Module: tf.compat.v1

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Bring in all of the public TensorFlow interface into this module.

Modules

app module: Generic entry point script.

audio module: Public API for tf.audio namespace.

autograph module: Conversion of eager-style Python into TensorFlow graph code.

bitwise module: Operations for manipulating the binary representations of integers.

compat module: Compatibility functions.

config module: Public API for tf.config namespace.

data module: tf.data.Dataset API for input pipelines.

debugging module: Public API for tf.debugging namespace.

distribute module: Library for running a computation across multiple devices.

distributions module: Core module for TensorFlow distribution objects and helpers.

dtypes module: Public API for tf.dtypes namespace.

errors module: Exception types for TensorFlow errors.

estimator module: Estimator: High level tools for working with models.

experimental module: Public API for tf.experimental namespace.

feature_column module: Public API for tf.feature_column namespace.

flags module: Import router for absl.flags. See https://github.com/abseil/abseil-py

gfile module: Import router for file_io.

graph_util module: Helpers to manipulate a tensor graph in python.

image module: Image ops.

initializers module: Public API for tf.initializers namespace.

io module: Public API for tf.io namespace.

keras module: Implementation of the Keras API, the high-level API of TensorFlow.

layers module: Public API for tf.keras.internal.legacy.layers namespace.

linalg module: Operations for linear algebra.

lite module: Public API for tf.lite namespace.

logging module: Logging and Summary Operations.

lookup module: Public API for tf.lookup namespace.

losses module: Loss operations for use in neural networks.

manip module: Operators for manipulating tensors.

math module: Math Operations.

metrics module: Evaluation-related metrics.

mixed_precision module: Public API for tf.mixed_precision namespace.

mlir module: Public API for tf.mlir namespace.

nest module: Functions that work with structures.

nn module: Primitive Neural Net (NN) Operations.

profiler module: Public API for tf.profiler namespace.

python_io module: Python functions for directly manipulating TFRecord-formatted files.

quantization module: Public API for tf.quantization namespace.

queue module: Public API for tf.queue namespace.

ragged module: Ragged Tensors.

random module: Public API for tf.random namespace.

raw_ops module: Public API for tf.raw_ops namespace.

resource_loader module: Resource management library.

saved_model module: Public API for tf.saved_model namespace.

sets module: Tensorflow set operations.

signal module: Signal processing operations.

sparse module: Sparse Tensor Representation.

spectral module: Public API for tf.spectral namespace.

strings module: Operations for working with string Tensors.

summary module: Operations for writing summary data, for use in analysis and visualization.

sysconfig module: System configuration library.

test module: Testing.

tpu module: Ops related to Tensor Processing Units.

train module: Support for training models.

types module: Public TensorFlow type definitions.

user_ops module: Public API for tf.user_ops namespace.

version module: Public API for tf.version namespace.

xla module: Public API for tf.xla namespace.

Classes

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

class AttrValue: A ProtocolMessage

class ConditionalAccumulator: A conditional accumulator for aggregating gradients.

class ConditionalAccumulatorBase: A conditional accumulator for aggregating gradients.

class ConfigProto: A ProtocolMessage

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: A ProtocolMessage

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: A ProtocolMessage

class GradientTape: Record operations for automatic differentiation.

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

class GraphDef: A protobuf containing the graph of operations.

class GraphKeys: Standard names to use for graph collections.

class GraphOptions: A ProtocolMessage

class HistogramProto: A ProtocolMessage

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 IndexedSlicesSpec: Type specification for a tf.IndexedSlices.

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: A ProtocolMessage

class MetaGraphDef: A ProtocolMessage

class Module: Base neural network module class.

class NameAttrList: A ProtocolMessage

class NodeDef: A ProtocolMessage

class OpError: The base class for TensorFlow exceptions.

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

class OptimizerOptions: A ProtocolMessage

class OptionalSpec: Type specification for tf.experimental.Optional.

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 RaggedTensorSpec: Type specification for a tf.RaggedTensor.

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: A ProtocolMessage

class RunOptions: A ProtocolMessage

class Session: A class for running TensorFlow operations.

class SessionLog: A ProtocolMessage

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 SparseTensorSpec: Type specification for a tf.sparse.SparseTensor.

class SparseTensorValue: SparseTensorValue(indices, values, dense_shape)

class Summary: A ProtocolMessage

class SummaryMetadata: A ProtocolMessage

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

class Tensor: A tf.Tensor represents a multidimensional array of elements.

