Code Snippet:

Code Snippet:

import torch
import torch.nn as nn

class TemporalTransformer(nn.Module):
    def __init__(self, input_dim, embed_dim, num_heads):
        super(TemporalTransformer, self).__init__()
        self.encoder = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads)
        self.decoder = nn.Linear(embed_dim, input_dim)

    def forward(self, x):
        x = self.encoder(x) + x
        return self.decoder(x)

model = TemporalTransformer(10, 128, 8)

This code snippet is a compact representation of a Temporal Transformer Network. This type of model is specifically designed to handle sequential data such as time series data, user behavior, and sequential text. It combines the power of both transformer-based models and traditional RNN-based models by adding residual connections.

In this snippet, the TemporalTransformer class initializes a PyTorch model consisting of two main components: the encoder and the decoder. The encoder is a transformer encoder layer, whereas the decoder is a linear layer that transforms the output into the desired embedding dimension. The forward method takes an input tensor x, applies the residual connection, and passes it through the decoder.

This model architecture is particularly useful for forecasting stock prices, traffic analysis, or any other applications requiring sequential data processing.

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