Where do I call the BatchNormalization function in Keras?

If you are using Keras for deep learning, you may have come across the BatchNormalization function. BatchNormalization is a technique used in neural networks to improve the training process and speed up convergence.

So, where do you call the BatchNormalization function in Keras? The answer is that you typically call it within a Keras model, specifically after a layer that generates activations. In other words, you would add BatchNormalization after a Dense or Conv2D layer, for example.

Here is an example of how you can add BatchNormalization to a Keras model:

“`python
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization

model = Sequential()
model.add(Dense(64, activation=’relu’, input_shape=(100,)))
model.add(BatchNormalization())
model.add(Dense(10, activation=’softmax’))
“`

In this example, we first add a Dense layer with 64 units and ReLU activation function. Then, we add BatchNormalization to the model. After that, we add another Dense layer with 10 units and softmax activation function. This is a simple example, but it shows how you can integrate BatchNormalization into your Keras model.

Overall, calling the BatchNormalization function in Keras is a simple process that can help improve the training and convergence of your neural network models.