Update '*var' according to the AddSign update.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ApplyPowerSign
mt <- beta1 * m{t-1} + (1 - beta1) * g update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g variable <- variable - lr_t * update


Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ApplyProximalAdagrad
accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}


Update '*var' as FOBOS algorithm with fixed learning rate.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ApplyProximalGradientDescent
prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}


Update '*var' according to the RMSProp algorithm.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ApplyRMSProp
Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero.
mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom


Returns the truth value of abs(x-y) < tolerance element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ApproximateEqual


Returns the index with the largest value across dimensions of a tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ArgMax
Note that in case of ties the identity of the return value is not guaranteed.


Returns the index with the smallest value across dimensions of a tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ArgMin
Note that in case of ties the identity of the return value is not guaranteed.


Computes the trignometric inverse sine of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Asin
The tf.math.asin operation returns the inverse of tf.math.sin, such that if y = tf.math.sin(x) then, x = tf.math.asin(y).


Computes inverse hyperbolic sine of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Asinh
Given an input tensor, this function computes inverse hyperbolic sine for every element in the tensor. Both input and output has a range of [-inf, inf].


Asserts that the given condition is true.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Assert
If condition evaluates to false, print the list of tensors in data. summarize determines how many entries of the tensors to print.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssertCardinalityDataset


A transformation that asserts which transformations happen next.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssertNextDataset
This transformation checks whether the camel-case names (i.e. "FlatMap", not "flat_map") of the transformations following this transformation match the list of names in the transformations argument. If there is a mismatch, the transformation raises an exception.
The check occurs when iterating over the contents of the dataset, which means that the check happens after any static optimizations are applied to the dataset graph.


Update 'ref' by assigning 'value' to it.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Assign
This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value.


Update 'ref' by adding 'value' to it.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssignAdd
This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.


Adds a value to the current value of a variable.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssignAddVariableOp
Any ReadVariableOp with a control dependency on this op is guaranteed to see the incremented value or a subsequent newer one.


Update 'ref' by subtracting 'value' from it.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssignSub
This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value.


Subtracts a value from the current value of a variable.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssignSubVariableOp
Any ReadVariableOp with a control dependency on this op is guaranteed to see the decremented value or a subsequent newer one.


Assigns a new value to a variable.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AssignVariableOp
Any ReadVariableOp with a control dependency on this op is guaranteed to return this value or a subsequent newer value of the variable.


Converts each entry in the given tensor to strings.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AsString
Supports many numeric types and boolean.
For Unicode, see the https://www.tensorflow.org/tutorials/representation/unicode tutorial.


Computes the trignometric inverse tangent of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Atan
The tf.math.atan operation returns the inverse of tf.math.tan, such that if y = tf.math.tan(x) then, x = tf.math.atan(y).


Computes arctangent of y/x element-wise, respecting signs of the arguments.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Atan2
This is the angle ( \theta \in [-\pi, \pi] ) such that [ x = r \cos(\theta) ] and [ y = r \sin(\theta) ] where (r = \sqrt(x^2 + y^2) ).


Computes inverse hyperbolic tangent of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Atanh
Given an input tensor, this function computes inverse hyperbolic tangent for every element in the tensor. Input range is [-1,1] and output range is [-inf, inf]. If input is -1, output will be -inf and if the input is 1, output will be inf. Values outside the range will have nan as output.


Produces a visualization of audio data over time.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AudioSpectrogram
Spectrograms are a standard way of representing audio information as a series of slices of frequency information, one slice for each window of time. By joining these together into a sequence, they form a distinctive fingerprint of the sound over time.
This op expects to receive audio data as an input, stored as floats in the range -1 to 1, together with a window width in samples, and a stride specifying how far to move the window between slices. From this it generates a three dimensional output. The first dimension is for the channels in the input, so a stereo audio input would have two here for example. The second dimension is time, with successive frequency slices. The third dimension has an amplitude value for each frequency during that time slice.
This means the layout when converted and saved as an image is rotated 90 degrees clockwise from a typical spectrogram. Time is descending down the Y axis, and the frequency decreases from left to right.
Each value in the result represents the square root of the sum of the real and imaginary parts of an FFT on the current window of samples. In this way, the lowest dimension represents the power of each frequency in the current window, and adjacent windows are concatenated in the next dimension.
To get a more intuitive and visual look at what this operation does, you can run tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the resulting spectrogram as a PNG image.


Outputs a Summary protocol buffer with audio.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AudioSummary
The summary has up to max_outputs summary values containing audio. The audio is built from tensor 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 sample_rate.
The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values:


Outputs a Summary protocol buffer with audio.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AudioSummaryV2
The summary has up to max_outputs summary values containing audio. The audio is built from tensor 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 sample_rate.
The tag argument is a scalar Tensor of type string. It is used to build the tag of the summary values:


Creates a dataset that shards the input dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AutoShardDataset
Creates a dataset that shards the input dataset by num_workers, returning a sharded dataset for the index-th worker. This attempts to automatically shard a dataset by examining the Dataset graph and inserting a shard op before the inputs to a reader Dataset (e.g. CSVDataset, TFRecordDataset).
This dataset will throw a NotFound error if we cannot shard the dataset automatically.


Performs average pooling on the input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AvgPool
Each entry in output is the mean of the corresponding size ksize window in value.


Performs 3D average pooling on the input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AvgPool3D
Each entry in output is the mean of the corresponding size ksize window in value.


Computes gradients of average pooling function.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AvgPool3DGrad


Computes gradients of the average pooling function.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.AvgPoolGrad


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BandedTriangularSolve


Defines a barrier that persists across different graph executions.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Barrier
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 'complete' and 'incomplete' 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.


