64
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argMax' :: (OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tidx) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build Int64
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-- | Update <tt>ref</tt> by assigning <a>value</a> to it.
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--
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-- This operation outputs "ref" after the assignment is done. This makes
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-- it easier to chain operations that need to use the reset value.
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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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-- | Return the reduction indices for computing gradients of s0 op s1 with
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-- broadcast.
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--
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-- This is typically used by gradient computations for a broadcasting
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-- operation.
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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 x of type SrcT to y of DstT.
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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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-- | Concatenates tensors along one dimension.
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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' :: TensorType a => OpParams -> Shape -> [a] -> Tensor Build a
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-- | Returns the truth value of (x == y) element-wise.
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--
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-- <ul>
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-- <li>NOTE*: <tt>Equal</tt> supports broadcasting. More about
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-- broadcasting <a>here</a></li>
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-- </ul>
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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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-- | Creates a tensor filled with a scalar value.
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--
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-- This operation creates a tensor of shape <tt>dims</tt> and fills it
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-- with <a>value</a>.
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--
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-- For example:
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--
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-- ```prettyprint # Output tensor has shape [2, 3]. fill([2, 3], 9)
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-- ==> [[9, 9, 9] [9, 9, 9]] ```
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fill :: TensorType t => Tensor v'1 Int32 -> Tensor v'2 t -> Tensor Build t
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fill' :: TensorType t => OpParams -> Tensor v'1 Int32 -> Tensor v'2 t -> Tensor Build t
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-- | Return a tensor with the same shape and contents as the input tensor
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-- or value.
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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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-- | Multiply the matrix "a" by the matrix "b".
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--
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-- The inputs must be two-dimensional matrices and the inner dimension of
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-- "a" (after being transposed if transpose_a is true) must match the
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-- outer dimension of "b" (after being transposed if transposed_b is
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-- true).
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--
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-- <ul>
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-- <li>Note*: The default kernel implementation for MatMul on GPUs uses
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-- cublas.</li>
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-- </ul>
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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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-- | Computes the mean of elements across dimensions of a tensor.
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--
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-- Reduces <tt>input</tt> along the dimensions given in
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-- <tt>reduction_indices</tt>. Unless <tt>keep_dims</tt> is true, the
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-- rank of the tensor is reduced by 1 for each entry in
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-- <tt>reduction_indices</tt>. If <tt>keep_dims</tt> is true, the reduced
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-- dimensions are retained with length 1.
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mean :: (OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * 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 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tidx) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
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-- | Returns x * y element-wise.
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--
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-- <ul>
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-- <li>NOTE*: <tt>Mul</tt> supports broadcasting. More about broadcasting
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-- <a>here</a></li>
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-- </ul>
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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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-- | Computes numerical negative value element-wise.
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--
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-- I.e., \(y = -x\).
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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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-- | Returns a one-hot tensor.
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--
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-- The locations represented by indices in <tt>indices</tt> take value
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-- <tt>on_value</tt>, while all other locations take value
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-- <tt>off_value</tt>.
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--
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-- If the input <tt>indices</tt> is rank <tt>N</tt>, the output will have
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-- rank `N+1`, The new axis is created at dimension <tt>axis</tt>
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-- (default: the new axis is appended at the end).
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--
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-- If <tt>indices</tt> is a scalar the output shape will be a vector of
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-- length <tt>depth</tt>.
