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代做3RD ANU-Assignment 2调试Python作业

Extended Call for Papers for the 
3RD ANU ANNUAL BIO-INSPIRED COMPUTING STUDENT CONFERENCE http://cs.anu.edu.au/~tom/conf/ABCs202-/  
also being used for 
COMP4660/8420 Assignment 2: Neural Networks 
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Submission Due: Sunday 31st May (Week 11) at 11:55pm via EasyChair. 
 
Context 
This assignment extends the previous one in a way that is up to you; there should be some 
connection to the previous assignment, but remember, don’t get too stressed. 
In either case, you should take into account the marking comments from assignment 1, and 
any peer review comments you may get, to improve the text as appropriate to the new 
assignment. So, what do you do? You can either extend you current paper using a deep 
learning approach OR an evolutionary algorithms approach. If you want to do a completely 
different 2nd assignment send me an email to ask if I agree as I worry those will be too hard. 
Examples: if you extend the current with an evolutionary algorithm, you could adjust the 
parameters of your neural network or pruning or input processing or all the weights or … 
Similarly, you could use a CNN on the larger version of the data set (not all datasets have 
this) and then apply the same pruning or … to the fully connected layers at the end. 
 
Deliverables (all similar to before.) 
1. A report (about 4-6 pages of text content with a MAXIMUM of 10 pages, including 
references, diagrams, graphs and tables). Remember to keep your report clear and 
concise. I say that the text content should be about 4-6 pages to signal that we will not 
be counting lines, but if it is 10 pages long and it is all diagrams then it is very clear 
that there is too little text. Conversely, if it is 10 pages long and there is just one 
diagram then there is too much text, but 6 pages with one diagram is fine, and so on. 
2. Your assignment 2 is an extension of assignment 1 so we expect a significant 
proportion of the text to be the same and just extended and improved in assignment 
2. It is your own words so re-using the text as we explicitly allow is not plagiarism. 
3. Support documents in a zip file 
a. PyTorch or Python source code file(s), plus the data set you used for your 
assignment, in original and preprocessed form. In one zip folder. 
b. Copies of the technique and dataset papers. This is for consistency, and makes 
marking easier as it clearly identifies the papers chosen. 
c. Source documents for your report. This could be a Word document or a folder of 
Latex source or … 
 
Submission Method 
Please submit your assignment via the EasyChair conference management system: 
https://www.easychair.org/conferences/?conf=abcs2020. Your submission will be a second 
version of your previous paper as far as EasyChair is concerned – I will re-open the site 
 
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about a week before the assignment is due. You can then just submit using the paper_v2 and 
support_v2 fields. Something like this is common in conferences, when your paper is 
accepted you then modify based on review comments (markers and peer reviewers in this 
case) and then submit a final version (often called a “camera-ready” version). 
Objectives 
The purpose for this assignment is for you to: 
• Develop a good understanding of deep learning or evolutionary algorithms and 
enhance your skills in implementing them in PyTorch / Python 
• Enhance your approach to investigating and solving a real-world data set / problem 
• Develop improved understanding of reporting investigations in a conference paper 
• Some experience in using Google scholar to find citing/cited papers 
 
Task Description 
Your task is to: 
1. devise a classification or regression problem to investigate using the data set; 
2. implement in PyTorch / Python to solve the problem and implement a method to 
determine the performance of the technique(s) you used; 
3. implement a technique from the literature (paper selected as for assignment 1) and 
determine its benefit or lack of benefit (to keep it simple, we expect you to simply 
extend the work you did in assignment 1 simply using DL/EA, rather than doing a 
brand new assignment); 
4. compare your results with results published in the dataset paper reporting results on 
the data set you chose (dataset paper as chosen for assignment 1); and 
5. write a report on your work 
 
Data set 
Either continue with your existing dataset or for some datasets an expanded version will be 
available. 
Your report should indicate which dataset you used, what modifications you made to the 
encodings if you needed to do so, and cite the technique paper you chose. Other academic 
papers can generally be found by using Google scholar. Google scholar will have links to one 
or more paper repositories. You can get access if you do this from ANU campus as the 
library subscribes to most of the large document repositories. Some papers are in multiple 
repositories so if you cannot find a free copy then ask your tutor for advice. From off 
campus, if you log into the ANU Virtual Proxy server then usually you can access the same 
electronic resources. You should also mention in your report any different topologies and 
analyses used in your experimentation. 
 
Design of a Problem 
Detail in your report what you want to model in the data set and explain what the inputs 
and outputs you will use to develop the neural network model. 
 
