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 ( 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 ...

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