程序代写案例-COMP9444-Assignment 2

2021/7/20 COMP9444 Assignment 2
https://www.cse.unsw.edu.au/~cs9444/21T2/hw2/ 1/5
COMP9444 Neural Networks and Deep Learning
Term 2, 2021
Proje
ct 2 - Simpsons Character Classification
Due: Friday 6 August, 23:59 pm
Marks: 30% of final assessment
Introduction
For this assignment you will be writing a Pytorch program that learns to classify 14 different
Simpsons Characters using the grey scale images we provide.
Example images of 9 different Simpsons Characters from the dataset.
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, configuration file config.py, skeleton file student.py and data
zip file data.zip . You will need to unzip the data file in the same directory to create a
subdirectory named data. 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
2021/7/20 COMP9444 Assignment 2
https://www.cse.unsw.edu.au/~cs9444/21T2/hw2/ 2/5
You must NOT modify hw2main.py in any way. You should only submit student.py
(If you wish, you can modify config.py in order to switch between CPU and GPU usage)
The provided file hw2main.py handles the following:
loading the images from the data directory
splitting the data into training and validation sets (in the ratio specified by train_val_split)
data transformation: images are loaded and converted to tensors; this allows the network
to work with the data; you can optionally modify and add your own transformation steps,
and you can specify different transformations for the training and testing phase if you wish
loading the data using DataLoader() provided by pytorch with your specified batch_size in
student.py
You should aim to keep your code backend-agnostic in the sense that it can run on either a
CPU or GPU. This is generally achieved using the .to(device) function. If you do not have access
to a GPU, you should at least ensure that your code runs correctly on a CPU.
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 (i.e. those available on the CSE machines). You must adhere to these
constraints:
1. your model must be defined in a class called network.
2. the savedModel.pth file you submit must be generated by the student.py file you submit.
3. make sure your version of pytorch and torchvision produce a savedModel.pth with the
correct format to run on the CSE machines (which use Torch1.8.1 and TorchVision0.9.1).
4. your submission (including savedModel.pth) must be under 50MB and you cannot load any
external assets in the network class.
5. while you may train on a GPU, you must ensure your model is able to be evaluated on a
CPU.
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
2021/7/20 COMP9444 Assignment 2
https://www.cse.unsw.edu.au/~cs9444/21T2/hw2/ 3/5
At the top of your code, in a block of comments, you must provide a brief answer (about 300-
500 words) to this Question:
Briefly describe how your program works, and explain any design and training
decisions you made along the way.
You should try to cover the following points in your Answer:
a. choice of architecture, algorithms and enhancements (if any)
b. choice of loss function and optimiser
c. choice of image transformations
d. tuning of metaparameters
e. use of validation set, and any other steps taken to improve generalization and avoid
overfitting
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". Enter your Group Name
and select the Group Type "hw2". 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
In order to avoid technical problems, you are strongly advised to submit from the command line,
and not via the give Web interface. 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 Friday 6 August, 23:59. 15% penalty will be applied to the (maximum)
mark for every 24 hours late after the deadline.
2021/7/20 COMP9444 Assignment 2
https://www.cse.unsw.edu.au/~cs9444/21T2/hw2/ 4/5
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 data.zip and also disjoint
from the holdout test set).
Marking Scheme
After submissions have closed, your code wil be run on a holdout test set (i.e. a set of images
and labels that we do not make available to you, but which we will use to test your model).
Marks will be allocated as follows:
14 marks for algorithms, design choices and answer to the Question
  2 marks for coding style and comments
14 marks based on performance on the (unseen) test set
The performance mark will be based on the accuracy of your network performance on the
testing dataset, which is disjoint from the validation dataset we use to test your model at
submission time.
General Advice:
Use the variable train_val_split to help you make design decisions aimed to avoid
overfitting to the training data. At the very end, you may wish to re-train using the entire
training set.
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.
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.
2021/7/20 COMP9444 Assignment 2
https://www.cse.unsw.edu.au/~cs9444/21T2/hw2/ 5/5
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.
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!

欢迎咨询51作业君
51作业君 51作业君

Email:51zuoyejun

@gmail.com

添加客服微信: ITCSdaixie