How Batch Normalization works?
- During each training iteration (epoch), BN takes a mini batch of data and normalizes the activations (outputs) of a hidden layer. This normalization transforms the activations to have a mean of 0 and a standard deviation of 1.
- While normalization helps with stability, it can also disrupt the network’s learned features. To compensate, BN introduces two learnable parameters: gamma and beta. Gamma rescales the normalized activations, and beta shifts them, allowing the network to recover the information present in the original activations.
It ensures that each element or component is in the right proportion before distributing the inputs into the layers and each layer is normalized before being passed to the next layer.
Correct Batch Size:
- Resonable sized mini-batches must be taken into consideration during training. It performs better with large batch sizes as it computes more accurate batch statistics.
- Leading it to be more stable gradients and faster convergence.
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
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