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Module: tf.compat.v1

Bring in all of the public TensorFlow interface into this module.


app module: Generic entry point script.

audio module: Public API for namespace.

autograph module: Conversion of eager-style 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.

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.

flags module: Import router for absl.flags. See

gfile module: Import router for file_io.

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

image module: Image ops.

initializers module: Public API for tf.initializers namespace.

io module: Public API for namespace.

keras module: Implementation of the Keras API, the high-level API of 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.

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.

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: Public API for tf.raw_ops namespace.

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.

types module: Public TensorFlow type definitions.

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.


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: The base class for TensorFlow exceptions.

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

class OptimizerOptions: A ProtocolMessage

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

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.