TensorFlow 2 version
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TensorFlow root package
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: Functions for Python 2 vs. 3 compatibility.
config module: Public API for tf.config namespace.
contrib module: Contrib module containing volatile or experimental code.
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.
gfile module: Import router for file_io.
graph_util module: Helpers to manipulate a tensor graph in python.
image module: Image processing and decoding 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.
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: Note: tf.raw_ops provides direct/low level access to all TensorFlow ops. See the RFC
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.
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: Represents an optional potentially containing a structured value.
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.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: 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 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