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 plain 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 meant to be a high-level API for TensorFlow.

layers module: Public API for tf.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: Public API for tf.nest namespace.

nn module: Wrappers for 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 ProtocolMessage

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

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 tensor is a multidimensional array of elements represented by a

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

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.

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 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(...): 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.

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

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.

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(...): 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 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(...): Logical AND function.

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. 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 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_and_dequantize_v4(...): Returns the gradient of QuantizeAndDequantizeV4.

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.

recompute_grad(...): An eager-compatible version of recompute_grad.

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 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 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.

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(...): Scatter updates into a new tensor 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 input tensor for values on the innermost dimension.

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 the max 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 the sum 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(...)

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. This emulates C semantics in that

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(...): Return the elements where condition is True (multiplexing 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. Creates file and recursively

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 \(\zeta(x, q)\).

Other Members
AUTO_REUSE
COMPILER_VERSION	'7.3.1 20180303'
CXX11_ABI_FLAG	0
GIT_VERSION	'v2.4.0-rc4-71-g582c8d236cb'
GRAPH_DEF_VERSION	561
GRAPH_DEF_VERSION_MIN_CONSUMER	0
GRAPH_DEF_VERSION_MIN_PRODUCER	0
MONOLITHIC_BUILD	0
QUANTIZED_DTYPES
VERSION	'2.4.0'
version	'2.4.0'
bfloat16	tf.dtypes.DType
bool	tf.dtypes.DType
complex128	tf.dtypes.DType
complex64	tf.dtypes.DType
double	tf.dtypes.DType
float16	tf.dtypes.DType
float32	tf.dtypes.DType
float64	tf.dtypes.DType
half	tf.dtypes.DType
int16	tf.dtypes.DType
int32	tf.dtypes.DType
int64	tf.dtypes.DType
int8	tf.dtypes.DType
newaxis	None
qint16	tf.dtypes.DType
qint32	tf.dtypes.DType
qint8	tf.dtypes.DType
quint16	tf.dtypes.DType
quint8	tf.dtypes.DType
resource	tf.dtypes.DType
string	tf.dtypes.DType
uint16	tf.dtypes.DType
uint32	tf.dtypes.DType
uint64	tf.dtypes.DType
uint8	tf.dtypes.DType
variant	tf.dtypes.DType