python-pytorch 实现seq2seq+luong general concat attention笔记1.0.10

上篇文章是使用luong的dot计算分数方法实现seq2seq attention简单对话，这篇文件使用general记分方法

只需要替换Attention类

1. general注意力
   就是在dot方法基础上，对hidden做一个线性变换

class Attention(nn.Module):
    def __init__(self):
        super(Attention, self).__init__()
        self.va=nn.Linear(hidden_size,hidden_size,bias=False)
        
    def forward(self, hidden, encoder_outputs):
        """
        hidden:[layer_num,batch_size,hidden_size]
        encoder_outputs:[seq_len,batch_size,hidden_size]
        """
        score=encoder_outputs.permute(1,0,2).bmm(self.va(hidden).permute(1,2,0))# [batch_size,seq_len,layer_num]
        
        attr=nn.functional.softmax(score,dim=1)# [batch_size,seq_len,layer_num]
        
        context=attr.permute(0,2,1).bmm(encoder_outputs.permute(1,0,2))

        
        return context,attr

2. concat注意力
   这里需要注意的是，在计算出tanh后，需要自定义个va的矩阵相乘，大小是当前的[batch_size,hidden_size*2]

class Attention(nn.Module):
    def __init__(self):
        super(Attention, self).__init__()
        self.wa=nn.Linear(hidden_size*2,hidden_size*2,bias=False)
        self.wa1=nn.Linear(hidden_size*2,hidden_size,bias=False)
        
    def forward(self, hidden, encoder_outputs):
        """
        hidden:[layer_num,batch_size,hidden_size]
        encoder_outputs:[seq_len,batch_size,hidden_size]
        """
        
        hiddenchange=hidden.repeat(encoder_outputs.size(0),1,1)#[seq_len,batch_size,hidden_size]
        
        concated=torch.cat([hiddenchange.permute(1,0,2),encoder_outputs.permute(1,0,2)],dim=-1)# [batch_size,seq_len,hidden_size*2]
        waed=self.wa(concated)# [batch_size,seq_len,hidden_size*2]
        tanhed=torch.tanh(waed)# [batch_size,seq_len,hidden_size*2]
        
        self.va=nn.Parameter(torch.FloatTensor(encoder_outputs.size(1),hidden_size*2))#[batch_size,hidden_size*2]
#         print("tanhed size",tanhed.size(),self.va.unsqueeze(2).size())
        
        attr=tanhed.bmm(self.va.unsqueeze(2))# [batch_size,seq_len,1]
        
        context=attr.permute(0,2,1).bmm(encoder_outputs.permute(1,0,2))# [batch_size,1,seq_len]

        return context,attr
        

三者训练结果对比

文字太多存不下，放到另外一篇文章了，链接是：https://mp.csdn.net/mp_blog/creation/success/139053790

总结

• 将线性变换设置bias为True的时候，收敛会变快
• 之前学习率设置是0.001，然后改为0.05后，起始loss变为1.xxxx，4000次迭代loss就达到了0.00x

完整代码

参见链接https://blog.csdn.net/m0_60688978/article/details/139053661

参考

https://blog.csdn.net/m0_60688978/article/details/139044526