Module: tf.contrib

Contrib module containing volatile or experimental code.

Modules

autograph module: This is the legacy module for AutoGraph, kept for backward compatibility.

batching module: Ops and modules related to batch.

bayesflow module: Ops for representing Bayesian computation.

checkpoint module: Tools for working with object-based checkpoints.

cloud module: Module for cloud ops.

cluster_resolver module: Standard imports for Cluster Resolvers.

compiler module: A module for controlling the Tensorflow/XLA JIT compiler.

constrained_optimization module: A library for performing constrained optimization in TensorFlow.

copy_graph module: Functions to copy elements between graphs.

crf module: Linear-chain CRF layer.

cudnn_rnn module: Ops for fused Cudnn RNN models.

data module: Experimental API for building input pipelines.

deprecated module: Non-core alias for the deprecated tf.X_summary ops.

distribute module: A distributed computation library for TF.

distributions module: Classes representing statistical distributions and ops for working with them.

eager module: TensorFlow Eager execution prototype.

estimator module: estimator python module.

factorization module: Ops and modules related to factorization.

feature_column module: Experimental utilities for tf.feature_column.

ffmpeg module: Working with audio using FFmpeg.

framework module: Framework utilities.

graph_editor module: TensorFlow Graph Editor.

grid_rnn module: GridRNN cells

image module: Ops for image manipulation.

input_pipeline module: Ops and modules related to input_pipeline.

integrate module: Integration and ODE solvers.

keras module: Implementation of the Keras API meant to be a high-level API for TensorFlow.

kernel_methods module: Ops and estimators that enable explicit kernel methods in TensorFlow.

labeled_tensor module: Labels for TensorFlow.

layers module: Ops for building neural network layers, regularizers, summaries, etc.

learn module: High level API for learning (DEPRECATED).

legacy_seq2seq module: Deprecated library for creating sequence-to-sequence models in TensorFlow.

linear_optimizer module: Ops for training linear models.

lookup module: Ops for lookup operations.

losses module: Ops for building neural network losses.

memory_stats module: Ops for memory statistics.

metrics module: Ops for evaluation metrics and summary statistics.

mixed_precision module: Library for mixed precision training.

model_pruning module: Model pruning implementation in tensorflow.

nn module: Module for variants of ops in tf.nn.

opt module: A module containing optimization routines.

optimizer_v2 module: Distribution-aware version of Optimizer.

periodic_resample module: Custom op used by periodic_resample.

predictor module: Modules for Predictors.

proto module: Ops and modules related to proto.

quantization module: Ops for building quantized models.

quantize module: Functions for rewriting graphs for quantized training.

receptive_field module: Module that declares the functions in tf.contrib.receptive_field's API.

recurrent module: Recurrent computations library.

reduce_slice_ops module: reduce by slice

remote_fused_graph module: Remote fused graph ops python library.

resampler module: Ops and modules related to resampler.

rnn module: RNN Cells and additional RNN operations.

rpc module: Ops and modules related to RPC.

saved_model module: SavedModel contrib support.

seq2seq module: Ops for building neural network seq2seq decoders and losses.

signal module: Signal processing operations.

slim module: Slim is an interface to contrib functions, examples and models.

solvers module: Ops for representing Bayesian computation.

sparsemax module: Module that implements sparsemax and sparsemax loss, see [1].

specs module: Init file, giving convenient access to all specs ops.

staging module: contrib module containing StagingArea.

stat_summarizer module: Exposes the Python wrapper for StatSummarizer utility class.

stateless module: Stateless random ops which take seed as a tensor input.

summary module: TensorFlow Summary API v2.

tensor_forest module: Random forest implementation in tensorflow.

tensorboard module: tensorboard module containing volatile or experimental code.

testing module: Testing utilities.

tfprof module: tfprof is a tool that profile various aspect of TensorFlow model.

timeseries module: A time series library in TensorFlow (TFTS).

tpu module: Ops related to Tensor Processing Units.

training module: Training and input utilities.

util module: Utilities for dealing with Tensors.