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tensorflow-haskell/docs/haddock/tensorflow-core-ops-0.1.0.0/tensorflow-core-ops.txt
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-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/
-- | Haskell wrappers for Core Tensorflow Ops.
--
-- Code generated signatures for the Ops in libtensorflow_c.
@package tensorflow-core-ops
@version 0.1.0.0
module TensorFlow.GenOps.Core
-- | Receives the named tensor from send_device on recv_device.
--
-- _HostRecv requires its input on host memory whereas _Recv requires its
-- input on device memory.
_HostRecv :: (TensorType tensor_type) => Int64 -> Tensor Value tensor_type
-- | Receives the named tensor from send_device on recv_device.
_Recv :: (TensorType tensor_type) => Int64 -> Tensor Value tensor_type
-- | Sends the named tensor from send_device to recv_device.
_Send :: (TensorType t) => Int64 -> Tensor v1 t -> ControlNode
-- | A graph node which represents an argument to a function.
_Arg :: (TensorType t) => Int64 -> Tensor Value t
-- | Update '*var' according to the RMSProp algorithm.
--
-- Note that in dense implement of this algorithm, ms and mom will update
-- even if the grad is zero, but in this sparse implement, ms and mom
-- will not update in iterations the grad is zero.
--
-- 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 tindices -> Tensor Value t
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 t -> Tensor v10 t -> Tensor Value t
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Tensor Value 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor Value t
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor Value 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 Int64 -> Tensor Value t
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Tensor Value t
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor Value 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor Value 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 tindices -> Tensor Value 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 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
-- | 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 character. If
-- <tt>delimiter</tt> is an empty string, each element of <tt>input</tt>
-- is split into individual 1 character strings.
--
-- 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)
-- | 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
-- | 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
-- | 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
-- | 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 v1 t -> Tensor v2 tindices -> Tensor v3 t -> 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) ==&gt; ["ac", "bd"] tf.reduce_join(a, 1) ==&gt; ["ab", "cd"]
-- tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==&gt; ["ac", "bd"]
-- tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==&gt; ["ab", "cd"]
-- tf.reduce_join(a, 0, keep_dims=True) ==&gt; [["ac", "bd"]]
-- tf.reduce_join(a, 1, keep_dims=True) ==&gt; [["ab"], ["cd"]]
-- tf.reduce_join(a, 0, separator=".") ==&gt; ["a.c", "b.d"]
-- tf.reduce_join(a, [0, 1]) ==&gt; ["acbd"] tf.reduce_join(a, [1, 0])
-- ==&gt; ["abcd"] tf.reduce_join(a, []) ==&gt; ["abcd"] ```
reduceJoin :: Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor Value ByteString
-- | 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 v1 t -> Tensor v2 tindices -> Tensor v3 t -> Tensor Value t
-- | Adds 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.
--
-- 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>
scatterAdd :: (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 t -> Tensor Value t
-- | 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 v1 t -> Tensor v2 tindices -> Tensor v3 t -> 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 v1 t -> Tensor v2 t -> Tensor Value t
-- | 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 v1 t -> Tensor v2 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
-- | Applies softmax to a batched N-D <tt>SparseTensor</tt>.
--
-- The inputs represent an N-D SparseTensor with logical shape `[..., B,
-- C]` (where `N &gt;= 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
-- | 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 '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | 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)
-- | 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
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
-- | Push an element onto the stack.
stackPush :: (TensorType t) => Tensor v1 ByteString -> 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
-- | Interleave the values from the `data` tensors into a single tensor.
--
-- Builds a merged tensor such that
--
-- merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
--
-- For example, if each `indices[m]` is scalar or vector, we have
--
-- # 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) &lt; (n,j)` the slice
-- `data[n][j]` will appear in the merged result.
--
-- For example:
--
-- 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
-- | Returns the number of work units this Reader has finished processing.
readerNumWorkUnitsCompleted :: Tensor v1 ByteString -> Tensor Value Int64
-- | 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 v1 ByteString -> Tensor v2 ByteString -> (Tensor Value ByteString, Tensor Value ByteString)
-- | 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)
-- | A Reader that outputs fixed-length records from a file.
fixedLengthRecordReader :: Int64 -> Tensor Value ByteString
-- | 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
-- | 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 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
-- | 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
-- | Sends the named tensor from send_device to recv_device.
--
-- _HostSend requires its input on host memory whereas _Send requires its
-- input on device memory.
_HostSend :: (TensorType t) => Int64 -> Tensor v1 t -> ControlNode
-- | 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
-- | 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
-- | 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 :: Tensor Value ByteString
-- | 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
-- | 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
-- | 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
-- | 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
-- | Decode the first frame of a GIF-encoded image to a uint8 tensor.
