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)
