what s the difference between self-attention mechanism and full-connection layer

Question

Can you explain with the help of code what's the difference between "self-attention mechanism" and "full-connection" layer?

Answer 1

A self-attention mechanism computes contextual relationships between input elements, while a fully connected (dense) layer applies learned weights independently to each input without considering relationships.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Fully Connected Layer: Applies a learned transformation to each input independently.
• Self-Attention Mechanism: Computes query-key-value relationships to capture dependencies.
• Matrix Multiplication: Used for attention score computation.
• Softmax Layer: Normalizes attention scores for importance weighting.

Hence, while a fully connected layer processes inputs independently, self-attention dynamically models relationships between inputs, making it more effective for capturing dependencies.