tensorflow::ops

Raise a exception to abort the process when called.

Computes the absolute value of a tensor.

Returns the element-wise sum of a list of tensors.

Applies a gradient to a given accumulator.

Returns the number of gradients aggregated in the given accumulators.

Updates the accumulator with a new value for global_step.

Extracts the average gradient in the given ConditionalAccumulator.

Computes acos of x element-wise.

Computes inverse hyperbolic cosine of x element-wise.

Returns x + y element-wise.

Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.

Add all input tensors element wise.

Add a SparseTensor to a SparseTensorsMap return its handle.

Returns x + y element-wise.

Adjust the contrast of one or more images.

Adjust the hue of one or more images.

Adjust the saturation of one or more images.

Computes the "logical and" of elements across dimensions of a tensor.

Generates labels for candidate sampling with a learned unigram distribution.

Returns the argument of a complex number.

Computes the "logical or" of elements across dimensions of a tensor.

Update '*var' according to the adadelta scheme.

Update '*var' according to the adagrad scheme.

Update '*var' according to the proximal adagrad scheme.

Update '*var' according to the Adam algorithm.

Update '*var' according to the AddSign update.

Update '*var' according to the centered RMSProp algorithm.

Update '*var' according to the Ftrl-proximal scheme.

Update '*var' according to the Ftrl-proximal scheme.

Update '*var' by subtracting 'alpha' * 'delta' from it.

Update '*var' according to the momentum scheme.

Update '*var' according to the AddSign update.

Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.

Update '*var' as FOBOS algorithm with fixed learning rate.

Update '*var' according to the RMSProp algorithm.

Returns min/max k values and their indices of the input operand in an approximate manner.

Returns the truth value of abs(x-y) < tolerance element-wise.

Returns the index with the largest value across dimensions of a tensor.

Returns the index with the smallest value across dimensions of a tensor.

Converts each entry in the given tensor to strings.

Computes the trignometric inverse sine of x element-wise.

Computes inverse hyperbolic sine of x element-wise.

Asserts that the given condition is true.

Update 'ref' by assigning 'value' to it.

Update 'ref' by adding 'value' to it.

Update 'ref' by subtracting 'value' from it.

Computes the trignometric inverse tangent of x element-wise.

Computes arctangent of y/x element-wise, respecting signs of the arguments.

Computes inverse hyperbolic tangent of x element-wise.

Performs average pooling on the input.

Performs 3D average pooling on the input.

Computes gradients of average pooling function.

Defines a barrier that persists across different graph executions.

Closes the given barrier.

Computes the number of incomplete elements in the given barrier.

For each key, assigns the respective value to the specified component.

Computes the number of complete elements in the given barrier.

Takes the given number of completed elements from a barrier.

Multiplies slices of two tensors in batches.

Multiplies slices of two tensors in batches.

Multiplies slices of two tensors in batches.

BatchToSpace for 4-D tensors of type T.

BatchToSpace for N-D tensors of type T.

Compute the regularized incomplete beta integral \(I_x(a, b)\).

Adds bias to value.

The backward operation for "BiasAdd" on the "bias" tensor.

Counts the number of occurrences of each value in an integer array.

Bitcasts a tensor from one type to another without copying data.

Return the shape of s0 op s1 with broadcast.

Broadcast an array for a compatible shape.

Bucketizes 'input' based on 'boundaries'.

Cast x of type SrcT to y of DstT.

Returns element-wise smallest integer not less than x.

Checks a tensor for NaN and Inf values.

Clips tensor values to a specified min and max.

Greedily selects a subset of bounding boxes in descending order of score,.

Converts two real numbers to a complex number.

Computes the complex absolute value of a tensor.

Computes the ids of the positions in sampled_candidates that match true_labels.

Concatenates tensors along one dimension.

A conditional accumulator for aggregating gradients.

Returns the complex conjugate of a complex number.

Shuffle dimensions of x according to a permutation and conjugate the result.

