Batch Normalization in TensorFlow
In the provided pseudo code, we have used a simple neural network model with batch normalization using TensorFlow’s Keras API. We have added, the batch normalization layer using ‘tf.keras.layers.BatchNormalization()‘ to normalize the activations of the previous layer.
import tensorflow as tf
# Define a simple model
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, input_shape=(784,)),
tf.keras.layers.BatchNormalization(), # Add Batch Normalization layer
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=5, batch_size=32)
What is Batch Normalization In Deep Learning?
Internal covariate shift is a major challenge encountered while training deep learning models. Batch normalization was introduced to address this issue. In this article, we are going to learn the fundamentals and need of Batch normalization. We are also going to perform batch normalization.
Table of Content
- What is Batch Normalization?
- Need for Batch Normalization
- Fundamentals of Batch Normalization
- Batch Normalization in TensorFlow
- Batch Normalization in PyTorch
- Benefits of Batch Normalization
- Conclusion