Closes the given barrier.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BarrierClose
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.


Computes the number of incomplete elements in the given barrier.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BarrierIncompleteSize


For each key, assigns the respective value to the specified component.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BarrierInsertMany
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.


Computes the number of complete elements in the given barrier.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BarrierReadySize


Takes the given number of completed elements from a barrier.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BarrierTakeMany
This operation concatenates completed-element component tensors along the 0th dimension to make a single component tensor.
Elements come out of the barrier when they are complete, and in the order in which they were placed into the barrier. The indices output provides information about the batch in which each element was originally inserted into the barrier.


Batches all input tensors nondeterministically.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Batch
When many instances of this Op are being run concurrently with the same container/shared_name in the same device, some will output zero-shaped Tensors and others will output Tensors of size up to max_batch_size.
All Tensors in in_tensors are batched together (so, for example, labels and features should be batched with a single instance of this operation.
Each invocation of batch emits an id scalar which will be used to identify this particular invocation when doing unbatch or its gradient.
Each op which emits a non-empty batch will also emit a non-empty batch_index Tensor, which, is a [K, 3] matrix where each row contains the invocation's id, start, and length of elements of each set of Tensors present in batched_tensors.
Batched tensors are concatenated along the first dimension, and all tensors in in_tensors must have the first dimension of the same size.
in_tensors: The tensors to be batched. num_batch_threads: Number of scheduling threads for processing batches of work. Determines the number of batches processed in parallel. max_batch_size: Batch sizes will never be bigger than this. batch_timeout_micros: Maximum number of microseconds to wait before outputting an incomplete batch. allowed_batch_sizes: Optional list of allowed batch sizes. If left empty, does nothing. Otherwise, supplies a list of batch sizes, causing the op to pad batches up to one of those sizes. The entries must increase monotonically, and the final entry must equal max_batch_size. grad_timeout_micros: The timeout to use for the gradient. See Unbatch. batched_tensors: Either empty tensors or a batch of concatenated Tensors. batch_index: If out_tensors is non-empty, has information to invert it. container: Controls the scope of sharing of this batch. id: always contains a scalar with a unique ID for this invocation of Batch. shared_name: Concurrently running instances of batch in the same device with the same container and shared_name will batch their elements together. If left empty, the op name will be used as the shared name. T: the types of tensors to be batched.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchCholesky


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchCholeskyGrad


Creates a dataset that batches batch_size elements from input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchDataset


Creates a dataset that batches batch_size elements from input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchDatasetV2


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchFFT


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchFFT2D


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchFFT3D


Batches all the inputs tensors to the computation done by the function.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchFunction
So, for example, in the following code
If more than one session.run call is simultaneously trying to compute b the values of a will be gathered, non-deterministically concatenated along the first axis, and only one thread will run the computation.
Assumes that all arguments of the function are Tensors which will be batched along their first dimension.
Arguments that are captured, are not batched. The session.run call which does the concatenation, will use the values of the captured tensors available to it. Therefore, typical uses of captured tensors should involve values which remain unchanged across session.run calls. Inference is a good example of this.
SparseTensor is not supported. The return value of the decorated function must be a Tensor or a list/tuple of Tensors.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchIFFT


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchIFFT2D


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchIFFT3D


Multiplies slices of two tensors in batches.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatMul
Multiplies all slices of Tensor x and y (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 adj_x or adj_y flag to True, which are by default False.
The input tensors x and y are 2-D or higher with shape [..., r_x, c_x] and [..., r_y, c_y].
The output tensor is 2-D or higher with shape [..., r_o, c_o], where:
output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])


Multiplies slices of two tensors in batches.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatMulV2
Multiplies all slices of Tensor x and y (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 adj_x or adj_y flag to True, which are by default False.
The input tensors x and y are 2-D or higher with shape [..., r_x, c_x] and [..., r_y, c_y].
The output tensor is 2-D or higher with shape [..., r_o, c_o], where:
output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixBandPart


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixDeterminant


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixDiag


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixDiagPart


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixInverse


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixSetDiag


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixSolve


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixSolveLs


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchMatrixTriangularSolve


Batch normalization.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchNormWithGlobalNormalization
This op is deprecated. Prefer tf.nn.batch_normalization.


Gradients for batch normalization.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchNormWithGlobalNormalizationGrad
This op is deprecated. See tf.nn.batch_normalization.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchSelfAdjointEig


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchSelfAdjointEigV2


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchSvd


BatchToSpace for 4-D tensors of type T.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchToSpace
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 batch dimension are moved in spatial blocks to the height and width dimensions, followed by cropping along the height and width dimensions.
crops = [[crop_top, crop_bottom], [crop_left, crop_right]]


BatchToSpace for N-D tensors of type T.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BatchToSpaceND
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 crops to produce the output. This is the reverse of SpaceToBatch. See below for a precise description.
This operation is equivalent to the following steps:
Reshape input to reshaped of shape: [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape), input_shape[1], ..., input_shape[N-1]]
Permute dimensions of reshaped to produce permuted of shape [batch / prod(block_shape),
input_shape[1], block_shape[0], ..., input_shape[M], block_shape[M-1],
input_shape[M+1], ..., input_shape[N-1]]
input_shape[1] * block_shape[0], ..., input_shape[M] * block_shape[M-1],
input_shape[M+1], ..., input_shape[N-1]]
input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
input_shape[M+1], ..., input_shape[N-1]]
Some examples:
(1) For the following input of shape [4, 1, 1, 1], block_shape = [2, 2], and crops = [[0, 0], [0, 0]]:
The output tensor has shape [1, 2, 2, 1] and value:
(2) For the following input of shape [4, 1, 1, 3], block_shape = [2, 2], and crops = [[0, 0], [0, 0]]:
The output tensor has shape [1, 2, 2, 3] and value:
(3) For the following input of shape [4, 2, 2, 1], block_shape = [2, 2], and crops = [[0, 0], [0, 0]]:
The output tensor has shape [1, 4, 4, 1] and value:
(4) For the following input of shape [8, 1, 3, 1], block_shape = [2, 2], and crops = [[0, 0], [2, 0]]:
The output tensor has shape [2, 2, 4, 1] and value:


