Lab 2: Neural Network Implementation

This project implements a neural network using PyTorch and TorchVision. It includes training and evaluation of a ResNet-18 model on the CIFAR-10 dataset. The project allows customization of hyperparameters such as learning rate, optimizer, and computing device.

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Requirements

• Python 3.x
• PyTorch
• TorchVision

Install the required libraries using:

pip install torch torchvision

Usage

Run with Default Parameters

python lab2.py

Run with Custom Parameters

python lab2.py --lr 0.01 --device cuda --opt adam --c7

Parameters Desctiption

Parameter	Description	Default Value	Options/Constraints
--lr	Learning rate	0.1	Must be a float < 1
--device	Computing device	cpu	cpu, cuda
--num_workers	Number of I/O workers	2	Must be an integer
--data_path	Dataset download directory	./data	Path to directory
--opt	Optimizer selection	sgd	sgd, nesterov, adam, adagrad, adadelta
--c7	Disable batch normalization in ResNet-18	False	Set to True to disable batch normalization

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Project Structure

├── lab2.py               # Main script for training and evaluation
├── plotting.py           # Script for generating figures
├── README.md             # Project documentation
└── data/                 # Directory for storing the dataset (default)
Additional Files
plotting.py: Generates figures for the report, such as training loss and test accuracy plots.

Example Commands

Train the model with a learning rate of 0.01 on a GPU using the Adam optimizer:

python lab2.py --lr 0.01 --device cuda --opt adam

Train the model without batch normalization:

python lab2.py --c7 Output The script will print the training loss and test accuracy for each epoch. Example output:

Epoch 1, Train Loss: 1.2345, Test Accuracy: 0.5678
Epoch 2, Train Loss: 1.1234, Test Accuracy: 0.6789

Notes

Ensure the dataset is downloaded to the correct directory (./data by default).

Experiment with different hyperparameters to observe their impact on performance.

If you encounter GPU memory issues, reduce the batch size or switch to the CPU.