Module: contrib

Module: tf.contrib

Module tf.contrib

Defined in tensorflow/contrib/__init__.py.

contrib module containing volatile or experimental code.

Modules

bayesflow module: Ops for representing Bayesian computation.

cloud module: Module for cloud ops.

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.

cudnn_rnn module: Ops for fused Cudnn RNN models.

data module: tf.contrib.data.Dataset API for input pipelines.

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.

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.

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

linalg module: Linear algebra libraries.

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.

nccl module: Functions for using NVIDIA nccl collective ops.

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.

saved_model module: SavedModel contrib support.

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.

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.

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.

util module: Utilities for dealing with Tensors.