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselI0


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselI0e


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselI1


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselI1e


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselJ0


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselJ1


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselK0


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselK0e


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselK1


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselK1e


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselY0


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BesselY1


Compute the regularized incomplete beta integral \(I_x(a, b)\).
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Betainc
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.


Adds bias to value.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BiasAdd
This is a special case of tf.add where bias is restricted to be 1-D. Broadcasting is supported, so value may have any number of dimensions.


The backward operation for "BiasAdd" on the "bias" tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BiasAddGrad
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.


Adds bias to value.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BiasAddV1
This is a deprecated version of BiasAdd and will be soon removed.
This is a special case of tf.add where bias is restricted to be 1-D. Broadcasting is supported, so value may have any number of dimensions.


Counts the number of occurrences of each value in an integer array.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Bincount
Outputs a vector with length size and the same dtype as weights. If weights are empty, then index i stores the number of times the value i is counted in arr. If weights are non-empty, then index i stores the sum of the value in weights at each index where the corresponding value in arr is i.
Values in arr outside of the range [0, size) are ignored.


Bitcasts a tensor from one type to another without copying data.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Bitcast
Given a tensor input, this operation returns a tensor that has the same buffer data as input with datatype type.
If the input datatype T is larger than the output datatype type then the shape changes from [...] to [..., sizeof(T)/sizeof(type)].
If T is smaller than type, the operator requires that the rightmost dimension be equal to sizeof(type)/sizeof(T). The shape then goes from [..., sizeof(type)/sizeof(T)] to [...].
tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype (e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast() gives module error. For example,


Elementwise computes the bitwise AND of x and y.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BitwiseAnd
The result will have those bits set, that are set in both x and y. The computation is performed on the underlying representations of x and y.


Elementwise computes the bitwise OR of x and y.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BitwiseOr
The result will have those bits set, that are set in x, y or both. The computation is performed on the underlying representations of x and y.


Elementwise computes the bitwise XOR of x and y.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BitwiseXor
The result will have those bits set, that are different in x and y. The computation is performed on the underlying representations of x and y.


Computes the LSTM cell forward propagation for all the time steps.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BlockLSTM
This is equivalent to applying LSTMBlockCell in a loop, like so:


Computes the LSTM cell backward propagation for the entire time sequence.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BlockLSTMGrad
This implementation is to be used in conjunction of LSTMBlock.


Computes the LSTM cell backward propagation for the entire time sequence.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BlockLSTMGradV2
This implementation is to be used in conjunction of BlockLSTMV2.


Computes the LSTM cell forward propagation for all the time steps.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BlockLSTMV2
This is equivalent to applying LSTMBlockCell in a loop, like so:


Aggregates the summary of accumulated stats for the batch.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesAggregateStats
The summary stats contains gradients and hessians accumulated for each node, feature dimension id and bucket.


Bucketize each feature based on bucket boundaries.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesBucketize
An op that returns a list of float tensors, where each tensor represents the bucketized values for a single feature.


Calculates gains for each feature and returns the best possible split information for the feature.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCalculateBestFeatureSplit
The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split.
In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature.


Calculates gains for each feature and returns the best possible split information for each node. However, if no split is found, then no split information is returned for that node.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCalculateBestFeatureSplitV2
The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split.
In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature.


Calculates gains for each feature and returns the best possible split information for the feature.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCalculateBestGainsPerFeature
The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split.
In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
The length of output lists are all of the same length, num_features. The output shapes are compatible in a way that the first dimension of all tensors of all lists are the same and equal to the number of possible split nodes for each feature.


Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCenterBias


Creates a tree ensemble model and returns a handle to it.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCreateEnsemble


Create the Resource for Quantile Streams.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesCreateQuantileStreamResource


Deserializes a serialized tree ensemble config and replaces current tree
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesDeserializeEnsemble
ensemble.


Creates a handle to a BoostedTreesEnsembleResource
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesEnsembleResourceHandleOp


Debugging/model interpretability outputs for each example.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesExampleDebugOutputs
It traverses all the trees and computes debug metrics for individual examples, such as getting split feature ids and logits after each split along the decision path used to compute directional feature contributions.


Flush the quantile summaries from each quantile stream resource.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesFlushQuantileSummaries
An op that outputs a list of quantile summaries of a quantile stream resource. Each summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank) for a single feature.


Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesGetEnsembleStates


Makes the summary of quantiles for the batch.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesMakeQuantileSummaries
An op that takes a list of tensors (one tensor per feature) and outputs the quantile summaries for each tensor.


Makes the summary of accumulated stats for the batch.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesMakeStatsSummary
The summary stats contains gradients and hessians accumulated into the corresponding node and bucket for each example.


Runs multiple additive regression ensemble predictors on input instances and
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesPredict
computes the logits. It is designed to be used during prediction. It traverses all the trees and calculates the final score for each instance.