Does nothing.

Computes a 2-D convolution given 4-D input and filter tensors.

Computes the gradients of convolution with respect to the filter.

Computes the gradients of convolution with respect to the filter.

Computes the gradients of convolution with respect to the input.

Computes the gradients of convolution with respect to the input.

Computes a 3-D convolution given 5-D input and filter tensors.

Computes the gradients of 3-D convolution with respect to the filter.

Computes the gradients of 3-D convolution with respect to the input.

Computes cos of x element-wise.

Computes hyperbolic cosine of x element-wise.

Increments 'ref' until it reaches 'limit'.

Extracts crops from the input image tensor and resizes them.

Computes the gradient of the crop_and_resize op wrt the input boxes tensor.

Computes the gradient of the crop_and_resize op wrt the input image tensor.

Compute the pairwise cross product.

Compute the cumulative product of the tensor x along axis.

Compute the cumulative sum of the tensor x along axis.

Returns the dimension index in the destination data format given the one in.

Permute input tensor from src_format to dst_format.

Identity op for gradient debugging.

Identity op for gradient debugging.

Decode and Crop a JPEG-encoded image to a uint8 tensor.

Decode web-safe base64-encoded strings.

Decode the first frame of a BMP-encoded image to a uint8 tensor.

Convert CSV records to tensors.

Decompress strings.

Decode the frame(s) of a GIF-encoded image to a uint8 tensor.

Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.

Convert JSON-encoded Example records to binary protocol buffer strings.

Decode a JPEG-encoded image to a uint8 tensor.

Reinterpret the bytes of a string as a vector of numbers.

Decode a PNG-encoded image to a uint8 or uint16 tensor.

Reinterpret the bytes of a string as a vector of numbers.

Makes a copy of x.

Delete the tensor specified by its handle in the session.

Counts the number of occurrences of each value in an integer array.

DepthToSpace for tensors of type T.

Computes a 2-D depthwise convolution given 4-D input and filter tensors.

Computes the gradients of depthwise convolution with respect to the filter.

Computes the gradients of depthwise convolution with respect to the input.

Dequantize the 'input' tensor into a float or bfloat16 Tensor.

Deserialize and concatenate SparseTensors from a serialized minibatch.

Deserialize SparseTensor objects.

Destroys the temporary variable and returns its final value.

Returns a diagonal tensor with a given diagonal values.

Returns the diagonal part of the tensor.

Computes Psi, the derivative of Lgamma (the log of the absolute value of.

Computes the grayscale dilation of 4-D input and 3-D filter tensors.

Computes the gradient of morphological 2-D dilation with respect to the filter.

Computes the gradient of morphological 2-D dilation with respect to the input.

Returns x / y element-wise.

Returns 0 if the denominator is zero.

Draw bounding boxes on a batch of images.

Draw bounding boxes on a batch of images.

Partitions data into num_partitions tensors using indices from partitions.

Interleave the values from the data tensors into a single tensor.

Computes the (possibly normalized) Levenshtein Edit Distance.

Computes the exponential linear function.

Creates a tensor with the given shape.

Encode strings into web-safe base64 format.

JPEG-encode an image.

JPEG encode input image with provided compression quality.

PNG-encode an image.

Ensures that the tensor's shape matches the expected shape.

Returns the truth value of (x == y) element-wise.

Computes the Gauss error function of x element-wise.

Computes the complementary error function of x element-wise.

TODO: add doc.

Computes the euclidean norm of elements across dimensions of a tensor.

Computes exponential of x element-wise.

Inserts a dimension of 1 into a tensor's shape.

Computes exp(x) - 1 element-wise.

Extracts a glimpse from the input tensor.

Extract patches from images and put them in the "depth" output dimension.

Extract the shape information of a JPEG-encoded image.

Extract patches from input and put them in the "depth" output dimension.

A queue that produces elements in first-in first-out order.

Output a fact about factorials.

Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same shape and type.