--
-- GIF with frame or transparency compression are not supported convert
-- animated GIF from compressed to uncompressed by:
--
-- convert $src.gif -coalesce $dst.gif
decodeGif :: Tensor v1 ByteString -> Tensor Value Word8
-- | Deprecated. Disallowed in GraphDef version &gt;= 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
-- | 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 &gt;=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
batchMatrixSolve :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | 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 the gradient of bilinear interpolation.
resizeBilinearGrad :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Float -> Tensor v2 t -> Tensor Value t
-- | Output a fact about factorials.
fact :: Tensor Value ByteString
-- | Delete the tensor specified by its handle in the session.
deleteSessionTensor :: Tensor v1 ByteString -> ControlNode
-- | 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
-- | Get the value of the tensor specified by its handle.
getSessionTensor :: (TensorType dtype) => Tensor v1 ByteString -> Tensor Value dtype
batchMatrixInverse :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor Value t
-- | Generate a glob pattern matching all sharded file names.
shardedFilespec :: Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor Value ByteString
-- | 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
-- | Store the input tensor in the state of the current session.
getSessionHandle :: (TensorType t) => Tensor v1 t -> Tensor Value ByteString
-- | Table initializer that takes two tensors for keys and values
-- respectively.
initializeTable :: (TensorType tkey, TensorType tval) => Tensor v1 ByteString -> Tensor v2 tkey -> Tensor v3 tval -> ControlNode
-- | 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 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
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 Int64 -> Tensor Value t
-- | 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
-- | Computes gradients for the exponential linear (Elu) operation.
eluGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Computes gradient of the FractionalAvgPool function.
--
-- Unlike FractionalMaxPoolGrad, we don't need to find arg_max for
-- FractionalAvgPoolGrad, we just need to evenly back-propagate each
-- element of out_backprop to those indices that form the same pooling
-- cell. Therefore, we just need to know the shape of original input
-- tensor, instead of the whole tensor.
fractionalAvgPoolGrad :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 Int64 -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Int64 -> Tensor Value t
-- | 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
-- | 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
-- | Computes the number of incomplete elements in the given barrier.
barrierIncompleteSize :: Tensor v1 ByteString -> Tensor Value Int32
-- | 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)
-- | Computes the number of complete elements in the given barrier.
barrierReadySize :: Tensor v1 ByteString -> Tensor Value Int32
-- | 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 v1 ByteString -> ControlNode
-- | A Reader that outputs the lines of a file delimited by '\n'.
textLineReader :: Tensor Value ByteString
-- | 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)
-- | Exits the current frame to its parent frame.
--
-- Exit makes its input `data` available to the parent frame.
refExit :: (TensorType 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
-- | 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
-- | 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) ==&gt; [-2.25 - 4.75j, 3.25 - 5.75j] ```
conj :: (TensorType t, OneOf '[Complex Double, Complex Float] t) => Tensor v1 t -> Tensor Value t
-- | 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
-- | Delete the TensorArray from its resource container. This enables
--
-- the user to close and release the resource in the middle of a
-- step/run.
tensorArrayClose :: Tensor v1 ByteString -> 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
-- | Get the current size of the TensorArray.
tensorArraySize :: Tensor v1 ByteString -> Tensor v2 Float -> Tensor Value Int32
-- | 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).
tensorArrayConcat :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Float -> (Tensor Value dtype, Tensor Value Int64)
-- | 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
-- | 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
-- | Pack the elements from the TensorArray into output <a>value</a>.
--
-- <ul>
-- <li>*WARNING: This op is deprecated.**</li>
-- </ul>
--
-- Instead of this op, use <tt>TensorArrayGather</tt> with `indices =
-- RangeOp(0, TensorArraySizeOp)`.
--
-- All elements must have the same shape.
tensorArrayPack :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Float -> Tensor Value dtype
-- | 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]) =&gt; [0, 0, 0],
-- [0, 2, 0], [0, 5, 0] ```
concatOffset :: Tensor v1 Int32 -> [Tensor v2 Int32] -> [Tensor Value Int32]
-- | 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 v1 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
-- | Push an element onto the tensor_array.
tensorArrayWrite :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Tensor Value Float
-- | 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)
-- ==&gt; [[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 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, ..., N,
-- N]`, then the output is a tensor of rank `k - 1` with dimensions `[I,
-- J, K, ..., 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) ==&gt; [[1, 2, 3, 4], [5, 6, 7, 8]]
--
-- which has shape (2, 4) ```
matrixDiagPart :: (TensorType t) => Tensor v1 t -> Tensor Value t
-- | Computes the number of elements in the given queue.
queueSize :: Tensor v1 ByteString -> Tensor Value Int32
-- | 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
-- | Returns element-wise smallest integer in not less than x.
ceil :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> 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 :: Tensor Value ByteString
-- | A placeholder op that passes though <tt>input</tt> when its output is
-- not fed.
placeholderWithDefault :: (TensorType dtype) => Tensor v1 dtype -> Tensor Value dtype
-- | 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
-- | Restore a Reader to its initial clean state.
readerReset :: Tensor v1 ByteString -> ControlNode
-- | 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
batchMatrixSetDiag :: (TensorType t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Delete the stack from its resource container.
stackClose :: Tensor v1 ByteString -> ControlNode
-- | 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 &lt;&lt; (num_bits - 1) - 1), (1 &lt;&lt; (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 &lt;&lt; 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 which elements of x are NaN.
isNan :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
-- | 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) ==&gt; [[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) ==&gt; [[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1], [2,
-- 1, 1]] ```
where' :: Tensor v1 Bool -> Tensor Value Int64
-- | 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 ==&gt; [2, 4, 6] idx ==&gt; [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)
-- | Return a strided slice from <tt>input</tt>.
