LAB 8.3

import numpy as np
import matplotlib.pyplot as plt
# Activation function
def sigmoid(t):
    return 1 / (1 + np.exp(-t))

# Derivative of sigmoid function
def sigmoid_derivative(p):
    return p * (1 - p)

# Class definition
class NeuralNetwork:
    def __init__(self, x, y, n):
        self.input = x
        self.weights1 = np.random.rand(self.input.shape[1], n)  # n nodes in the hidden layer
        self.weights2 = np.random.rand(n, 1)
        self.y = y
        self.output = np.zeros(y.shape)

    def feedforward(self):
        self.layer1 = sigmoid(np.dot(self.input, self.weights1))
        self.layer2 = sigmoid(np.dot(self.layer1, self.weights2))
        return self.layer2

    def backprop(self):
        d_weights2 = np.dot(self.layer1.T, 2 * (self.y - self.output) * sigmoid_derivative(self.output))
        d_weights1 = np.dot(self.input.T, np.dot(2 * (self.y - self.output) * sigmoid_derivative(self.output),
        self.weights2.T) * sigmoid_derivative(self.layer1))
        self.weights1 += d_weights1
        self.weights2 += d_weights2

    def train(self, X, y):
        self.output = self.feedforward()
        self.backprop()

# Each row is a training example, each column is [X1, X2, X3]
X=np.random.random((50,3))
y_list=np.array(([((X[n][0]+X[n][1]+X[n][2])/3)for n in range(50)]),dtype=float)
y=np.array(([y_list]), dtype=float).T
loss = []
iterr = [x for x in range (0,1001,1)]
print ("Input : \n" + str(X))
print ("Actual Output: \n" + str(y))
NN = NeuralNetwork(X, y, 5) # number of nodes in the hidden layer
print("\n*** Neural network training started...")
for i in range(1001):  # trains the NN 1,001 times
  loss.append((np.mean (np.square(y-NN.feedforward ()))))
  if i % 100 == 0:
    print("\n*** iteration # " + str(i))
    print("Predicted Output: \n" + str(NN.feedforward()))
    print("Loss function is: \n" + str(np.mean(np.square(y - NN.feedforward()))))  # mean sum squared loss
  NN.train(X, y)


for i in range(len(loss)):
  print(loss[i])

plt.plot(iterr,loss)
plt.savefig('pic_0_1.png')