This repository contains two networks specified in .yaml files. The berlin networks are fully connected networks with 3 hidden layers of 500 units each. The composite networks have the same structure, but at each hidden layer the original input is concatenated to the output before being sent to the next layer. classify.py provides both a pylearn2 implementation for classification and a slow numpy implementation for testing intermediate results. driver.py can be used to run multiple different versions of the two networks to generate multiple data points for speed and performance testing. plot.py can be used to plot the accuracy of the networks versus training time and versus training epochs. fixed_autoencoder.pkl contains a premade autoencoder layer with the identity matrix. This is read by the composite_network.yaml and should be copied with the composite_network.yaml.