GRU¶
- class continual.GRU(input_size, hidden_size, num_layers=1, bias=True, dropout=0.0, device=None, dtype=None, *args, **kwargs)[source]¶
Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
For each element in the input sequence, each layer computes the following function:
where is the hidden state at time t, is the input at time t, is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and , , are the reset, update, and new gates, respectively. is the sigmoid function, and is the Hadamard product.
In a multilayer GRU, the input of the -th layer () is the hidden state of the previous layer multiplied by dropout where each is a Bernoulli random variable which is with probability
dropout
.- 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
num_layers (int) – Number of recurrent layers. E.g., setting
num_layers=2
would mean stacking two GRUs together to form a stacked GRU, with the second GRU taking in outputs of the first GRU and computing the final results. Default: 1bias (bool) – If
False
, then the layer does not use bias weights b_ih and b_hh. Default:True
dropout (float) – If non-zero, introduces a Dropout layer on the outputs of each GRU layer except the last layer, with dropout probability equal to
dropout
. Default: 0
- Inputs: input, h_0
input: tensor of shape containing the features of the input sequence. The input can also be a packed variable length sequence. See
torch.nn.utils.rnn.pack_padded_sequence()
ortorch.nn.utils.rnn.pack_sequence()
for details.h_0: tensor of shape containing the initial hidden state for each element in the batch. Defaults to zeros if not provided.
where:
- Outputs: output, h_n
output: tensor of shape containing the output features (h_t) from the last layer of the GRU, for each t. If a
torch.nn.utils.rnn.PackedSequence
has been given as the input, the output will also be a packed sequence.h_n: tensor of shape containing the final hidden state for each element in the batch.
- Variables:
weight_ih_l[k] – the learnable input-hidden weights of the layer (W_ir|W_iz|W_in), of shape (3*hidden_size, input_size) for k = 0. Otherwise, the shape is (3*hidden_size, num_directions * hidden_size)
weight_hh_l[k] – the learnable hidden-hidden weights of the layer (W_hr|W_hz|W_hn), of shape (3*hidden_size, hidden_size)
bias_ih_l[k] – the learnable input-hidden bias of the layer (b_ir|b_iz|b_in), of shape (3*hidden_size)
bias_hh_l[k] – the learnable hidden-hidden bias of the layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)
Note
All the weights and biases are initialized from where
Note
For bidirectional GRUs are not supported.
Note
Contrary to the module version found in torch.nn, this module assumes batch first, channel next, and temporal dimension last.
Examples:
gru = co.GRU(input_size=10, hidden_size=20, num_layers=2) # B, C, T x = torch.randn(1, 10, 16) # torch API h0 = torch.randn(2, 1, 20) output, hn = gru(x, h0) # continual inference API gru.set_state(h0) firsts = gru.forward_steps(x[:,:,:-1]) last = gru.forward_step(x[:,:,-1]) assert torch.allclose(firsts, output[:, :, :-1]) assert torch.allclose(last, output[:, :, -1])