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

class TensorArraySpec: Type specification for a tf.TensorArray.

class TensorInfo: A ProtocolMessage

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 TypeSpec: Specifies a TensorFlow value type.

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. (deprecated)

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

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.

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

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

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

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.

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 element-wise.

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(...): Return the ceiling of the input, element-wise.

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.

control_flow_v2_enabled(...): Returns True if v2 control flow is enabled.

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 or bfloat16 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_control_flow_v2(...): Opts out of control flow v2.

disable_eager_execution(...): Disables eager execution.

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

disable_tensor_equality(...): Compare Tensors by their id and be hashable.

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 a safe divide which returns 0 if y (denominator) 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(...): Tensor contraction over specified indices and outer product.

enable_control_flow_v2(...): Use control flow v2.

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

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

enable_tensor_equality(...): Compare Tensors with element-wise comparison and thus be unhashable.

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. In statistics, for non-negative values of \(x\), the error function has the following interpretation: for a random variable \(Y\) that is normally distributed with mean 0 and variance \(1/\sqrt{2}\), \(erf(x)\) is the probability that \(Y\) falls in the range \([−x, x]\).

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

executing_eagerly(...): Checks whether the current thread has eager execution enabled.

executing_eagerly_outside_functions(...): Returns True if executing eagerly, even if inside a graph function.

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

expand_dims(...): Returns a tensor with a length 1 axis inserted at index axis. (deprecated arguments)

expm1(...): Computes exp(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

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 via per-channel floats

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.

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(...): Compiles a function into a callable TensorFlow graph. (deprecated arguments) (deprecated arguments) (deprecated arguments)

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

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.

grad_pass_through(...): Creates a grad-pass-through op with the forward behavior provided in f.

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(...): Promise to the TF runtime that the input tensor is a constant. (deprecated)

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 TF-native type that can be passed to many TF ops.

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 evenly-spaced values in an interval along a given axis.

linspace(...): Generates evenly-spaced values in an interval along a given axis.

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(...): Transforms elems by applying fn to each element unstacked on axis 0. (deprecated arguments)

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 to zero.

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

matrix_diag(...): Returns a batched diagonal tensor with 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 adjoints (conjugate transposes).

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(...): Solve systems of linear equations with upper or lower triangular matrices.

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.

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 an element-wise x * y.

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 one (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_index_shuffle(...): Outputs the position of value in a permutation of [0, ..., max_index].

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 the contents of file.

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.

recompute_grad(...): Defines a function as a recompute-checkpoint for the tape auto-diff.

reduce_all(...): Computes tf.math.logical_and of elements across dimensions of a tensor. (deprecated arguments)

reduce_any(...): Computes tf.math.logical_or of elements across dimensions of a tensor. (deprecated arguments)

reduce_join(...): Joins all strings into a single string, or joins along an axis.

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

reduce_max(...): Computes tf.math.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 tf.math.minimum of elements across dimensions of a tensor. (deprecated arguments)

reduce_prod(...): Computes tf.math.multiply 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.

repeat(...): Repeat elements of input.

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.

reverse_sequence(...): Reverses variable length slices. (deprecated arguments) (deprecated arguments)

reverse_v2(...): Reverses specific dimensions of a tensor.

rint(...): Returns element-wise integer closest to x.

roll(...): Rolls the elements of a tensor along an axis.

round(...): Rounds the values of a tensor to the nearest integer, element-wise.

rsqrt(...): Computes reciprocal of square root of x element-wise.

saturate_cast(...): Performs a safe saturating cast of value to dtype.

scalar_mul(...): Multiplies a scalar times a Tensor or IndexedSlices object.