Add the quantile summaries to each quantile stream resource.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceAddSummaries
An op that adds a list of quantile summaries to a quantile stream resource. Each summary Tensor is rank 2, containing summaries (value, weight, min_rank, max_rank) for a single feature.


Deserialize bucket boundaries and ready flag into current QuantileAccumulator.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceDeserialize
An op that deserializes bucket boundaries and are boundaries ready flag into current QuantileAccumulator.


Flush the summaries for a quantile stream resource.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceFlush
An op that flushes the summaries for a quantile stream resource.


Generate the bucket boundaries for each feature based on accumulated summaries.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceGetBucketBoundaries
An op that returns a list of float tensors for a quantile stream resource. Each tensor is Rank 1 containing bucket boundaries for a single feature.


Creates a handle to a BoostedTreesQuantileStreamResource.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesQuantileStreamResourceHandleOp


Serializes the tree ensemble to a proto.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesSerializeEnsemble


Aggregates the summary of accumulated stats for the batch.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesSparseAggregateStats
The summary stats contains gradients and hessians accumulated for each node, bucket and dimension id.


Calculates gains for each feature and returns the best possible split information for the feature.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit
The split information is the best threshold (bucket id), gains and left/right node contributions per node for each feature.
It is possible that not all nodes can be split on each feature. Hence, the list of possible nodes can differ between the features. Therefore, we return node_ids_list for each feature, containing the list of nodes that this feature can be used to split.
In this manner, the output is the best split per features and per node, so that it needs to be combined later to produce the best split for each node (among all possible features).
The output shapes are compatible in a way that the first dimension of all tensors are the same and equal to the number of possible split nodes for each feature.


Runs multiple additive regression ensemble predictors on input instances and
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesTrainingPredict
computes the update to cached logits. It is designed to be used during training. It traverses the trees starting from cached tree id and cached node id and calculates the updates to be pushed to the cache.


Updates the tree ensemble by either adding a layer to the last tree being grown
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesUpdateEnsemble
or by starting a new tree.


Updates the tree ensemble by adding a layer to the last tree being grown
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BoostedTreesUpdateEnsembleV2
or by starting a new tree.


Return the shape of s0 op s1 with broadcast.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BroadcastArgs
Given s0 and s1, tensors that represent shapes, compute r0, the broadcasted shape. s0, s1 and r0 are all integer vectors.


Return the reduction indices for computing gradients of s0 op s1 with broadcast.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BroadcastGradientArgs
This is typically used by gradient computations for a broadcasting operation.


Broadcast an array for a compatible shape.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BroadcastTo
Broadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a Tensor to a shape, it starts with the trailing dimensions, and works its way forward.
For example,
In the above example, the input Tensor with the shape of [1, 3] is broadcasted to output Tensor with shape of [3, 3].
When doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized.
However, broadcast_to does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, broadcast_to might be fused to subsequent operation and then be optimized away, however.)


Bucketizes 'input' based on 'boundaries'.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Bucketize
For example, if the inputs are boundaries = [0, 10, 100] input = [[-5, 10000] [150, 10] [5, 100]]
then the output will be output = [[0, 3] [3, 2] [1, 3]]


Records the bytes size of each element of input_dataset in a StatsAggregator.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.BytesProducedStatsDataset


Creates a dataset that caches elements from input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CacheDataset
A CacheDataset will iterate over the input_dataset, and store tensors. If the cache already exists, the cache will be used. If the cache is inappropriate (e.g. cannot be opened, contains tensors of the wrong shape / size), an error will the returned when used.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CacheDatasetV2


An n-way switch statement which calls a single branch function.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Case


Cast x of type SrcT to y of DstT.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cast


Returns element-wise smallest integer not less than x.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Ceil


Checks a tensor for NaN and Inf values.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CheckNumerics
When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is.


Checks a tensor for NaN, -Inf and +Inf values.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CheckNumericsV2
When run, reports an InvalidArgument error if tensor has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes tensor as-is. Unlike CheckNumerics (V1), CheckNumericsV2 distinguishes -Inf and +Inf in the errors it throws.


Computes the Cholesky decomposition of one or more square matrices.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cholesky
The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices.
The input has to be symmetric and positive definite. Only the lower-triangular part of the input will be used for this operation. The upper-triangular part will not be read.
The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices [..., :, :].


Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CholeskyGrad
For an explanation see "Differentiation of the Cholesky algorithm" by Iain Murray http://arxiv.org/abs/1602.07527


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ChooseFastestBranchDataset


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ChooseFastestDataset


Clips tensor values to a specified min and max.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ClipByValue
Given a tensor t, this operation returns a tensor of the same type and shape as t with its values clipped to clip_value_min and clip_value_max. Any values less than clip_value_min are set to clip_value_min. Any values greater than clip_value_max are set to clip_value_max.


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CloseSummaryWriter


Receives a tensor value broadcast from another device.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveBcastRecv


Broadcasts a tensor value to one or more other devices.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveBcastSend


Mutually accumulates multiple tensors of identical type and shape.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveGather


Mutually accumulates multiple tensors of identical type and shape.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveGatherV2


An Op to permute tensors across replicated TPU instances.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectivePermute
Each instance supplies its own input.
For example, suppose there are 4 TPU instances: [A, B, C, D]. Passing source_target_pairs=[[0,1],[1,2],[2,3],[3,0]] gets the outputs: [D, A, B, C].


