description: ## TensorFlow

Module: tf

View source on GitHub

TensorFlow

pip install tensorflow

Modules

audio module: Public API for tf.audio namespace.

autodiff module: Public API for tf.autodiff 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.

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.

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

image module: Image ops.

initializers module: Keras initializer serialization / deserialization.

io module: Public API for tf.io namespace.

keras module: Implementation of the Keras API meant to be a high-level API for TensorFlow.

linalg module: Operations for linear algebra.

lite module: Public API for tf.lite namespace.

lookup module: Public API for tf.lookup namespace.

losses module: Built-in loss functions.

math module: Math Operations.

metrics module: Built-in 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.

optimizers module: Built-in optimizer classes.

profiler module: Public API for tf.profiler namespace.

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.

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.

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.

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 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 GradientTape: Record operations for automatic differentiation.

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

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

class IndexedSlicesSpec: Type specification for a tf.IndexedSlices.

class Module: Base neural network module class.

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

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

class RaggedTensor: Represents a ragged tensor.

class RaggedTensorSpec: Type specification for a tf.RaggedTensor.

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

class SparseTensor: Represents a sparse tensor.

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

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 TensorShape: Represents the shape of a Tensor.

class TensorSpec: Describes a tf.Tensor.

class TypeSpec: Specifies a TensorFlow value type.

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

class Variable: See the variable guide.

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

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

class constant_initializer: Initializer that generates tensors with constant values.

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

class ones_initializer: Initializer that generates tensors initialized to 1.

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 zeros_initializer: Initializer that generates tensors initialized to 0.

Functions

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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().

constant(...): Creates a constant tensor from a tensor-like object.

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

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

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

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

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

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

device(...): Specifies the device for ops created/executed in this context.

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.

eig(...): Computes the eigen decomposition of a batch of matrices.

eigvals(...): Computes the eigenvalues of one or more matrices.

einsum(...): Tensor contraction over specified indices and outer product.

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.

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

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

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

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.

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

fingerprint(...): Generates fingerprint values.

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

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

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

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

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

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

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.

inside_function(...): Indicates whether the caller code is executing inside a tf.function.

is_tensor(...): Checks whether x is a TF-native type that can be passed to many TF ops.

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

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

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

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

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

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.

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

make_tensor_proto(...): Create a TensorProto.

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

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

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

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

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.

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

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

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 of all ones that has the same shape as the input.

pad(...): Pads a tensor.

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

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

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

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

quantize_and_dequantize_v4(...): Returns the gradient of QuantizeAndDequantizeV4.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

reshape(...): Reshapes a tensor.

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

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

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

round(...): Rounds the values of a tensor to the nearest integer, 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. (deprecated argument values)

scatter_nd(...): Scatter updates into a new tensor according to indices.

searchsorted(...): Searches input tensor for values on the innermost dimension.

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

shape(...): Returns a tensor containing the shape of the input 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 N-D tensors of type T.

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

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.

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

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).

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

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

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

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

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.

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.

transpose(...): Transposes a, where a is a Tensor.

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

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.

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

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

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

where(...): Return the elements where condition is True (multiplexing x and y).

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

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

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