How do I use dropout in neural networks?

How do I use dropout in neural networks?

Dropout is a regularization technique used to prevent overfitting in neural networks. You can implement it by randomly setting a fraction of the input units to zero during training.

What fraction of units should I drop?

A common practice is to drop around 20-50% of the units. You can experiment with different rates to see what works best for your model.

When should I apply dropout in my model?

Typically, dropout is applied after activation functions in hidden layers, but not in the output layer.

Does dropout affect the training time?

Yes, dropout can increase training time since the model needs to learn with different subsets of data in each iteration.