Dropout Layer Explained in the Context of CNN

What are dropout layers and why are they applied after dense layers?

The Dropout Layer is a regularization technique used in CNN (and other deep learning models) to help prevent overfitting. Overfitting occurs when a model demonstrates high performance on the training data but struggles to generalize well to unseen data.

One way to prevent overfitting is by building ensembles of neural networks with different architectures. However, this approach is computationally costly and would require a lot of programming work. Another method to simulate a diverse range of network architectures with a single model is by incorporating ‘dropout’, which involves randomly disabling nodes during the training phase.

The dropout layer functions by randomly deactivating a portion of input units during each training update. This implies that during forward propagation, certain neurons in the network are ‘dropped out’ or temporarily disregard, along with their associated connections, based on a specific probability. The remaining neurons were then rescaled by a factor of (1/(1-droped_rate)) to account for the dropped neurons during training.

By excluding neurons, the model becomes less dependent on specific ones and encourages the network to acquire more robust and…