Module: tf.contrib

Module tf.contrib

contrib module containing volatile or experimental code.

Members

bayesflow module: Ops for representing Bayesian computation.

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

copy_graph module: Functions to copy elements between graphs.

crf module: Linear-chain CRF layer. See the CRF (contrib) guide.

cudnn_rnn module: Ops for fused Cudnn RNN models.

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

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

factorization module: Ops and modules related to factorization.

framework module: Framework utilities. See the Framework (contrib) guide.

graph_editor module: TensorFlow Graph Editor. See the Graph Editor (contrib) guide.

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. See the Integrate (contrib) guide.

labeled_tensor module: Labels for TensorFlow.

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

learn module: High level API for learning. See the Learn (contrib) guide.

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

linalg module: Linear algebra libraries. See the Linear Algebra (contrib) guide.

linear_optimizer module: Ops for training linear models.

lookup module: Ops for lookup operations.

losses module: Ops for building neural network losses. See Losses (contrib).

metrics module: Ops for evaluation metrics and summary statistics.

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

nn module: Module for deprecated ops in tf.nn.

opt module: A module containing optimization routines.

quantization module: Ops for building quantized models.

rnn module: RNN Cells and additional RNN operations. See RNN and Cells (contrib) guide.

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

session_bundle module

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

solvers module: Ops for representing Bayesian computation.

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

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

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.

training module: Training and input utilities. See Training (contrib) guide.

util module: Utilities for dealing with Tensors. See Utilities (contrib) guide.

Defined in tensorflow/contrib/__init__.py.