Recognizing CIFAR-10 images with deep learning

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

Figure 3.9: An example of CIFAR-10 images

The images in this section are from Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf. They are part of the CIFAR-10 dataset (toronto.edu): https://www.cs.toronto.edu/~kriz/cifar.html.

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

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