NYCU_AI_2024

National Yang Ming Chiao Tung University Graduate Course, Artificial Intelligence.
國立陽明交通大學 2024 Fall Semester 人工智慧 楊元福教授

Assignment Description

This repository contains three assignment folders:

• HW1
• HW2
• HW3

Installation Steps

1. Clone this repository to your local machine:

   git clone https://github.com/Vincent-Lien/NYCU_AI_2024.git
   cd NYCU_AI_2024

2. Install the required Python packages:

   pip install -r requirement.txt

• This repository is based on Python 3.10.

Running Assignments

HW1

HW1 is a Jupyter Notebook. Please open and run HW1_code.ipynb using Jupyter Notebook or Jupyter Lab.

HW2

HW2 is also a Jupyter Notebook. Please open and run HW2_code.ipynb using Jupyter Notebook or Jupyter Lab.

HW3

HW3 contains a Python script and some related data. Please follow these steps to run it:

1. Navigate to the HW3 folder:

   cd HW3

2. Run the main.py script:

   python main.py [--model {Net,CNN}] [--batch-size N] [--lr LR] [--epochs N]

   Additional parameters:

   • --model: Choose model architecture between Net or CNN (default: Net)
   • --batch-size: Set batch size for training (default: 64)
   • --lr: Set learning rate (default: 1.0)
   • --epochs: Set number of epochs to train (default: 14)

3. Or run all models using the provided shell script:

   ./run_all_models.sh

   This script will execute all models sequentially and save the results.

   Example configurations:

   Models: Net, CNN
   Batch sizes: 16, 64, 256
   Learning rates: 0.1, 0.01, 0.001
   Epochs: 10, 100, 500
   

   The script will evaluate model performance across these parameter combinations.

   Resources and References

   • For a comprehensive Python and NumPy tutorial, visit CS231n Python NumPy Tutorial