--
-- The output tensor is a tensor with dimensions implied by
-- <tt>begin</tt>, <tt>end</tt>, and <tt>strides</tt>, whose values are
-- extracted from <tt>begin</tt>.
--
-- Specifically, the result tensor at index `(i[0], i[1], ..., i[n-1])`
-- will obtain the value `input[begin[0] + i[0] * stride[0], ..., `
-- `begin[n-1] + i[n-1] * stride[n-1])]`.
--
-- <ul>
-- <li>Requirements*: `0 != strides[i] for i in [0, n)`</li>
-- </ul>
stridedSlice :: (TensorType index, OneOf '[Int32, Int64] index, TensorType t) => Tensor v1 t -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor Value t
-- | A queue that randomizes the order of elements.
randomShuffleQueue :: Tensor Value ByteString
-- | 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
-- | 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 index, OneOf '[Int32, Int64] index, TensorType t) => Tensor v1 t -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor v5 t -> 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]) ==&gt; [[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]) ==&gt; [[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]) ==&gt; [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]) ==&gt; [[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]) ==&gt; [[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])
-- ==&gt; [[[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, []) ==&gt; 7 ```
reshape :: (TensorType t, TensorType tshape, OneOf '[Int32, Int64] tshape) => Tensor v1 t -> Tensor v2 tshape -> Tensor Value t
-- | A queue that produces elements in first-in first-out order.
fIFOQueue :: Tensor Value ByteString
-- | 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)
-- | 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)
-- | Randomly crop <tt>image</tt>.
--
-- <a>size</a> is a 1-D int64 tensor with 2 elements representing the
-- crop height and width. The values must be non negative.
--
-- This Op picks a random location in <tt>image</tt> and crops a
-- <tt>height</tt> by <tt>width</tt> rectangle from that location. The
-- random location is picked so the cropped area will fit inside the
-- original image.
randomCrop :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor Value t
-- | Cast x of type SrcT to y of DstT.
--
-- _HostCast requires its input and produces its output in host memory.
_HostCast :: (TensorType dstT, TensorType srcT) => Tensor v1 srcT -> Tensor Value dstT
-- | 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 v1 ByteString -> ControlNode
-- | 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 &lt;= begin[i] &lt;= begin[i] + size[i] &lt;= Di
-- for i in [0, n)</li>
-- </ul>
slice :: (TensorType index, OneOf '[Int32, Int64] index, TensorType t) => Tensor v1 t -> Tensor v2 index -> Tensor v3 index -> 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 index, OneOf '[Int32, Int64] index, TensorType t) => Tensor v1 index -> Tensor v2 index -> Tensor v3 index -> Tensor v4 index -> Tensor v5 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
-- | 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) ==&gt; 12 ```
size :: (TensorType t, TensorType out_type, OneOf '[Int32, Int64] out_type) => Tensor v1 t -> Tensor Value out_type
-- | 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 :: Tensor Value ByteString
-- | 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
-- | 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
-- | 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]
-- | 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)
-- | 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 ==&gt; [1, 2, 4, 7, 8] idx ==&gt; [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)
-- | 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
-- | 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 ...```
tensorArraySplit :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor v3 Int64 -> Tensor v4 Float -> Tensor Value Float
-- | 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]
-- | 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
-- | Raise a exception to abort the process when called.
--
-- Returns nothing but an exception.
abort :: ControlNode
-- | 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)
-- | 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
-- | 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] &lt;
-- 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
-- | Returns the number of records this Reader has produced.
--
-- This is the same as the number of ReaderRead executions that have
-- succeeded.
readerNumRecordsProduced :: Tensor v1 ByteString -> Tensor Value Int64
-- | 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)
-- | Returns immutable tensor from memory region.
--
-- The current implementation memmaps the tensor from a file.
immutableConst :: (TensorType dtype) => Tensor Value dtype
-- | Returns the min of x and y (i.e. x &lt; 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
-- | 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 &gt;= 0 means use the index (starting at zero) of the
-- split line based on <tt>delimiter</tt>.</li>
-- </ul>
initializeTableFromTextFile :: Int64 -> Int64 -> Tensor v1 ByteString -> Tensor v2 ByteString -> ControlNode
-- | 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) ==&gt; [1, 2, 3, 4] ```
diagPart :: (TensorType t, OneOf '[Complex Double, Complex Float, Int32, Int64, 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
-- | 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>.
tensorArrayScatter :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 t -> Tensor v4 Float -> Tensor Value Float
-- | 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) ==&gt; 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
-- | Return a tensor with the same shape and contents as the input tensor
-- or value.
identity :: (TensorType t) => Tensor v1 t -> Tensor Value t
-- | 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
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 tindices -> Tensor Value t
-- | 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:
--
-- # 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 tindices, OneOf '[Int32, Int64] tindices, TensorType tparams) => Tensor v1 tparams -> Tensor v2 tindices -> Tensor Value tparams
-- | Checks whether a tensor has been initialized.
--
-- Outputs boolean scalar indicating whether the tensor has been
-- initialized.
isVariableInitialized :: (TensorType dtype) => Tensor v1 dtype -> Tensor Value Bool
-- | Concatenates tensors along one dimension.
concat :: (TensorType t) => Tensor v1 Int32 -> [Tensor v2 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 t, OneOf '[Int32, Int64] t, TensorType tout, OneOf '[Int32, Int64] tout) => Tensor v1 t -> Tensor v2 tout -> Tensor v3 tout -> Tensor Value tout
-- | 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
-- | Performs average pooling on the input.
