author: | ishanashah |

score: | 7 / 10 |

Problem Statement:

The core idea is to reduce internal covariate shift.

Internal covariate shift is the change in the distributions of network activations due to a change in netwrok parameters during training.

Internal covariate shift causes layers to continuously adapt to a new distribution (the layers are chasing a moving target).

Batch normalization reduces interval covariate shift via a normalization step that fixes the means and variances of layer inputs.

Normalization step goes between linear and non-linear layers.

Normalize the activations of a layer so they have 0 mean and unit variance.

Normalization allows the use of much higher learning rates because gradients no longer depend on the scale or the initial values of the parameters.

Removing dropout from a batch-normalized network improves accuracy, possibly because the activations observed for a training example are affected by the random selection of examples in the same mini-batch.

Implementation:

Compute mean and standard deviation over all batches.

In the case of convolutional layers, compute mean and standard deviation over all batches and all spatial locations of a single channel.

Subtract mean and divide by standard deviation for all activations.

During test time there are no batches, so use a running average mean and standard deviation from training time.

Experimental Results:

Batch normalization makes the network train much faster and makes the input distribution more stable.

Batch normalization achieves the same accuracy with 14 times fewer training steps.

## TL;DR

- Batch normalization reduces the change in distributions of network activations during training.
- Batch normalization makes activations have 0 mean and unit variance.
- Batch normalization makes network training much faster.