Module: tf.contrib.rnn

Defined in tensorflow/contrib/rnn/__init__.py.

RNN Cells and additional RNN operations.

See RNN and Cells (contrib) guide.

Classes

class AttentionCellWrapper: Basic attention cell wrapper.

class BasicLSTMCell: Basic LSTM recurrent network cell.

class BasicRNNCell: The most basic RNN cell.

class BidirectionalGridLSTMCell: Bidirectional GridLstm cell.

class CompiledWrapper: Wraps step execution in an XLA JIT scope.

class Conv1DLSTMCell: 1D Convolutional LSTM recurrent network cell.

class Conv2DLSTMCell: 2D Convolutional LSTM recurrent network cell.

class Conv3DLSTMCell: 3D Convolutional LSTM recurrent network cell.

class ConvLSTMCell: Convolutional LSTM recurrent network cell.

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

class DeviceWrapper: Operator that ensures an RNNCell runs on a particular device.

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

class EmbeddingWrapper: Operator adding input embedding to the given cell.

class FusedRNNCell: Abstract object representing a fused RNN cell.

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

class GLSTMCell: Group LSTM cell (G-LSTM).

class GRUBlockCell: Block GRU cell implementation.

class GRUBlockCellV2: Temporary GRUBlockCell impl with a different variable naming scheme.

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

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

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

class InputProjectionWrapper: Operator adding an input projection to the given cell.

class IntersectionRNNCell: Intersection Recurrent Neural Network (+RNN) cell.

class LSTMBlockCell: Basic LSTM recurrent network cell.

class LSTMBlockFusedCell: FusedRNNCell implementation of LSTM.

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

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

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

class LayerNormBasicLSTMCell: LSTM unit with layer normalization and recurrent dropout.

class LayerRNNCell: Subclass of RNNCells that act like proper tf.Layer objects.

class MultiRNNCell: RNN cell composed sequentially of multiple simple cells.

class NASCell: Neural Architecture Search (NAS) recurrent network cell.

class OutputProjectionWrapper: Operator adding an output projection to the given cell.

class PhasedLSTMCell: Phased LSTM recurrent network cell.

class RNNCell: Abstract object representing an RNN cell.

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

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

class TimeReversedFusedRNN: This is an adaptor to time-reverse a FusedRNNCell.

class UGRNNCell: Update Gate Recurrent Neural Network (UGRNN) cell.

Functions

best_effort_input_batch_size(...): Get static input batch size if available, with fallback to the dynamic one.

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.

transpose_batch_time(...): Transposes the batch and time dimensions of a Tensor.