Adding Self-Attention to a Convolutional Neural Network: PyTorch Deep Learning Tutorial Section 13

Welcome to the thirteenth section of our PyTorch Deep Learning Tutorial! In this section, we will explore how to add self-attention mechanism to a convolutional neural network (CNN) using PyTorch.

What is Self-Attention?

Self-attention is a mechanism that helps a model to focus on different parts of the input data with different weights. It allows the model to learn relationships between different input elements and to attend to the most important parts of the input.

How to Add Self-Attention to a CNN in PyTorch?

To add self-attention to a CNN in PyTorch, we can use the torch.nn.MultiheadAttention module. This module takes the input features and computes the self-attention scores and attention weights for each input element. We can then use these attention weights to weight the input features before passing them through the CNN layers.

import torch
import torch.nn as nn

class SelfAttentionCNN(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_heads):
        super(SelfAttentionCNN, self).__init__()
        
        self.attention = nn.MultiheadAttention(input_dim, num_heads)
        self.conv = nn.Conv2d(input_dim, hidden_dim, kernel_size=3)
        self.fc = nn.Linear(hidden_dim, 10)
    
    def forward(self, x):
        x, _ = self.attention(x, x, x)
        x = self.conv(x)
        x = self.fc(x)
        
        return x

Training the Model

After defining the model with self-attention mechanism, we can train it just like any other PyTorch model. We can use standard training loops with optimizer and loss functions to train the model on our dataset.

Conclusion

Adding self-attention to a CNN can help improve the model’s performance by allowing it to focus on important parts of the input data. In this tutorial, we explored how to add self-attention to a CNN in PyTorch using the torch.nn.MultiheadAttention module. We hope this tutorial was helpful in understanding how to incorporate self-attention into your deep learning models!