Table Of Contents
Table Of Contents

rnn and contrib.rnn

Build-in recurrent neural network layers are provided in the following two modules:

mxnet.gluon.rnn Recurrent neural network module.
mxnet.gluon.contrib.rnn Contrib recurrent neural network module.

Recurrent Cells

rnn.LSTMCell Long-Short Term Memory (LSTM) network cell.
rnn.GRUCell Gated Rectified Unit (GRU) network cell.
rnn.RecurrentCell Abstract base class for RNN cells
rnn.SequentialRNNCell Sequentially stacking multiple RNN cells.
rnn.BidirectionalCell Bidirectional RNN cell.
rnn.DropoutCell Applies dropout on input.
rnn.ZoneoutCell Applies Zoneout on base cell.
rnn.ResidualCell Adds residual connection as described in Wu et al, 2016 (
contrib.rnn.Conv1DRNNCell 1D Convolutional RNN cell.
contrib.rnn.Conv2DRNNCell 2D Convolutional RNN cell.
contrib.rnn.Conv3DRNNCell 3D Convolutional RNN cells
contrib.rnn.Conv1DLSTMCell 1D Convolutional LSTM network cell.
contrib.rnn.Conv2DLSTMCell 2D Convolutional LSTM network cell.
contrib.rnn.Conv3DLSTMCell 3D Convolutional LSTM network cell.
contrib.rnn.Conv1DGRUCell 1D Convolutional Gated Rectified Unit (GRU) network cell.
contrib.rnn.Conv2DGRUCell 2D Convolutional Gated Rectified Unit (GRU) network cell.
contrib.rnn.Conv3DGRUCell 3D Convolutional Gated Rectified Unit (GRU) network cell.
contrib.rnn.VariationalDropoutCell Applies Variational Dropout on base cell.
contrib.rnn.LSTMPCell Long-Short Term Memory Projected (LSTMP) network cell.

Recurrent Layers

rnn.RNN Applies a multi-layer Elman RNN with tanh or ReLU non-linearity to an input sequence.
rnn.LSTM Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.
rnn.GRU Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.