--
-- Each entry in <tt>output</tt> is the mean of the corresponding size
-- <tt>ksize</tt> window in <a>value</a>.
avgPool :: (TensorType t, OneOf '[Word16, Double, Float] 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 :: Tensor Value 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)
-- | Outputs random values from a normal distribution.
--
-- The generated values will have mean 0 and standard deviation 1.
randomStandardNormal :: (TensorType t, OneOf '[Int32, Int64] t, TensorType dtype, OneOf '[Word16, Double, Float] dtype) => Tensor v1 t -> Tensor Value dtype
-- | 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
-- | 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_box` 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,
--
-- # 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 -> (Tensor Value t, Tensor Value t, Tensor Value Float)
-- | Returns the truth value of (x &gt; 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
-- | Makes its input available to the next iteration.
refNextIteration :: (TensorType 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 &gt;=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
-- | Does nothing. Serves as a control trigger for scheduling.
--
-- Only useful as a placeholder for control edges.
controlTrigger :: ControlNode
-- | 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 v1 t -> Tensor v2 tindices -> Tensor v3 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
-- | 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
-- | 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
-- | 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 v1 t -> Tensor v2 t -> Tensor Value t
-- | 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 gradients of the maxpooling function.
maxPoolGradWithArgmax :: (TensorType t, OneOf '[Word16, Float] t, TensorType targmax, OneOf '[Int32, Int64] targmax) => Tensor v1 t -> Tensor v2 t -> Tensor v3 targmax -> 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
-- | Returns a tensor of zeros with the same shape and type as x.
zerosLike :: (TensorType t) => Tensor v1 t -> Tensor Value t
-- | Reads and outputs the entire contents of the input filename.
readFile :: Tensor v1 ByteString -> 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
-- | Transforms a serialized tensorflow.TensorProto proto into a Tensor.
parseTensor :: (TensorType out_type) => Tensor v1 ByteString -> Tensor Value out_type
-- | 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
-- | 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'
-- | 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 v1 ByteString -> Tensor v2 tin -> Tensor v3 tout -> ControlNode
-- | 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
batchSelfAdjointEig :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> 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
-- | 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
-- | Unpack the data from the input value into TensorArray elements.
--
-- <ul>
-- <li>*WARNING: This op is deprecated.**</li>
-- </ul>
--
-- Instead of this op, use <tt>TensorArrayScatter</tt> with `indices =
-- RangeOp(0, SizeOp(value)[0])`.
tensorArrayUnpack :: (TensorType t) => Tensor v1 ByteString -> Tensor v2 t -> Tensor v3 Float -> Tensor Value Float
batchMatrixDeterminant :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> 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
-- | 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)
-- | 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)
-- ==&gt; [[9, 9, 9] [9, 9, 9]] ```
fill :: (TensorType t) => Tensor v1 Int32 -> Tensor v2 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 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
-- | 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
-- | Return the same ref tensor as the input ref tensor.
refIdentity :: (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
-- | 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 v1 ByteString -> Tensor v2 tin -> Tensor v3 tout -> ControlNode
batchIFFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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 ==&gt; [1, 2, 4, 7, 8] idx ==&gt;
-- [0, 0, 1, 2, 2, 2, 3, 4, 4] count ==&gt; [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)
-- | Gather values or slices from <tt>params</tt> according to
-- <tt>indices</tt>.
--
-- <tt>params</tt> is a Tensor of rank <tt>R</tt> and <tt>indices</tt> is
-- a Tensor of rank <tt>M</tt>.
--
-- <tt>indices</tt> must be integer tensor, containing indices into
-- <tt>params</tt>. It must be shape `[d_0, ..., d_N, R]` where `0 &lt; R
-- &lt;= M`.
--
-- The innermost dimension of <tt>indices</tt> (with length <tt>R</tt>)
-- corresponds to indices into elements (if `R = M`) or slices (if `R
-- &lt; M`) along the <tt>N</tt>th dimension of <tt>params</tt>.
--
-- Produces an output tensor with shape
--
-- <ul>
-- <li><i>d_0, ..., d_{n-1}, params.shape[R</i> , ...,
-- params.shape[M-1]].</li>
-- </ul>
--
-- Some examples below.
--
-- Simple indexing into a matrix:
--
-- 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:
--
-- 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:
--
-- 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:
--
-- 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:
--
-- 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:
--
-- 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 tindices, OneOf '[Int32, Int64] tindices, TensorType tparams) => Tensor v1 tparams -> Tensor v2 tindices -> Tensor Value tparams
-- | Read an element from the TensorArray into output <a>value</a>.
tensorArrayRead :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> 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 v1 ByteString -> Tensor v2 ByteString -> Tensor v3 Int64 -> (Tensor Value ByteString, Tensor Value ByteString)
-- | 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
batchMatrixBandPart :: (TensorType t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor Value t
-- | 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
-- | Forwards the <tt>index</tt>th element of <tt>inputs</tt> to
-- <tt>output</tt>.
