dropout layers

Dropout layers are a type of regularization technique used in deep learning neural networks to prevent overfitting. They work by randomly dropping out (setting to zero) a certain percentage of the neurons in the layer during each training iteration. This forces the remaining neurons to learn more independent features and reduces the likelihood of the network relying too heavily on certain neurons.

During inference, all neurons are used, but their outputs are scaled by the dropout rate to compensate for the fact that fewer neurons were used during training. Dropout layers can be added to any layer in a neural network, but are typically used after a fully connected layer or convolutional layer. Dropout rates typically range from 0.1 to 0.5, depending on the complexity of the network and the size of the dataset.