First Neural Network

Google colab link:

• https://colab.research.google.com/drive/1YsDhmFcN9i8C3LBlhAMR4ATqwqoA8avX#scrollTo=1XKwZ0nfSsew

Python Files

• NeuralNetwork.py: This file hold the Neural Network class that is used in other files
• NNExample.py: This file trains and plots the loss of the NN throughout training process (should be first file to be ran)
• NNTests.py: This file contains tests for the NN

Packages used:

• Numpy, matplotlib, and unittest

Class Neural Network:

• Contains 7 class attributes
• Contains 8 class methods

Info:

• Creates a 3-6-2 Neural Network
• 3 nodes for input layer
• 6 nodes for hidden layer
• 2 nodes for output layer
• Activation function used is a sigmoid
• Uses back propergation to update the weights
• Trains the NN 1,000 times
• Plots the loss over number of training iterations
• Prints out the shape of both weight matrices
• Prints out the predicted value for the test case and the rounded value of the test case

Questions:

• What are the dimensions of weight matrix 1 and weight matrix 2?
  • The dimension of weight matrix 1 is: 3x6
  • The dimension of weight matrix 2 is: 6x2
• Test [1,1,1]. What are the predicted y values for it?
  • The predicted y values for this sample are: [0.98363427, 0.0157898] (subject to change)
  • The rounded y values for this sample are: [1,0]
• Could you guess what is the meaning of y1 and y2?
  • My guess what the meaning of y1 and y2 mean is that the outcome is [0,1] when the input is even in binary and [1,0] when the outcome is odd in binary. For example 000, 010, 100 are all [0,1] while the sequences 001, 011, 111 are all [1,0]. This makes me believe that this is determining whether the binary sequence is even or odd.