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 matplotlib.pyplot as plt # CIFAR_10 is a set of 60K images 32x32 pixels on 3 channels...