#Program instructions:

Run 'run_program.py' to generate output.

Depending on the number of iterations the program can take a long time to run.

Sample output files are included. 50_iteration_results.txt contains a subset of the predictions made, as well as the RMSE at each iteration and the final prediction accuracy.

For comparison, I also included 2_iteration_results.txt, which contains the same information, and shows that the prediction gets much more accurate with the number of iterations our BackPropNetwork is allowed to make.

#Reasoning for the network architecture values chosen:

Number of hidden nodes:

I chose a small number of hidden nodes because the task was not too computatinally complex. As I increased the number of hidden nodes the execution time of my program increased very quickly.

Initialization of weights:

I chose to use small initial weights because larger values can drive layer 1 nodes to saturation quickly. Increasing training time.

Frequency of weight updates:

I updated weights for each feedForward iteration (each input data)

Choice of learning rate:

I chose a somewhat large value for my learning rate because I wanted to speed up the training time. There is a lot of data to train on.

Momentum value

I chose a small momentum value as to not strongly bias future weight trainings.

#Dependencies Python 2.7.12 pip install numpy