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--
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-- If <tt>indices</tt> is a vector of length <tt>features</tt>, the
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-- output shape will be: ``` features x depth if axis == -1 depth x
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-- features if axis == 0 ```
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--
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-- If <tt>indices</tt> is a matrix (batch) with shape `[batch,
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-- features]`, the output shape will be: ``` batch x features x depth if
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-- axis == -1 batch x depth x features if axis == 1 depth x batch x
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-- features if axis == 0 ```
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--
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-- Examples =========
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--
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-- Suppose that
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--
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-- ``` indices = [0, 2, -1, 1] depth = 3 on_value = 5.0 off_value = 0.0
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-- axis = -1 ```
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--
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-- Then output is `[4 x 3]`:
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--
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-- ```output = [5.0 0.0 0.0] // one_hot(0) [0.0 0.0 5.0] // one_hot(2)
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-- [0.0 0.0 0.0] // one_hot(-1) [0.0 5.0 0.0] // one_hot(1) ```
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--
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-- Suppose that
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--
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-- ``` indices = [0, 2, -1, 1] depth = 3 on_value = 0.0 off_value = 3.0
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-- axis = 0 ```
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--
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-- Then output is `[3 x 4]`:
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--
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-- ```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
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-- 0.0 3.0 3.0] // ^ one_hot(0) // ^ one_hot(2) // ^ one_hot(-1) // ^
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-- one_hot(1) ``` Suppose that
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--
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-- ``` indices = [[0, 2], [1, -1]] depth = 3 on_value = 1.0 off_value =
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-- 0.0 axis = -1 ```
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--
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-- Then output is `[2 x 2 x 3]`:
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--
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-- ```output = [ [1.0, 0.0, 0.0] // one_hot(0) [0.0, 0.0, 1.0] //
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-- one_hot(2) ][ [0.0, 1.0, 0.0] // one_hot(1) [0.0, 0.0, 0.0] //
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-- one_hot(-1) ]```
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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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-- | Packs a list of <tt>N</tt> rank-<tt>R</tt> tensors into one
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-- rank-`(R+1)` tensor.
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--
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-- Packs the <tt>N</tt> tensors in <tt>values</tt> into a tensor with
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-- rank one higher than each tensor in <tt>values</tt>, by packing them
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-- along the <tt>axis</tt> dimension. Given a list of tensors of shape
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-- `(A, B, C)`;
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--
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-- if `axis == 0` then the <tt>output</tt> tensor will have the shape
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-- `(N, A, B, C)`. if `axis == 1` then the <tt>output</tt> tensor will
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-- have the shape `(A, N, B, C)`. Etc.
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--
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-- For example:
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--
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-- ```prettyprint # <tt>x</tt> is [1, 4] # <tt>y</tt> is [2, 5] #
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-- <tt>z</tt> is [3, 6] pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] #
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-- Pack along first dim. pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5,
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-- 6]] ```
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--
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-- This is the opposite of <a>unpack</a>.
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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' :: (MonadBuild m, TensorType a) => OpParams -> Shape -> m (Tensor Value a)
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-- | Creates a sequence of numbers.
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--
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-- This operation creates a sequence of numbers that begins at
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-- <tt>start</tt> and extends by increments of <tt>delta</tt> up to but
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-- not including <tt>limit</tt>.
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--
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-- For example:
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--
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-- ``` # <tt>start</tt> is 3 # <tt>limit</tt> is 18 # <tt>delta</tt> is 3
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-- tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] ```
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range :: OneOf ((:) * Int32 ((:) * Int64 ((:) * 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 ((:) * 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 rectified linear: `max(features, 0)`.
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relu :: OneOf ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))) t => Tensor v'1 t -> Tensor Build t
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relu' :: OneOf ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))) t => OpParams -> Tensor v'1 t -> Tensor Build t
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-- | Computes rectified linear gradients for a Relu operation.
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reluGrad :: OneOf ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * 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 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))) t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
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-- | Reshapes a tensor.
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--
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-- Given <tt>tensor</tt>, this operation returns a tensor that has the
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-- same values as <tt>tensor</tt> with shape <a>shape</a>.
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--
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-- If one component of <a>shape</a> is the special value -1, the size of
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-- that dimension is computed so that the total size remains constant. In
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-- particular, a <a>shape</a> of `[-1]` flattens into 1-D. At most one
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-- component of <a>shape</a> can be -1.
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--
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-- If <a>shape</a> is 1-D or higher, then the operation returns a tensor
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-- with shape <a>shape</a> filled with the values of <tt>tensor</tt>. In
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-- this case, the number of elements implied by <a>shape</a> must be the
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-- same as the number of elements in <tt>tensor</tt>.
