How to do it...

This section walks through the steps to calculate the cost function.

1. Set a  learning rate value of 0.1 to incrementally change the weights and bias until a final output is selected using the following script:

learningRate = 0.1

2. Initiate a Python list called allCosts using the following script. 

allCosts = []

3. Create a for loop that will iterate through 100,000 scenarios using the following script:

4. Plot the cost values collected over the 100,000 iterations using the following script:

plt.plot(all_costs)
plt.title('Cost Value over 100,000 iterations')
plt.xlabel('Iteration')
plt.ylabel('Cost Value')
plt.show()

5. The final values of the weights and bias can be viewed using the following script:

print('The final values of w1, w2, and b')
print('---------------------------------')
print('w1 = {}'.format(w1))
print('w2 = {}'.format(w2))
print('b = {}'.format(b))