refSelect :: (TensorType t) => Tensor v1 Int32 -> [Tensor v2 t] -> Tensor Value t
-- | 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
-- | 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 v1 ByteString -> Tensor v2 tin -> Tensor v3 tout -> Tensor Value tout
-- | 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)) ==&gt; [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])) ==&gt; [1, 2, 3, 1] ```
squeeze :: (TensorType t) => Tensor v1 t -> Tensor Value 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
-- | 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
-- | 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
-- | 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)
-- | 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
-- | 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]) =&gt; [[1, 4], [2, 5], [3, 6]] #
-- Pack along first dim. pack([x, y, z], axis=1) =&gt; [[1, 2, 3], [4, 5,
-- 6]] ```
--
-- This is the opposite of <a>unpack</a>.
pack :: (TensorType t) => [Tensor v1 t] -> Tensor Value t
-- | 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
-- | 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)
-- | 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 diagonals of the innermost matrices. These will be overwritten by
-- the values in <tt>diagonal</tt>. The batched matrices must be square.
--
-- The output is computed as follows:
--
-- Assume <tt>input</tt> has `k+1` dimensions `[I, J, K, ..., N, N]` and
-- <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:
--
-- <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
-- | Update '*var' according to the RMSProp algorithm.
--
-- Note that in dense implement of this algorithm, ms and mom will update
-- even if the grad is zero, but in this sparse implement, ms and mom
-- will not update in iterations the grad is zero.
--
-- mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta =
-- learning_rate * gradient / sqrt(mean_square + epsilon)
--
-- ms &lt;- rho * ms_{t-1} + (1-rho) * grad * grad mom &lt;- momentum *
-- mom_{t-1} + lr * grad / sqrt(ms + epsilon) var &lt;- var - mom
applyRMSProp :: (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 v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor Value t
-- | Returns a constant tensor.
const :: (TensorType dtype) => Tensor Value dtype
-- | 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
-- | 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
-- | Debug NaN Value Counter Op
--
-- Counts number of NaNs in the input tensor, for debugging.
debugNanCount :: (TensorType t) => Tensor v1 t -> Tensor Value Int64
-- | 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
batchMatrixDiag :: (TensorType t) => Tensor v1 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]
-- | 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 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) ==&gt; [[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
batchMatrixDiagPart :: (TensorType t) => Tensor v1 t -> Tensor Value t
-- | Computes gradient of the FractionalMaxPool function.
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
-- | 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
-- | 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
-- | 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)
-- | 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)
-- | 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 v1 t -> Tensor v2 Bool -> (Tensor Value t, Tensor Value t)
-- | 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
-- | Returns the truth value of NOT x element-wise.
logicalNot :: Tensor v1 Bool -> Tensor Value Bool
-- | Gradients for Local Response Normalization.
lRNGrad :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
-- | 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
-- | 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
-- | 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)
-- | 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
batchCholeskyGrad :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Gather specific elements from the TensorArray into output
-- <a>value</a>.
--
-- All elements selected by <tt>indices</tt> must have the same shape.
tensorArrayGather :: (TensorType dtype) => Tensor v1 ByteString -> Tensor v2 Int32 -> Tensor v3 Float -> Tensor Value dtype
-- | 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
-- | Training via negative sampling.
negTrain :: Int64 -> Tensor v1 Float -> Tensor v2 Float -> Tensor v3 Int32 -> Tensor v4 Int32 -> Tensor v5 Float -> ControlNode
-- | 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.
tensorArrayGrad :: Tensor v1 ByteString -> Tensor v2 Float -> Tensor Value ByteString
-- | 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
-- | Does nothing. Only useful as a placeholder for control edges.
noOp :: ControlNode
-- | Makes its input available to the next iteration.
nextIteration :: (TensorType t) => Tensor v1 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
-- | 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)
-- | 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
-- | 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 t, OneOf '[Int32, Int64] t, TensorType dtype, OneOf '[Word16, Double, Float] dtype) => Tensor v1 t -> Tensor v2 dtype -> Tensor v3 dtype -> Tensor v4 dtype -> Tensor v5 dtype -> Tensor Value dtype
-- | 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
-- | Computes exponential linear: `exp(features) - 1` if &lt; 0,
-- <tt>features</tt> otherwise.
--
-- See <a>Fast and Accurate Deep Network Learning by Exponential Linear
-- Units (ELUs)</a>
elu :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value t
-- | Outputs all keys and values in the table.
lookupTableExport :: (TensorType tkeys, TensorType tvalues) => Tensor v1 ByteString -> (Tensor Value tkeys, Tensor Value tvalues)
-- | Computes the number of elements in the given table.
lookupTableSize :: Tensor v1 ByteString -> Tensor Value Int64
-- | Computes gradients of the average pooling function.
avgPoolGrad :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 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)
-- | 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)
-- | 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
-- | 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
-- | Pop the element at the top of the stack.
stackPop :: (TensorType elem_type) => Tensor v1 ByteString -> Tensor Value elem_type
-- | 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 :: Tensor Value ByteString
batchSelfAdjointEigV2 :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t)
batchMatrixTriangularSolve :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
batchMatrixSolveLs :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 Double -> Tensor Value t
batchSvd :: (TensorType t, OneOf '[Complex Double, Complex Float, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value t, Tensor Value t)
-- | Outputs a <tt>Summary</tt> protocol buffer with a tensor.
tensorSummary :: (TensorType t) => Tensor v1 t -> Tensor Value ByteString
-- | Computes softmax cross entropy cost and gradients to backpropagate.
