Dropout

Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped out” randomly. This means that their contribution to the activation of downstream neurons is temporally removed on the forward pass, and any weight updates are not applied to the neuron on the backward pass.

How Dropout Works

• Random Deactivation: During each training iteration (or epoch), individual neurons are randomly selected and temporarily removed from the network, along with all their incoming and outgoing connections. The probability of a neuron being dropped is a hyperparameter and is typically set between 0.2 and 0.5.

• Impact on Learning: When neurons are dropped, the network structure changes, meaning the model has to learn to adapt to a different architecture each time. This randomness helps the model to avoid overfitting to specific patterns in the training data.

• At Test Time: During inference or evaluation, dropout is not applied. Instead, all neurons are used, but their outputs are scaled down by the dropout rate to balance the fact that more neurons are active compared to the training phase.

Real Life Example

Imagine a company where projects are handled by teams of employees. To ensure that no single employee becomes too crucial to the completion of a project (akin to overfitting), the company adopts a policy where each day, a random subset of employees doesn't come to work. As a result:

• Team Adaptation: The remaining team members must adapt and learn to handle tasks they might not typically do. This situation is similar to how dropout forces the remaining neurons in a neural network to adapt and learn from different subsets of features.

• Project Robustness: Over time, this policy ensures that the project's success doesn't hinge on any single employee. Each employee develops a more versatile skill set, similar to how a neural network learns more generalized features.

• Full Team Utilization: When it's time to present the project to a client (analogous to the model's evaluation phase), all employees participate, bringing together their diverse skills honed through this process.

Impact on the Model

As a result, the network becomes less sensitive to the specific weights of neurons. This leads to two main benefits:

• Reduction of Overfitting: By dropping different sets of neurons, it ensures that the network does not become overly dependent on any one neuron and thus can generalize better.

• Network Robustness: Dropout forces the network to learn more robust features that are useful in conjunction with many different random subsets of the other neurons.

Keras Example

Dropout can be easily implemented in TensorFlow using the Keras API. Here is an example of how to add Dropout layer to a neural network model:

In this example, Dropout(0.5) means that each neuron in the previous layer has a 50% chance of being excluded from the next training pass. Common dropout rates are between 20% and 50%. Finding the right rate is often a matter of trial and error and can depend heavily on the specific dataset and model architecture.