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 (https://arxiv.org/abs/1609.08144).
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.

Table Of Contents

• rnn and contrib.rnn
  • Recurrent Cells
  • Recurrent Layers

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