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Implementation 
Choose an appropriate measure to report the results produced by your extended technique 
and compare to your results using neural networks. You can use the measure used in the 
research papers that have the results of their predictions on the data set you have used and 
would like to compare your results with. Remember to cite the papers in your report. 
 
Report 
The report must be in the style of an academic paper and must conform to the Lecture Notes 
in Computer Science conference paper proceedings format, but with the margins changed to 
2 cm and header/footer to 1 cm. The template for the report can be downloaded from Wattle 
by clicking the link named “AssignmentReportTemplate-LNCS-Office2007.zip” and needs 
the margins to be modified. If you prefer to use Latex , there is a link on the ABCs conference 
website which takes you to Springer’s current pages. Any template from there will also need 
the margins to be modified. Use the Springer citation style as found in the template file you 
use. 
Your report should have a meaningful title, which indicates what you have done. 
"COMP8420 Assignment" is not a meaningful title. Your title and content should NOT 
mention the course: we are modelling the assignment so that you are making a conference 
paper submission. Your u number should be showing in your email address, and only there. 
Your affiliation would be "Research School of Computer Science, Australian National 
University, Canberra Australia". Please do not forget to include your name as the single 
author. 
A suggested structure for the report: 
• Abstract – A paragraph that summarises the work you did, the results you found and 
whether it was better, same or worse than a published research paper for the same 
dataset. An abstract is similar to an executive summary of the entire report. 
• Introduction – A description of the motivation for the choice of the data set, the 
problem that you modelled using a neural network and an outline of the 
investigations that you carried out using the model. This section should also include 
a brief background to the problem and the methods used perform the analysis. 
Remember to use citations! 
• Method – A description of the technique(s) you implemented and details of the 
investigations or tests you conducted using the technique. 
• Results and Discussion – Presentation of results from the investigations and detailed 
analysis of the results including comparison of your results with the results 
published in a research paper on the data set. Remember to use citations! 
• Conclusion and Future Work – A statement on your findings and how your work 
can be extended or how might it be improved. Even if you have conducted a 
thorough investigation there is always work left to do. Outlining future work is 
VERY important as it shows that you have thought about the problem and have a 
deeper understanding then just stating a conclusion. 
 
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• References – You should have a few more than last time, as this is the 2nd version of 
your paper (or it's a 2nd paper). Don’t forget to cite source, target and other papers 
you got information from. 
A comment: you should read papers in relevant areas of the literature (e.g. on similar topics) 
to get more of a feel for how to write and layout academic papers. This forms part of the 
learning you should get out of advanced courses such as COMP4xxx/COMP8xxx given that 
the ANU is a research intensive University. 
 
Peer Marking 
I have assumed you are all participating, if you wish to not participate, you will need to 
withdraw (via a Wattle choice item in the Assignments tab) otherwise you’re in and the 
mark from this counts – this is because everyone who participated last year found this very 
valuable. I’ll start allocating papers to everyone who doesn’t withdraw by the end of 
Thursday. 
 
Conference 
Will we really hold it as a physical conference? I hope we do go beyond a virtual conference, 
but it will be up to you all. We will discuss it during the rest of the semester. 
 
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Assessment Guide ( /30 i.e. marked out of 30) worth 15% of overall course marks 
Abstract ( /2) 
Clear and concise abstract summarising the work done 
Method section / Data Set and Model Design ( /8) 
Valid reasons for choosing the data set 
Good level of problem complexity 
Clear and valid investigation aims 
Model design that clearly serves the purpose of the investigations 
Appropriate measures used to determine the performance of the neural network and 
predictions 
Good explanation of the model design 
Appropriate choice for the inputs and outputs of the prediction model with valid reasons 
Evidence of good understanding of the relevant literature 
Results and Discussion ( /6) 
Good methods used to evaluate the model including an appropriate split of the train, test / 
validation data OR evolutionary algorithm cross-over / mutation settings to maintain 
diversity. 
Good level of detail used to analyse results 
Conclusion and Future Work ( /2) 
Appropriate conclusion of the work 
Appropriate work suggested to extend and/or improve the work 
References and citations ( /2) 
References used as appropriate 
Presentation ( /6) 
Good report structure 
Report is legible, clear and concise 
Clear presentation of results including appropriate use of figures, tables and graphs 
Report conforms to required style (including the use of appropriate language) and length 
Consistent style used for citing references 
Correct grammar and spelling 
Implementation ( /4) 
Structure of the code is legible and well organised 
Evidence of good code design - appropriate level of modularity, encapsulation and 
reusability of the code 
Code is comprehensible with appropriate names of coding items e.g. code files, functions, 
variables 
Code executes without errors 
Good comments in the code 
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