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Module: tf

TensorFlow 2.0 version View source on GitHub

Modules

app module

audio module

autograph module

bitwise module

compat module

config module

contrib module: contrib module containing volatile or experimental code.

data module

debugging module

distribute module

distributions module

dtypes module

errors module

estimator module

experimental module

feature_column module

gfile module

graph_util module

image module

initializers module

io module

keras module

layers module

linalg module

lite module

logging module

lookup module

losses module

manip module

math module

metrics module

nest module

nn module

profiler module

python_io module

quantization module

queue module

ragged module

random module

raw_ops module

resource_loader module

saved_model module

sets module

signal module

sparse module

spectral module

strings module

summary module

sysconfig module

test module

tpu module

train module

user_ops module

version module

xla module

Classes

class AggregationMethod: A class listing aggregation methods used to combine gradients.

class AttrValue

class ConditionalAccumulator: A conditional accumulator for aggregating gradients.

class ConditionalAccumulatorBase: A conditional accumulator for aggregating gradients.

class ConfigProto

class CriticalSection: Critical section.

class DType: Represents the type of the elements in a Tensor.

class DeviceSpec: Represents a (possibly partial) specification for a TensorFlow device.

class Dimension: Represents the value of one dimension in a TensorShape.

class Event

class FIFOQueue: A queue implementation that dequeues elements in first-in first-out order.

class FixedLenFeature: Configuration for parsing a fixed-length input feature.

class FixedLenSequenceFeature: Configuration for parsing a variable-length input feature into a Tensor.

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

class GPUOptions

class GradientTape: Record operations for automatic differentiation.

class Graph: A TensorFlow computation, represented as a dataflow graph.

class GraphDef

class GraphKeys: Standard names to use for graph collections.

class GraphOptions

class HistogramProto

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

class IndexedSlices: A sparse representation of a set of tensor slices at given indices.

class InteractiveSession: A TensorFlow Session for use in interactive contexts, such as a shell.

class LMDBReader: A Reader that outputs the records from a LMDB file.

class LogMessage

class MetaGraphDef

class Module: Base neural network module class.

class NameAttrList

class NodeDef

class OpError: A generic error that is raised when TensorFlow execution fails.

class Operation: Represents a graph node that performs computation on tensors.

class OptimizerOptions

class PaddingFIFOQueue: A FIFOQueue that supports batching variable-sized tensors by padding.

class PriorityQueue: A queue implementation that dequeues elements in prioritized order.

class QueueBase: Base class for queue implementations.

class RaggedTensor: Represents a ragged tensor.

class RandomShuffleQueue: A queue implementation that dequeues elements in a random order.

class ReaderBase: Base class for different Reader types, that produce a record every step.

class RegisterGradient: A decorator for registering the gradient function for an op type.

class RunMetadata

class RunOptions

class Session: A class for running TensorFlow operations.

class SessionLog

class SparseConditionalAccumulator: A conditional accumulator for aggregating sparse gradients.

class SparseFeature: Configuration for parsing a sparse input feature from an Example.

class SparseTensor: Represents a sparse tensor.

class SparseTensorValue: SparseTensorValue(indices, values, dense_shape)

class Summary

class SummaryMetadata

class TFRecordReader: A Reader that outputs the records from a TFRecords file.

class Tensor: Represents one of the outputs of an Operation.

class TensorArray: Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays.

class TensorInfo

class TensorShape: Represents the shape of a Tensor.

class TensorSpec: Describes a tf.Tensor.

class TextLineReader: A Reader that outputs the lines of a file delimited by newlines.

class UnconnectedGradients: Controls how gradient computation behaves when y does not depend on x.

class VarLenFeature: Configuration for parsing a variable-length input feature.

class Variable: See the Variables Guide.

class VariableAggregation: Indicates how a distributed variable will be aggregated.

class VariableScope: Variable scope object to carry defaults to provide to get_variable.

class VariableSynchronization: Indicates when a distributed variable will be synced.

