COMP219 - 2020 - Second CA Assignment

Assessment Information

1 Objective

2 DNN-based Classifification

2.1 Requirement and Description

Language and Platform Python (version 3.5 or above) and Tensorflflow or Keras (latest version). You can use some libraries available on Python platform, including numpy, scipy, scikit-learn, and matplotlib. If you intend to use libraries other than these, please consult the demonstrator or the lecturer.

Learning Task You can choose either classifification (preferred) or regression, but needs to be the same choice as your Assignment 1 submission.

Assignment Tasks You need to implement the following functionalities:

f1 design and build two difffferent deep neural networks, one with convolutional layer and the other without convolutional layer;

f2 apply model evaluation on the learned models. For the materials on model evaluation, you may take a look at the metrics explained in the lecture “model evaluation”. You are required to implement by yourself (i.e., do not call built-in libraries)

(a) the cross-validation of 5 subsamples,

(b) the confusion matrix, and

(c) the ROC curve for one class vs. all other classes

for

(a) the two neural networks you trained in f1, and

(b) the two traditional machine learning algorithms in the fifirst assignment.

Please also summarise your observation on the results.

Additional Requirements We have additional requirements that,

1. the marker can run your code directly, i.e., see the results of functionality f1 by loading the saved models, without training.

2. You need to provide clear instructions on how to train the two models. The instructions may be e.g., a difffferent command or an easy way of adapting the source code.

Documentation You need to write a proper document

1. detailing how to run your program, including the software dependencies,

2. explaining how the functionalities and additional requirements are implemented, and

3. providing the details of your implementation, including e.g., the meaning of parameters and variables, the description of your model evaluation, etc.

Submission fifiles Your submission should include the following fifiles:

2.2 Marking Criteria

Note 1 At the beginning of the document, please include a check list indicating whether the below marking points have been implemented successfully. Unless exceptional cases, the length of the submitted document needs to be within 4 pages (A4 paper, 11pt font size).

functionality f1: 50%

3functionality f2: 50%

3 Deadlines and How to Submit

”.tar” or ”.zip”

For example, “201191838.tar” or “201191838.zip” if your student number is 201191838.

Submissions with other fifilename will not be accepted. Also, in the submission fifiles, please do not include your name.

4 Q&A

A: As indicated, you can have one class vs. all other classes, where all other classes are deemed as a single class.

A: If you think some functionality is hard to implement, please explain in the document. The marker will then evaluate your explanation to give you a reasonable mark.

A: You can upload them again if needed. Note that, the marker won’t be able to access the fifirst assignment when they are marking the second assignment.

A: Marking is based on the quality of your implementation and your documentation, and will not take the runtime into consideration. On the other hand, you are recommended to explain the details of your program (including the runtime) in your document.

Q: Which version of Tensorflflow shall we use?