TensorFlow Tutorial - Visual Analysis

Notes by Magnus Erik Hvass Pedersen: https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual...

In some of the previous tutorials on Convolutional Neural Networks, we showed the convolutional filter weights, see e.g. Tutorial #02 and #06. But it was impossible to determine what the convolutional filters might be recognizing in the input image from merely looking at the filter-weights.

In this tutorial we will present a basic method for visually analysing the inner-workings of a neural network. The idea is to generate an image that maximizes individual features inside the neural network. The image is initialized with a little random noise and then gradually changed using the gradient of the given feature with regard to the input image.

This method for visual analysis of a neural network is also known as feature maximization or activation maximization.

This builds on the previous tutorials. You should be familiar with neural networks in general (e.g. Tutorial #01 and #02), and knowledge of the Inception model is also helpful (Tutorial #07).