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

class constant_initializer: Initializer that generates tensors with constant values.

class glorot_normal_initializer: The Glorot normal initializer, also called Xavier normal initializer.

class glorot_uniform_initializer: The Glorot uniform initializer, also called Xavier uniform initializer.

class name_scope: A context manager for use when defining a Python op.

class ones_initializer: Initializer that generates tensors initialized to 1.

class orthogonal_initializer: Initializer that generates an orthogonal matrix.

class random_normal_initializer: Initializer that generates tensors with a normal distribution.

class random_uniform_initializer: Initializer that generates tensors with a uniform distribution.

class truncated_normal_initializer: Initializer that generates a truncated normal distribution.

class uniform_unit_scaling_initializer: Initializer that generates tensors without scaling variance.

class variable_scope: A context manager for defining ops that creates variables (layers).

class variance_scaling_initializer: Initializer capable of adapting its scale to the shape of weights tensors.

class zeros_initializer: Initializer that generates tensors initialized to 0.

Functions

Assert(...): Asserts that the given condition is true.

NoGradient(...): Specifies that ops of type op_type is not differentiable.

NotDifferentiable(...): Specifies that ops of type op_type is not differentiable.

Print(...): Prints a list of tensors. (deprecated)

abs(...): Computes the absolute value of a tensor.

accumulate_n(...): Returns the element-wise sum of a list of tensors.

acos(...): Computes acos of x element-wise.

acosh(...): Computes inverse hyperbolic cosine of x element-wise.

add(...): Returns x + y element-wise.

add_check_numerics_ops(...): Connect a tf.debugging.check_numerics to every floating point tensor.

add_n(...): Adds all input tensors element-wise.

add_to_collection(...): Wrapper for Graph.add_to_collection() using the default graph.

add_to_collections(...): Wrapper for Graph.add_to_collections() using the default graph.

all_variables(...): Use tf.compat.v1.global_variables instead. (deprecated)

angle(...): Returns the element-wise argument of a complex (or real) tensor.

arg_max(...): Returns the index with the largest value across dimensions of a tensor. (deprecated)

arg_min(...): Returns the index with the smallest value across dimensions of a tensor. (deprecated)

argmax(...): Returns the index with the largest value across axes of a tensor. (deprecated arguments)

argmin(...): Returns the index with the smallest value across axes of a tensor. (deprecated arguments)

argsort(...): Returns the indices of a tensor that give its sorted order along an axis.

as_dtype(...): Converts the given type_value to a DType.

as_string(...): Converts each entry in the given tensor to strings. Supports many numeric

asin(...): Computes the trignometric inverse sine of x element-wise.

asinh(...): Computes inverse hyperbolic sine of x element-wise.

assert_equal(...): Assert the condition x == y holds element-wise.

assert_greater(...): Assert the condition x > y holds element-wise.

assert_greater_equal(...): Assert the condition x >= y holds element-wise.

assert_integer(...): Assert that x is of integer dtype.

assert_less(...): Assert the condition x < y holds element-wise.

assert_less_equal(...): Assert the condition x <= y holds element-wise.

assert_near(...): Assert the condition x and y are close element-wise.

assert_negative(...): Assert the condition x < 0 holds element-wise.

assert_non_negative(...): Assert the condition x >= 0 holds element-wise.

assert_non_positive(...): Assert the condition x <= 0 holds element-wise.

assert_none_equal(...): Assert the condition x != y holds for all elements.

assert_positive(...): Assert the condition x > 0 holds element-wise.

assert_proper_iterable(...): Static assert that values is a "proper" iterable.

assert_rank(...): Assert x has rank equal to rank.

assert_rank_at_least(...): Assert x has rank equal to rank or higher.

assert_rank_in(...): Assert x has rank in ranks.

assert_same_float_dtype(...): Validate and return float type based on tensors and dtype.

assert_scalar(...): Asserts that the given tensor is a scalar (i.e. zero-dimensional).

assert_type(...): Statically asserts that the given Tensor is of the specified type.

assert_variables_initialized(...): Returns an Op to check if variables are initialized.

assign(...): Update ref by assigning value to it.

assign_add(...): Update ref by adding value to it.

assign_sub(...): Update ref by subtracting value from it.

atan(...): Computes the trignometric inverse tangent of x element-wise.

atan2(...): Computes arctangent of y/x element-wise, respecting signs of the arguments.

atanh(...): Computes inverse hyperbolic tangent of x element-wise.

batch_gather(...): Gather slices from params according to indices with leading batch dims. (deprecated)

batch_scatter_update(...): Generalization of tf.compat.v1.scatter_update to axis different than 0. (deprecated)

batch_to_space(...): BatchToSpace for 4-D tensors of type T.

batch_to_space_nd(...): BatchToSpace for N-D tensors of type T.

