Adding dropouts to prevent overfitting

The most popular method to prevent overfitting in neural networks is adding dropouts. In Chapter 2, Feed-Forward Neural Networks, we introduced dropouts, and we've used dropouts throughout the book. In the following recipe, we demonstrate, just like Chapter 2, Feed-Forward Neural Networks, the difference in performance when adding dropouts. This time, we will be using the cifar10 dataset.

How to do it...

1. We start by importing all libraries as follows:

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from...