Mutually reduces multiple tensors of identical type and shape.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveReduce


Mutually reduces multiple tensors of identical type and shape.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CollectiveReduceV2


Greedily selects a subset of bounding boxes in descending order of score,
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CombinedNonMaxSuppression
This operation performs non_max_suppression on the inputs per batch, across all classes. Prunes 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. Also 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 the final boxes, scores and classes tensor returned after performing non_max_suppression.


Compare values of input to threshold and pack resulting bits into a uint8.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CompareAndBitpack
Each comparison returns a boolean true (if input_value > threshold) or and false otherwise.
This operation is useful for Locality-Sensitive-Hashing (LSH) and other algorithms that use hashing approximations of cosine and L2 distances; codes can be generated from an input via:
Given an input shaped [s0, s1, ..., s_n], the output is a uint8 tensor shaped [s0, s1, ..., s_n / 8].


Converts two real numbers to a complex number.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Complex
Given a tensor real representing the real part of a complex number, and a tensor imag representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form \(a + bj\), where a represents the real part and b represents the imag part.
The input tensors real and imag must have the same shape.


Computes the complex absolute value of a tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ComplexAbs
Given a tensor x of complex numbers, this operation returns a tensor of type float or double that is the absolute value of each element in x. All elements in x must be complex numbers of the form \(a + bj\). The absolute value is computed as \( \sqrt{a^2 + b^2}\).


Compresses a dataset element.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CompressElement


Computes the ids of the positions in sampled_candidates that match true_labels.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ComputeAccidentalHits
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 'removing' the sampled labels that match the true labels by making the classifier sure that they are sampled labels.


Computes the static batch size of a dataset sans partial batches.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ComputeBatchSize


Concatenates tensors along one dimension.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Concat


Creates a dataset that concatenates input_dataset with another_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConcatenateDataset


Computes offsets of concat inputs within its output.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConcatOffset
This is typically used by gradient computations for a concat operation.


Concatenates tensors along one dimension.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConcatV2


A conditional accumulator for aggregating gradients.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConditionalAccumulator
The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator.


Sets up the centralized structures for a distributed TPU system.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConfigureDistributedTPU


Sets up TPUEmbedding in a distributed TPU system.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConfigureTPUEmbedding


Returns the complex conjugate of a complex number.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conj
Given a tensor input of complex numbers, this operation returns a tensor of complex numbers that are the complex conjugate of each element in input. The complex numbers in input 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 \(a - bj\).


Shuffle dimensions of x according to a permutation and conjugate the result.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConjugateTranspose
The output y has the same rank as x. The shapes of x and y satisfy: y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1] y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])


Returns a constant tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Const


This op consumes a lock created by MutexLock.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ConsumeMutexLock
This op exists to consume a tensor created by MutexLock (other than direct control dependencies). It should be the only that consumes the tensor, and will raise an error if it is not. Its only purpose is to keep the mutex lock tensor alive until it is consumed by this op.


Does nothing. Serves as a control trigger for scheduling.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.ControlTrigger
Only useful as a placeholder for control edges.


Computes a 2-D convolution given 4-D input and filter tensors.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv2D
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:
In detail, with the default NHWC format,
Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides, strides = [1, stride, stride, 1].


Computes the gradients of convolution with respect to the filter.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv2DBackpropFilter


Computes the gradients of convolution with respect to the input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv2DBackpropInput


Computes a 3-D convolution given 5-D input and filter tensors.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv3D
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.


Computes the gradients of 3-D convolution with respect to the filter.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv3DBackpropFilter


Computes the gradients of 3-D convolution with respect to the filter.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv3DBackpropFilterV2


Computes the gradients of 3-D convolution with respect to the input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv3DBackpropInput


Computes the gradients of 3-D convolution with respect to the input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Conv3DBackpropInputV2


Copy a tensor from CPU-to-CPU or GPU-to-GPU.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Copy
Performs CPU-to-CPU or GPU-to-GPU deep-copying of tensor, depending on the device on which the tensor is allocated. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details.
Unlike the CopyHost Op, this op does not have HostMemory constraint on its input or output.


Copy a tensor to host.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CopyHost
Performs CPU-to-CPU deep-copying of tensor. N.B.: If the all downstream attached debug ops are disabled given the current gRPC gating status, the output will simply forward the input tensor without deep-copying. See the documentation of Debug* ops for more details.
Unlike the Copy Op, this op has HostMemory constraint on its input or output.


Computes cos of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cos
Given an input tensor, this function computes cosine of every element in the tensor. Input range is (-inf, inf) and output range is [-1,1]. If input lies outside the boundary, nan is returned.


Computes hyperbolic cosine of x element-wise.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cosh
Given an input tensor, this function computes hyperbolic cosine of every element in the tensor. Input range is [-inf, inf] and output range is [1, inf].


Increments 'ref' until it reaches 'limit'.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CountUpTo


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CreateSummaryDbWriter


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CreateSummaryFileWriter


Extracts crops from the input image tensor and resizes them.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CropAndResize
Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change.
Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes. The cropped boxes are all resized (with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width]. The result is a 4-D tensor [num_boxes, crop_height, crop_width, depth]. The resizing is corner aligned. In particular, if boxes = [[0, 0, 1, 1]], the method will give identical results to using tf.image.resize_bilinear() or tf.image.resize_nearest_neighbor()(depends on the method argument) with align_corners=True.


Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CropAndResizeGradBoxes


Computes the gradient of the crop_and_resize op wrt the input image tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CropAndResizeGradImage


Compute the pairwise cross product.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cross
a and b 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.


