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test_output
NeuralNetwork.py
README.md
logicGates.py
test.py

README.md

Implementing Neural Network Backpropagation and Stochastic Gradient Descent

Aakash Pydi


The neural network with forward propagation and back-propagation pass is implemented in neural_network.py. The functionality of the classes defined in logic_gates.py relies on this neural network. The class representations of the AND, OR, NOT, and XOR gates is given in logic_gates.py. These calsses were modified to derive the weight (theta) values of their corresponding neural network through the use of backword propagation. The training data set was generated in the train() method associated with each class.

The implemented classes were tested in test.py. The output of executing test.py is given below. Note that, the output of test.py is also attached at the end of this file.

Comparison of Theta I handcrafted for HW02 vs the one's learnt from back-propagation

AND Gate

Handcrafted Weight Layer_0 Derived Backpropagation Weight Layer_0
-10 -10.9117
6 7.2167
6 7.2167

OR Gate

Handcrafted Weight Layer_0 Derived Backpropagation Weight Layer_0
-1 -3.3639
2 7.2062
2 7.2062

NOT Gate

Handcrafted Weight Layer_0 Derived Backpropagation Weight Layer_0
1 3.3257
-2 -6.8652

XOR Gate

Handcrafted Weight Layer_0 Derived Backpropagation Weight Layer_0
-25, -25 6.8729, 2.5445
-50, 50 -4.6067, -6.2229
50, -50 -4.6419, -6.4120
handcrafted Weight Layer_1 Derived Backpropagation Weight Layer_1
-25 -4.4001
50 9.3367
50 -9.5169


Corresponding Output of the Test Script

AND Gate Output

test_and


OR Gate Output

test_or


NOT Gate Output

test_not


XOR Gate Output

test_xor


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