Training Keras Model with BatchNorm Layer using Tensorflow

Batch normalization is a technique used to improve the performance and stability of deep learning models. In Keras, we can easily implement batch normalization using the BatchNormalization layer provided by the Tensorflow library.

Importing Libraries

Before we start training our model with batch normalization, we need to import the necessary libraries in our Python code:


import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import BatchNormalization


Creating the Model

Next, we need to create our neural network model in Keras. We can add the BatchNormalization layer to our model by simply including it as a part of our model architecture:


model = keras.Sequential([
keras.layers.Dense(64, input_shape=(784,)),
keras.layers.BatchNormalization(),
keras.layers.Activation('relu'),
keras.layers.Dense(10, activation='softmax')
])


Compiling the Model

After creating our model, we need to compile it with the appropriate loss function, optimizer, and metrics:


model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])


Training the Model

Finally, we can train our model on a dataset using the fit() method. The batch normalization layer will help stabilize and improve the training process:


model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))


Conclusion

Using the BatchNormalization layer in Keras with Tensorflow can greatly improve the training process of our deep learning models. By incorporating batch normalization, we can achieve better performance and stability in our neural network models.