COMP9444 Neural Networks and Deep Learning
Term 2, 2020
Project 2 - Rating Prediction
Due: Sunday 9 August, 23:59 pm
Marks: 30% of final assessment
Note: hw2main.py has been updated at 9:40am on Friday 24 July. Specifically, line 104 was changed to:
outputs = student.convertNetOutput(net(inputs, length)).flatten()
Please either download the new version, or edit your own hw2main.py by adding the command .flatten()
Introduction
For this assignment you will be writing a Pytorch program that learns to read product reviews in text format and predict an integer rating from 1 to 5 stars associated with each review.
Getting Started
Copy the archive hw2.zip into your own filespace and unzip it. This should create an hw2 directory containing the main file hw2main.py, skeleton file student.py and data file train.json . Your task is to complete the file student.py in such a way that it can be run in conjunction with hw2main.py by typing
python3 hw2main.py
You must NOT modify hw2main.py in any way. You should ONLY modify student.py
The provided file hw2main.py handles the following:
Loading the data from train.json
Splitting the data into training and validation sets (in the ratio specified by trainValSplit)
Data Processing: strings are converted to lower case, and lengths of the reviews are calculated and added to the dataset (this allows for dynamic padding). You can optionally add your own preprocessing, postprocessing and stop_words (Note that none of this is necessarily required, but it is possible).
Vectorization, using torchtext GloVe vectors 6B.
Batching, using the BucketIterator() prodived by torchtext so as to batch together reviews of similar length. This is not necessary for accuracy but will speed up training since the total sequence length can be reduced for some batches.
The code is structured to be backend-agnostic. That is, if a GPU is present, it will automatically be used; otherwise, the CPU will be used. This is the purpose of the .to(device) function being called on several operations.
Please take some time to read through hw2main.py and understand what it does.
Constraints
We have tried to structure hw2main.py so as to allow as much flexibility as possible in the design of your student.py code. You are free to create additional variables, functions, classes, etc., so long as your code runs correctly with hw2main.py unmodified, and you are only using the approved packages. You must adhere to the these constraints:
Your model must be defined in a class named network.
The savedModel.pth file you submit must be generated by the student.py file you submit.
Your submission (including savedModel.pth) must be under 50MB and you cannot load any external assets in the network class.
While you may train on a GPU, you must ensure your model is able to be evaluated on a CPU.
The GloVe vectors are stored in a subdirectory called .vector_cache. You are restricted to using GloVe vectors 6B, but you are free to specify the value of dim (50, 100, 200 or 300).
You must ensure that we can load your code and test it. This will involve importing your student.py file, creating an instance of your network class, restoring the parameters from your savedModel.pth, loading our own test dataset, processing according to what you specified in your student.py file, and calculating accuracy and score.
You may NOT download or load data other than what we have provided. If we find your submitted model has been trained on external data you will receive zero marks for the assignment.
Question
At the top of your code, in a block of comments, you must provide a brief answer (one or two paragraphs) to this Question:
Briefly describe how your program works, and explain any design and training decisions you made along the way.
Marking Scheme
After submissions have closed, your code wil be run on a holdout test set (i.e. a set of reviews and ratings that we do not make available to you, but which we will use to test your model). Marks will be allocated as follows:
12 marks for Algorithms, Style, Comments and Answer to the Question
18 marks based on performance on the (unseen) test set
The performance mark will be a function of the Weighted score, which is:
(1.0 × Correct predictions percentage) + (0.4 × One star away percentage)
Groups
This assignment may be done individually, or in groups of two students. Groups are determined by an SMS field called hw2group. Every student has initially been assigned a unique hw2group which is "h" followed by their studentID number, e.g. h1234567. If you plan to complete the assignment individually, you don't need to do anything (but, if you do create a group with only you as a member, that's ok too). If you wish to form a group, go to the COMP9444 WebCMS page and click on "Groups" in the left hand column, then click "Create". Leave the "Group Type" as "Default". After creating a Group, click "Edit", search for the other member, and click "Add". WebCMS assigns a unique group ID to each group, in the form of "g" followed by six digits (e.g. g012345). We will periodically run a script to load these values into SMS. You must ensure there are no more than two members in your group, and no-one is a member of two different groups.
Submission
You should submit your trained model and Python code by typing
give cs9444 hw2 student.py savedModel.pth
You must submit your trained model savedModel.pth as well as the Python code student.py
You can submit as many times as you like - later submissions by either group member will overwrite previous submissions by either group member. You can check that your submission has been received by using the following command:
9444 classrun -check
The submission deadline is Sunday 9 August, 23:59. 15% penalty will be applied to the (maximum) mark for every 24 hours late after the deadline.
Additional information may be found in the FAQ and will be considered as part of the specification for the project. You should check this page regularly.
When you submit, the system will check that your model can be successfully loaded, and evaluate it on data randomly chosen from a third dataset (disjoint from train.json and also disjoint from the holdout test set).
Common Questions:
Can I train on the full dataset if I find it? No. You should NOT attempt to reconstruct the test set by searching the Internet. We will retrain a random selection of submissions, as well as those achieving high accuracy. If your code attempts to search or load external assets, or we find a mismatch between your submittied code and saved model, you will receive zero marks.
My model is only slightly larger than 50MB, can you still accept it? No, the 50MB limit is part of the assignment specification and is quite generous. You should be able to get away with much less.
Can we assume you will call net.eval() on our model prior to testing? Yes.
Can we assume a max length on the reviews? No. But nothing will be significantly longer than what is present in the training set.
General Advice:
You have been provided only rudimentary skeleton code that saves your model and prints the loss and accuracy at various inputs. You will almost certainly need to expand on this code so as to have a clearer understanding of what your model is doing.
If you find your training accuracy is high, but the submission accuracy is low, you are overfitting to the training data.
Try to be methodical in your development. Blindly modifying code, looking at the output, then modifying again can cause you go around in circles. A better approach is to keep a record of what you have tried, and what outcome you observed. Decide on a hypothesis you want to test, run an experiment and record the result. Then move on to the next idea.
You should consider the submission test script to be the final arbiter with regard to whether a certain approach is valid. If you try something, and the submission test runs and you get a good accuracy then the approach is valid. If it causes errors then it is not valid.
Do Not leave this assignment to the last minute. Get started early, and submit early in order to ensure your code runs correctly. Marks from automated testing are final. You should aim to be uploading your final submission at least two hours before the deadline. It is likely that close to the deadline, the wait time on submission test results will increase.
Plagiarism Policy
Your program must be entirely your own work. Plagiarism detection software will be used to compare all submissions pairwise and serious penalties will be applied, particularly in the case of repeat offences.
DO NOT COPY FROM OTHERS; DO NOT ALLOW ANYONE TO SEE YOUR CODE
Please refer to the UNSW Policy on Academic Integrity and Plagiarism if you require further clarification on this matter.
Good luck!