LSTMCell

class torch.nn.LSTMCell(input_size, hidden_size, bias=True, device=None, dtype=None)

A long short-term memory (LSTM) cell.

\[\begin{array}{ll} i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\ f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\ g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\ o = \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\ c' = f \odot c + i \odot g \\ h' = o \odot \tanh(c') \\ \end{array}\]

where \(\sigma\) is the sigmoid function, and \(\odot\) is the Hadamard product.

Parameters:

• input_size (int) – The number of expected features in the input x

• hidden_size (int) – The number of features in the hidden state h

• bias (bool) – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

Inputs: input, (h_0, c_0)

• input of shape (batch, input_size) or (input_size): tensor containing input features

• h_0 of shape (batch, hidden_size) or (hidden_size): tensor containing the initial hidden state

• c_0 of shape (batch, hidden_size) or (hidden_size): tensor containing the initial cell state

  If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.

Outputs: (h_1, c_1)

• h_1 of shape (batch, hidden_size) or (hidden_size): tensor containing the next hidden state

• c_1 of shape (batch, hidden_size) or (hidden_size): tensor containing the next cell state

Variables:

• weight_ih (torch.Tensor) – the learnable input-hidden weights, of shape (4*hidden_size, input_size)

• weight_hh (torch.Tensor) – the learnable hidden-hidden weights, of shape (4*hidden_size, hidden_size)

• bias_ih – the learnable input-hidden bias, of shape (4*hidden_size)

• bias_hh – the learnable hidden-hidden bias, of shape (4*hidden_size)

Note

All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{1}{\text{hidden\_size}}\)

On certain ROCm devices, when using float16 inputs this module will use different precision for backward.

Examples:

>>> rnn = nn.LSTMCell(10, 20)  # (input_size, hidden_size)
>>> input = torch.randn(2, 3, 10)  # (time_steps, batch, input_size)
>>> hx = torch.randn(3, 20)  # (batch, hidden_size)
>>> cx = torch.randn(3, 20)
>>> output = []
>>> for i in range(input.size()[0]):
...     hx, cx = rnn(input[i], (hx, cx))
...     output.append(hx)
>>> output = torch.stack(output, dim=0)