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

bincount(...): Counts the number of occurrences of each value in an integer array.

bitcast(...): Bitcasts a tensor from one type to another without copying data.

boolean_mask(...): Apply boolean mask to tensor.

broadcast_dynamic_shape(...): Computes the shape of a broadcast given symbolic shapes.

broadcast_static_shape(...): Computes the shape of a broadcast given known shapes.

broadcast_to(...): Broadcast an array for a compatible shape.

case(...): Create a case operation.

cast(...): Casts a tensor to a new type.

ceil(...): Returns element-wise smallest integer not less than x.

check_numerics(...): Checks a tensor for NaN and Inf values.

cholesky(...): Computes the Cholesky decomposition of one or more square matrices.

cholesky_solve(...): Solves systems of linear eqns A X = RHS, given Cholesky factorizations.

clip_by_average_norm(...): Clips tensor values to a maximum average L2-norm. (deprecated)

clip_by_global_norm(...): Clips values of multiple tensors by the ratio of the sum of their norms.

clip_by_norm(...): Clips tensor values to a maximum L2-norm.

clip_by_value(...): Clips tensor values to a specified min and max.

colocate_with(...): DEPRECATED FUNCTION

complex(...): Converts two real numbers to a complex number.

concat(...): Concatenates tensors along one dimension.

cond(...): Return true_fn() if the predicate pred is true else false_fn(). (deprecated arguments)

confusion_matrix(...): Computes the confusion matrix from predictions and labels.

conj(...): Returns the complex conjugate of a complex number.

constant(...): Creates a constant tensor.

container(...): Wrapper for Graph.container() using the default graph.

control_dependencies(...): Wrapper for Graph.control_dependencies() using the default graph.

convert_to_tensor(...): Converts the given value to a Tensor.

convert_to_tensor_or_indexed_slices(...): Converts the given object to a Tensor or an IndexedSlices.

convert_to_tensor_or_sparse_tensor(...): Converts value to a SparseTensor or Tensor.

cos(...): Computes cos of x element-wise.

cosh(...): Computes hyperbolic cosine of x element-wise.

count_nonzero(...): Computes number of nonzero elements across dimensions of a tensor. (deprecated arguments) (deprecated arguments)

count_up_to(...): Increments 'ref' until it reaches 'limit'. (deprecated)

create_partitioned_variables(...): Create a list of partitioned variables according to the given slicing. (deprecated)

cross(...): Compute the pairwise cross product.

cumprod(...): Compute the cumulative product of the tensor x along axis.

cumsum(...): Compute the cumulative sum of the tensor x along axis.

custom_gradient(...): Decorator to define a function with a custom gradient.

decode_base64(...): Decode web-safe base64-encoded strings.

decode_compressed(...): Decompress strings.

decode_csv(...): Convert CSV records to tensors. Each column maps to one tensor.

decode_json_example(...): Convert JSON-encoded Example records to binary protocol buffer strings.

decode_raw(...): Convert raw byte strings into tensors. (deprecated arguments)

delete_session_tensor(...): Delete the tensor for the given tensor handle.

depth_to_space(...): DepthToSpace for tensors of type T.

dequantize(...): Dequantize the 'input' tensor into a float Tensor.

deserialize_many_sparse(...): Deserialize and concatenate SparseTensors from a serialized minibatch.

device(...): Wrapper for Graph.device() using the default graph.

diag(...): Returns a diagonal tensor with a given diagonal values.

diag_part(...): Returns the diagonal part of the tensor.

