Chapter 6.2 - Output Projection

Overview

We just gave our AI multiple "heads" to think about a sentence. But right now, they all have separate answers. It's time for a team meeting! Output Projection is how we mix all their ideas together into one amazing conclusion.

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🎯 Why we do it

Rationale

If you ask 4 friends for advice, you get 4 different answers. You need to combine their advice to make your final decision. The Output Projection layer takes all the glued-together answers from the different heads and scrambles them up nicely so the AI can use the best parts of each!

🛠️ How we do it

Methodology

We use a simple Linear Layer (just a matrix multiplication) to mix everything up. We multiply our glued-together vector by a special matrix called W_O (Weight Output).

import torch
import torch.nn as nn

# All the heads glued their answers together
glued_answers = torch.tensor([0.5, 0.2, 0.9, 0.1])

# The mixer (Output Projection)
mixer = nn.Linear(4, 4)

# Mix it up!
final_answer = mixer(glued_answers)
print("The team has decided:", final_answer)