An Op to sum inputs across replicated TPU instances.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CrossReplicaSum
Each instance supplies its own input.
For example, suppose there are 8 TPU instances: [A, B, C, D, E, F, G, H]. Passing group_assignment=[[0,2,4,6],[1,3,5,7]] sets A, C, E, G as group 0, and B, D, F, H as group 1. Thus we get the outputs: [A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H].


Reads out the CSR components at batch index.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CSRSparseMatrixComponents
This op is meant only for debugging / testing, and its interface is not expected to be stable.


Convert a (possibly batched) CSRSparseMatrix to dense.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CSRSparseMatrixToDense


Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CSRSparseMatrixToSparseTensor


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CSVDataset


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CSVDatasetV2


Performs beam search decoding on the logits given in input.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CTCBeamSearchDecoder
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.


Performs greedy decoding on the logits given in inputs.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CTCGreedyDecoder
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 '*', the sequence "A B B * B B" becomes "A B 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.


Calculates the CTC Loss (log probability) for each batch entry. Also calculates
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CTCLoss
the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.


Calculates the CTC Loss (log probability) for each batch entry. Also calculates
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CTCLossV2
the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM.


A RNN backed by cuDNN.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNN
Computes the RNN from the input and initial states, with respect to the params buffer.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is false.


Backprop step of CudnnRNN.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNBackprop
Compute the backprop of both data and weights in a RNN.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in for forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.


Backprop step of CudnnRNN.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNBackpropV2
Compute the backprop of both data and weights in a RNN. Takes an extra "host_reserved" inupt than CudnnRNNBackprop, which is used to determine RNN cudnnRNNAlgo_t and cudnnMathType_t.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. reserve_space: The same reserve_space produced in the forward operation. host_reserved: The same host_reserved produced in the forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.


Backprop step of CudnnRNNV3.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNBackpropV3
Compute the backprop of both data and weights in a RNN. Takes an extra "sequence_lengths" input than CudnnRNNBackprop.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, input_size]. If time_major is false, the shape is [batch_size, seq_length, input_size]. input_h: If time_major is true, this is a 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. If time_major is false, the shape is [batch_size, num_layer * dir, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore sequence_lengths: a vector of lengths of each input sequence. output: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. If time_major is false, the shape is [batch_size, seq_length, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. output_backprop: A 3-D tensor with the same shape as output in the forward pass. output_h_backprop: A 3-D tensor with the same shape as output_h in the forward pass. output_c_backprop: A 3-D tensor with the same shape as output_c in the forward pass. time_major: Indicates whether the input/output format is time major or batch major. reserve_space: The same reserve_space produced in the forward operation. input_backprop: The backprop to input in the forward pass. Has the same shape as input. input_h_backprop: The backprop to input_h in the forward pass. Has the same shape as input_h. input_c_backprop: The backprop to input_c in the forward pass. Has the same shape as input_c. params_backprop: The backprop to the params buffer in the forward pass. Has the same shape as params.


Converts CudnnRNN params from canonical form to usable form.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNCanonicalToParams
Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences.
Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases.
num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.


Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNCanonicalToParamsV2
Writes a set of weights into the opaque params buffer so they can be used in upcoming training or inferences.
Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases.
num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. num_params_weights: number of weight parameter matrix for all layers. num_params_biases: number of bias parameter vector for all layers. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. num_proj: The output dimensionality for the projection matrices. If None or 0, no projection is performed.


Computes size of weights that can be used by a Cudnn RNN model.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNParamsSize
Return the params size that can be used by the Cudnn RNN model. Subsequent weight allocation and initialization should use this size.
num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. params_size: The size of the params buffer that should be allocated and initialized for this RNN model. Note that this params buffer may not be compatible across GPUs. Please use CudnnRNNParamsWeights and CudnnRNNParamsBiases to save and restore them in a way that is compatible across different runs.


Retrieves CudnnRNN params in canonical form.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNParamsToCanonical
Retrieves a set of weights from the opaque params buffer that can be saved and restored in a way compatible with future runs.
Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases.
num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. num_params: number of parameter sets for all layers. Each layer may contain multiple parameter sets, with each set consisting of a weight matrix and a bias vector. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout.


Retrieves CudnnRNN params in canonical form. It supports the projection in LSTM.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNParamsToCanonicalV2
Retrieves a set of weights from the opaque params buffer that can be saved and restored in a way compatible with future runs.
Note that the params buffer may not be compatible across different GPUs. So any save and restoration should be converted to and from the canonical weights and biases.
num_layers: Specifies the number of layers in the RNN model. num_units: Specifies the size of the hidden state. input_size: Specifies the size of the input state. num_params_weights: number of weight parameter matrix for all layers. num_params_biases: number of bias parameter vector for all layers. weights: the canonical form of weights that can be used for saving and restoration. They are more likely to be compatible across different generations. biases: the canonical form of biases that can be used for saving and restoration. They are more likely to be compatible across different generations. rnn_mode: Indicates the type of the RNN model. input_mode: Indicate whether there is a linear projection between the input and The actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. dir = (direction == bidirectional) ? 2 : 1 dropout: dropout probability. When set to 0., dropout is disabled. seed: the 1st part of a seed to initialize dropout. seed2: the 2nd part of a seed to initialize dropout. num_proj: The output dimensionality for the projection matrices. If None or 0, no projection is performed.


A RNN backed by cuDNN.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNV2
Computes the RNN from the input and initial states, with respect to the params buffer. Produces one extra output "host_reserved" than CudnnRNN.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: A 3-D tensor with the shape of [seq_length, batch_size, input_size]. input_h: A 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore output: A 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true. host_reserved: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true. It is output on host memory rather than device memory.


