Could you implement this change to the model below:

Considerations:

Fusion Layer: An additional layer could allow the model to better combine information from the encoder and decoder.
Suggestion:

Add Cross-Attention Mechanism: Introduce a layer that performs attention over the encoder outputs and the decoder outputs:

class TCNEncoderTransformerDecoder(nn.Module):
    # ...

    def forward(self, src, tgt):
        # Encoder
        memory = self.encoder(src)
        memory = self.positional_encoding(memory)
        
        # Decoder
        tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt.size(1)).to(tgt.device)
        output = self.decoder(tgt, memory, tgt_mask)
        
        # Additional Attention Layer
        output, _ = self.attention_layer(output, memory, memory)
        
        # Final Output Layer
        output = self.fc_out(output)
        return output
Add a Dense Fusion Layer: After the decoder, concatenate or add the encoder outputs and pass them through a fully connected layer.

combined = torch.cat((output, memory), dim=-1)
final_output = self.final_fc_layer(combined)
Experiment with Different Fusion Strategies: Try different ways of combining encoder and decoder outputs to see which yields the best performance.


Mode:
# TCN Encoder
class TCNEncoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, kernel_size, dropout):
        super(TCNEncoder, self).__init__()
        layers = []
        num_levels = num_layers

        in_channels = input_size
        out_channels = hidden_size

        for i in range(num_levels):
            dilation_size = 2 ** i
            padding = (kernel_size - 1) * dilation_size
            conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding, dilation=dilation_size)
            conv = nn.utils.parametrizations.weight_norm(conv)
            layers += [conv, nn.ReLU(), nn.Dropout(dropout)]
            in_channels = hidden_size  # For next layer

        self.network = nn.Sequential(*layers)
        self.kernel_size = kernel_size
        self.num_levels = num_levels

    def forward(self, x):
        # x shape: (batch_size, seq_length, input_size)
        x = x.permute(0, 2, 1)  # Reshape input for Conv1d: (batch_size, input_size, seq_length)
        x = self.network(x)
        total_padding = (self.kernel_size - 1) * (2 ** self.num_levels - 1)
        x = x[:, :, :-total_padding] if total_padding != 0 else x  # Remove extra padding
        x = x.permute(0, 2, 1)  # Back to (batch_size, seq_length, hidden_size)
        return x

    
# Transformer Decoder with masked multi-head attention
class TransformerDecoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
        super(TransformerDecoder, self).__init__()
        self.embedding = nn.Linear(input_size, hidden_size)
        self.positional_encoding = PositionalEncoding(hidden_size, dropout)
        decoder_layer = nn.TransformerDecoderLayer(d_model=hidden_size, nhead=num_heads, dropout=dropout)
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
        self.fc_out = nn.Linear(hidden_size, input_size)

    def forward(self, tgt, memory, tgt_mask):
        # tgt: (batch_size, tgt_seq_length, input_size)
        tgt_emb = self.embedding(tgt)
        tgt_emb = self.positional_encoding(tgt_emb)
        memory = memory.transpose(0, 1)  # (src_seq_length, batch_size, hidden_size)
        tgt_emb = tgt_emb.transpose(0, 1)  # (tgt_seq_length, batch_size, hidden_size)
        output = self.transformer_decoder(tgt_emb, memory, tgt_mask=tgt_mask)
        output = output.transpose(0, 1)  # Back to (batch_size, tgt_seq_length, hidden_size)
        output = self.fc_out(output)
        return output

    
# TCN-Encoder-Transformer-Decoder Model
class TCNEncoderTransformerDecoder(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, kernel_size, num_heads, dropout):
        super(TCNEncoderTransformerDecoder, self).__init__()
        self.encoder = TCNEncoder(input_size, hidden_size, num_layers, kernel_size, dropout)
        self.decoder = TransformerDecoder(input_size, hidden_size, num_layers, num_heads, dropout)
        self.positional_encoding = PositionalEncoding(hidden_size, dropout)  # For encoder outputs

    def forward(self, src, tgt):
        # src: (batch_size, src_seq_length, input_size)
        # tgt: (batch_size, tgt_seq_length, input_size)
        # Encoder
        memory = self.encoder(src)  # (batch_size, src_seq_length, hidden_size)
        memory = self.positional_encoding(memory)  # Add positional encoding to memory
        # Decoder
        tgt_mask = nn.Transformer.generate_square_subsequent_mask(tgt.size(1)).to(tgt.device)  # (tgt_seq_length, tgt_seq_length)
        output = self.decoder(tgt, memory, tgt_mask)
        return output


# Positional Encoding class (unchanged)
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        
        pe = torch.zeros(max_len, d_model)  # Create positional encoding matrix
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)  # Apply sine to even indices
        pe[:, 1::2] = torch.cos(position * div_term)  # Apply cosine to odd indices
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)  # Save positional encoding matrix

    def forward(self, x):
        x = x + self.pe[:, :x.size(1), :].to(x.device)  # Add positional encoding
        return self.dropout(x)