Introduction

The present session will introduce the Python classes and how to create your own Artificial Neural Network (ANN) class. The class will implement the __init__, feedforward, and backpropagation methods.

Each method will be described in detail to understand the ANN’s inner work. Then, the handwriting MNIST dataset will be used to train and test our ANN.

Objectives

• Learn the basics of using Python Classes
• Create methods for: create, feedforward, and backpropagation
• Train the ANN
• Test the ANN

Pending tasks (Your work)

• Create a Python function to evaluate the ANN’s performance by passing all the test images(mnist_test.csv) and compare the target with the ANN’s results.
• Make a plot of the ANN’s evolution performance for at least 10 combinations of the number of hidden nodes, learning rate, and the number of epochs.