Module: tf

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.

Other Members
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

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf