GRUCell#

classtorch.nn.modules.rnn.GRUCell(input_size,hidden_size,bias=True,device=None,dtype=None)[source]#

A gated recurrent unit (GRU) cell.

$$\begin{array}{l}r=\sigma (W_{ir}x+b_{ir}+W_{hr}h+b_{hr})\\ z=\sigma (W_{iz}x+b_{iz}+W_{hz}h+b_{hz})\\ n=\tanh (W_{in}x+b_{in}+r\odot (W_{hn}h+b_{hn}))\\ h^{\mathrm{\prime }}=(1-z)\odot n+z\odot h\end{array}$$r=σ(Wir​x+bir​+Whr​h+bhr​)z=σ(Wiz​x+biz​+Whz​h+bhz​)n=tanh(Win​x+bin​+r⊙(Whn​h+bhn​))h′=(1−z)⊙n+z⊙h​

where$\sigma$σ is the sigmoid function, and$\odot$⊙ is the Hadamard product.

Parameters

• input_size (int) – The number of expected features in the inputx

• hidden_size (int) – The number of features in the hidden stateh

• bias (bool) – IfFalse, then the layer does not use bias weightsb_ih andb_hh. Default:True

Inputs: input, hidden

• input : tensor containing input features

• hidden : tensor containing the initial hiddenstate for each element in the batch.Defaults to zero if not provided.

Outputs: h’

• h’ : tensor containing the next hidden statefor each element in the batch

Shape:

• input:$(N,H_{in})$(N,Hin​) or$(H_{in})$(Hin​) tensor containing input features where$H_{in}$Hin​ =input_size.

• hidden:$(N,H_{out})$(N,Hout​) or$(H_{out})$(Hout​) tensor containing the initial hiddenstate where$H_{out}$Hout​ =hidden_size. Defaults to zero if not provided.

• output:$(N,H_{out})$(N,Hout​) or$(H_{out})$(Hout​) tensor containing the next hidden state.

Variables

• weight_ih (torch.Tensor) – the learnable input-hidden weights, of shape(3*hidden_size, input_size)

• weight_hh (torch.Tensor) – the learnable hidden-hidden weights, of shape(3*hidden_size, hidden_size)

• bias_ih – the learnable input-hidden bias, of shape(3*hidden_size)

• bias_hh – the learnable hidden-hidden bias, of shape(3*hidden_size)

Note

All the weights and biases are initialized from$\mathcal{U}(-\sqrt{k},\sqrt{k})$U(−k​,k​)where$k=\frac{1}{\text{hidden\_size}}$k=hidden_size1​

On certain ROCm devices, when using float16 inputs this module will usedifferent precision for backward.

Examples:

>>>rnn=nn.GRUCell(10,20)>>>input=torch.randn(6,3,10)>>>hx=torch.randn(3,20)>>>output=[]>>>foriinrange(6):...hx=rnn(input[i],hx)...output.append(hx)