代写AIC2100、代做Python编程语言

日期：2025-06-04 09:36

AIC2100 AI Programming with Python

Lab 6

Yonsei University

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You must follow the specification thorougly!

• Any typos (including whitespace and linebreak) will result in a point deduction.

• If you’re asked to write the comment or docstring, you must add them.

• If some Python libraries are prohibited, importing them will result in 0 points.

• Depending on the lab content, additional rules can be added.

• Please read the specification document carefully and submit your code.

• We won't accept appeals for point deductions based on misinterpreting the lab specification

documentation.

• If any specification is unclear, please post your question on the Q&A board.

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Please refer to the guidelines noted in previous labs. They remain applicable for this and

subsequent labs.

Any updates in guidelines will be announced again.

Coding guideline

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Notation

• To clearly instruct the lab specifications, (1) we use “˽” to depict a whitespace (blank)

character and (2) “¤” for a “\n” (newline) character.

• Underlined text refers to the user input. -> input()

• New notations will be demonstrated additionally on there first mention.

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Importing modules/libraries

You may be asked or permitted to use some specific modules/libraries. You are allowed to use

only specified ones in each problem. For example, if math module is mentioned in problem 1 but

not in problem 2, you are not allowed to use math module in problem 2.

Some custom modules may be required to use in the implementation. We will provide such

module python codes via LearnUs if necessary.

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Class

In this lab, you will be asked to implement class. If you are asked to implement

the class only, you should not include the main program in your code, just like

the function.

Unexpected execution of the main program during the grading can result in 0

points, so please be careful!!

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Class Docstring

You must put the docstring inside the class itself and all class methods, just

like you did in the function.

• 10 points will be deducted per lab problem with missing or insufficient docstrings.

• Don't forget to fill in the docstrings in the provided template code, too!

• Each docstring must be written in English and contain at least 20 characters.

class MyClass:

"""Class docstring"""

def __init__(self):

"""Method docstring"""

(your code continues…)

def method1(self, a, b):

"""Method docstring"""

(your code continues…)

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[Recap] Marking Rule : Comments

• 10 points will be deducted per lab problem with missing or insufficient comments.

• Please put brief comments (#...) with all your code (including functions).

• You must include at least two comments in your code.

• Each comment must be at least 10 characters long and written in English.

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[Recap] Marking Rule : Docstrings

• 10 points will be deducted per lab problem with missing or insufficient docstrings in all

functions.

• Please provide docstrings with all functions.

• Each function must include at least one docstring.

• Each docstring must be written in English and contain at least 20 characters.

• A docstring should briefly explain :

• The purpose of each parameter (if any).

• What the function computes.

• What the function returns to the caller (if anything).

def your_function(*your_args):

"""Your docstring"""

(your code continues…)

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Comment vs Docstring in Function

• If you execute the following code,

• …here’s the result.

• They are different! Be careful not to confuse them.

• Docstring is a “string variable”.

• Comment is not a variable.

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Programming Problems: Function

In some programming problems, you will be asked to implement a function. To solve such problems, you

must read the problem specification to understand:

1. What is the name of the asked function?

2. What input(s) does the asked function receive?

3. What should the function compute?

4. What should the function return?

▪ Unless requested in the problem specification, in your function you should not read input from the user,

and you should not print the results of your function!

▪ To test your function, you must write your own main program, which must call your function with

appropriate input values. In the main program, you can then print the value(s) returned from your

function to check for correctness.

• Do not hand in such a main program that you used to test your function!

• Only the function itself must be handed in.

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Programming Problems: Function

Here’s an example. Assume that you are asked to implement a function my_add that takes two

numbers as parameters and returns their addition.

This is the correct program.

This is the incorrect program.

1. Wrong function name → No point

2. Wrong parameter input → No point

3. Missing docstring → 10 points deduction

4. Including print (or any implementation not requested in the lab

specification) in the main program part → No point

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Problem 1

Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and

a test set of 10,000 examples. Each example is a 28 x 28 grayscale image, associated with a label from 10

classes.

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747

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Problem 1

Labels

Each training and test example is assigned to one of the following labels:

0. T-shirt/top 1. Trouser 2. Pullover

3. Dress 4. Coat 5. Sandal

6. Shirt 7. Sneaker 8. Bag

9. Ankle boot

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747

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Problem 1

Write a classifier model code to classify clothes using the given Fashion MNIST dataset and Pytorch.

