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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 op_type
is not differentiable.
Print(...)
: Prints a list of tensors. (deprecated)