Benefits of Batch Normalization
- Faster Convergence: By stabilizing the gradients, BN allows you to use higher learning rates, which can significantly speed up training.
- Reduced Internal Covariate Shift: As the network trains, the distribution of activations within a layer can change (internal covariate shift). BN helps mitigate this by normalizing activations before subsequent layers, making the training process less sensitive to these shifts.
- Initialization Insensitivity: BN makes the network less reliant on the initial weight values, allowing for more robust training and potentially better performance.
Batch Normalization Implementation in PyTorch
Batch Normalization (BN) is a critical technique in the training of neural networks, designed to address issues like vanishing or exploding gradients during training. In this tutorial, we will implement batch normalization using PyTorch framework.
Table of Content
- What is Batch Normalization?
- How Batch Normalization works?
- Implementing Batch Normalization in PyTorch
- Benefits of Batch Normalization