Table Of Contents
Table Of Contents


mxnet.ndarray.RNN(data=None, parameters=None, state=None, state_cell=None, state_size=_Null, num_layers=_Null, bidirectional=_Null, mode=_Null, p=_Null, state_outputs=_Null, projection_size=_Null, lstm_state_clip_min=_Null, lstm_state_clip_max=_Null, lstm_state_clip_nan=_Null, out=None, name=None, **kwargs)

Applies recurrent layers to input data. Currently, vanilla RNN, LSTM and GRU are implemented, with both multi-layer and bidirectional support.

When the input data is of type float32 and the environment variables MXNET_CUDA_ALLOW_TENSOR_CORE and MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION are set to 1, this operator will try to use pseudo-float16 precision (float32 math with float16 I/O) precision in order to use Tensor Cores on suitable NVIDIA GPUs. This can sometimes give significant speedups.

Vanilla RNN

Applies a single-gate recurrent layer to input X. Two kinds of activation function are supported: ReLU and Tanh.

With ReLU activation function:

\[h_t = relu(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\]

With Tanh activtion function:

\[h_t = \tanh(W_{ih} * x_t + b_{ih} + W_{hh} * h_{(t-1)} + b_{hh})\]

Reference paper: Finding structure in time - Elman, 1988.


Long Short-Term Memory - Hochreiter, 1997.

\[\begin{split}\begin{array}{ll} i_t = \mathrm{sigmoid}(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ f_t = \mathrm{sigmoid}(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\ o_t = \mathrm{sigmoid}(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ c_t = f_t * c_{(t-1)} + i_t * g_t \\ h_t = o_t * \tanh(c_t) \end{array}\end{split}\]


Gated Recurrent Unit - Cho et al. 2014.

The definition of GRU here is slightly different from paper but compatible with CUDNN.

\[\begin{split}\begin{array}{ll} r_t = \mathrm{sigmoid}(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \mathrm{sigmoid}(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) * n_t + z_t * h_{(t-1)} \\ \end{array}\end{split}\]
  • data (NDArray) – Input data to RNN

  • parameters (NDArray) – Vector of all RNN trainable parameters concatenated

  • state (NDArray) – initial hidden state of the RNN

  • state_cell (NDArray) – initial cell state for LSTM networks (only for LSTM)

  • state_size (int (non-negative), required) – size of the state for each layer

  • num_layers (int (non-negative), required) – number of stacked layers

  • bidirectional (boolean, optional, default=0) – whether to use bidirectional recurrent layers

  • mode ({'gru', 'lstm', 'rnn_relu', 'rnn_tanh'}, required) – the type of RNN to compute

  • p (float, optional, default=0) – drop rate of the dropout on the outputs of each RNN layer, except the last layer.

  • state_outputs (boolean, optional, default=0) – Whether to have the states as symbol outputs.

  • projection_size (int or None, optional, default='None') – size of project size

  • lstm_state_clip_min (double or None, optional, default=None) – Minimum clip value of LSTM states. This option must be used together with lstm_state_clip_max.

  • lstm_state_clip_max (double or None, optional, default=None) – Maximum clip value of LSTM states. This option must be used together with lstm_state_clip_min.

  • lstm_state_clip_nan (boolean, optional, default=0) – Whether to stop NaN from propagating in state by clipping it to min/max. If clipping range is not specified, this option is ignored.

  • out (NDArray, optional) – The output NDArray to hold the result.


out – The output of this function.

Return type

NDArray or list of NDArrays