手写RNN前向计算 VS Pytorch.RNN

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batch_size , seq ,num_layers = 2,3,2 input_size , hidden_size = 2,3 input = torch.randn(batch_size, seq, input_size) h_0 = torch.zeros(num_layers,batch_size,hidden_size) # 调用Pytorch RNN rnn = nn.RNN(input_size,hidden_size,num_layers, batch_first=True) output , h_n = rnn(input, h_0) print('Pytorch RNN:') print(output) # 显示Pytorch RNN 权重weights和偏置bias # for name, value in rnn.named_parameters(): #   print(name) #   print(value)   # rnn_forward计算 def rnn_forward(input,weight_ih,weight_hh,bias_ih,bias_hh,h_0):     batch_size, seq, input_size = input.shape     hidden_size = weight_ih.shape[0]     hout_layer = torch.zeros(batch_size, seq, hidden_size)     '''     计算矩阵的行列数     weight_ih: input_size*hidden_size     weight_hh: hidden_size*hidden_size     bias_ih,bias_hh:hidden_size     '''     for t in range(seq):         x= input[:,t,:]         times_ih = torch.mm(weight_ih,torch.t(x))         times_hh = torch.mm(weight_hh,torch.t(h_0.squeeze(0)))         h_0 = torch.tanh(torch.t(times_ih)+bias_ih+torch.t(times_hh)+bias_hh)         hout_layer[:,t,:] = h_0         #h_0 的行列数为batch_size*hidden_size       return hout_layer ,h_0     # 第一层输出 hout_layer0,h0_layer0 = rnn_forward(input,rnn.weight_ih_l0,rnn.weight_hh_l0,rnn.bias_ih_l0,rnn.bias_hh_l0,h_0[0,:,:]) # 第二层输出 hout_layer1,h0_layer1 = rnn_forward(hout_layer0,rnn.weight_ih_l1,rnn.weight_hh_l1,rnn.bias_ih_l1,rnn.bias_hh_l1,h_0[1,:,:])   h_0[0,:,:] = h0_layer0 h_0[1,:,:] = h0_layer1 print('RNN_forward:') print(hout_layer1)