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 fall into exactly one of ten possible classes. The potential classes for the images are airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. Please also refer to the first bullet point in the See also section.
Most image datasets will be too large to fit into memory. What we can do with TensorFlow is set up an image pipeline to read in one batch at a time from a file. We do this by essentially setting up an image reader, and then creating a batch queue that operates on the ...
