RNN 模型出现 Val Loss 不断增加的现象，如何优化？

The increasing validation loss indicates that the model is overfitting to the training data and is not generalizing well to the validation data. To optimize the model and reduce overfitting, you can try the following approaches:

1. Regularization techniques: Add regularization techniques like dropout or weight decay to prevent overfitting. For example, you can add dropout layers after the embedding layer and LSTM layer to randomly drop out some units during training.

2. Adjust learning rate: Try lowering the learning rate to allow the model to converge more slowly and potentially find a better solution.

3. Increase model capacity: If the model is underfitting and not capturing enough complexity, you can increase the hidden_dim or add more layers to the LSTM to increase the model's capacity.

4. Data augmentation: Augment the training data by applying random transformations to create additional training examples. This can help the model generalize better and reduce overfitting.

Here is an updated version of the code with added dropout regularization:

class RNNClassifier(nn.Module):  
    def __init__(self, vocab_size, embedding_dim, hidden_dim, label_size, padding_idx):  
        super(RNNClassifier, self).__init__()  
        self.vocab_size = vocab_size  
        self.embedding_dim = embedding_dim  
        self.hidden_dim = hidden_dim  
        self.label_size = label_size  
        self.num_layers = 2
  
        # Embedding Layer  
        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=padding_idx)  
          
        # RNN Layer  
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=self.num_layers, batch_first=True)  
        
        # Dropout Layer
        self.dropout = nn.Dropout(0.5)
          
        # Output Layer  
        self.fc = nn.Linear(hidden_dim, label_size)  
  
    def zero_state(self, batch_size):  
        hidden = torch.zeros(self.num_layers, batch_size, self.hidden_dim)  
        cell = torch.zeros(self.num_layers, batch_size, self.hidden_dim) 
        return hidden, cell  
  
    def forward(self, text):  
        # text shape = [batch_size, seq_len]  
        # Embedding  
        emb = self.embedding(text)  # shape = [batch_size, seq_len, embedding_dim]  
          
        # LSTM Layer  
        h0, c0 = self.zero_state(text.size(0))  # shape = [batch_size, hidden_dim]  
        output, (hn, cn) = self.lstm(emb, (h0, c0))  # output shape = [batch_size, seq_len, hidden_dim], hn shape = [batch_size, num_layers, hidden_dim], cn shape = [batch_size, num_layers, hidden_dim]  
          
        # Dropout
        output = self.dropout(output)
        
        # Output Layer  
        output = self.fc(output[:, -1, :])  # use the last output at the last step for classification  
        return output

You can experiment with different hyperparameters and regularization techniques to further optimize the model.