Python Deep Learning Projects

Dropout

Dropout is a regularization technique used to prevent overfitting. During training, it is implemented by randomly sampling a neural network from the original neural network during each forward and backward propagation, and then training this subset network on the batch of input data. During testing, no dropout is implemented. The test results are obtained as an ensemble of all of the sampled networks:

Figure 8.23: Dropout, as shown in the Dropout: A Simple Way to Prevent Neural Networks from Overfitting paper (Source: http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)

In Keras, implementing Dropout is easy. First, import ...