The mechanism through which dropout works has already been explained in other. I will just comment on one explanation for why it improves performance. To see how dropout works , I build a deep net in Keras and tried to validate it on the CIFAR-dataset. Here I will illustrate the effectiveness of dropout layers with a simple example. The primary idea is to randomly drop components of neural network (outputs) from a layer of neural network.
However, a very simple approximate averaging method works well in practice.
The idea is to use a single neural net at test time without dropout. The weights of this network are scaled-down versions of the trained weights. If a unit is retained with probability p during training, the outgoing weights of that . The key idea is to randomly drop units (along with their connections) from the neural network during training. Dropout is a technique for addressing this problem. This prevents units from co-adapting too much.
During training, dropout samples from an exponential number of different “thinned” networks. Are pooling layers added before or. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. Continue reading “Why dropout works”