--
-- Unlike <tt>SoftmaxCrossEntropyWithLogits</tt>, this operation does not
-- accept a matrix of label probabilities, but rather a single label per
-- row of features. This label is considered to have probability 1.0 for
-- the given row.
--
-- Inputs are the logits, not probabilities.
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)
-- | 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 t, OneOf '[Word16, Float] t, TensorType targmax, OneOf '[Int32, Int64] targmax) => Tensor v1 t -> (Tensor Value t, Tensor Value targmax)
-- | 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)
-- | 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
-- | 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) ==&gt;
-- [[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
batchIFFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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
-- | 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
-- | 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
-- | Gradients for batch normalization.
--
-- This op is deprecated. See `tf.nn.batch_normalization`.
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)
-- | 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
-- | 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) ==&gt; [2, 4, 3, 0, 1] ```
invertPermutation :: (TensorType t, OneOf '[Int32, Int64] t) => Tensor v1 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) ==&gt; [[ 1, 5] [11, 28]]
-- ```
mirrorPadGrad :: (TensorType t, TensorType tpaddings, OneOf '[Int32, Int64] tpaddings) => Tensor v1 t -> Tensor v2 tpaddings -> Tensor Value t
-- | 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) ==&gt;
-- [[[[ 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) ==&gt;
-- [[[[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) ==&gt;
-- [[[[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
-- | Computes a 2-D 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, out_channels]`, this op performs the
-- following:
--
-- <ol>
-- <li>Flattens the filter to a 2-D matrix with shape `[filter_height *
-- filter_width * in_channels, output_channels]`.</li>
-- <li>Extracts image patches from the input tensor to form a *virtual*
-- tensor of shape `[batch, out_height, out_width, filter_height *
-- filter_width * in_channels]`.</li>
-- <li>For each patch, right-multiplies the filter matrix and the image
-- patch vector.</li>
-- </ol>
--
-- In detail, with the default NHWC format,
--
-- output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di,
-- strides[2] * j + dj, q] * filter[di, dj, q, k]
--
-- Must have `strides[0] = strides[3] = 1`. For the most common case of
-- the same horizontal and vertices strides, `strides = [1, stride,
-- stride, 1]`.
conv2D :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Computes the gradients of convolution with respect to the input.
conv2DBackpropInput :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 Int32 -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
-- | Produce a string tensor that encodes the state of a Reader.
--
-- Not all Readers support being serialized, so this can produce an
-- Unimplemented error.
readerSerializeState :: Tensor v1 ByteString -> 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) => Tensor Value dtype
-- | 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
-- | Computes gradients of the maxpooling function.
maxPoolGrad :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor v3 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
-- | 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 t, OneOf '[Int32, Int64] t, TensorType dtype, OneOf '[Word16, Double, Float] dtype) => Tensor v1 t -> Tensor Value dtype
-- | 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
-- | 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 t -> Tensor v6 t -> Tensor v7 t -> Tensor v8 tindices -> Tensor Value t
-- | Computes the gradients of depthwise convolution with respect to the
-- filter.
depthwiseConv2dNativeBackpropFilter :: (TensorType t, OneOf '[Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> Tensor Value t
-- | Computes a 3-D convolution given 5-D <tt>input</tt> and <a>filter</a>
-- tensors.
--
-- In signal processing, cross-correlation is a measure of similarity of
-- two waveforms as a function of a time-lag applied to one of them. This
-- is also known as a sliding dot product or sliding inner-product.
--
-- Our Conv3D implements a form of cross-correlation.
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
-- | Returns the truth value of (x &gt;= 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
-- | 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
-- | Computes the gradients of 3-D convolution with respect to the filter.
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
-- | 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
-- | 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
-- | 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 v1 t] -> (Tensor Value t, Tensor Value Int32)
-- | Computes the gradients of 3-D convolution with respect to the filter.
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
-- | 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
-- | 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
-- | Computes gradients of max pooling function.
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
-- | 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
-- | 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
-- | 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
-- | Restore a reader to a previously saved state.
--
-- Not all Readers support being restored, so this can produce an
-- Unimplemented error.
readerRestoreState :: Tensor v1 ByteString -> Tensor v2 ByteString -> ControlNode
-- | 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) ==&gt; [2, 2, 3] ```
shape :: (TensorType t, TensorType out_type, OneOf '[Int32, Int64] out_type) => Tensor v1 t -> Tensor Value out_type
-- | Computes softmax cross entropy cost and gradients to backpropagate.
--
-- Inputs are the logits, not probabilities.
softmaxCrossEntropyWithLogits :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> (Tensor Value t, Tensor Value t)
-- | Performs max pooling on the input.
maxPool :: (TensorType t, OneOf '[Word16, Float] t) => Tensor v1 t -> Tensor Value t
-- | Computes the gradient of morphological 2-D dilation with respect to
-- the input.
