Coursework 2: Neural networks 
ENG4200 Introduction to Artificial Intelligence and Machine Learning 4 
1. Key Information 
• Worth 30% of overall grade 
• Submission 1 (/2): Report submission 
• Deadline uploaded on Moodle 
• Submission 2 (/2): Code submission to CodeGrade 
• Deadline uploaded on Moodle (the same as for report) 
2. Training data 
The training dataset has been generated by maximum flow analysis between nodes 12 and 2. The 
feature dataset has 19 fields, which of each represents the maximum flow capacity of each of the 
19 edges, taking the values of 0, 1, and 2. The output dataset has 20 fields, where the first 19 
fields refer to the actual flow taking place on each of the 19 edges, and the last one refers to the 
maximum flow possible between nodes 12 and 2. 
 
Figure 1 The network used to generate training dataset. This information is just to help you understand the training 
dataset; you must not generate additional training dataset to train your neural network. 
 3. What you will do 
You have to create and train a neural network with the following requirement/note: 
• Only the provided training dataset should be used, i.e. furthur traning dataset must NOT be 
created by performing maximum flow analysis over the network in Figure 1. 
• The accuracy on a hidden test dataset will be evaluated by a customised function as 
follows, where the accuracy on the maximum flow field is weighted by 50%, and other 19 
fields share the rest 50% (you may design your loss function accordingly): 
 
 
 You should prepare two submissions, code submission and report submission. In blue colour are 
assessment criteria. 
• Code submission should include two files (example code uploaded on Moodle): 
o A .py file with two functions 
▪ demo_train demonstrates the training process for a few epochs. It has three 
inputs: (1) the file name of taining feature data (.csv), (2) the file name of 
training output data (.csv), and (3) the number of epochs. It needs to do two 
things: (1) it needs to print out a graph with two curves of training and 
validation accuracy, respectively; and (2) save the model as .keras file. 
▪ predict_in_df makes predictions on a provided feature data. It has two 
inputs: (1) the file name of a trained NN model (.keras) and (2) the file name 
of the feature data (.csv). It needs to return the predictions by the NN model 
as a dataframe that has the same format as ‘train_Y’. 
o A .keras file of your trained model 
▪ This will be used to test the hidden test dataset on CodeGrade. 
 
o You can test your files on CodeGrade. There is no limit in the number of 
submissions on CodeGrade until the deadline. 
 
o Assessment criteria 
▪ 5% for the code running properly addressing all requirements. 
▪ 10% for a third of the highest accuracy, 7% (out of 10%) for a third of the 
second highest accuracy, and 5% (out of 10%) for the rest. 
 
• Report submission should be at maximum 2 pages on the following three questions and 
one optional question: 
o Parametric studies of hyperparameters (e.g. structure, activators, optimiser, learning 
rate, etc.): how did you test different values, what insights have you obtained, and 
how did you decide the final setting of your model? 
o How did you address overfitting and imbalanced datasets? 
o How did you decide your loss function? 
o [Optional] Any other aspects you’d like to highlight (e.g. using advanced methods 
such as graphical neural network and/or transformer)? 
 
o [Formatting] Neither cover page nor content list is required. Use a plain word 
document with your name and student ID in the first line. 
 
o Assessment criteria 
▪ 5% for each of the questions, evaluated by technical quality AND 
writing/presentation 
▪ Any brave attempts of methods (e.g. Graphical Neural Network, Transformer, 
or Physics-Informed Neural Network using physical relationships e.g. that 
the flows going in and out of a node should be balanced) that go beyond 
what we learned in classroom will earn not only the top marks for report, but 
also (unless the accuracy is terribly off) will earn a full 10% mark for 
accuracy in the code submission part. If you have made such attempts, don’t 
forget to highlight your efforts on the report. 
 
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