Analyze the model below and suggest possible improvements :
Does the TCN effectively capture both local and long-term dependencies in the input sequence, given the model’s kernel size and dilation rates?
Is weight normalization on each Conv1D layer necessary, or could another normalization technique perform better?
How does the model handle boundary effects due to padding?
Is the decoder architecture optimally tuned in terms of layers and heads?
Is masking implemented correctly to prevent future information leakage in the decoder?
How well does the embedding layer handle the input dimensions of the target sequence?
Is the positional encoding effective for the sequence length and temporal nature of the data?
Could this model benefit from an additional layer that combines the encoder and decoder outputs, such as a final attention or dense layer?
result sMAPE: 23~25%
in_seq_len = 5
in_seq_len = 2
input_size_ = 414
hidden_size_ = 256
num_layers_ = 4
kernel_size_ = 5
num_heads_ = 4
dropout_ = 0.05
learning_rate_ = 0.0001
num_epochs_ = 50
gradient_clip_val_ = 0.5
# 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)