Recognizing CIFAR-10 images with deep learning

The CIFAR-10 dataset contains 60,000 color images of 32 x 32 pixels in 3 channels divided into 10 classes. Each class contains 6,000 images. The training set contains 50,000 images, while the test sets provides 10,000 images. This image taken from the CIFAR repository (https://www.cs.toronto.edu/~kriz/cifar.html) describes a few random examples from the 10 classes:

The goal is to recognize previously unseen images and assign them to one of the 10 classes. Let us define a suitable deep net.

First of all we import a number of useful modules, define a few constants, and load the dataset:

from keras.datasets import cifar10
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.optimizers import SGD, Adam, RMSprop
import...