ENGR 421 DASC 521

Homework 03: Multilayer Perceptron for Multiclass Discrimination

In this homework, you will implement a multilayer perceptron for multiclass discrimination in R,

Matlab, or Python. Here are the steps you need to follow:

1. Read Section 11.7.3 from the textbook.

2. You are given a multivariate classification data set, which contains 1000 clothing images

of size 28 pixels × 28 pixels (i.e., 784 pixels). These images are from five distinct

classes, namely, T-shirt, trouser, dress, sneaker, and bag. The figure below shows five

sample figures from each class. You are given two data files:

a. hw03_images.csv: letter images,

b. hw03_labels.csv: corresponding image labels (1: T-shirt, 2: trouser, 3: dress,

4: sneaker, and 5: bag).

3. Divide the data set into two parts by assigning the first 500 images to the training set and

the remaining 500 images to the test set.

4. Train a multilayer perceptron for multiclass discrimination using the sigmoid activation

function for twenty nodes in the hidden layer (� = 20) and using the softmax activation

function for five nodes in the output layer. You should develop a backpropagation

algorithm under batch learning scenario with the following learning parameters.

eta <- 0.0005

epsilon <- 1e-3

max_iteration <- 500

You should initialize the weight parameters to the values given in initial_W.csv

and initial_V.csv files before running your algorithm.

5. Draw the objective function values throughout the iterations. Your figure should be

similar to the following figure.

6. Calculate the confusion matrix for the data points in your training set using the

discrimination rule you will develop using the trained multilayer perceptron. Your

confusion matrix should be similar to the following matrix.

y_train

y_predicted 1 2 3 4 5

1 101 1 4 0 0

2 0 82 0 0 0

3 6 3 108 0 0

4 0 0 0 100 0

5 0 0 0 0 95

7. Calculate the confusion matrix for the data points in your test set using the discrimination

rule you will develop using the trained multilayer perceptron. Your confusion matrix

should be similar to the following matrix.

y_test

y_predicted 1 2 3 4 5

1 84 1 2 0 2

2 0 87 1 0 0

3 13 3 101 0 2

4 0 0 0 103 1

5 2 0 1 0 97

0 100 200 300 400 500

200

400

600

800

Iteration

Error

What to submit: You need to submit your source code in a single file (.R file if you are using R,

.m file if you are using Matlab, or .py file if you are using Python) and a short report explaining

your approach (.doc, .docx, or .pdf file). You will put these two files in a single zip file named as

STUDENTID.zip, where STUDENTID should be replaced with your 7-digit student number.

How to submit: Submit the zip file you created to Blackboard. Please follow the exact style

mentioned and do not send a zip file named as STUDENTID.zip.