Different Types of Normalization in Deep Neural Networks

In this article, we will understand normalization and forms of Different Types of Normalization in Deep Neural Networks

What is normalization?

We can define normalization as a procedure that is used to change the value of the numeric variable to a typical form of scale without making contrasts between values. Normalization has always been the major component of deep learning. Now let us learn about the different types of normalization in deep neural networks.

Benefits of Normalization:

1. It helps in normalizing each feature as some have higher numerical values than others.

2. It helps in reducing internal Covariate shifts to improve the training.

3. It helps in faster optimization and restricts the weight to a certain range.

4. It helps the network in regularization.

Different types of normalization in deep neural networks

Deep learning involves training carefully deep neural networks and the various design decisions which affect the training regime of these deep networks.  Different types of normalization in deep neural networks.

  • Batch Normalization
  • Weight Normalization
  • Layer Normalization
  • Group Normalization
  • Weight standardization

Now let us know in detail about the different types of normalization in deep neural networks.

Batch Normalization

Batch normalization helps in standardizing the inputs to a particular layer in a network which should have approximately zero means and unit variance. Mathematically speaking, the Batch normalization layer helps in transforming each input in the mini-batch by subtracting the input mean in the current mini-batch and dividing it by the standard deviation.

Different Types of Normalization in Deep Neural Networks

Weight normalization

In order to cover the disadvantages of batch normalization, weight normalization was introduced. It helps in decoupling the length from the direction of the weight vector and hence reparametrize the network to speed up the training.

Layer Normalization

Layer Normalization helps in normalizing the activations along the feature direction instead of mini-batch direction, which overcomes the disadvantages of batch normalization. It also normalizes each feature of the activations to zero mean and unit variance.

Group Normalization

It is like layer normalization, but it divides the features into a certain group and normalizes each group separately. It also performs better than layer normalization.

Different Types of Normalization in Deep Neural Networks

Weight Standardization

This method is used in transforming the weights of any layer, which have zero mean and unit variance. This could be a convolution layer, RNN layer, or linear layer. Any layer with shape(N, *) where * represents 1 or more dimensions, weight standardization, transforms the weights along the * dimension(s).

Concluding thoughts

Normalization helps in speeding and stabilizing the learning process. If in case the training in large batches is not an issue and the network doesn’t have recurrent connections, batch normalizations can be used and in case the training is with smaller batches or complex layers then group normalization or weight standardization can be used.

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