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

dimension_at_index(...): Compatibility utility required to allow for both V1 and V2 behavior in TF.

dimension_value(...): Compatibility utility required to allow for both V1 and V2 behavior in TF.

disable_eager_execution(...): Disables eager execution.

disable_resource_variables(...): Opts out of resource variables. (deprecated)

disable_v2_behavior(...): Disables TensorFlow 2.x behaviors.

disable_v2_tensorshape(...): Disables the V2 TensorShape behavior and reverts to V1 behavior.

div(...): Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)

div_no_nan(...): Computes an unsafe divide which returns 0 if the y is zero.

divide(...): Computes Python style division of x by y.

dynamic_partition(...): Partitions data into num_partitions tensors using indices from partitions.

dynamic_stitch(...): Interleave the values from the data tensors into a single tensor.

edit_distance(...): Computes the Levenshtein distance between sequences.

einsum(...): A generalized contraction between tensors of arbitrary dimension.

enable_eager_execution(...): Enables eager execution for the lifetime of this program.

enable_resource_variables(...): Creates resource variables by default.

enable_v2_behavior(...): Enables TensorFlow 2.x behaviors.

enable_v2_tensorshape(...): In TensorFlow 2.0, iterating over a TensorShape instance returns values.

encode_base64(...): Encode strings into web-safe base64 format.

ensure_shape(...): Updates the shape of a tensor and checks at runtime that the shape holds.

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

erf(...): Computes the Gauss error function of x element-wise.

erfc(...): Computes the complementary error function of x element-wise.

executing_eagerly(...): Returns True if the current thread has eager execution enabled.

exp(...): Computes exponential of x element-wise. \(y = e^x\).

expand_dims(...): Inserts a dimension of 1 into a tensor's shape. (deprecated arguments)

expm1(...): Computes exponential of x - 1 element-wise.

extract_image_patches(...): Extract patches from images and put them in the "depth" output dimension.

extract_volume_patches(...): Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.

eye(...): Construct an identity matrix, or a batch of matrices.

fake_quant_with_min_max_args(...): Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.

fake_quant_with_min_max_args_gradient(...): Compute gradients for a FakeQuantWithMinMaxArgs operation.

fake_quant_with_min_max_vars(...): Fake-quantize the 'inputs' tensor of type float via global float scalars min

fake_quant_with_min_max_vars_gradient(...): Compute gradients for a FakeQuantWithMinMaxVars operation.

fake_quant_with_min_max_vars_per_channel(...): Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d],

fake_quant_with_min_max_vars_per_channel_gradient(...): Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.

fft(...): Fast Fourier transform.

fft2d(...): 2D fast Fourier transform.

fft3d(...): 3D fast Fourier transform.

fill(...): Creates a tensor filled with a scalar value.

fingerprint(...): Generates fingerprint values.

fixed_size_partitioner(...): Partitioner to specify a fixed number of shards along given axis.

floor(...): Returns element-wise largest integer not greater than x.

floor_div(...): Returns x // y element-wise.

floordiv(...): Divides x / y elementwise, rounding toward the most negative integer.

floormod(...): Returns element-wise remainder of division. When x < 0 xor y < 0 is

foldl(...): foldl on the list of tensors unpacked from elems on dimension 0.

foldr(...): foldr on the list of tensors unpacked from elems on dimension 0.

function(...): Creates a callable TensorFlow graph from a Python function.

gather(...): Gather slices from params axis axis according to indices.

gather_nd(...): Gather slices from params into a Tensor with shape specified by indices.

get_collection(...): Wrapper for Graph.get_collection() using the default graph.

get_collection_ref(...): Wrapper for Graph.get_collection_ref() using the default graph.

get_default_graph(...): Returns the default graph for the current thread.

get_default_session(...): Returns the default session for the current thread.

get_local_variable(...): Gets an existing local variable or creates a new one.

get_logger(...): Return TF logger instance.

get_seed(...): Returns the local seeds an operation should use given an op-specific seed.

get_session_handle(...): Return the handle of data.

get_session_tensor(...): Get the tensor of type dtype by feeding a tensor handle.

get_static_value(...): Returns the constant value of the given tensor, if efficiently calculable.

get_variable(...): Gets an existing variable with these parameters or create a new one.

get_variable_scope(...): Returns the current variable scope.

global_norm(...): Computes the global norm of multiple tensors.

global_variables(...): Returns global variables.

global_variables_initializer(...): Returns an Op that initializes global variables.

gradients(...): Constructs symbolic derivatives of sum of ys w.r.t. x in xs.

