This is the final code for the thesis "Exploring and Comparing Visualization Techniques for Neural Networks" at Osnabrück University, written by Antonia Hain, handed in September 26th, 2018.

am_alexnet.py is the main module that can to be executed. It will start the optimization process automatically. Per default, a random unit in the last layer is optimized using a combination of regularizations.

All relevant parameters for activation maximization can be adjusted in am_constants.py. There, the parametrizations for the regularizers can be changed and a unit for optimization can be selected.

am_tools.py contains methods helping with the optimization process.

caffe_classes.py contains the classes learned by the network.

The network weights file is too large to be uploaded on GitHub. Please download bvlc_alexnet.npy from this source: https://www.cs.toronto.edu/~guerzhoy/tf_alexnet/

TensorFlow is required in order to run the code.