Dropout and Batch Normalization

Introduction

Before we dive into the main topic, let's understand the problem we are trying to solve. When training deep neural networks, there are two common issues we often face - overfitting and internal covariate shift.

Overfitting occurs when the model learns the training data too well, including its noise and outliers, which leads to poor performance on unseen data.

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

Two common techniques to combat these problems are Dropout and Batch Normalization. These are forms of regularization and optimization techniques in deep learning.

Dropout

Dropout is a regularization technique used to prevent overfitting. During training, some number of layer outputs are randomly ignored or 'dropped out'. This dropout rate is usually set between 0.2 and 0.5. At each training stage, individual nodes are either 'dropped out' of the network with probability 1 - dropout_rate or kept with probability dropout_rate.

Here's how you can implement dropout in PyTorch:

import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.dropout = nn.Dropout(p=0.2)
        self.fc1 = nn.Linear(10, 5)
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.dropout(x)
        return x

In this example, nn.Dropout(p=0.2) is used to create a dropout layer that will drop out 20% of the neurons. Neurons to drop are selected randomly in every forward call.

Batch Normalization

Batch Normalization is an optimization technique that addresses internal covariate shift. It normalizes the output from the activation function in each batch, hence the name 'batch normalization'. This helps in speeding up the learning process and provides regularization, reducing the need for dropout.

Here's how you can implement batch normalization in PyTorch:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.batch_norm = nn.BatchNorm1d(10)
        self.fc1 = nn.Linear(10, 5)
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.batch_norm(x)
        return x

In this example, nn.BatchNorm1d(10) is used to create a batch normalization layer that normalizes the input from the previous layer.

Conclusion

Both Dropout and Batch Normalization are powerful techniques to improve the performance and stability of your neural network. Dropout helps to avoid overfitting, while Batch Normalization helps to make the training faster and more stable. By using these techniques, you can ensure that your network learns the data in an effective way.

In the next sections, we will dive deeper into other regularization and optimization techniques that can help you to further improve your model.