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
input_size – The number of expected features in the input x
hidden_size – The number of features in the hidden state h
num_layers – Number of recurrent layers. E.g., setting
num_layers=2would 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: 1
bias – If
False, then the layer does not use bias weights b_ih and b_hh. Default:
batch_first – If
True, then the input and output tensors are provided as (batch, seq, feature). Default:
dropout – 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
bidirectional – If
True, becomes a bidirectional GRU. Default:
- Inputs: input, h_0
input of shape (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See
h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.
- Outputs: output, h_n
output of shape (seq_len, batch, num_directions * hidden_size): tensor containing the output features h_t from the last layer of the GRU, for each t. If a
torch.nn.utils.rnn.PackedSequencehas been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated using
output.view(seq_len, batch, num_directions, hidden_size), with forward and backward being direction 0 and 1 respectively.
Similarly, the directions can be separated in the packed case.
h_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len
Like output, the layers can be separated using
h_n.view(num_layers, num_directions, batch, hidden_size).
Input1: tensor containing input features where and L represents a sequence length.
Input2: tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided. where If the RNN is bidirectional, num_directions should be 2, else it should be 1.
Output2: tensor containing the next hidden state for each element in the batch
~GRU.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)
~GRU.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)
~GRU.bias_ih_l[k] – the learnable input-hidden bias of the layer (b_ir|b_iz|b_in), of shape (3*hidden_size)
~GRU.bias_hh_l[k] – the learnable hidden-hidden bias of the layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)
All the weights and biases are initialized from where
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype
torch.float164) V100 GPU is used, 5) input data is not in
PackedSequenceformat persistent algorithm can be selected to improve performance.
>>> rnn = nn.GRU(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = torch.randn(2, 3, 20) >>> output, hn = rnn(input, h0)