A RNN backed by cuDNN.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CudnnRNNV3
Computes the RNN from the input and initial states, with respect to the params buffer. Accepts one extra input "sequence_lengths" than CudnnRNN.
rnn_mode: Indicates the type of the RNN model. input_mode: Indicates whether there is a linear projection between the input and the actual computation before the first layer. 'skip_input' is only allowed when input_size == num_units; 'auto_select' implies 'skip_input' when input_size == num_units; otherwise, it implies 'linear_input'. direction: Indicates whether a bidirectional model will be used. Should be "unidirectional" or "bidirectional". dropout: Dropout probability. When set to 0., dropout is disabled. seed: The 1st part of a seed to initialize dropout. seed2: The 2nd part of a seed to initialize dropout. input: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, input_size]. If time_major is false, the shape is [batch_size, seq_length, input_size]. input_h: If time_major is true, this is a 3-D tensor with the shape of [num_layer * dir, batch_size, num_units]. If time_major is false, the shape is [batch_size, num_layer * dir, num_units]. input_c: For LSTM, a 3-D tensor with the shape of [num_layer * dir, batch, num_units]. For other models, it is ignored. params: A 1-D tensor that contains the weights and biases in an opaque layout. The size must be created through CudnnRNNParamsSize, and initialized separately. Note that they might not be compatible across different generations. So it is a good idea to save and restore sequence_lengths: a vector of lengths of each input sequence. output: If time_major is true, this is a 3-D tensor with the shape of [seq_length, batch_size, dir * num_units]. If time_major is false, the shape is [batch_size, seq_length, dir * num_units]. output_h: The same shape has input_h. output_c: The same shape as input_c for LSTM. An empty tensor for other models. is_training: Indicates whether this operation is used for inference or training. time_major: Indicates whether the input/output format is time major or batch major. reserve_space: An opaque tensor that can be used in backprop calculation. It is only produced if is_training is true.


Compute the cumulative product of the tensor x along axis.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cumprod
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:
By setting the exclusive kwarg to True, an exclusive cumprod is performed instead:
By setting the reverse kwarg to True, the cumprod is performed in the opposite direction:
This is more efficient than using separate tf.reverse ops.
The reverse and exclusive kwargs can also be combined:


Compute the cumulative sum of the tensor x along axis.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Cumsum
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:
By setting the exclusive kwarg to True, an exclusive cumsum is performed instead:
By setting the reverse kwarg to True, the cumsum is performed in the opposite direction:
This is more efficient than using separate tf.reverse ops.
The reverse and exclusive kwargs can also be combined:


Compute the cumulative product of the tensor x along axis.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.CumulativeLogsumexp
By default, this op performs an inclusive cumulative log-sum-exp, which means that the first element of the input is identical to the first element of the output:
By setting the exclusive kwarg to True, an exclusive cumulative log-sum-exp is performed instead:
Note that the neutral element of the log-sum-exp operation is -inf, however, for performance reasons, the minimal value representable by the floating point type is used instead.
By setting the reverse kwarg to True, the cumulative log-sum-exp is performed in the opposite direction.


Returns the dimension index in the destination data format given the one in
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DataFormatDimMap
the source data format.


Permute input tensor from src_format to dst_format.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DataFormatVecPermute
Input tensor must be a vector of size 4, or a 4x2 tensor.
For example, with src_format of NHWC, dst_format of NCHW, and inputs:
and
, the outputs will be (respectively):
and


Creates a dataset that reads data from the tf.data service.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DataServiceDataset


Returns the cardinality of input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetCardinality
Returns the cardinality of input_dataset.


Creates a dataset from the given graph_def.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetFromGraph
Creates a dataset from the provided graph_def.


Returns a serialized GraphDef representing input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetToGraph
Returns a graph representation for input_dataset.


Returns a serialized GraphDef representing input_dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetToGraphV2
Returns a graph representation for input_dataset.


Outputs the single element from the given dataset.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetToSingleElement


Writes the given dataset to the given file using the TFRecord format.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DatasetToTFRecord


Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.Dawsn


Identity op for gradient debugging.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugGradientIdentity
This op is hidden from public in Python. It is used by TensorFlow Debugger to register gradient tensors for gradient debugging. This op operates on non-reference-type tensors.


Identity op for gradient debugging.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugGradientRefIdentity
This op is hidden from public in Python. It is used by TensorFlow Debugger to register gradient tensors for gradient debugging. This op operates on reference-type tensors.


Provides an identity mapping of the non-Ref type input tensor for debugging.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugIdentity
Provides an identity mapping of the non-Ref type input tensor for debugging.


Debug Identity V2 Op.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugIdentityV2
Provides an identity mapping from input to output, while writing the content of the input tensor by calling DebugEventsWriter.
The semantics of the input tensor depends on tensor_debug_mode. In typical usage, the input tensor comes directly from the user computation only when graph_debug_mode is FULL_TENSOR (see protobuf/debug_event.proto for a list of all the possible values of graph_debug_mode). For the other debug modes, the input tensor should be produced by an additional op or subgraph that computes summary information about one or more tensors.


Debug NaN Value Counter Op.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugNanCount
Counts number of NaNs in the input tensor, for debugging.


