What methods are used to implement layer normalization in transformer architectures for stability

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Can you name the methods which are used to implement layer normalization in transformer architecture for stability?
Nov 21 in Generative AI by Ashutosh
• 8,790 points
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1 answer to this question.

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The methods that are used to implement layer normalization in transformer architectures for stability are as follows: 

  • Normalize Activations: Compute mean and variance across features, then scale and shift.
  • Apply Learnable Parameters: Use learnable scale (γ\gamma) and shift (β\beta).

Here is the code snippet you can refer to:

The above is used to stabilize training dynamics, speed up convergence, and is applied after self-attention and feedforward sub-layers in Transformers.

Hence, by referring to the code above, you can implement layer normalization in transformer architectures for stability. 

answered Nov 21 by Ashutosh
• 8,790 points

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