Transformers and Attention Mechanisms

1. Introduction

Transformers are a type of neural network architecture that has revolutionized the field of natural language processing (NLP) and beyond. They rely heavily on the attention mechanism to draw global dependencies between input and output.

2. Key Concepts

3. Attention Mechanism

The attention mechanism allows the model to weigh the importance of different words in a sentence when making predictions. The idea is to compute a context vector that is a weighted sum of the input embeddings.

3.1 Scaled Dot-Product Attention

Given an input set of queries \(Q\), keys \(K\), and values \(V\), attention is calculated as follows:

    Attention(Q, K, V) = softmax(QK^T / sqrt(d_k))V
    

4. Transformers

Transformers consist of an encoder and a decoder. The encoder processes the input sequence and passes its representation to the decoder, which generates the output sequence.

4.1 Architecture Overview

    Encoder: 
    - Input Embedding
    - Positional Encoding
    - Multi-Head Attention
    - Feed Forward Neural Network
    - Layer Normalization

    Decoder:
    - Input Embedding
    - Positional Encoding
    - Masked Multi-Head Attention
    - Multi-Head Attention (with encoder output)
    - Feed Forward Neural Network
    - Layer Normalization
    

5. Code Example

Here’s a simple implementation of the scaled dot-product attention:

import numpy as np

def scaled_dot_product_attention(Q, K, V):
    d_k = K.shape[-1]
    scores = np.dot(Q, K.T) / np.sqrt(d_k)
    weights = softmax(scores)
    output = np.dot(weights, V)
    return output

def softmax(x):
    exp_x = np.exp(x - np.max(x))
    return exp_x / exp_x.sum(axis=-1, keepdims=True)
    

6. Best Practices

• Use pre-trained models when fine-tuning for specific tasks.
• Experiment with different attention heads to capture various aspects of the input.
• Regularly validate your model to avoid overfitting.
• Utilize learning rate schedulers for better convergence.

7. FAQ