Transformer Neural Network: Visualized and Implemented from Scratch

Transformer Neural Network has become a popular choice for various natural language processing tasks due to its effectiveness in capturing long-range dependencies and its parallelism. In this article, we will explore the workings of a Transformer Neural Network and show how it can be implemented from scratch.

What is a Transformer Neural Network?

A Transformer Neural Network is a type of neural network architecture that is based on self-attention mechanisms. It was introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. The key innovation of the Transformer architecture is the use of self-attention mechanisms that allow the model to focus on different parts of the input sequence independently, without the need for recurrent connections or convolutions.

How does a Transformer Neural Network work?

A Transformer Neural Network consists of an encoder and a decoder. The encoder takes an input sequence and processes it using self-attention mechanisms to capture the relationships between different parts of the sequence. The decoder then generates an output sequence based on the information encoded in the input sequence.

Implementing a Transformer Neural Network from Scratch

Implementing a Transformer Neural Network from scratch can be a challenging task, but it can also be a rewarding learning experience. Below is a simplified version of a Transformer Neural Network implemented in Python:

# Import necessary libraries
import torch
import torch.nn as nn

# Define the Transformer model
class Transformer(nn.Module):
    def __init__(self, input_size, hidden_size, num_heads, num_layers):
        super(Transformer, self).__init__()
        
        self.encoder_layers = nn.TransformerEncoderLayer(input_size, num_heads, hidden_size)
        self.encoder = nn.TransformerEncoder(self.encoder_layers, num_layers)
        
        self.decoder_layers = nn.TransformerDecoderLayer(input_size, num_heads, hidden_size)
        self.decoder = nn.TransformerDecoder(self.decoder_layers, num_layers)
        
    def forward(self, src, tgt):
        memory = self.encoder(src)
        output = self.decoder(tgt, memory)
        
        return output

Conclusion

In this article, we have explored the Transformer Neural Network architecture and shown how it can be implemented from scratch using PyTorch. The Transformer architecture has proven to be a powerful tool for natural language processing tasks, and understanding how it works can help you develop more effective models for your own projects.