The Normalization layer in Keras is a preprocessing layer that normalizes the activations of a layer for each individual sample. This means that the mean and standard deviation are calculated over the activations of each sample, and then used to normalize those activations. This can help to improve the stability of the model and make it more robust to noise.
The Normalization layer has the following properties:
- It is a preprocessing layer, which means that it is applied to the input data before it is passed to the next layer.
- It normalizes the activations of a layer for each individual sample.
- It can help to improve the stability of the model and make it more robust to noise.
The Normalization layer can be used in any type of model, but it is most commonly used in recurrent neural networks. This is because recurrent neural networks are prone to exploding or vanishing gradients, and the Normalization layer can help to mitigate this problem.
To use the Normalization layer in Keras, you can import it from the `keras.layers` module:
from keras.layers import Normalization
Then, you can add the layer to your model like this:
model.add(Normalization())
The Normalization layer has a number of optional arguments that you can use to configure it. These arguments include:
- `axis`: The axis along which to normalize the activations. The default value is `-1`, which means to normalize along the last axis.
- `center`: Whether to center the activations before normalizing them. The default value is `True`.
- `scale`: Whether to scale the activations after normalizing them. The default value is `True`.
- `epsilon`: A small value to add to the denominator of the normalization equation to avoid numerical errors. The default value is `1e-6`.
The Normalization layer is a powerful tool that can be used to improve the performance of machine learning models. It is a good practice to use the Normalization layer whenever possible, especially when working with recurrent neural networks.
