-柚子皮-
#RNN
rnn=nn.RNN(10,20,2) #(each_input_size, hidden_state, num_layers)
input=torch.randn(5,3,10) # (seq_len, batch, input_size)
h0=torch.randn(2,3,20) #(num_layers * num_directions, batch, hidden_size)
output,hn=rnn(input,h0)
print(output.size(),hn.size())
#LSTM
rnn=nn.LSTM(10,20,2) #(each_input_size, hidden_state, num_layers)
input=torch.randn(5,3,10) # (seq_len, batch, input_size)
h0=torch.randn(2,3,20) #(num_layers * num_directions, batch, hidden_size)
c0=torch.randn(2,3,20) #(num_layers * num_directions, batch, hidden_size)
output,(hn,cn)=rnn(input,(h0,c0)) #seq_len x batch x hidden*bi_directional
print(output.size(),hn.size(),cn.size())
#GRU
rnn=nn.GRU(10,20,2)
input=torch.randn(5,3,10)
h0=torch.randn(2,3,20)
output,hn=rnn(input,h0)
print(output.size(),hn.size())
gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=dropout, bidirectional=True)
from: -柚子皮-
ref:[LSTM和GRU原理及pytorch代码，输入输出大小说明]