It is important to be able to extend CNN models for image recognition so that we understand how to increase the depth of the network. This may increase the accuracy of our predictions if we have enough data. Extending the depth of CNN networks is done in a standard fashion: we just repeat the convolution, max pool, and ReLU in series until we are satisfied with the depth. Many of the more accurate image recognition networks operate in this fashion.
Implementing an advanced CNN
Getting ready
In this recipe, we will implement a more advanced method of reading image data and use a larger CNN to do image recognition on the CIFAR10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html). This dataset has 60,000 32x32 images that...