Compute gradients for a FakeQuantWithMinMaxArgs operation.

Fake-quantize the 'inputs' tensor of type float via global float scalars.

Compute gradients for a FakeQuantWithMinMaxVars operation.

Fake-quantize the 'inputs' tensor of type float via per-channel floats.

Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.

Creates a tensor filled with a scalar value.

Generates fingerprint values.

A Reader that outputs fixed-length records from a file.

Generates labels for candidate sampling with a learned unigram distribution.

Returns element-wise largest integer not greater than x.

Returns x // y element-wise.

Returns element-wise remainder of division.

Performs fractional average pooling on the input.

Performs fractional max pooling on the input.

Batch normalization.

Gradient for batch normalization.

Gradient for batch normalization.

Gradient for batch normalization.

Batch normalization.

Batch normalization.

Performs a padding as a preprocess during a convolution.

Performs a resize and padding as a preprocess during a convolution.

Gather slices from params according to indices.

Gather slices from params into a Tensor with shape specified by indices.

Gather slices from params axis axis according to indices.

Store the input tensor in the state of the current session.

Store the input tensor in the state of the current session.

Get the value of the tensor specified by its handle.

Returns the truth value of (x > y) element-wise.

Returns the truth value of (x >= y) element-wise.

Gives a guarantee to the TF runtime that the input tensor is a constant.

Convert one or more images from HSV to RGB.

Return histogram of values.

Outputs a Summary protocol buffer with a histogram.

Return a tensor with the same shape and contents as the input tensor or value.

Returns a list of tensors with the same shapes and contents as the input.

A Reader that outputs the queued work as both the key and value.

Compute the lower regularized incomplete Gamma function P(a, x).

Compute the upper regularized incomplete Gamma function Q(a, x).

Returns the imaginary part of a complex number.

Returns immutable tensor from memory region.

Says whether the targets are in the top K predictions.

Says whether the targets are in the top K predictions.

Adds v into specified rows of x.

Subtracts v into specified rows of x.

Updates specified rows 'i' with values 'v'.

Computes the reciprocal of x element-wise.

Computes the inverse permutation of a tensor.

Returns which elements of x are finite.

Returns which elements of x are Inf.

Returns which elements of x are NaN.

Checks whether a tensor has been initialized.

L2 Loss.

A Reader that outputs the records from a LMDB file.

Local Response Normalization.

Generates labels for candidate sampling with a learned unigram distribution.

Returns the truth value of (x < y) element-wise.

Returns the truth value of (x <= y) element-wise.

Computes the log of the absolute value of Gamma(x) element-wise.

Computes natural logarithm of x element-wise.

Computes natural logarithm of (1 + x) element-wise.

Computes log softmax activations.

Generates labels for candidate sampling with a log-uniform distribution.

Returns the truth value of x AND y element-wise.

Returns the truth value of NOT x element-wise.

Returns the truth value of x OR y element-wise.

Forwards the input to the output.

Op removes all elements in the underlying container.

Op returns the number of incomplete elements in the underlying container.

Op peeks at the values at the specified key.

Op returns the number of elements in the underlying container.

Stage (key, values) in the underlying container which behaves like a hashtable.

Op removes and returns the values associated with the key.

Op removes and returns a random (key, value)

Multiply the matrix "a" by the matrix "b".

Returns the set of files matching one or more glob patterns.

Copy a tensor setting everything outside a central band in each innermost matrix to zero.

Returns a batched diagonal tensor with a given batched diagonal values.

Returns the batched diagonal part of a batched tensor.

Returns the batched diagonal part of a batched tensor.

Returns the batched diagonal part of a batched tensor.

Returns a batched diagonal tensor with given batched diagonal values.

Returns a batched diagonal tensor with given batched diagonal values.

Returns a batched matrix tensor with new batched diagonal values.

Returns a batched matrix tensor with new batched diagonal values.

Returns a batched matrix tensor with new batched diagonal values.

