This is the second part of my introduction to building an image recognition system with TensorFlow. In the first part we built a softmax classifier to label images from the CIFAR-10 dataset. We achieved an accuracy of around 25-30%. Since there are 10 different and equally likely categories, labeling the images randomly we’d expect an accuracy of 10%. So we’re already a lot better than random, but there’s still plenty of room for improvement.
In this post, I’ll describe how to build a neural network that performs the same task. Let’s see by how much we can increase our prediction accuracy!