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
-- | 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
-- | 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
-- | Computes rectified linear gradients for a Relu operation.
reluGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Computes rectified linear 6: `min(max(features, 0), 6)`.
relu6 :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 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
-- | 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
-- | 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
-- | Computes softplus: `log(exp(features) + 1)`.
softplus :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor Value 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
-- | Computes softsign gradients for a softsign operation.
softsignGrad :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Tensor v1 t -> Tensor v2 t -> Tensor Value t
-- | Returns the truth value of (x &lt;= 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
-- | Computes log softmax activations.
--
-- For each batch <tt>i</tt> and class <tt>j</tt> we have
--
-- logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i])))
logSoftmax :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
-- | Says whether the targets are in the top <tt>K</tt> predictions.
--
-- This outputs a <tt>batch_size</tt> bool array, an entry `out[i]` is
-- <tt>true</tt> if the prediction for the target class is among the top
-- <tt>k</tt> predictions among all predictions for example <tt>i</tt>.
-- Note that the behavior of <tt>InTopK</tt> differs from the
-- <tt>TopK</tt> op in its handling of ties; if multiple classes have the
-- same prediction value and straddle the top-<tt>k</tt> boundary, all of
-- those classes are considered to be in the top <tt>k</tt>.
--
-- More formally, let
--
-- \(predictions_i\) be the predictions for all classes for example
-- <tt>i</tt>, \(targets_i\) be the target class for example <tt>i</tt>,
-- \(out_i\) be the output for example <tt>i</tt>,
--
-- $$out_i = predictions_{i, targets_i} in
-- TopKIncludingTies(predictions_i)$$
inTopK :: (TensorType t, OneOf '[Int32, Int64] t) => Int64 -> Tensor v1 Float -> Tensor v2 t -> Tensor Value Bool
-- | 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) ==&gt; [[[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
-- | 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
-- | 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.
--
-- If <tt>k</tt> varies dynamically, use <tt>TopKV2</tt> below.
topK :: (TensorType t, OneOf '[Int16, Int32, Int64, Int8, Word16, Word8, Double, Float] t) => Int64 -> Tensor v1 t -> (Tensor Value t, Tensor Value Int32)
-- | 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)
-- | Performs fractional max pooling on the input.
--
-- Fractional max pooling is slightly different than regular max pooling.
-- In regular max pooling, you downsize an input set by taking the
-- maximum value of smaller N x N subsections of the set (often 2x2), and
-- try to reduce the set by a factor of N, where N is an integer.
-- Fractional max pooling, as you might expect from the word
-- "fractional", means that the overall reduction ratio N does not have
-- to be an integer.
--
-- The sizes of the pooling regions are generated randomly but are fairly
-- uniform. For example, let's look at the height dimension, and the
-- constraints on the list of rows that will be pool boundaries.
--
-- First we define the following:
--
-- <ol>
-- <li>input_row_length : the number of rows from the input set</li>
-- <li>output_row_length : which will be smaller than the input</li>
-- <li>alpha = input_row_length / output_row_length : our reduction
-- ratio</li>
-- <li>K = floor(alpha)</li>
-- <li>row_pooling_sequence : this is the result list of pool boundary
-- rows</li>
-- </ol>
--
-- Then, row_pooling_sequence should satisfy:
--
-- <ol>
-- <li>a[0] = 0 : the first value of the sequence is 0</li>
-- <li>a[end] = input_row_length : the last value of the sequence is the
-- size</li>
-- <li>K &lt;= (a[i+1] - a[i]) &lt;= K+1 : all intervals are K or K+1
-- size</li>
-- <li>length(row_pooling_sequence) = output_row_length+1</li>
-- </ol>
--
-- For more details on fractional max pooling, see this paper:
-- <a>Benjamin Graham, Fractional Max-Pooling</a>
fractionalMaxPool :: (TensorType t, OneOf '[Int32, Int64, Double, Float] t) => Tensor v1 t -> (Tensor Value t, Tensor Value Int64, Tensor Value Int64)
-- | 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)` is one if `(num_lower &lt; 0 ||
-- (m-n) &lt;= num_lower)) &amp;&amp; (num_upper &lt; 0 || (n-m) &lt;=
-- num_upper)`, and zero otherwise.
--
-- 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) ==&gt; [[ 0, 1, 2, 3] [-1, 0, 1, 2]
-- [ 0, -1, 0, 1] [ 0, 0, -1, 0]],
--
-- tf.matrix_band_part(input, 2, 1) ==&gt; [[ 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) ==&gt; Upper
-- triangular part. tf.matrix_band_part(input, -1, 0) ==&gt; Lower
-- triangular part. tf.matrix_band_part(input, 0, 0) ==&gt; Diagonal. ```
matrixBandPart :: (TensorType t) => Tensor v1 t -> Tensor v2 Int64 -> Tensor v3 Int64 -> Tensor Value t
-- | Reinterpret the bytes of a string as a vector of numbers.
decodeRaw :: (TensorType out_type, OneOf '[Int16, Int32, Int64, Int8, Word8, Double, Float] out_type) => Tensor v1 ByteString -> Tensor Value out_type
-- | 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
-- | 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 t, OneOf '[Int32, Int64] t, TensorType dtype, OneOf '[Word16, Double, Float] dtype) => Tensor v1 t -> Tensor Value dtype
-- | 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], ==&gt; [1, 2], [5, 6]] [3,
-- 4]] ```
randomShuffle :: (TensorType t) => Tensor v1 t -> Tensor Value t
-- | 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 -> Tensor Value Int64
-- | 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 -> Tensor Value 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 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
-- | A graph node which represents a return value of a function.