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

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

group(...): Create an op that groups multiple operations.

guarantee_const(...): Gives a guarantee to the TF runtime that the input tensor is a constant.

hessians(...): Constructs the Hessian of sum of ys with respect to x in xs.

histogram_fixed_width(...): Return histogram of values.

histogram_fixed_width_bins(...): Bins the given values for use in a histogram.

identity(...): Return a tensor with the same shape and contents as input.

identity_n(...): Returns a list of tensors with the same shapes and contents as the input

ifft(...): Inverse fast Fourier transform.

ifft2d(...): Inverse 2D fast Fourier transform.

ifft3d(...): Inverse 3D fast Fourier transform.

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

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

imag(...): Returns the imaginary part of a complex (or real) tensor.

import_graph_def(...): Imports the graph from graph_def into the current default Graph. (deprecated arguments)

init_scope(...): A context manager that lifts ops out of control-flow scopes and function-building graphs.

initialize_all_tables(...): Returns an Op that initializes all tables of the default graph. (deprecated)

initialize_all_variables(...): See tf.compat.v1.global_variables_initializer. (deprecated)

initialize_local_variables(...): See tf.compat.v1.local_variables_initializer. (deprecated)

initialize_variables(...): See tf.compat.v1.variables_initializer. (deprecated)

invert_permutation(...): Computes the inverse permutation of a tensor.

is_finite(...): Returns which elements of x are finite.

is_inf(...): Returns which elements of x are Inf.

is_nan(...): Returns which elements of x are NaN.

is_non_decreasing(...): Returns True if x is non-decreasing.

is_numeric_tensor(...): Returns True if the elements of tensor are numbers.

is_strictly_increasing(...): Returns True if x is strictly increasing.

is_tensor(...): Checks whether x is a tensor or "tensor-like".

is_variable_initialized(...): Tests if a variable has been initialized.

lbeta(...): Computes \(ln(|Beta(x)|)\), reducing along the last dimension.

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

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

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

lin_space(...): Generates values in an interval.

linspace(...): Generates values in an interval.

load_file_system_library(...): Loads a TensorFlow plugin, containing file system implementation. (deprecated)

load_library(...): Loads a TensorFlow plugin.

load_op_library(...): Loads a TensorFlow plugin, containing custom ops and kernels.

local_variables(...): Returns local variables.

local_variables_initializer(...): Returns an Op that initializes all local variables.

log(...): Computes natural logarithm of x element-wise.

log1p(...): Computes natural logarithm of (1 + x) element-wise.

log_sigmoid(...): Computes log sigmoid of x element-wise.

logical_and(...): Returns the truth value of x AND y element-wise.

logical_not(...): Returns the truth value of NOT x element-wise.

logical_or(...): Returns the truth value of x OR y element-wise.

logical_xor(...): Logical XOR function.

make_ndarray(...): Create a numpy ndarray from a tensor.

make_template(...): Given an arbitrary function, wrap it so that it does variable sharing.

make_tensor_proto(...): Create a TensorProto.

map_fn(...): map on the list of tensors unpacked from elems on dimension 0.

matching_files(...): Returns the set of files matching one or more glob patterns.

matmul(...): Multiplies matrix a by matrix b, producing a * b.

matrix_band_part(...): Copy a tensor setting everything outside a central band in each innermost matrix

matrix_determinant(...): Computes the determinant of one or more square matrices.

matrix_diag(...): Returns a batched diagonal tensor with a given batched diagonal values.

matrix_diag_part(...): Returns the batched diagonal part of a batched tensor.

matrix_inverse(...): Computes the inverse of one or more square invertible matrices or their

matrix_set_diag(...): Returns a batched matrix tensor with new batched diagonal values.

matrix_solve(...): Solves systems of linear equations.

matrix_solve_ls(...): Solves one or more linear least-squares problems.

matrix_square_root(...): Computes the matrix square root of one or more square matrices:

matrix_transpose(...): Transposes last two dimensions of tensor a.

matrix_triangular_solve(...): Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.

maximum(...): Returns the max of x and y (i.e. x > y ? x : y) element-wise.

meshgrid(...): Broadcasts parameters for evaluation on an N-D grid.

min_max_variable_partitioner(...): Partitioner to allocate minimum size per slice.

minimum(...): Returns the min of x and y (i.e. x < y ? x : y) element-wise.

