RNNCell — PyTorch 2.7 documentation (original) (raw)

class torch.ao.nn.quantized.dynamic.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', dtype=torch.qint8)[source][source]¶

An Elman RNN cell with tanh or ReLU non-linearity. A dynamic quantized RNNCell module with floating point tensor as inputs and outputs. Weights are quantized to 8 bits. We adopt the same interface as torch.nn.RNNCell, please see https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for documentation.

Examples:

rnn = nn.RNNCell(10, 20) input = torch.randn(6, 3, 10) hx = torch.randn(3, 20) output = [] for i in range(6): ... hx = rnn(input[i], hx) ... output.append(hx)