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

scatter_add(...): Adds sparse updates to the variable referenced by resource.

scatter_div(...): Divides a variable reference by sparse updates.

scatter_max(...): Reduces sparse updates into a variable reference using the max operation.

scatter_min(...): Reduces sparse updates into a variable reference using the min operation.

scatter_mul(...): Multiplies sparse updates into a variable reference.

scatter_nd(...): Scatters updates into a tensor of shape shape according to indices.

scatter_nd_add(...): Applies sparse addition to individual values or slices in a Variable.

scatter_nd_sub(...): Applies sparse subtraction to individual values or slices in a Variable.

scatter_nd_update(...): Applies sparse updates to individual values or slices in a Variable.

scatter_sub(...): Subtracts sparse updates to a variable reference.

scatter_update(...): Applies sparse updates to a variable reference.

searchsorted(...): Searches for where a value would go in a sorted sequence.

segment_max(...): Computes the maximum along segments of a tensor.

segment_mean(...): Computes the mean along segments of a tensor.

segment_min(...): Computes the minimum along segments of a tensor.

segment_prod(...): Computes the product along segments of a tensor.

segment_sum(...): Computes the sum along segments of a tensor.

self_adjoint_eig(...): Computes the eigen decomposition of a batch of self-adjoint matrices.

self_adjoint_eigvals(...): Computes the eigenvalues of one or more self-adjoint matrices.

sequence_mask(...): Returns a mask tensor representing the first N positions of each cell.

serialize_many_sparse(...): Serialize N-minibatch SparseTensor into an [N, 3] Tensor.

serialize_sparse(...): Serialize a SparseTensor into a 3-vector (1-D Tensor) object.

serialize_tensor(...): Transforms a Tensor into a serialized TensorProto proto.

set_random_seed(...): Sets the graph-level random seed for the default graph.

setdiff1d(...): Computes the difference between two lists of numbers or strings.

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

shape_n(...): Returns shape of tensors.

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

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

sin(...): Computes sine of x element-wise.

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

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

slice(...): Extracts a slice from a tensor.

sort(...): Sorts a tensor.

space_to_batch(...): SpaceToBatch for 4-D tensors of type T.

space_to_batch_nd(...): SpaceToBatch for N-D tensors of type T.

space_to_depth(...): SpaceToDepth for tensors of type T.

sparse_add(...): Adds two tensors, at least one of each is a SparseTensor. (deprecated arguments)

sparse_concat(...): Concatenates a list of SparseTensor along the specified dimension. (deprecated arguments)

sparse_fill_empty_rows(...): Fills empty rows in the input 2-D SparseTensor with a default value.

sparse_mask(...): Masks elements of IndexedSlices.

sparse_matmul(...): Multiply matrix "a" by matrix "b".

sparse_maximum(...): Returns the element-wise max of two SparseTensors.

sparse_merge(...): Combines a batch of feature ids and values into a single SparseTensor. (deprecated)

sparse_minimum(...): Returns the element-wise min of two SparseTensors.

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

sparse_reduce_max(...): Computes tf.sparse.maximum of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)

sparse_reduce_max_sparse(...): Computes the max of elements across dimensions of a SparseTensor. (deprecated arguments)

sparse_reduce_sum(...): Computes tf.sparse.add of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments)

sparse_reduce_sum_sparse(...): Computes the sum of elements across dimensions of a SparseTensor. (deprecated arguments)

sparse_reorder(...): Reorders a SparseTensor into the canonical, row-major ordering.

sparse_reset_shape(...): Resets the shape of a SparseTensor with indices and values unchanged.

sparse_reshape(...): Reshapes a SparseTensor to represent values in a new dense shape.

sparse_retain(...): Retains specified non-empty values within a SparseTensor.

sparse_segment_mean(...): Computes the mean along sparse segments of a tensor.

sparse_segment_sqrt_n(...): Computes the sum along sparse segments of a tensor divided by the sqrt(N).

sparse_segment_sum(...): Computes the sum along sparse segments of a tensor.

sparse_slice(...): Slice a SparseTensor based on the start and size.

sparse_softmax(...): Applies softmax to a batched N-D SparseTensor.

sparse_split(...): Split a SparseTensor into num_split tensors along axis. (deprecated arguments)

sparse_tensor_dense_matmul(...): Multiply SparseTensor (or dense Matrix) (of rank 2) "A" by dense matrix

sparse_tensor_to_dense(...): Converts a SparseTensor into a dense tensor.

sparse_to_dense(...): Converts a sparse representation into a dense tensor. (deprecated)

sparse_to_indicator(...): Converts a SparseTensor of ids into a dense bool indicator tensor.

sparse_transpose(...): Transposes a SparseTensor

split(...): Splits a tensor value into a list of sub tensors.

sqrt(...): Computes element-wise square root of the input tensor.

