RNN and Cells (contrib)
[TOC]
Module for constructing RNN Cells and additional RNN operations.
Base interface for all RNN Cells
- @{tf.contrib.rnn.RNNCell}
Core RNN Cells for use with TensorFlow’s core RNN methods
- @{tf.contrib.rnn.BasicRNNCell}
- @{tf.contrib.rnn.BasicLSTMCell}
- @{tf.contrib.rnn.GRUCell}
- @{tf.contrib.rnn.LSTMCell}
- @{tf.contrib.rnn.LayerNormBasicLSTMCell}
Classes storing split RNNCell state
- @{tf.contrib.rnn.LSTMStateTuple}
Core RNN Cell wrappers (RNNCells that wrap other RNNCells)
- @{tf.contrib.rnn.MultiRNNCell}
- @{tf.contrib.rnn.LSTMBlockWrapper}
- @{tf.contrib.rnn.DropoutWrapper}
- @{tf.contrib.rnn.EmbeddingWrapper}
- @{tf.contrib.rnn.InputProjectionWrapper}
- @{tf.contrib.rnn.OutputProjectionWrapper}
- @{tf.contrib.rnn.DeviceWrapper}
- @{tf.contrib.rnn.ResidualWrapper}
Block RNNCells
- @{tf.contrib.rnn.LSTMBlockCell}
- @{tf.contrib.rnn.GRUBlockCell}
Fused RNNCells
- @{tf.contrib.rnn.FusedRNNCell}
- @{tf.contrib.rnn.FusedRNNCellAdaptor}
- @{tf.contrib.rnn.TimeReversedFusedRNN}
- @{tf.contrib.rnn.LSTMBlockFusedCell}
LSTM-like cells
- @{tf.contrib.rnn.CoupledInputForgetGateLSTMCell}
- @{tf.contrib.rnn.TimeFreqLSTMCell}
- @{tf.contrib.rnn.GridLSTMCell}
RNNCell wrappers
- @{tf.contrib.rnn.AttentionCellWrapper}
- @{tf.contrib.rnn.CompiledWrapper}
Recurrent Neural Networks
TensorFlow provides a number of methods for constructing Recurrent Neural Networks.
- @{tf.contrib.rnn.static_rnn}
- @{tf.contrib.rnn.static_state_saving_rnn}
- @{tf.contrib.rnn.static_bidirectional_rnn}
- @{tf.contrib.rnn.stack_bidirectional_dynamic_rnn}
附
引
（1）The Unreasonable Effectiveness of Recurrent Neural Networks