ML 4 – BACKPROPAGATION ALGORITHM

4. BUILD AN ARTIFICIAL NEURAL NETWORK BY IMPLEMENTING THE BACKPROPAGATION ALGORITHM AND TEST THE SAME USING APPROPRIATE DATASETS.

SOLUTION

NO DATASET

lab4.py

import numpy as np
X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float)
y = np.array(([92], [86], [89]), dtype=float)
X = X/np.amax(X, axis=0)
y = y/100

class Neural_Network(object):
def __init__(self):
self.inputSize = 2
self.outputSize = 1
self.hiddenSize = 3
self.W1 = np.random.randn(self.inputSize, self.hiddenSize)
self.W2 = np.random.randn(self.hiddenSize, self.outputSize)

def forward(self, X):
self.z = np.dot(X, self.W1)
self.z2 = self.sigmoid(self.z)
self.z3 = np.dot(self.z2, self.W2)
o = self.sigmoid(self.z3)
return o

def sigmoid(self, s):
return 1/(1+np.exp(-s))

def sigmoidPrime(self, s):
return s * (1 – s)

def backward(self, X, y, o):
self.o_error = y – o
self.o_delta = self.o_error*self.sigmoidPrime(o)
self.z2_error = self.o_delta.dot(self.W2.T)
self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2)
self.W1 += X.T.dot(self.z2_delta)
self.W2 += self.z2.T.dot(self.o_delta)

def train (self, X, y):
o = self.forward(X)
self.backward(X, y, o)

NN = Neural_Network()
for i in range(1000):
print (“nInput: n” + str(X))
print (“nActual Output: n” + str(y))
print (“nPredicted Output: n” + str(NN.forward(X)))
print (“nLoss: n” + str(np.mean(np.square(y – NN.forward(X)))))
NN.train(X, y)

STEPS & OUTPUT:

to view steps & output click HERE