CS 5000: Theory of Computability
Assignment 3
Learning Objectives
1. ANNs
2. Backpropagation
Problem 1 (5 points)
Implement the three-layer ANN shown in Fig. 1 from Chapter 4 of Tom
Mitchell’s text on machine learning. The ANN is trained to approximate the
identity function that maps eight binary strings with exactly one 1 to themselves. Your implementation should include the class ANN.java or ANN.py.
The class should implement three methods: build(), train(numIterations),
and fit(input).
The build() method constructs the three-layer network in Fig. 1. The
train(numIterations) method takes the number of iterations and uses the
backpropagation algorithm to train the ANN for the specified number of iterations. You can also implement another version of this method, train(error),
that keeps training the ANN until the error drops below the value specified
by the parameter. Finally, the method fit(input) takes a 8-bit binary string
as input and returns the output binary string, i.e., the values in the 8 output
nodes. For simplicity, you can represent the inputs and outputs as integer
arrays. But this is just a recommendation.
Fig. 2 shows a trained ANN for this problem from Chapter 4 of Mitchell’s
text. Your ANN’s weight do not have to be exactly equal to these. So long
1
Figure 1: ANN before training.
2
Figure 2: ANN after training.
3
as your ANN maps each 8-binary string with exactly one 1 to itself, it is
correct.
Also, implement the methods save() and restore(). The method save()
should save the trained network into a file. If you use Python, you can use
the pickle package. If you use JAVA, you may want to try ObjectOutputStream.writeObject() and ObjectInputStream.readObject().
What to Submit
Submit your ANN.py or ANN.java via Canvas.
Happy Hacking!
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