Normalization is a technique used to transform the values of a dataset so that they have a mean of 0 and a standard deviation of 1. This can be helpful for machine learning algorithms, as it can help to improve the convergence of the model and make it more robust to noise.
There are two main types of normalization: batch normalization and layer normalization.
- Batch normalization normalizes the activations of a layer for each batch of data. This means that the mean and standard deviation are calculated over the current batch of data, and then used to normalize the activations of that batch.
- Layer normalization 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.
Batch normalization is typically used in deep neural networks, as it can help to improve the convergence of the model and make it more robust to noise. Layer normalization is typically used in recurrent neural networks, as it can help to improve the stability of the model.
In Keras, normalization can be performed using the `BatchNormalization` or `LayerNormalization` layers. The following code shows how to use the `BatchNormalization` layer:
from keras.layers import BatchNormalization
model.add(BatchNormalization())
The following code shows how to use the `LayerNormalization` layer:
from keras.layers import LayerNormalization
model.add(LayerNormalization(axis=-1))
The `axis` argument to the `LayerNormalization` layer specifies the axis along which the normalization should be performed. In the above code, the normalization is performed along the last axis, which is the axis of features.
Normalization is a powerful technique that can be used to improve the performance of machine learning algorithms. It is a good practice to use normalization whenever possible, especially when working with deep neural networks or recurrent neural networks.
