Layer normalization

1. What is layer normalization?
2. Why is layer normalization important?
3. What is the use of normalization in CNN?
4. Why layer normalization in RNN?

What is layer normalization?

Layer normalization normalizes each of the inputs in the batch independently across all features. As batch normalization is dependent on batch size, it's not effective for small batch sizes. Layer normalization is independent of the batch size, so it can be applied to batches with smaller sizes as well.

Why is layer normalization important?

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accuracy.

What is the use of normalization in CNN?

Normalization is a pre-processing technique used to standardize data. In other words, having different sources of data inside the same range. Not normalizing the data before training can cause problems in our network, making it drastically harder to train and decrease its learning speed.

Why layer normalization in RNN?

In a layer normalized RNN, the normalization terms make it invariant to re-scaling all of the summed inputs to a layer, which results in much more stable hidden-to-hidden dynamics.