Preventing Overfitting with Dropout
Dropout is a regularization technique used to prevent deep learning models from becoming overfitted.
During neural network training, it randomly excludes certain neurons to discourage the model from relying too heavily on specific patterns.
Why is Dropout Needed?
When a neural network model is too complex, it can lead to the problem of overfitting to the training data.
This reduces the model's ability to generalize to new data.
Dropout helps by deactivating certain neurons during training, preventing the model from becoming overly dependent on specific neurons, and thus enabling it to generalize better.
Before: Certain neurons overly focus on specific patterns → Overfitting occurs
After: Randomly deactivate neurons → Learn a variety of features, improving generalization performance
How Does Dropout Work?
Dropout works by randomly deactivating neurons during the training process to prevent reliance on specific neurons in the network.
1. Removing Certain Neurons During Training
At each step of training, neurons are deactivated with a certain probability.
For example, if the dropout rate is 50%, half of the neurons are randomly removed at each step.
Training Step: Among neurons A, B, C, D, A and C are deactivated
Deactivated neurons do not participate in calculations during that step.
2. Using All Neurons During Testing
In the prediction phase, all neurons are activated, but their weights are adjusted to account for the dropout rate.
Dropout Rate: 50%
Among neurons A, B, C, D, E, A and C were deactivated during training
During testing, all neurons are active but the weights are adjusted
Dropout is one of the powerful strategies to enhance the generalization performance of neural network models.
In the next lesson, we will explore the stabilization technique of learning using Batch Normalization.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.