Computes the maximum of elements across dimensions of a tensor.

Performs max pooling on the input.

Performs 3D max pooling on the input.

Computes gradients of 3D max pooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes second-order gradients of the maxpooling function.

Computes gradients of the maxpooling function.

Performs max pooling on the input.

Performs max pooling on the input and outputs both max values and indices.

Returns the max of x and y (i.e.

Computes the mean of elements across dimensions of a tensor.

Forwards the value of an available tensor from inputs to output.

Merges summaries.

V2 format specific: merges the metadata files of sharded checkpoints.

Computes the minimum of elements across dimensions of a tensor.

Returns the min of x and y (i.e.

Pads a tensor with mirrored values.

Returns element-wise remainder of division.

Returns x * y element-wise.

Draws samples from a multinomial distribution.

Returns x * y element-wise.

TODO: add doc.

Computes numerical negative value element-wise.

Returns the next representable value of x1 in the direction of x2, element-wise.

Makes its input available to the next iteration.

Does nothing.

Greedily selects a subset of bounding boxes in descending order of score,.

Greedily selects a subset of bounding boxes in descending order of score,.

Greedily selects a subset of bounding boxes in descending order of score,.

Greedily selects a subset of bounding boxes in descending order of score,.

Greedily selects a subset of bounding boxes in descending order of score,.

Greedily selects a subset of bounding boxes in descending order of score,.

Returns the truth value of (x != y) element-wise.

Finds values of the n-th order statistic for the last dimension.

Returns a one-hot tensor.

Returns a tensor of ones with the same shape and type as x.

Op removes all elements in the underlying container.

Op returns the number of incomplete elements in the underlying container.

Op peeks at the values at the specified key.

Op returns the number of elements in the underlying container.

Stage (key, values) in the underlying container which behaves like a ordered.

Op removes and returns the values associated with the key.

Op removes and returns the (key, value) element with the smallest.

Pads a tensor with zeros.

Pads a tensor.

A queue that produces elements in first-in first-out order.

Concatenates a list of N tensors along the first dimension.

Interleave the values from the data tensors into a single tensor.

Outputs random values from a normal distribution.

Transforms a vector of brain.Example protos (as strings) into typed tensors.

Transforms a vector of tf.Example protos (as strings) into typed tensors.

Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.

Transforms a vector of tf.io.SequenceExample protos (as strings) into typed tensors.

Transforms a tf.Example proto (as a string) into typed tensors.

Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.

Transforms a serialized tensorflow.TensorProto proto into a Tensor.

A placeholder op for a value that will be fed into the computation.

A placeholder op that passes through input when its output is not fed.

Compute the polygamma function \(\psi^{(n)}(x)\).

Computes the power of one value to another.

An identity op that triggers an error if a gradient is requested.

Prints a list of tensors.

Prints a string scalar.

A queue that produces elements sorted by the first component value.

Computes the product of elements across dimensions of a tensor.

Quantizes then dequantizes a tensor.

Quantizes then dequantizes a tensor.

Quantizes then dequantizes a tensor.

Returns the gradient of QuantizeAndDequantizeV4.

Convert the quantized 'input' tensor into a lower-precision 'output', using the.

Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.

Returns x + y element-wise, working on quantized buffers.

Produces the average pool of the input tensor for quantized types.

Quantized Batch normalization.

Adds Tensor 'bias' to Tensor 'input' for Quantized types.

Concatenates quantized tensors along one dimension.

Computes a 2D convolution given quantized 4D input and filter tensors.

Quantized Instance normalization.

Perform a quantized matrix multiplication of a by the matrix b.

Produces the max pool of the input tensor for quantized types.

Returns x * y element-wise, working on quantized buffers.

Computes Quantized Rectified Linear: max(features, 0)

Computes Quantized Rectified Linear 6: min(max(features, 0), 6)

Computes Quantized Rectified Linear X: min(max(features, 0), max_value)

Resize quantized images to size using quantized bilinear interpolation.

