我们如何解释下面的验证损失？

如何解决我们如何解释下面的验证损失？

我在 CNN+LSTM 编码器和解码器模型中训练了不同数量的层。 我面临的问题对我来说很奇怪。验证损失在 3.*** 左右波动。从下面的损失图中我们可以看出。我有3 CNN layer+1 layer BLSTM at encoder and 1 LSTM at decoder

3 layer CNN+2 layers of BLSTM at encoder and 1 layer LSTM at encoder

我也尝试过从 0.1 到 0.000001 的重量衰减。但我仍然得到这种类型的损失图。请注意，模型的准确性在验证和训练集上都在增加。验证损失怎么可能仍然在 3 左右，但准确度却在增加？有人能解释一下吗？

谢谢 ` 类编码器（nn.Module）： def init(self,height,width,enc_hid_dim,dec_hid_dim,dropout): super().init() self.height=高度 self.enc_hid_dim=enc_hid_dim self.width=width

    self.layer0 = nn.Sequential(
            nn.Conv2d(1,8,kernel_size=(3,3),stride =(1,1),padding=1),nn.ReLU(),nn.Batchnorm2d(8),nn.MaxPool2d(2,2),)
    self.layer1 = nn.Sequential(
            nn.Conv2d(8,32,nn.Batchnorm2d(32),)
    self.layer2 = nn.Sequential(
            nn.Conv2d(32,64,nn.Batchnorm2d(64),2)
            )

    self.rnn = nn.LSTM(self.height//8*64,self.enc_hid_dim,bidirectional=True)
    
    self.fc = nn.Linear(enc_hid_dim * 2,dec_hid_dim)
    
    self.dropout = nn.Dropout(dropout)
    self.cnn_dropout = nn.Dropout(p=0.2)
    
def forward(self,src,in_data_len,train):
    batch_size = src.shape[0]
    out = self.layer0(src)
    out = self.layer1(out)
    out = self.layer2(out)
    out = self.dropout(out) # torch.Size([batch,channel,h,w])
    out = out.permute(3,2,1) # (width,batch,channels)
    out.contiguous()
    out = out.reshape(-1,batch_size,self.height//8*64) #(w,(height,channels)) 
   
    width = out.shape[0] 
    src_len = in_data_len.numpy()*(width/self.width) 
    src_len = src_len + 0.999 # in case of 0 length value from float to int
    src_len = src_len.astype('int')
    out = pack_padded_sequence(out,src_len.tolist(),batch_first=False)
    outputs,hidden_out = self.rnn(out)
    hidden=hidden_out[0]
    cell=hidden_out[1]
    # output: t,b,f*2  hidden: 2,f
    outputs,output_len = pad_packed_sequence(outputs,batch_first=False)

    hidden = torch.tanh(self.fc(torch.cat((hidden[-2,:,:],hidden[-1,:]),dim = 1)))
    cell = torch.tanh(self.fc(torch.cat((cell[-2,cell[-1,dim = 1)))
    
    return outputs,hidden,cell,output_len

类解码器（nn.Module）： def init(self,output_dim,emb_dim,dropout,attention): super().init()

    self.output_dim = output_dim
    self.attention = attention
    
    self.embedding = nn.Embedding(output_dim,emb_dim)
    
    self.rnn = nn.LSTM((enc_hid_dim * 2) + emb_dim,dec_hid_dim)
    
    self.fc_out = nn.Linear((enc_hid_dim * 2) + dec_hid_dim + emb_dim,output_dim)
    
    self.dropout_layer = nn.Dropout(dropout)
    
    
def forward(self,input,encoder_outputs,train):
     
    input=torch.topk(input,1)[1] 
    
    embedded = self.embedding(input)
    if train:
        embedded=self.dropout_layer(embedded)
    embedded = embedded.permute(1,2)
    
    #embedded = [1,batch size,emb dim]
    
    a = self.attention(hidden,encoder_outputs)
            
    #a = [batch size,src len]
    
    a = a.unsqueeze(1)
    
    #a = [batch size,1,src len]
    
    encoder_outputs = encoder_outputs.permute(1,2)
    
    #encoder_outputs = [batch size,src len,enc hid dim * 2]
    
    weighted = torch.bmm(a,encoder_outputs)
    
    weighted = weighted.permute(1,2)
    
    #weighted = [1,enc hid dim * 2]
    
    rnn_input = torch.cat((embedded,weighted),dim = 2)
    
    output,hidden_out = self.rnn(rnn_input (hidden.unsqueeze(0),cell.unsqueeze(0)))
    hidden=hidden_out[0]
    cell=hidden_out[1]
    
    assert (output == hidden).all()
    
    embedded = embedded.squeeze(0)
    output = output.squeeze(0)
    weighted = weighted.squeeze(0)
    
    prediction = self.fc_out(torch.cat((output,weighted,embedded),dim = 1))
    
    return prediction,hidden.squeeze(0),cell.squeeze(0)

`