Activity 16: Neural Network

This activity uses the same training and test sets as with the previous activities. The algorithm for the neural network requires us to "label" the classes that will be used for the training. These labels will then be used to classify the test set. If a test object gives an array similar to the "label" of a certain class, it is assigned to that class. Note that we used five nodes for the output layer. This was done so that there will be a high difference between classes.

Figure 1. Training set labels.

The implementation of the neural network is separated in two. One, with a learning rate of 0.1 while the other has a learning rate of 2.5. The results are shown below (click for a larger image)

Figure 2. Summary of results. Left: Learning rate = 0.1. Right: Learning rate = 2.5.

Cells with red shading indicates wrong classification. These false results are observed only with a neural network with low learning rate. For 2.5 learning rate, the classification is 100% accurate. Also, compared to the previous methods, neural network shows a sharp distinction between classes. The values shown in figure 2 demonstrate this.


For this activity, I'll give myself a 9 for completing the activity and for the effort of figuring out how to implement a five node output layer neural network.