Closes the given queue.

Dequeues a tuple of one or more tensors from the given queue.

Dequeues n tuples of one or more tensors from the given queue.

Dequeues n tuples of one or more tensors from the given queue.

Enqueues a tuple of one or more tensors in the given queue.

Enqueues zero or more tuples of one or more tensors in the given queue.

Returns true if queue is closed.

Returns true if queue is closed.

Computes the number of elements in the given queue.

Converts one or more images from RGB to HSV.

Counts the number of occurrences of each value in an integer array.

Outputs random values from the Gamma distribution(s) described by alpha.

Outputs random values from a normal distribution.

Outputs random values from the Poisson distribution(s) described by rate.

Randomly shuffles a tensor along its first dimension.

A queue that randomizes the order of elements.

Outputs random values from a uniform distribution.

Outputs random integers from a uniform distribution.

Creates a sequence of numbers.

Reads and outputs the entire contents of the input filename.

Returns the number of records this Reader has produced.

Returns the number of work units this Reader has finished processing.

Returns the next record (key, value pair) produced by a Reader.

Returns up to num_records (key, value) pairs produced by a Reader.

Restore a Reader to its initial clean state.

Restore a reader to a previously saved state.

Produce a string tensor that encodes the state of a Reader.

Returns the real part of a complex number.

Returns x / y element-wise for real types.

Computes the reciprocal of x element-wise.

Emits randomized records.

Joins a string Tensor across the given dimensions.

Makes its input available to the next iteration.

Forwards the indexth element of inputs to output.

Forwards the ref tensor data to the output port determined by pred.

Check if the input matches the regex pattern.

Replaces matches of the pattern regular expression in input with the replacement string provided in rewrite.

Computes rectified linear: max(features, 0).

Computes rectified linear 6: min(max(features, 0), 6).

Computes a range that covers the actual values present in a quantized tensor.

Converts the quantized input tensor into a lower-precision output.

Resize images to size using area interpolation.

Resize images to size using bicubic interpolation.

Resize images to size using bilinear interpolation.

Resize images to size using nearest neighbor interpolation.

Update '*var' according to the adadelta scheme.

Update '*var' according to the adagrad scheme.

Update '*var' according to the proximal adagrad scheme.

Update '*var' according to the Adam algorithm.

Update '*var' according to the Adam algorithm.

Update '*var' according to the AddSign update.

Update '*var' according to the centered RMSProp algorithm.

Update '*var' according to the Ftrl-proximal scheme.

Update '*var' according to the Ftrl-proximal scheme.

Update '*var' by subtracting 'alpha' * 'delta' from it.

Update '*var' according to the momentum scheme.

Update '*var' according to the momentum scheme.

Update '*var' according to the AddSign update.

Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.

Update '*var' as FOBOS algorithm with fixed learning rate.

Update '*var' according to the RMSProp algorithm.

Increments variable pointed to by 'resource' until it reaches 'limit'.

Applies sparse addition to individual values or slices in a Variable.

TODO: add doc.

TODO: add doc.

Applies sparse subtraction to individual values or slices in a Variable.

Applies sparse updates to individual values or slices within a given.

var: Should be from a Variable().

Update relevant entries in '*var' and '*accum' according to the adagrad scheme.

Update entries in '*var' and '*accum' according to the proximal adagrad scheme.

Update '*var' according to the centered RMSProp algorithm.

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

Update relevant entries in '*var' and '*accum' according to the momentum scheme.

Update relevant entries in '*var' and '*accum' according to the momentum scheme.

Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.

Sparse update '*var' as FOBOS algorithm with fixed learning rate.

Update '*var' according to the RMSProp algorithm.

Restores a tensor from checkpoint files.

Restores a tensor from checkpoint files.

Restores tensors from a V2 checkpoint.

Returns element-wise integer closest to x.

Rounds the values of a tensor to the nearest integer, element-wise.

Computes reciprocal of square root of x element-wise.

