RNN¶
- class
dragon.vm.torch.nn.RNN(
input_size,
hidden_size,
nonlinearity='relu',
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False
)[source]¶ Apply a multi-layer Elman RNN. [Elman, 1990].
Examples:
m = torch.nn.RNN(32, 64) x = torch.ones(8, 32, 256) outputs, hidden = m(x)
__init__¶
RNN.__init__(
input_size,
hidden_size,
nonlinearity='relu',
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False
)[source]¶Create a
RNNmodule.- Parameters:
- input_size (int) – The dimension of input.
- hidden_size (int) – The dimension of hidden state.
- nonlinearity ({'tanh', 'relu'}, optional) – The nonlinearity.
- num_layers (int, optional, default=1) – The number of recurrent layers.
- bias (bool, optional, default=True) – True to use bias.
- batch_first (bool, optional, default=False) – True to use order [N, T, C] otherwise [T, N, C].
- dropout (number, optional, default=0) – The dropout ratio.
- bidirectional (bool, optional, default=False) – Whether to create a bidirectional rnn.