OCMP5329 辅导 、讲解 python 程序设计
OCMP5329 - Deep Learning
Coding Assignment
This is an individual assignment and should be completed independently.
Due: End of day on Friday of Week 4
1. Task description
Based on the codes given in Tutorial: Multilayer Neural Network, you are required to accomplish a multi-class classification task on the provided dataset.
In this assignment, you are expected to implement the modules specified in the marking table.
You must guarantee that the submitted codes are self-complete, and the newly implemented modules can be successfully run in common Python environment.
You are allowed to use Deep Learning frameworks (e.g. PyTorch). You are encouraged not to use these deep learning frameworks if you want to challenge yourself for a deeper understanding. In this case, scientific computing packages, such as NumPy and SciPy, can be used to manually implement the auto-grad functions.
If you have any questions about the assignment, please contact the teaching team.
The dataset can be downloaded from Canvas. There are 10 classes in this dataset. The dataset has been split into training set and test set.
2. Instructions to hand in the assignment
2.1 Go to Canvas and upload the report. The report should include each member’s details (student ID and name).
2.2 The report must include a link of your code and data (e.g. a shared Google Cloud folder, so we can easily run it on Colab). Clearly provide instructions on how to run your code in the appendix of the report or include a readme.txt in your shared folder.
Don’t update the code/data any more after the submission. If the latest modified time of the shared folder is significantly late after the submission deadline, the whole submission will be taken as a late submission.
2.3 The report must clearly show (i) details of your modules, (ii) the predicted results from your classifier on test examples, (iii) run-time, and (iv) hardware and software specifications of the computer that you used for performance evaluations.
2.4 There is no special format to follow for the report but please make it as clear as possible and similar to a research paper.
2.5 The use of ChatGPT or other AI tools is prohibited in the assignments. A plagiarism checker will be used.
Late submission
Suppose you hand in work after the deadline.
If you have not been granted special consideration or arrangements:
– A penalty of 5% of the maximum marks will be taken per day (or part) late. After 10 days, you will be awarded a mark of zero.
– For example, if an assignment is worth 40% of the final mark and you are one hour late submitting, then the maximum marks possible would be 38%.
– For example, if an assignment is worth 40% of the final mark and you are 28 hours late submitting, then the maximum marks possible marks would be 36%.
– Warning: submission sites get very slow near deadlines.
– Submit early; you can resubmit if there is time before the deadline.
3. Marking scheme
Category Criterion
Report [50] Introduction [5]
- What’s the aim of the study?
- Why is the study important?
Methods [15]
- Problem formulation and pre-processing (if any) [3]
- The principle of different modules [4]
- What is the design of your best model [4]
- Implementation details and hyper-parameters [4]
Experiments and results (with Figures or Tables) [20]
- Performance in terms of different evaluation metrics [5]
- Extensive analysis, including hyperparameter analysis, ablation studies and comparison methods [5]
- Meaningful discussion of the results [5]
- Justification on your best model [5]
Discussion and conclusion [5]
- Meaningful conclusion and reflection
Other [5]
- At the discretion of the marker: for impressing the marker, excelling expectation, etc. Examples include fast code, using LATEX, etc.
Modules [45] More than one hidden layer [5]
ReLU activation [5]
Weight decay [5]
Momentum SGD [5]
Dropout [5]
Softmax and cross-entropy loss [5]
Mini-batch training [5]
Batch normalisation [5]
Other advanced operations (e.g., GELU, Adam) [5]
* Please make a highlight if you have one you think is advanced.
Code [5] Code runs within a feasible time [5]
Code Penalties [-]
Well organised, commented and documented [5]
Badly written code: [-20]
Not including instructions on how to run your code: [-30]
Late submission