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Table of Contents
Typedefs | |
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| typedef
A type for representing the output of ops that produce more than one output, or a list of tensors. |
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(:: v, const char *msg) |
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(const :: & v, const char *msg) |
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(std::ostream & os, const & x) |
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Classes | |
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A | |
Represents a tensor value that can be used as an operand to an . | |
A type for representing the input to ops that require a list of tensors. | |
Represents a node in the computation graph. | |
Represents a tensor value produced by an . | |
A | |
Denotes success or failure of a call in Tensorflow. | |
Represents an n-dimensional array of values. |
Structs | |
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A helper struct to hold the scopes that would be used by a function constructing a composite op. | |
Hash class that can be used for e.g. storing Outputs in an unordered_map. |
Namespaces | |
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std::vector< > OutputList
A type for representing the output of ops that produce more than one output, or a list of tensors.
std::function< void(const &)> StatusCallback
tensorflow::string * TfCheckOpHelper( :: v, const char *msg)
tensorflow::string * TfCheckOpHelperOutOfLine( const :: & v, const char *msg)
std::ostream & operator<<( std::ostream & os, const & x)
Functions | |
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( element, *parent, int64 index) | |
( *parent, *element, int64 index) | |
CopyElementToSlice( element, *parent, int64 index)
MaybeMoveSliceToElement( *parent, *element, int64 index)
Typedefs | |
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Functions | |
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(const & scope, const & inp) |
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(const & scope, const & inp) |
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(const :: & scope, :: tag, :: tensor, :: sample_rate) | |
(const :: & scope, :: tag, :: tensor, :: sample_rate, const AudioSummary::Attrs & attrs) | |
(const TensorProto & x) |
Color to use for pixels with non-finite values. |
(const & scope, const & val) | |
(const & scope, const T & v, const TensorShape shape) | |
(const & scope, const std::initializer_list< T > & v, const TensorShape shape) | |
(const & scope, const TensorProto & proto) | |
(const :: & scope, :: tag, :: tensor) | |
(const :: & scope, :: tag, :: tensor, const ImageSummary::Attrs & attrs) | |
(int64 x) |
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(int64 x) |
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() const |
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(It represents the value of a *pixel in the output image).Non-finite values in the input tensor are *replaced by this tensor in the output image.The default value is the color *red.**Arguments |
number of batch elements to generate images for. |
Classes | |
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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 . | |
Computes acos of x element-wise. | |
Computes inverse hyperbolic cosine of x element-wise. | |
Returns x + y element-wise. | |
an | |
all input tensors element wise. | |
a | |
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 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 asin 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 atan of x element-wise. | |
Computes arctangent of | |
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. | |
for 4-D tensors of type T. | |
for N-D tensors of type T. | |
Computes the Bessel i0e function of | |
Computes the Bessel i1e function of | |
Compute the regularized incomplete beta integral \(I_x(a, b)\). | |
Adds | |
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'. | |
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. | |
Compare values of | |
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 | |
Computes the gradients of convolution with respect to the filter. | |
Computes the gradients of convolution with respect to the input. | |
Computes a 3-D convolution given 5-D | |
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 | |
Compute the cumulative sum of the tensor | |
Returns the dimension index in the destination data format given the one in. | |
Returns the permuted vector/tensor in the destination data format given the. | |
op for gradient debugging. | |
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 first frame of a GIF-encoded image to a uint8 tensor. | |
Convert JSON-encoded Example records to binary protocol buffer strings. | |
Decode a JPEG-encoded image to a uint8 tensor. | |
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 | |
Delete the tensor specified by its handle in the session. | |
for tensors of type T. | |
Computes a 2-D depthwise convolution given 4-D | |
Computes the gradients of depthwise convolution with respect to the filter. | |
Computes the gradients of depthwise convolution with respect to the input. | |
the 'input' tensor into a float . | |
Deserialize and concatenate | |
Deserialize | |
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 (the log of the absolute value of. | |
Computes the grayscale dilation of 4-D | |
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. | |
Partitions | |
Interleave the values from the | |
Computes the (possibly normalized) Levenshtein Edit Distance. | |
Computes exponential linear: | |
Creates a tensor with the given shape. | |
Encode strings into web-safe base64 format. | |
JPEG-encode an image. | |
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 | |
Computes the complementary error function of | |
Computes exponential of x element-wise. | |