The files you need to submit are as follows:

1. Training script (File name must be “lab6_p1.py”)

We have provided a basic skeleton code to LearnUs. Complete and extend the provided LearnUs skeleton

code to include all parts necessary to train your model (see next slide for details).

2. Trained weights (.pth file, File name must be “lab6_p1.pth”)

You must also submit the weights trained based on the code. Scoring is done based on (1) the weights and

(2) the model structure of the code you wrote, and you will be given a score based on whether 12 new

clothing data are correctly classified (see slide 21 for details).

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Problem 1

1. Set network

In this section, write code to define and instantiate the layers of your neural

network with appropriate input and output dimensions.

2. Initialize weight (if needed)

Here, add code to apply a weight initialization scheme to each learnable layer

before training.

3. Set activation function

In this part, specify the activation functions that should follow your layers.

4. Set model

Combine your previously defined layers and activations into a single model

container and move it to the chosen device with .to method.

5. Set loss and optimizer

Write code to choose a loss function and instantiate an optimizer using your

model’s parameters.

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Problem 1

train

In this stage, the model learns patterns

by repeatedly processing the training

data, comparing its predictions to the

true labels, and adjusting its internal

parameters to reduce errors. Through

this iterative optimization, the network’s

weights are refined so that its outputs

become increasingly accurate.

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Problem 1

eval

In this phase, the model is set to

inference mode and processes a single

input image to produce a set of class

scores. It then selects and returns the

class with the highest score as its final

prediction, without altering any model

parameters.

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Problem 1

Generate trainer: Instantiates FashionMNIST Trainer, setting random seed,

device, data loaders, and model skeleton.

Run train: Executes the training loop to optimize the model’s weights on the

FashionMNIST training set.

Evaluate on test set: Computes and prints overall accuracy of the trained model

on the test dataset.

Pick a sample and predict: Randomly selects one test image and its true label,

runs it through eval(), and prints both the actual label and the model’s predicted

label.

Export model: Saves the current model’s learned weights to a file (lab6_p1.pth).

Import model: Loads the saved weights back into the model to restore its state.

Re-evaluate on test set: Runs eval_all() again to confirm the imported weights

yield the same test accuracy as before.

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Problem 1

Note 1. Do not change the seed value (2025) in the code! We will check whether the weights you

submit are derived from the code you actually wrote. This is used to check for plagiarism.

Note 2. The recommended versions of the libraries provided are as follows (latest versions as of

2025/05):

numpy ==2.2.5

torch==2.7.0

torchvision==0.22.0

matplotlib==3.10.3

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Problem 1

Note 3.

This Lab6 Problem1 is scored in the following guide.

1. There are 12 clothing images that are not in the Fashion MNIST dataset.

2. We will classify the 12 images using your submitted model and code.

3. [1] Whether the image was classified correctly and [2] the classification accuracy are evaluated, and up to 6-7

points are given per image. (Since there are 12 images, the total score is 80 points.)

4. Once you have submitted your code and model to Gradescope, you can view the scores for the 12 images. If

you’re not satisfied with your scores, you may retrain your model and resubmit both your code and model.

Note 4.

Do not use different data for model train and test! (Even if it is the Fashion MNIST dataset)

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Marking Criteria

• Score is only given to programs that compile and produce the correct output with Python version

3.

• No points for programs that are named wrongly. Please refer to the following slide for the

required file names.

• Points are deducted for programs that produce warnings.

• Please pay particular attention to the requested output format of your programs. Deviating from

the requested output format results in points deductions.

• Gradescope only allows code to run for up to 10 minutes!

• If your code takes longer than 10 minutes to execute due to excessive computation, it will not be

graded and will be marked as incorrect.

• (10 minutes is a very generous limit for this Lab assignment, but please still pay attention to the

efficiency of your code.)

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Plagiarism

• Plagiarism (Cheating)

– This is an individual assignment. All or some submissions are checked for plagiarism.

• We will not inform you which problems will be checked.

– Once detected, measures will be taken for all students involved in the plagiarism incident

(including the "source'' of the plagiarized code).

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Please prepare the files for the programming problems. The names of the files, their due dates, and the

archive file names are given in the table above.

• Please upload your file by the stated due date on Gradescope.

• Please pay attention to file names.

Deliverables, Due Date and Submission

Problem File name Due

1

lab6_p1.py

lab6_p1.pth

Friday

June 6, 2025,

23:59