attention_decoder代码解析:
class AttnDecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
super(AttnDecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size # 另一种语言的词汇量
self.dropout_p = dropout_p
self.max_length = max_length
self.embedding = nn.Embedding(self.output_size, self.hidden_size)
self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_p)
self.gru = nn.GRU(self.hidden_size, self.hidden_size)
self.out = nn.Linear(self.hidden_size, self.output_size)
def forward(self, input, hidden, encoder_outputs): # forward的参数是decoder的输入
# decoder的input是另一种语言的词汇,要么是target,要么是上一个单元返回的output中概率最大的一个
# 初始的hidden用的是encoder的最后一个hidden输出
embedded = self.embedding(input).view(1, 1, -1)
embedded = self.dropout(embedded)
# 将embedded的256词向量和hidden的256词向量合在一起,变成512维向量
# 再用线性全连接变成10维(最长句子词汇数),在算softmax,看
attn_weight = F.softmax(
self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1
)
# torch.cat用于粘贴,dim=1指dim1方向粘贴
# torch.bmm是批矩阵乘操作,attention里将encoder的输出和attention权值相乘
# bmm: (1,1,10)*(1,10,256),权重*向量,得到attention向量
# unsqueeze用于插入一个维度(修改维度)
attn_applied = torch.bmm(attn_weight.unsqueeze(0),
encoder_outputs.unsqueeze(0))
output = torch.cat((embedded[0], attn_applied[0]), 1)
output = self.attn_combine(output).unsqueeze(0)
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = F.log_softmax(self.out(output[0]), dim=1)
return output, hidden, attn_weight
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=device)
