Now that we have a decent understanding of the architectures of CNNs, let's get our hands dirty in Keras and apply a CNN.
For this example, we will use the famous CIFAR-10 face image dataset, which is conveniently available within the Keras domain. The dataset consists of 60,000, 32 x 32 color images with 10 target classes consisting of an airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. This is a smaller dataset than the one that was used for the AlexNet example. For more information, you can refer to https://www.cs.toronto.edu/~kriz/cifar.html.
In this CNN, we will use the following architecture to classify the image according to the 10 classes that we specified:
input->convolution 1 (32,3,3)->convolution 2(32,3,3)->pooling->dropout -> Output (Fully connected layer and softmax)
