Does batch normalization allow higher learning rate?

Using batch normalization allows us to use much higher learning rates, which further increases the speed at which networks train. Makes weights easier to initialize — Weight initialization can be difficult, and it’s even more difficult when creating deeper networks.

Why does batch normalization speed up training?

Using batch normalization makes the network more stable during training. This may require the use of much larger than normal learning rates, that in turn may further speed up the learning process.

How does batch normalization reduce overfitting?

It reduces overfitting because it has a slight regularization effects. Similar to dropout, it adds some noise to each hidden layer’s activations. Therefore, if we use batch normalization, we will use less dropout, which is a good thing because we are not going to lose a lot of information.

Why is scaling and shifting often applied after batch normalization?

This imposes that 2 extra parameters be learnt for each layer to increase training speed. This final transformation thus completes definition of Batch Normalization algorithm. Use of scaling and shifting is particularly much useful because, it provides more flexibility.

Why Batch Normalization is not good for RNN?

No, you cannot use Batch Normalization on a recurrent neural network, as the statistics are computed per batch, this does not consider the recurrent part of the network. Weights are shared in an RNN, and the activation response for each “recurrent loop” might have completely different statistical properties.

Why Batch Normalization is bad?

Not good for Recurrent Neural Networks Batch normalization can be applied in between stacks of RNN, where normalization is applied “vertically” i.e. the output of each RNN. But it cannot be applied “horizontally” i.e. between timesteps, as it hurts training because of exploding gradients due to repeated rescaling.