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--
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-- For example:
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--
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-- ```prettyprint # tensor <tt>t</tt> is [1, 2, 3, 4, 5, 6, 7, 8, 9] #
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-- tensor <tt>t</tt> has shape [9] reshape(t, [3, 3]) ==> [[1, 2, 3],
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-- [4, 5, 6], [7, 8, 9]]
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--
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-- # tensor <tt>t</tt> is [[[1, 1], [2, 2]], # [[3, 3], [4, 4]]] # tensor
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-- <tt>t</tt> has shape [2, 2, 2] reshape(t, [2, 4]) ==> [[1, 1, 2,
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-- 2], [3, 3, 4, 4]]
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--
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-- # tensor <tt>t</tt> is [[[1, 1, 1], # [2, 2, 2]], # [[3, 3, 3], # [4,
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-- 4, 4]], # [[5, 5, 5], # [6, 6, 6]]] # tensor <tt>t</tt> has shape [3,
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-- 2, 3] # pass '[-1]' to flatten <tt>t</tt> reshape(t, [-1]) ==> [1,
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-- 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
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--
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-- # -1 can also be used to infer the shape
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--
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-- # -1 is inferred to be 9: reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2,
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-- 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, 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
|
|
|
|
-- | Restore a tensor's value from a checkpoint file.
|
|
restore :: (MonadBuild m, TensorType a) => ByteString -> Tensor Ref a -> m ControlNode
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|
|
|
-- | Restore a tensor's value from a checkpoint file.
|
|
--
|
|
-- This version allows restoring from a checkpoint file that uses a
|
|
-- different tensor name than the variable.
|
|
restoreFromName :: (MonadBuild m, TensorType a) => ByteString -> ByteString -> Tensor Ref a -> m ControlNode
|
|
save :: (Rendered v, MonadBuild m, TensorType a) => ByteString -> [Tensor v a] -> m ControlNode
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|
|
|
-- | Create a constant scalar.
|
|
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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|
|
|
-- | 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 :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int32 ((:) * Int64 ((:) * Word16 ((:) * Double ((:) * Float ([] *)))))))) t => Tensor v'1 t -> Tensor Build t
|
|
sign' :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int32 ((:) * Int64 ((:) * Word16 ((:) * Double ((:) * Float ([] *)))))))) t => OpParams -> Tensor v'1 t -> Tensor Build t
|
|
|
|
-- | 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, OneOf ((:) * Int32 ((:) * Int64 ([] *))) out_type) => Tensor v'1 t -> Tensor Build out_type
|
|
size' :: (TensorType t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) out_type) => OpParams -> Tensor v'1 t -> Tensor Build out_type
|
|
|
|
-- | 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 :: OneOf ((:) * Word16 ((:) * Double ((:) * Float ([] *)))) t => Tensor v'1 t -> Tensor Build t
|
|
softmax' :: OneOf ((:) * Word16 ((:) * Double ((:) * Float ([] *)))) t => OpParams -> Tensor v'1 t -> Tensor Build t
|
|
|
|
-- | Computes softmax cross entropy cost and gradients to backpropagate.
|
|
--
|
|
-- Inputs are the logits, not probabilities.
|
|
softmaxCrossEntropyWithLogits :: OneOf ((:) * Word16 ((:) * Double ((:) * Float ([] *)))) t => Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build t)
|
|
softmaxCrossEntropyWithLogits' :: OneOf ((:) * Word16 ((:) * Double ((:) * Float ([] *)))) t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> (Tensor Build t, Tensor Build 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, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tindices) => Tensor v'1 tindices -> Tensor v'2 tindices -> Tensor v'3 t -> Tensor v'4 t -> Tensor Build t
|
|
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
|
|
|
|
-- | Returns x - y element-wise.
