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CISC/CMPE 452/COGS400 ASSIGNMENT 2

 ASSIGNMENT 2

BACKPROPAGATION
CISC/CMPE 452/COGS400
20 marks
General Instructions for Code and 
Submission (for all assignments) 
1. You can use any programming language (preferred Python, java, C, C++, Java) 
2. Make one zip file named as Asgn2_studentID which should include
a. A PDF file (template given on the next page) containing the ANN design, accuracy in 
precision, recall, and confusion matrix, comparison of accuracies of Model 1 and 2. 
Include a critical analysis about possible causes for the difference in accuracies.
b. Program code with comments in the code to explain what each of your program 
files and functions are for
3. Upload zip file to the OnQ site
4. Mark will be deducted based on late policy (-2 per day after due date until the end date 
after which no assignments will be accepted)
Assignment 2: The Original Dataset
■ You will use the handwritten character database for this assignment published by Lecun et 
al available at the following URL yann.lecun.com/exdb/mnist/ (accessible on OnQ as 
MNIST handwritten digit database, Yann LeCun, Corinna Cortes an.pdf). You will know 
about all the different models that were implemented to reduce the error of the initial 
model. 
Assignment 2 – Your Task
■ You will implement the Backpropagation learning algorithm for a multilayer (input, one hidden layer and 
one output layer) in the following two different ways to learn to classify handwritten characters using the 
MNIST training dataset and the corresponding label dataset. 
– Model 1: Using raw programming without using any predefined libraries to create, train or test 
the models. You can use a library to create a visualization or a chart from the predicted class 
values.
– Model 2: Using predefined libraries in data analytic tools like Keras or Scikit-learn. A) Implement 
one model with the same parameters as you use in Model 1. B) Try to improve Model 2A by trying 
other parameter values.
■ The input should be the training data and the output should be the corresponding digit class. Use 
sigmoidal output function at every layer and node. In other words, you must decide the following design 
criteria: 
– Initial weights and learning rate
– Training iterations and termination criteria
– Number of nodes in the layer
– Momentum
■ Use the predefined libraries as shown on the next slide to load the dataset in your program.
■ Compare the accuracies of models 1 and 2.
Assignment 2: Loading the Data in Python 
■ Using Keras package:
■ Using scikit-learn package:
■ Both will load same data but input features (i.e. pixel values for an image) will have different dimensions
– i.e. Multi-dimensional array of shape 28x28 Vs. One-dimensional array of shape 784
■ For more details, please check: 
– https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data
– https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_openml.html
Mark Distribution and Rubrics –
Total 20 Marks
■ 8 marks – For properly documented (with comments) fully functional executable code of Model 1 
achieving high accuracy. (5 marks if model functions but less optimally with some missing 
documentation, 0 for non-functional models)
■ 6 marks – For properly documented (with comments) fully functional executable code of Model 
2A (3 marks) and 2B (3 marks). (3 marks for non optimal coding and low accuracy, 0 for non￾functional models)
■ 6 marks – Complete report (pdf file) containing confusion matrix, overall precision, recall, and a 
critical discussion about lessons learned (choice of design factors and parameter values) from 
design choices, models’ performances and classification accuracies. You must present the
results for both the training and testing phases. (3 marks if results and parts of above reporting 
are missing, 0 if report is missing or none of the required information is presented in the report).
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