Debug Numeric Summary Op.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugNumericSummary
Provide a basic summary of numeric value types, range and distribution.
output: A double tensor of shape [14 + nDimensions], where nDimensions is the number of dimensions of the tensor's shape. The elements of output are: [0]: is initialized (1.0) or not (0.0). [1]: total number of elements [2]: NaN element count [3]: generalized -inf count: elements <= lower_bound. lower_bound is -inf by default. [4]: negative element count (excluding -inf), if lower_bound is the default -inf. Otherwise, this is the count of elements > lower_bound and < 0. [5]: zero element count [6]: positive element count (excluding +inf), if upper_bound is the default +inf. Otherwise, this is the count of elements < upper_bound and > 0. [7]: generalized +inf count, elements >= upper_bound. upper_bound is +inf by default. Output elements [1:8] are all zero, if the tensor is uninitialized. [8]: minimum of all non-inf and non-NaN elements. If uninitialized or no such element exists: +inf. [9]: maximum of all non-inf and non-NaN elements. If uninitialized or no such element exists: -inf. [10]: mean of all non-inf and non-NaN elements. If uninitialized or no such element exists: NaN. [11]: variance of all non-inf and non-NaN elements. If uninitialized or no such element exists: NaN. [12]: Data type of the tensor encoded as an enum integer. See the DataType proto for more details. [13]: Number of dimensions of the tensor (ndims). [14+]: Sizes of the dimensions.


Debug Numeric Summary V2 Op.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DebugNumericSummaryV2
Computes a numeric summary of the input tensor. The shape of the output depends on the tensor_debug_mode attribute. This op is used internally by TensorFlow Debugger (tfdbg) v2.
Supported values: 2 (CURT_HEALTH): Output a float32/64 tensor of shape [2]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is a bit which is set to 1 if the input tensor has an infinity or nan value, or zero otherwise.
3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st element is the tensor_id, if provided, and -1 otherwise. The remaining four slots are the total number of elements, -infs, +infs, and nans in the input tensor respectively.
4 (FULL_HEALTH): Output a float32/64 tensor of shape [11]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is the device_id, if provided, and -1 otherwise. The 3rd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The remaining elements hold the total number of elements, -infs, +infs, nans, negative finite numbers, zeros, and positive finite numbers in the input tensor respectively.
5 (SHAPE): Output a float32/64 tensor of shape [10]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The 3rd element holds the rank of the tensor. The 4th element holds the number of elements within the tensor. Finally the remaining 6 elements hold the shape of the tensor. If the rank of the tensor is lower than 6, the shape is right padded with zeros. If the rank is greater than 6, the head of the shape is truncated.
6 (FULL_NUMERICS): Output a float32/64 tensor of shape [22]. The 1st element is the tensor_id, if provided, and -1 otherwise. The 2nd element is the device_id, if provided, and -1 otherwise. The 3rd element holds the datatype value of the input tensor as according to the enumerated type in tensorflow/core/framework/types.proto. The 4th element holds the rank of the tensor. The 5th to 11th elements hold the shape of the tensor. If the rank of the tensor is lower than 6, the shape is right padded with zeros. If the rank is greater than 6, the head of the shape is truncated. The 12th to 18th elements hold the number of elements, -infs, +infs, nans, denormal floats, negative finite numbers, zeros, and positive finite numbers in the input tensor respectively. The final four elements hold the min value, max value, mean, and variance of the input tensor.
8 (REDUCE_INF_NAN_THREE_SLOTS): Output a float32/64 tensor of shape [3]. The 1st element is -inf if any elements of the input tensor is -inf, or zero otherwise. The 2nd element is +inf if any elements of the input tensor is +inf, or zero otherwise. The 3rd element is nan if any element of the input tensor is nan, or zero otherwise.


Decode and Crop a JPEG-encoded image to a uint8 tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeAndCropJpeg
The attr channels indicates the desired number of color channels for the decoded image.
If needed, the JPEG-encoded image is transformed to match the requested number of color channels.
The attr ratio 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.
It is equivalent to a combination of decode and crop, but much faster by only decoding partial jpeg image.


Decode web-safe base64-encoded strings.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeBase64
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 /.


Decode the first frame of a BMP-encoded image to a uint8 tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeBmp
The attr channels indicates the desired number of color channels for the decoded image.


Decompress strings.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeCompressed
This op decompresses each element of the bytes input Tensor, which is assumed to be compressed using the given compression_type.
The output is a string Tensor of the same shape as bytes, each element containing the decompressed data from the corresponding element in bytes.


Convert CSV records to tensors. Each column maps to one tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeCSV
RFC 4180 format is expected for the CSV records. (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field.


Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeGif
GIF images with frame or transparency compression are not supported. On Linux and MacOS systems, convert animated GIFs from compressed to uncompressed by running:
This op also supports decoding JPEGs and PNGs, though it is cleaner to use tf.io.decode_image.


Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeImage
Detects whether an image is a BMP, GIF, JPEG, or PNG, and performs the appropriate operation to convert the input bytes string into a Tensor of type dtype.


Decode a JPEG-encoded image to a uint8 tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeJpeg
The attr channels indicates the desired number of color channels for the decoded image.
If needed, the JPEG-encoded image is transformed to match the requested number of color channels.
The attr ratio 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.
This op also supports decoding PNGs and non-animated GIFs since the interface is the same, though it is cleaner to use tf.io.decode_image.


Convert JSON-encoded Example records to binary protocol buffer strings.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodeJSONExample
This op translates a tensor containing Example records, encoded using the standard JSON mapping, 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.


Reinterpret the bytes of a string as a vector of numbers.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodePaddedRaw


Decode a PNG-encoded image to a uint8 or uint16 tensor.
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.raw_ops.DecodePng
The attr channels indicates the desired number of color channels for the decoded image.
If needed, the PNG-encoded image is transformed to match the requested number of color channels.
This op also supports decoding JPEGs and non-animated GIFs since the interface is the same, though it is cleaner to use tf.io.decode_image.