|
|
--
|
|
-- <ul>
|
|
-- <li>NOTE*: <tt>Sub</tt> supports broadcasting. More about broadcasting
|
|
-- <a>here</a></li>
|
|
-- </ul>
|
|
sub :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int32 ((:) * Int64 ((:) * Word16 ((:) * Double ((:) * Float ([] *)))))))) t => Tensor v'1 t -> Tensor v'2 t -> Tensor Build t
|
|
sub' :: OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int32 ((:) * Int64 ((:) * Word16 ((:) * Double ((:) * Float ([] *)))))))) t => OpParams -> Tensor v'1 t -> Tensor v'2 t -> Tensor Build 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 :: (OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tidx) => Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
|
|
sum' :: (OneOf ((:) * (Complex Double) ((:) * (Complex Float) ((:) * Int16 ((:) * Int32 ((:) * Int64 ((:) * Int8 ((:) * Word16 ((:) * Word8 ((:) * Double ((:) * Float ([] *))))))))))) t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tidx) => OpParams -> Tensor v'1 t -> Tensor v'2 tidx -> Tensor Build t
|
|
|
|
-- | 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, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tperm) => Tensor v'1 t -> Tensor v'2 tperm -> Tensor Build t
|
|
transpose' :: (TensorType t, OneOf ((:) * Int32 ((:) * Int64 ([] *))) tperm) => OpParams -> Tensor v'1 t -> Tensor v'2 tperm -> Tensor Build t
|
|
|
|
-- | Random tensor from the unit normal distribution with bounded values.
|
|
--
|
|
-- This is a type-restricted version of <a>truncatedNormal</a>.
|
|
truncatedNormal :: (MonadBuild m, OneOf '[Word16, Double, Float] a) => Tensor v Int64 -> m (Tensor Value a)
|
|
truncatedNormal' :: (MonadBuild m, OneOf '[Word16, Double, Float] a) => OpParams -> Tensor v Int64 -> m (Tensor Value a)
|
|
|
|
-- | Use VariableV2 instead.
|
|
variable :: (MonadBuild m', TensorType dtype) => Shape -> m' (Tensor Ref dtype)
|
|
variable' :: (MonadBuild m', TensorType dtype) => OpParams -> Shape -> m' (Tensor Ref dtype)
|
|
|
|
-- | Create a constant vector.
|
|
vector :: TensorType a => [a] -> Tensor Build a
|
|
vector' :: TensorType a => OpParams -> [a] -> Tensor Build a
|
|
zeros :: (Num a, TensorType a) => Shape -> Tensor Build a
|
|
|
|
-- | Returns a tensor of zeros with the same shape and type as x.
|
|
zerosLike :: TensorType t => Tensor v'1 t -> Tensor Build t
|
|
zerosLike' :: TensorType t => OpParams -> Tensor v'1 t -> Tensor Build t
|
|
|
|
-- | Reshape a N-D tensor down to a scalar.
|
|
--
|
|
-- See <a>reshape</a>.
|
|
scalarize :: TensorType a => Tensor v a -> Tensor Build a
|
|
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)
|
|
|
|
|
|
-- | Parallel lookups on the list of tensors.
|
|
module TensorFlow.EmbeddingOps
|
|
|
|
-- | Looks up <tt>ids</tt> in a list of embedding tensors.
|
|
--
|
|
-- This function is used to perform parallel lookups on the list of
|
|
-- tensors in <tt>params</tt>. It is a generalization of <a>gather</a>,
|
|
-- where <tt>params</tt> is interpreted as a partition of a larger
|
|
-- embedding tensor.
|
|
--
|
|
-- The partition_strategy is "mod", we assign each id to partition `p =
|
|
-- id % len(params)`. For instance, 13 ids are split across 5 partitions
|
|
-- as: `[[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]`
|
|
--
|
|
-- The results of the lookup are concatenated into a dense tensor. The
|
|
-- returned tensor has shape `shape(ids) + shape(params)[1:]`.
|
|
embeddingLookup :: (MonadBuild m, Rendered v1, TensorType a, OneOf '[Int64, Int32] b, Num b) => [Tensor v1 a] -> Tensor v2 b -> m (Tensor Value a)
|
|
|
|
module TensorFlow.Gradient
|
|
|
|
-- | Gradient of <tt>y</tt> w.r.t. each element of <tt>xs</tt>.
|
|
gradients :: (MonadBuild m, Rendered v2, GradientCompatible a) => Tensor v1 a -> [Tensor v2 a] -> m [Tensor Value a]
|