part4.py

#import code from last time
from part2 import *

class NeuralNetwork(object):
    def __init__(self):        
        #Define Hyperparameters
        self.inputLayerSize = 2
        self.outputLayerSize = 1
        self.hiddenLayerSize = 3
        
        #Weights (parameters)
        self.W1 = np.random.randn(self.inputLayerSize, self.hiddenLayerSize)
        self.W2 = np.random.randn(self.hiddenLayerSize, self.outputLayerSize)
        
    def forwardPropagation(self, X):
        #Propagate inputs though network
        self.z2 = np.dot(X, self.W1)
        self.a2 = self.sigmoid(self.z2)
        self.z3 = np.dot(self.a2, self.W2)
        yHat = self.sigmoid(self.z3) 
        return yHat
        
    def sigmoid(self, z):
        #Apply sigmoid activation function to scalar, vector, or matrix
        return 1/(1+np.exp(-z))
    
    def sigmoidPrime(self, z):
        #Gradient of sigmoid
        return np.exp(-z)/((1+np.exp(-z))**2)
 
    def costFunction(self, X, y):
        #Compute cost for given X,y, use weights already stored in class.
        self.yHat = self.forwardPropagation(X)
        J = 0.5*sum((y-self.yHat)**2)
        return J
    
    def costFunctionPrime(self, X, y):
        #Compute derivative with respect to W1 and W2 for a given X and y:
        self.yHat = self.forwardPropagation(X)
        
        delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3))
        dJdW2 = np.dot(self.a2.T, delta3)
        
        delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2)
        dJdW1 = np.dot(X.T, delta2) 
        
        return dJdW1, dJdW2