Overview of Batch Normalization

Overview of Batch Normalization

Batch normalization is a technique to improve the training of deep neural networks by stabilizing and accelerating the learning process. Introduced by Sergey Ioffe and Christian Szegedy in 2015, it addresses the issue known as “internal covariate shift” where the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change....

Need for Batch Normalization in CNN model

Batch Normalization in CNN addresses several challenges encountered during training. There are following reasons highlight the need for batch normalization in CNN:...

How Does Batch Normalization Work in CNN?

Batch normalization works in convolutional neural networks (CNNs) by normalizing the activations of each layer across mini-batch during training. The working is discussed below:...

Applying Batch Normalization in CNN model using TensorFlow

In this section, we have provided a pseudo code, to illustrate how can we apply batch normalization in CNN model using TensorFlow. For applying batch normalization layers after the convolutional layers and before the activation functions, we use ‘tf.keras.layers.BatchNormalization()’....

Applying Batch Normalization in CNN model using PyTorch

In PyTorch, we can easily apply batch normalization in a CNN model....

Advantages of Batch Normalization in CNN

Fast ConvergenceImproved generalizationreduced sensitivityHigher learning ratesImprovement in model accuracy...

Conclusion

In conclusion, batch normalization stands as a pivotal technique in enhancing the training and performance of convolutional neural networks (CNNs). Its implementation addresses critical challenges such as internal covariate shift, thereby stabilizing training, accelerating convergence, and facilitating deeper network architectures....