tf.contrib.rnn

 

upper level

 

Modules

 

None

 

Classes

 

AttentionCellWrapper:

Basic attention cell wrapper.

BasicLSTMCell:

Basic LSTM recurrent network cell.

BasicRNNCell:

The most basic RNN cell.

BidirectionalGridLSTMCell:

Bidirectional GridLstm cell.

CompiledWrapper:

Wraps step execution in an XLA JIT scope.

Conv1DLSTMCell:

1D Convolutional LSTM recurrent network cell.

Conv2DLSTMCell:

2D Convolutional LSTM recurrent network cell.

Conv3DLSTMCell:

3D Convolutional LSTM recurrent network cell.

ConvLSTMCell:

Convolutional LSTM recurrent network cell.

CoupledInputForgetGateLSTMCell:

Long short-term memory unit (LSTM) recurrent network cell.

DeviceWrapper:

Operator that ensures an RNNCell runs on a particular device.

DropoutWrapper:

Operator adding dropout to inputs and outputs of the given cell.

EmbeddingWrapper:

Operator adding input embedding to the given cell.

FusedRNNCell:

Abstract object representing a fused RNN cell.

FusedRNNCellAdaptor:

This is an adaptor for RNNCell classes to be used with FusedRNNCell.

GLSTMCell:

Group LSTM cell (G-LSTM).

GRUBlockCell:

Block GRU cell implementation.

GRUBlockCellV2:

Temporary GRUBlockCell impl with a different variable naming scheme.

GRUCell:

Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).

GridLSTMCell:

Grid Long short-term memory unit (LSTM) recurrent network cell.

HighwayWrapper:

RNNCell wrapper that adds highway connection on cell input and output.

InputProjectionWrapper:

Operator adding an input projection to the given cell.

IntersectionRNNCell:

Intersection Recurrent Neural Network (+RNN) cell.

LSTMBlockCell:

Basic LSTM recurrent network cell.

LSTMBlockFusedCell:

FusedRNNCell implementation of LSTM.

LSTMBlockWrapper:

This is a helper class that provides housekeeping for LSTM cells.

LSTMCell:

Long short-term memory unit (LSTM) recurrent network cell.

LSTMStateTuple:

Tuple used by LSTM Cells for state_size, zero_state, and output state.

LayerNormBasicLSTMCell:

LSTM unit with layer normalization and recurrent dropout.

MultiRNNCell:

RNN cell composed sequentially of multiple simple cells.

NASCell:

Neural Architecture Search (NAS) recurrent network cell.

OutputProjectionWrapper:

Operator adding an output projection to the given cell.

PhasedLSTMCell:

Phased LSTM recurrent network cell.

RNNCell:

Abstract object representing an RNN cell.

ResidualWrapper:

RNNCell wrapper that ensures cell inputs are added to the outputs.

TimeFreqLSTMCell:

Time-Frequency Long short-term memory unit (LSTM) recurrent network cell.

TimeReversedFusedRNN:

This is an adaptor to time-reverse a FusedRNNCell.

UGRNNCell:

Update Gate Recurrent Neural Network (UGRNN) cell.

 

Functions

 

stack_bidirectional_dynamic_rnn(…):

Creates a dynamic bidirectional recurrent neural network.

stack_bidirectional_rnn(…):

Creates a bidirectional recurrent neural network.

static_bidirectional_rnn(…):

Creates a bidirectional recurrent neural network.

static_rnn(…):

Creates a recurrent neural network specified by RNNCell cell.

static_state_saving_rnn(…):

RNN that accepts a state saver for time-truncated RNN calculation.