AI4.py

# Import required libraries:
import numpy as np
# Define input features:
input_features = np.array([[0,0],[0,1],[1,0],[1,1]])
print (input_features.shape)
print (input_features)
# Define target output:

target_output = np.array([[0,1,1,1]])# Reshaping our target output into vector:
target_output = target_output.reshape(4,1)
print(target_output.shape)
print (target_output)

# Define weights:
weights = np.array([[0.1],[0.2]])
print(weights.shape)
print (weights)
# Bias weight:
bias = 0.3
# Learning Rate:
lr = 0.05
# Sigmoid function:
def sigmoid(x):
    return 1/(1+np.exp(-x))
    # Derivative of sigmoid function:
def sigmoid_der(x):
    return sigmoid(x)*(1-sigmoid(x))
    # Main logic for neural network:
# Running our code 10000 times:
for epoch in range(10000000):
    inputs = input_features
    #Feedforward input:
    in_o = np.dot(inputs, weights) + bias 
    #Feedforward output:
    out_o = sigmoid(in_o) 
    #Backpropogation
    #Calculating error
    error = out_o - target_output
    #Going with the formula:
    x = error.sum()


print(x)
#Calculating derivative:
derror_douto = error
douto_dino = sigmoid_der(out_o)
#Multiplying individual derivatives:
deriv = derror_douto * douto_dino #Multiplying with the 3rd individual derivative:
#Finding the transpose of input_features:
inputs = input_features.T
deriv_final = np.dot(inputs,deriv)
#Updating the weights values:
weights -= lr * deriv_final #Updating the bias weight value:
for i in deriv:
    bias -= lr * i #Check the final values for weight and biasprint (weights)
print (bias) #Taking inputs:
single_point = np.array([1,0]) #1st step:
result1 = np.dot(single_point, weights) + bias #2nd step:
result2 = sigmoid(result1) #Print final result
print(result2) #Taking inputs:
single_point = np.array([1,1]) #1st step:
result1 = np.dot(single_point, weights) + bias #2nd step:
result2 = sigmoid(result1) #Print final result
print(result2) #Taking inputs:
single_point = np.array([0,0]) #1st step:
result1 = np.dot(single_point, weights) + bias #2nd step:
result2 = sigmoid(result1) #Print final result
print(result2)