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Module: tf


pip install tensorflow


audio module: Public API for 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: 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 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.


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.


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.