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

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

squeeze(...): Removes dimensions of size 1 from the shape of a tensor. (deprecated arguments)

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

stop_gradient(...): Stops gradient computation.

strided_slice(...): Extracts a strided slice of a tensor (generalized Python array indexing).

string_join(...): Perform element-wise concatenation of a list of string tensors.

string_split(...): Split elements of source based on delimiter. (deprecated arguments)

string_strip(...): Strip leading and trailing whitespaces from the Tensor.

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

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

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

string_to_number(...): Converts each string in the input Tensor to the specified numeric type.

substr(...): Return substrings from Tensor of strings.

subtract(...): Returns x - y element-wise.

svd(...): Computes the singular value decompositions of one or more matrices.

switch_case(...): Create a switch/case operation, i.e. an integer-indexed conditional.

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

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

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

tensor_scatter_add(...): Adds sparse updates to an existing tensor according to indices.

tensor_scatter_nd_add(...): Adds sparse updates to an existing tensor according to indices.

tensor_scatter_nd_max(...): Apply a sparse update to a tensor taking the element-wise maximum.

tensor_scatter_nd_min(...)

tensor_scatter_nd_sub(...): Subtracts sparse updates from an existing tensor according to indices.

tensor_scatter_nd_update(...): Scatter updates into an existing tensor according to indices.

tensor_scatter_sub(...): Subtracts sparse updates from an existing tensor according to indices.

tensor_scatter_update(...): Scatter updates into an existing tensor according to indices.

tensordot(...): Tensor contraction of a and b along specified axes and outer product.

tile(...): Constructs a tensor by tiling a given tensor.

timestamp(...): Provides the time since epoch in seconds.

to_bfloat16(...): Casts a tensor to type bfloat16. (deprecated)

to_complex128(...): Casts a tensor to type complex128. (deprecated)

to_complex64(...): Casts a tensor to type complex64. (deprecated)

to_double(...): Casts a tensor to type float64. (deprecated)

to_float(...): Casts a tensor to type float32. (deprecated)

to_int32(...): Casts a tensor to type int32. (deprecated)

to_int64(...): Casts a tensor to type int64. (deprecated)

trace(...): Compute the trace of a tensor x.

trainable_variables(...): Returns all variables created with trainable=True.

transpose(...): Transposes a.

truediv(...): Divides x / y elementwise (using Python 3 division operator semantics).

truncated_normal(...): Outputs random values from a truncated normal distribution.

truncatediv(...): Returns x / y element-wise for integer types.

truncatemod(...): Returns element-wise remainder of division.

tuple(...): Group tensors together.

type_spec_from_value(...): Returns a tf.TypeSpec that represents the given value.

unique(...): Finds unique elements in a 1-D tensor.

unique_with_counts(...): Finds unique elements in a 1-D tensor.

unravel_index(...): Converts an array of flat indices into a tuple of coordinate arrays.

unsorted_segment_max(...): Computes the maximum along segments of a tensor.

unsorted_segment_mean(...): Computes the mean along segments of a tensor.

unsorted_segment_min(...): Computes the minimum along segments of a tensor.

unsorted_segment_prod(...): Computes the product along segments of a tensor.

unsorted_segment_sqrt_n(...): Computes the sum along segments of a tensor divided by the sqrt(N).

unsorted_segment_sum(...): Computes the sum along segments of a tensor.

unstack(...): Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.

variable_axis_size_partitioner(...): Get a partitioner for VariableScope to keep shards below max_shard_bytes.

variable_creator_scope(...): Scope which defines a variable creation function to be used by variable().

variable_op_scope(...): Deprecated: context manager for defining an op that creates variables.

variables_initializer(...): Returns an Op that initializes a list of variables.

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

verify_tensor_all_finite(...): Assert that the tensor does not contain any NaN's or Inf's.

where(...): Return the elements, either from x or y, depending on the condition.

where_v2(...): Returns the indices of non-zero elements, or multiplexes x and y.

while_loop(...): Repeat body while the condition cond is true.

wrap_function(...): Wraps the TF 1.x function fn into a graph function.

write_file(...): Writes contents to the file at input filename.

zeros(...): Creates a tensor with all elements set to zero.

zeros_like(...): Creates a tensor with all elements set to zero.

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

{
 tf.qint16,
 tf.qint16_ref,
 tf.qint32,
 tf.qint32_ref,
 tf.qint8,
 tf.qint8_ref,
 tf.quint16,
 tf.quint16_ref,
 tf.quint8,
 tf.quint8_ref
}