mod(...): Returns element-wise remainder of division. When x < 0 xor y < 0 is

model_variables(...): Returns all variables in the MODEL_VARIABLES collection.

moving_average_variables(...): Returns all variables that maintain their moving averages.

multinomial(...): Draws samples from a multinomial distribution. (deprecated)

multiply(...): Returns x * y element-wise.

negative(...): Computes numerical negative value element-wise.

no_gradient(...): Specifies that ops of type op_type is not differentiable.

no_op(...): Does nothing. Only useful as a placeholder for control edges.

no_regularizer(...): Use this function to prevent regularization of variables.

nondifferentiable_batch_function(...): Batches the computation done by the decorated function.

norm(...): Computes the norm of vectors, matrices, and tensors. (deprecated arguments)

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

numpy_function(...): Wraps a python function and uses it as a TensorFlow op.

one_hot(...): Returns a one-hot tensor.

ones(...): Creates a tensor with all elements set to 1.

ones_like(...): Creates a tensor with all elements set to 1.

op_scope(...): DEPRECATED. Same as name_scope above, just different argument order.

pad(...): Pads a tensor.

parallel_stack(...): Stacks a list of rank-R tensors into one rank-(R+1) tensor in parallel.

parse_example(...): Parses Example protos into a dict of tensors.

parse_single_example(...): Parses a single Example proto.

parse_single_sequence_example(...): Parses a single SequenceExample proto.

parse_tensor(...): Transforms a serialized tensorflow.TensorProto proto into a Tensor.

placeholder(...): Inserts a placeholder for a tensor that will be always fed.

placeholder_with_default(...): A placeholder op that passes through input when its output is not fed.

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

pow(...): Computes the power of one value to another.

print(...): Print the specified inputs.

py_func(...): Wraps a python function and uses it as a TensorFlow op.

py_function(...): Wraps a python function into a TensorFlow op that executes it eagerly.

qr(...): Computes the QR decompositions of one or more matrices.

quantize(...): Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.

quantize_v2(...): Please use tf.quantization.quantize instead.

quantized_concat(...): Concatenates quantized tensors along one dimension.

random_crop(...): Randomly crops a tensor to a given size.

random_gamma(...): Draws shape samples from each of the given Gamma distribution(s).

random_normal(...): Outputs random values from a normal distribution.

random_poisson(...): Draws shape samples from each of the given Poisson distribution(s).

random_shuffle(...): Randomly shuffles a tensor along its first dimension.

random_uniform(...): Outputs random values from a uniform distribution.

range(...): Creates a sequence of numbers.

rank(...): Returns the rank of a tensor.

read_file(...): Reads and outputs the entire contents of the input filename.

real(...): Returns the real part of a complex (or real) tensor.

realdiv(...): Returns x / y element-wise for real types.

reciprocal(...): Computes the reciprocal of x element-wise.

reduce_all(...): Computes the "logical and" of elements across dimensions of a tensor. (deprecated arguments)

reduce_any(...): Computes the "logical or" of elements across dimensions of a tensor. (deprecated arguments)

reduce_join(...): Joins a string Tensor across the given dimensions.

reduce_logsumexp(...): Computes log(sum(exp(elements across dimensions of a tensor))). (deprecated arguments)

reduce_max(...): Computes the maximum of elements across dimensions of a tensor. (deprecated arguments)

reduce_mean(...): Computes the mean of elements across dimensions of a tensor.

reduce_min(...): Computes the minimum of elements across dimensions of a tensor. (deprecated arguments)

reduce_prod(...): Computes the product of elements across dimensions of a tensor. (deprecated arguments)

reduce_sum(...): Computes the sum of elements across dimensions of a tensor. (deprecated arguments)

regex_replace(...): Replace elements of input matching regex pattern with rewrite.

register_tensor_conversion_function(...): Registers a function for converting objects of base_type to Tensor.

report_uninitialized_variables(...): Adds ops to list the names of uninitialized variables.

required_space_to_batch_paddings(...): Calculate padding required to make block_shape divide input_shape.

reset_default_graph(...): Clears the default graph stack and resets the global default graph.

reshape(...): Reshapes a tensor.

resource_variables_enabled(...): Returns True if resource variables are enabled.

reverse(...): Reverses specific dimensions of a tensor.

revers