Visualizing Deep Neural Networks Classes and Features – Ankivil

Neural networks are very powerful tools to classify data but they are very hard to debug. Indeed, they do a lot of computation with low level operations so they are like black boxes: we provide inputs and get outputs without any understanding on how the neural network is finding the results. Few years ago some scientists found ways to delve into the networks used for image categorization. Instead of doing backpropagation on weights like during the learning phase of a neural network, they did backpropagation on the images themselves: in the example below (edited from CS231n), considering x are inputs and w are weights, each learning step, the gradient (red) is applied to the x instead of the w. In this article, we will use the method and code from Google, Simonyan, Yosinski and Chollet to try to visualize the classes and convolutional layers learnt by popular neural networks. The code provided in this article uses the Keras library.