Generate a single randomly distorted bounding box for an image.

Generate a single randomly distorted bounding box for an image.

Saves the input tensors to disk.

Saves input tensors slices to disk.

Saves tensors in V2 checkpoint format.

Outputs a Summary protocol buffer with scalar values.

TODO: add doc.

Adds sparse updates to a variable reference.

Divides a variable reference by sparse updates.

Reduces sparse updates into a variable reference using the max operation.

Reduces sparse updates into a variable reference using the min operation.

Multiplies sparse updates into a variable reference.

Applies sparse addition to individual values or slices in a Variable.

Applies sparse subtraction to individual values or slices in a Variable.

Applies sparse updates to individual values or slices within a given.

Subtracts sparse updates to a variable reference.

Applies sparse updates to a variable reference.

Computes the maximum along segments of a tensor.

Computes the mean along segments of a tensor.

Computes the minimum along segments of a tensor.

Computes the product along segments of a tensor.

Computes the product along segments of a tensor.

Computes the sum along segments of a tensor.

Computes the sum along segments of a tensor.

TODO: add doc.

Computes scaled exponential linear: scale * alpha * (exp(features) - 1)

Serialize an N-minibatch SparseTensor into an [N, 3]Tensor object.

Serialize a SparseTensor into a [3]Tensor object.

Transforms a Tensor into a serialized TensorProto proto.

Computes the difference between two lists of numbers or strings.

Generate a sharded filename.

Generate a glob pattern matching all sharded file names.

Computes sigmoid of x element-wise.

Returns an element-wise indication of the sign of a number.

Computes sine of x element-wise.

Computes hyperbolic sine of x element-wise.

Computes softmax activations.

Computes softmax cross entropy cost and gradients to backpropagate.

TODO: add doc.

Computes softsign: features / (abs(features) + 1).

Applies a sparse gradient to a given accumulator.

Extracts the average sparse gradient in a SparseConditionalAccumulator.

Adds two SparseTensor objects to produce another SparseTensor.

The gradient operator for the SparseAdd op.

var: Should be from a Variable().

Update relevant entries in '*var' and '*accum' according to the adagrad scheme.

Update entries in '*var' and '*accum' according to the proximal adagrad scheme.

Update '*var' according to the centered RMSProp algorithm.

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

Update relevant entries in '*var' and '*accum' according to the momentum scheme.

Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.

Sparse update '*var' as FOBOS algorithm with fixed learning rate.

Update '*var' according to the RMSProp algorithm.

Counts the number of occurrences of each value in an integer array.

Concatenates a list of SparseTensor along the specified dimension.

A conditional accumulator for aggregating sparse gradients.

Generates sparse cross from a list of sparse and dense tensors.

Generates sparse cross from a list of sparse and dense tensors.

Generates sparse cross from a list of sparse and dense tensors.

Adds up a SparseTensor and a dense Tensor, using these special rules:

Component-wise divides a SparseTensor by a dense Tensor.

Component-wise multiplies a SparseTensor by a dense Tensor.

Fills empty rows in the input 2-D SparseTensor with a default value.

The gradient of SparseFillEmptyRows.

Multiply matrix "a" by matrix "b".

Computes the max of elements across dimensions of a SparseTensor.

Computes the max of elements across dimensions of a SparseTensor.

Computes the sum of elements across dimensions of a SparseTensor.

Computes the sum of elements across dimensions of a SparseTensor.

Reorders a SparseTensor into the canonical, row-major ordering.

Reshapes a SparseTensor to represent values in a new dense shape.

Computes the mean along sparse segments of a tensor.

Computes gradients for SparseSegmentMean.

Computes the mean along sparse segments of a tensor.

Computes the sum along sparse segments of a tensor divided by the sqrt of N.

Computes gradients for SparseSegmentSqrtN.

Computes the sum along sparse segments of a tensor divided by the sqrt of N.

Computes the sum along sparse segments of a tensor.

