GRU
-
class torch.nn.GRU(*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 timet
, is the hidden state of the layer at timet-1
or the initial hidden state at time0
, 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 – 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=2
would mean stacking two GRUs together to form astacked 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 weightsb_ih
andb_hh
. Default:True
-
batch_first – If
True
, then the input and output tensors are provided as (batch, seq, feature). Default:False
-
dropout – If non-zero, introduces a
Dropout
layer on the outputs of each GRU layer except the last layer, with dropout probability equal todropout
. Default: 0 -
bidirectional – If
True
, becomes a bidirectional GRU. Default:False
-
input_size – The number of expected features in the input
- 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. Seetorch.nn.utils.rnn.pack_padded_sequence()
for details. -
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.
-
input of shape
- 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 eacht
. If atorch.nn.utils.rnn.PackedSequence
has been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated usingoutput.view(seq_len, batch, num_directions, hidden_size)
, with forward and backward being direction0
and1
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 fort = seq_len
Like output, the layers can be separated using
h_n.view(num_layers, num_directions, batch, hidden_size)
.
-
- Shape:
-
- 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.
- Output1: where
- Output2: tensor containing the next hidden state for each element in the batch
- Input1: tensor containing input features where and
- Variables
-
-
~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)
fork = 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)
-
~GRU.weight_ih_l[k] – the learnable input-hidden weights of the layer (W_ir|W_iz|W_in), of shape
Note
All the weights and biases are initialized from where
- Orphan
Note
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype
torch.float16
4) V100 GPU is used, 5) input data is not inPackedSequence
format persistent algorithm can be selected to improve performance.Examples:
>>> rnn = nn.GRU(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = torch.randn(2, 3, 20) >>> output, hn = rnn(input, h0)
© 2019 Torch Contributors
Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.8.0/generated/torch.nn.GRU.html