TensorFlow

pip install tensorflow

Modules

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: Functions for Python 2 vs. 3 compatibility.

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 processing and decoding 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.

nest module: Public API for tf.nest namespace.

nn module: Wrappers for primitive Neural Net (NN) Operations.

optimizers module: Built-in optimizer classes.

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.

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 : Represents an optional potentially containing a structured value.

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.SparseTensor .

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

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 Variables 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.

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.

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

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

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

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

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.

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

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

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

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

get_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.

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

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

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

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

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

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

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.

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

make_tensor_proto(...) : Create a TensorProto.

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

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 x * y element-wise.

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

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

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

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 1.

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

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.

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 .

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.

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 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(...)

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 into sub tensors.

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

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_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.

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

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

transpose(...) : Transposes a .

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.

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

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

unravel_index(...) : Converts a flat index or array of flat indices into a tuple of

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, either from x or y , depending on the condition .

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

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

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

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