Inserts a dimension of 1 into a tensor's shape. | |
Computes exponential of x - 1 element-wise. | |
Extracts a glimpse from the input tensor. | |
Extract | |
Extract the shape information of a JPEG-encoded image. | |
Extract | |
A queue that produces elements in first-in first-out order. | |
a fact about factorials. | |
Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. | |
Compute gradients for a operation. | |
Fake-quantize the 'inputs' tensor of type float via global float scalars | |
Compute gradients for a operation. | |
Fake-quantize the 'inputs' tensor of type float and one of the shapes: | |
Compute gradients for a operation. | |
Creates a tensor filled with a scalar value. | |
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. | |
Batch normalization. | |
Performs a padding as a preprocess during a convolution. | |
Performs a resize and padding as a preprocess during a convolution. | |
slices from | |
slices from | |
slices from | |
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 | |
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 | |
Compute the upper regularized incomplete Gamma function | |
Returns the imaginary part of a complex number. | |
Returns immutable tensor from memory region. | |
Says whether the targets are in the top | |
Says whether the targets are in the top | |
Adds v into specified rows of x. | |
Subtracts | |
Updates specified rows with values in | |
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 | |
Generates values in an interval. | |
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. | |
(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) | |
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. | |
Returns a batched diagonal tensor with a given batched diagonal values. | |
Returns the batched diagonal part of a batched tensor. | |
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 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 | |
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. | |
Draws samples from a multinomial distribution. | |
Returns x * y element-wise. | |
Computes numerical negative value 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,. | |
Returns the truth value of (x != y) element-wise. | |
Finds values of the | |
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. | |
(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. | |
A queue that produces elements in first-in first-out order. | |
Interleave the values from the | |
Outputs random values from a normal distribution. | |
Transforms a vector of brain.Example protos (as strings) into typed tensors. | |
Transforms a vector of brain.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 . | |
Compute the polygamma function \(^{(n)}(x)\). | |
Computes the power of one value to another. | |
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. | |
Convert the quantized 'input' tensor into a lower-precision 'output', using the. | |
Returns x + y element-wise, working on quantized buffers. | |
Produces the average pool of the input tensor for quantized types. | |
Quantized Batch normalization. | |
Adds 'bias' to 'input' for Quantized types. | |
Computes a 2D convolution given quantized 4D input and filter tensors. | |
Perform a quantized matrix multiplication of | |
Produces the max pool of the input tensor for quantized types. | |
Returns x * y element-wise, working on quantized buffers. | |
Computes Quantized Rectified Linear: | |
Computes Quantized Rectified Linear 6: | |
Computes Quantized Rectified Linear X: | |
Packs a list of | |
Resize quantized | |
Closes the given queue. | |
Dequeues a tuple of one or more tensors from the given queue. | |
Dequeues | |
Dequeues | |
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. | |
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. | |
Returns the rank of a tensor. | |
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 | |
a Reader to its initial clean state. | |
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 across the given dimensions. | |
Makes its input available to the next iteration. | |
Forwards the | |
Forwards the ref tensor | |
Check if the input matches the regex pattern. | |
Replaces the match of pattern in input with rewrite. | |
Computes rectified linear: | |
Computes rectified linear 6: | |
Given a quantized tensor described by (input, input_min, input_max), outputs a. | |
Convert the quantized 'input' tensor into a lower-precision 'output', using the. | |
Reshapes a tensor. | |
Resize | |
Resize | |
Resize | |
Resize | |
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. | |
Increments variable pointed to by 'resource' until it reaches 'limit'. | |
Adds sparse | |
Applies sparse | |
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. | |
| |
Restores a tensor from checkpoint files. | |
Restores a tensor from checkpoint files. | |
Restores tensors from a V2 checkpoint. | |
Reverses specific dimensions of a tensor. | |
Reverses variable length slices. | |
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 | |
Adds sparse updates to a variable reference. | |
Divides a variable reference by sparse updates. | |
Reduces sparse updates into a variable reference using the | |
Reduces sparse updates into a variable reference using the | |
Multiplies sparse updates into a variable reference. | |
Scatter | |
Applies sparse addition between | |
Applies sparse addition to | |
Applies sparse subtraction between | |
Applies sparse | |
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 sum along segments of a tensor. | |
Computes scaled exponential linear: | |
Serialize an | |
Serialize a | |
Transforms a into a serialized TensorProto proto. | |
Computes the difference between two lists of numbers or strings. | |
Returns the shape of a tensor. | |
Returns shape of tensors. | |
Generate a sharded filename. | |
Generate a glob pattern matching all sharded file names. | |
Computes sigmoid of | |
