class LSTM(input_size: int, hidden_size: int, bias: bool = True)#

An LSTM recurrent layer.

The input has shape NLD or LD where:

  • N is the optional batch dimension

  • L is the sequence length

  • D is the input’s feature dimension

Concretely, for each element of the sequence, this layer computes:

\[\begin{split}\begin{aligned} i_t &= \sigma (W_{xi}x_t + W_{hi}h_t + b_{i}) \\ f_t &= \sigma (W_{xf}x_t + W_{hf}h_t + b_{f}) \\ g_t &= \text{tanh} (W_{xg}x_t + W_{hg}h_t + b_{g}) \\ o_t &= \sigma (W_{xo}x_t + W_{ho}h_t + b_{o}) \\ c_{t + 1} &= f_t \odot c_t + i_t \odot g_t \\ h_{t + 1} &= o_t \text{tanh}(c_{t + 1}) \end{aligned}\end{split}\]

The hidden state \(h\) and cell state \(c\) have shape NH or H, depending on whether the input is batched or not.

The layer returns two arrays, the hidden state and the cell state at each time step, both of shape NLH or LH.

  • input_size (int) – Dimension of the input, D.

  • hidden_size (int) – Dimension of the hidden state, H.

  • bias (bool) – Whether to use biases or not. Default: True.