View source on GitHub
|
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.