_Retval :: (TensorType t) => Int64 -> Tensor v1 t -> ControlNode
-- | 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 v1 t -> Tensor Value t
-- | Cast x of type SrcT to y of DstT.
cast :: (TensorType dstT, TensorType srcT) => Tensor v1 srcT -> Tensor Value dstT
-- | Increments <tt>ref</tt> until it reaches <tt>limit</tt>.
--
-- This operation outputs "ref" after the update is done. This makes it
-- easier to chain operations that need to use the updated value.
countUpTo :: (TensorType t, OneOf '[Int32, Int64] t) => Int64 -> Tensor v1 t -> Tensor Value t
-- | 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 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
-- | Returns the element-wise max of two SparseTensors.
--
-- Assumes the two SparseTensors have the same shape, i.e., no
-- broadcasting.
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)
-- | 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 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
-- | 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
-- | 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
-- | 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)) ==&gt; [1, 2] shape(expand_dims(t, 1)) ==&gt;
-- [2, 1] shape(expand_dims(t, -1)) ==&gt; [2, 1]
--
-- # <tt>t2</tt> is a tensor of shape [2, 3, 5] shape(expand_dims(t2, 0))
-- ==&gt; [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==&gt; [2, 3, 1, 5]
-- shape(expand_dims(t2, 3)) ==&gt; [2, 3, 5, 1] ```
--
-- This operation requires that:
--
-- `-1-input.dims() &lt;= dim &lt;= 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
-- | 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
-- | 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)
-- | 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
-- | Computes the gradient for the tanh of <tt>x</tt> wrt its input.
--
-- Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and
-- <tt>dy</tt> is the corresponding input gradient.
tanhGrad :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 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
-- | 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
-- | Computes cos of x element-wise.
cos :: (TensorType t, OneOf '[Complex Double, Complex Float, Word16, Double, Float] t) => Tensor v1 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
-- | 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
-- | 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
-- | 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
-- | Returns which elements of x are Inf.
isInf :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
-- | 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
-- | 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
-- | 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| &gt; 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 v1 t -> Tensor v2 t -> Tensor v3 t -> Tensor v4 t -> Tensor v5 tindices -> Tensor v6 t -> Tensor v7 t -> Tensor v8 t -> Tensor v9 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
batchFFT3D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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)
-- | Adds <tt>bias</tt> to <a>value</a>.
--
-- 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.
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
-- | 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 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
-- | 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
-- | 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
-- | Returns the max of x and y (i.e. x &gt; 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
-- | 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)
-- | Returns the truth value of (x &lt; 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
-- | 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) ==&gt; [[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
-- | 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
-- | 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 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
-- | 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) ==&gt; [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
-- | 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) ==&gt; [[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
-- | 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
-- | 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) ==&gt; [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
-- | 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
batchFFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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 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) ==&gt; [[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)
-- ==&gt; [[1, 2], [7, 8]]
--
-- ```
select :: (TensorType t) => Tensor v1 Bool -> Tensor v2 t -> Tensor v3 t -> Tensor Value t
-- | 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
-- | 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
-- | Computes the gradients of convolution with respect to the filter.
conv2DBackpropFilter :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor v2 Int32 -> Tensor v3 t -> 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
-- | Returns which elements of x are finite.
isFinite :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value Bool
-- | 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 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
-- | 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]) ==&gt; [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) ==&gt; [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) ==&gt; [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)
-- ==&gt; [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
-- | 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 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
-- | A Reader that outputs the records from a TensorFlow Records file.
tFRecordReader :: Tensor Value ByteString
-- | 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])) ==&gt; [[0 0 0 0]]
--
-- # Select two rows, two segment. tf.sparse_segment_sum(c,
-- tf.constant([0, 1]), tf.constant([0, 1])) ==&gt; [[ 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])) ==&gt; [[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 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
-- | 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
-- | 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) => Tensor Value dtype
-- | 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
-- | Creates a sequence of integers.
--
-- This operation creates a sequence of integers 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) ==&gt; [3, 6, 9, 12, 15] ```
range :: (TensorType tidx, OneOf '[Int32, Int64] 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
-- | Generates values in an interval.
--
-- A sequence of <tt>num</tt> evenly-spaced values are generated
-- beginning at <tt>start</tt>. If `num &gt; 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") =&gt; [ 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
-- | 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
-- | 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) ==&gt; [-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
-- | 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 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 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 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]) ==&gt; [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) ==&gt; [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) ==&gt; [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)
-- ==&gt; [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
batchIFFT :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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
-- | 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 v1 ByteString -> Tensor v2 ByteString -> Tensor v3 t -> ControlNode
-- | Returns element-wise largest integer not greater than x.
floor :: (TensorType t, OneOf '[Word16, Double, Float] t) => Tensor v1 t -> Tensor Value t
batchFFT2D :: Tensor v1 (Complex Float) -> Tensor Value (Complex Float)
-- | 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)
-- | 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)
batchCholesky :: (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,
--
-- 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:
--
-- # 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]
-- | 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
-- | 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)
-- | 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
-- | 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)
-- | 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
-- | 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
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