Python Deep Learning - Second Edition

Using Keras to classify images of objects

With Keras, it's easy to create neural nets, but it's also easy to download test datasets. Let's try to use the CIFAR-10 (Canadian Institute For Advanced Research, https://www.cs.toronto.edu/~kriz/cifar.html) dataset instead of MNIST. It consists of 60,000 32x32 RGB images, divided into 10 classes of objects, namely: airplanes, automobiles, birds, cats, deers, dogs, frogs, horses, ships, and trucks:

1. We'll import CIFAR-10 in the same way as we did MNIST:

from keras.datasets import cifar10from keras.layers.core import Dense, Activationfrom keras.models import Sequentialfrom keras.utils import np_utils

2. Then, we'll split the data into 50,000 training images and 10,000 testing images. Once again, we ...