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This project implements a perceptron and then neural network from scratch with backprop.


Patrick Phillips

This project implements a perceptron and then multilayer perceptron (neural net) from scratch and tests them on housing data from the US census in a simple classification problem deciding whether the house has price greater than $50,000.

First I implemented the perceptron algorithm in the folder perceptron. Experimenting with the performance as a function of iterations(which I plot below), I found that the accuracy did not really change as iterations increased. I use development data to loop through and choose which set of weights for iterations 1-50 performed best to then use on test data. By using the development data this way I help avoid overfitting.

I then create a two layer neural network and implement the apropriate backpropogation.I again use the development data to loop through and choose which set of weights over all iterations was optimal. Typically as the iterations increased the performance continued to increase, but I found that after about 10 iterations there was little or no improvement in performance on dev data, suggesting that the weights had converged to optimal values already.

I also experimented with different learn rates and had the most success with consistent and quick convergence to an accuracy of about 85% when I used a lr of .1, I found that sometimes the success % would get stuck at the .7567 accuracy with different combinations of hyperparameters, which indicates that a local maxima was likely found. With higher learning rates over .01 this problem didn’t occur, as the updates were significant enough to jump over this peak. I found that overall, the neural network was better than the perceptron. As shown in the graph above, I had results that peaked at roughly 85% accuracy which is about 5% better than the perceptron algorithm was able to do.