Computes gradients for SparseSegmentSum.

Computes the sum along sparse segments of a tensor.

Slice a SparseTensor based on the start and size.

The gradient operator for the SparseSlice op.

Applies softmax to a batched N-D SparseTensor.

Computes softmax cross entropy cost and gradients to backpropagate.

Returns the element-wise max of two SparseTensors.

Returns the element-wise min of two SparseTensors.

Split a SparseTensor into num_split tensors along one dimension.

Adds up a SparseTensor and a dense Tensor, producing a dense Tensor.

Multiply SparseTensor (of rank 2) "A" by dense matrix "B".

Computes square root of x element-wise.

Computes square of x element-wise.

Returns conj(x - y)(x - y) element-wise.

Packs a list of N rank-R tensors into one rank-(R+1) tensor.

Stage values similar to a lightweight Enqueue.

Op removes all elements in the underlying container.

Op peeks at the values at the specified index.

Op returns the number of elements in the underlying container.

Generate a randomly distorted bounding box for an image deterministically.

Formats a string template using a list of tensors.

Joins the strings in the given list of string tensors into one tensor;.

String lengths of input.

Converts all uppercase characters into their respective lowercase replacements.

Creates ngrams from ragged string data.

Split elements of input based on delimiter into a SparseTensor.

Split elements of source based on sep into a SparseTensor.

Strip leading and trailing whitespaces from the Tensor.

Converts each string in the input Tensor to its hash mod by a number of buckets.

Converts each string in the input Tensor to its hash mod by a number of buckets.

Converts each string in the input Tensor to its hash mod by a number of buckets.

Converts each string in the input Tensor to the specified numeric type.

Converts all lowercase characters into their respective uppercase replacements.

Return substrings from Tensor of strings.

Returns x - y element-wise.

Computes the sum of elements across dimensions of a tensor.

Forwards data to the output port determined by pred.

A Reader that outputs the records from a TensorFlow Records file.

Converts a sparse representation into a dense tensor.

Computes tan of x element-wise.

Computes hyperbolic tangent of x element-wise.

Returns a tensor that may be mutated, but only persists within a single step.

An array of Tensors of given size.

Delete the TensorArray from its resource container.

Concat the elements from the TensorArray into value value.

Gather specific elements from the TensorArray into output value.

Creates a TensorArray for storing the gradients of values in the given handle.

Creates a TensorArray for storing multiple gradients of values in the given handle.

Read an element from the TensorArray into output value.

Scatter the data from the input value into specific TensorArray elements.

Get the current size of the TensorArray.

Split the data from the input value into TensorArray elements.

Push an element onto the tensor_array.

Outputs a Summary protocol buffer with a tensor.

Outputs a Summary protocol buffer with a tensor and per-plugin data.

A Reader that outputs the lines of a file delimited by '\n'.

Provides the time since epoch in seconds.

Finds values and indices of the k largest elements for the last dimension.

Returns x / y element-wise, rounded towards zero.

Returns element-wise remainder of division.

Outputs random values from a truncated normal distribution.

Determine the script codes of a given tensor of Unicode integer code points.

Transcode the input text from a source encoding to a destination encoding.

Generates labels for candidate sampling with a uniform distribution.

Computes the maximum along segments of a tensor.

Computes the minimum along segments of a tensor.

Computes the product along segments of a tensor.

Computes the sum along segments of a tensor.

Op is similar to a lightweight Dequeue.

Holds state in the form of a tensor that persists across steps.

Reshapes a quantized tensor as per the Reshape op.

Selects elements from x or y, depending on condition.

A Reader that outputs the entire contents of a file as a value.

Writes contents to the file at input filename.

Returns 0 if x == 0, and x / y otherwise, elementwise.

Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.

Returns 0 if x == 0, and x * log(y) otherwise, elementwise.

Returns a tensor of zeros with the same shape and type as x.

Compute the Hurwitz zeta function \(\zeta(x, q)\).