Returns an element-wise indication of the sign of a number. | |
Computes sin of x element-wise. | |
Computes hyperbolic sine of x element-wise. | |
Returns the size of a tensor. | |
Return a slice from 'input'. | |
Returns a copy of the input tensor. | |
Computes softmax activations. | |
Computes softmax cross entropy cost and gradients to backpropagate. | |
Computes softplus: | |
Computes softsign: | |
for 4-D tensors of type T. | |
for N-D tensors of type T. | |
for tensors of type T. | |
Applies a sparse gradient to a given accumulator. | |
Extracts the average sparse gradient in a . | |
Adds two | |
The gradient operator for the 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. | |
Concatenates a list of | |
A conditional accumulator for aggregating sparse gradients. | |
Generates sparse cross from a list of sparse and dense tensors. | |
Adds up a SparseTensor and a dense , using these special rules: | |
Component-wise divides a SparseTensor by a dense . | |
Component-wise multiplies a SparseTensor by a dense . | |
Fills empty rows in the input 2-D | |
The gradient of . | |
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 . | |
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 . | |
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 the sum along sparse segments of a tensor. | |
a | |
The gradient operator for the op. | |
Applies softmax to a batched N-D | |
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. | |
a | |
Adds up a | |
SparseTensor (of rank 2) "A" by dense matrix "B". | |
Splits a tensor into | |
Splits a tensor into | |
Computes square root of x element-wise. | |
Computes square of x element-wise. | |
Returns (x - y)(x - y) element-wise. | |
Removes dimensions of size 1 from the shape of a tensor. | |
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. | |
Stops gradient computation. | |
Return a strided slice from | |
| |
Returns the gradient of | |
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 | |
elements of | |
elements of | |
Strip leading and trailing whitespaces from the . | |
Converts each string in the input to its hash mod by a number of buckets. | |
Converts each string in the input to its hash mod by a number of buckets. | |
Converts each string in the input to its hash mod by a number of buckets. | |
Converts each string in the input to the specified numeric type. | |
Return substrings from | |
Returns x - y element-wise. | |
Computes the sum of elements across dimensions of a tensor. | |
Forwards | |
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 | |
Returns a tensor that may be mutated, but only persists within a single step. | |
An array of Tensors of given size. | |
Delete the from its resource container. | |
the elements from the into value | |
specific elements from the into output | |
Creates a for storing the gradients of values in the given handle. | |
Creates a for storing multiple gradients of values in the given handle. | |
Read an element from the into output | |
Scatter the data from the input value into specific elements. | |
Get the current size of the . | |
the data from the input value into elements. | |
Push an element onto the tensor_array. | |
Outputs a | |
Outputs a | |
A Reader that outputs the lines of a file delimited by ' '. | |
Constructs a tensor by tiling a given tensor. | |
Provides the time since epoch in seconds. | |
Finds values and indices of the | |
Shuffle dimensions of x according to a permutation. | |
Returns x / y element-wise for integer types. | |
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. | |
Generates labels for candidate sampling with a uniform distribution. | |
Finds unique elements in a 1-D tensor. | |
Finds unique elements along an axis of a tensor. | |
Finds unique elements in a 1-D tensor. | |
Finds unique elements along an axis of a tensor. | |
Converts a flat index or array of flat indices into a tuple of. | |
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. | |
Unpacks a given dimension of a rank- | |
Op is similar to a lightweight Dequeue. | |
Holds state in the form of a tensor that persists across steps. | |
Returns locations of nonzero / true values in a tensor. | |
Selects elements from | |
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 * log(y) otherwise, elementwise. | |
Returns a tensor of zeros with the same shape and type as x. | |
Compute the Hurwitz zeta function \((x, q)\). |
Mul
Neg
ReduceAll
ReduceAny
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
Sub
NodeBuilder::NodeOut AsNodeOut( const & scope, const & inp)
std::vector< NodeBuilder::NodeOut > AsNodeOutList( const & scope, const & inp)
AudioSummary( const :: & scope, :: tag, :: tensor, :: sample_rate)
AudioSummary( const :: & scope, :: tag, :: tensor, :: sample_rate, const AudioSummary::Attrs & attrs)
TF_MUST_USE_RESULT Attrs BadColor( const TensorProto & x)
Color to use for pixels with non-finite values.
Defaults to Tensor
Const( const & scope, const & val)
Const( const & scope, const T & v, const TensorShape shape)
Const( const & scope, const std::initializer_list< T > & v, const TensorShape shape)
ConstFromProto( const & scope, const TensorProto & proto)
ImageSummary( const :: & scope, :: tag, :: tensor)
ImageSummary( const :: & scope, :: tag, :: tensor, const ImageSummary::Attrs & attrs)
Attrs MaxImages( int64 x)
Attrs MaxOutputs( int64 x)
::tensorflow::Node * node() const
image **If max_images is greater the summary value tags are *generated sequentially as *tag *tag etc **The bad_color argument is the color to use in the generated images for *non finite input values It is a uint8 D tensor of length channels *Each element must be in the range( It represents the value of a *pixel in the output image).Non-finite values in the input tensor are *replaced by this tensor in the output image.The default value is the color *red.**Arguments
number of batch elements to generate images for.
Defaults to 3
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