nn_implementation

A Python-only repository implementing and analyzing a neural network on the Fashion-MNIST dataset. It includes reusable modules, an experiment runner, a saved model checkpoint, and figures that visualize training and evaluation results.

Languages: Python (100%)

Table of Contents

• Overview
• Repository layout
• Setup
• Usage
• Results and artifacts
• Development notes
• License

Overview

This project provides a simple, modular neural network implementation and training pipeline, likely using PyTorch (inferred from the .pt checkpoint). Code is organized into:

• An experiment runner to orchestrate training/evaluation
• Custom layers/modules and utility functions
• Saved model weights and generated analysis plots

Repository layout

Top-level:

• .gitignore — Common Python ignores.
• .gitattributes — Attributes for Git.
• .DS_Store — macOS metadata (can be ignored).

Package directory: nn_implementation_code/

• init.py — Marks the directory as a Python package.
• base_experiment.py — Experiment/training loop orchestration.
• custom_functions.py — Utility functions (e.g., metrics, data handling, helpers).
• custom_modules.py — Model components or layers.
• data/ — Placeholder for data assets (empty in repo).
• fashion_mnist_sgd.pt — Saved model checkpoint (likely trained with SGD).
• q5_first_misclassified_per_class.png — Analysis figure (example of misclassifications).
• q6_test_loss_vs_epoch.png — Test loss curve.
• q6_train_loss_vs_epoch.png — Train loss curve.

Setup

Requirements (typical for PyTorch projects):

• Python 3.9+ recommended
• PyTorch and torchvision
• matplotlib, numpy

Example:

python -m venv .venv
. .venv/bin/activate  # or .venv\Scripts\activate on Windows

pip install torch torchvision matplotlib numpy

Usage

As the repository structure implies modular code, a typical usage pattern is:

• Edit or create a small script to import base_experiment.py, define hyperparameters, and run training/evaluation.
• Alternatively, if base_experiment.py can be run directly, invoke it with appropriate arguments. Check the file’s CLI (if present) or adapt the import example below.

Example: running an experiment from a driver script

# run_experiment.py
from nn_implementation_code.base_experiment import main  # or a suitable entry function

if __name__ == "__main__":
    # Provide config via arguments or dict (adjust to match base_experiment.py interface)
    # e.g., epochs, batch_size, lr, model config, dataset path, etc.
    main(
        epochs=10,
        batch_size=64,
        learning_rate=0.01,
        optimizer="sgd",
        seed=42,
    )

Then:

python run_experiment.py

Loading the saved checkpoint:

import torch
from nn_implementation_code.custom_modules import build_model  # hypothetical factory

model = build_model()
state = torch.load("nn_implementation_code/fashion_mnist_sgd.pt", map_location="cpu")
model.load_state_dict(state)
model.eval()

Note: Adjust imports and function names to match the actual APIs in base_experiment.py and custom_modules.py.

Results and artifacts

The repository includes example outputs:

• Training/validation curves:
  • q6_train_loss_vs_epoch.png
  • q6_test_loss_vs_epoch.png
• Misclassification analysis:
  • q5_first_misclassified_per_class.png
• Trained weights:
  • fashion_mnist_sgd.pt

Use these to verify your local runs or to perform further analysis.

Development notes

• base_experiment.py likely contains the training loop, evaluation, logging, and plotting hooks. Extend it to add features like:
  • Better CLI/argparse configuration
  • Checkpointing and resume
  • TensorBoard logging
• custom_modules.py and custom_functions.py are the main extension points for models and utilities:
  • Add architectures or preprocessing pipelines
  • Implement additional metrics or data transforms
• Place dataset caches or auxiliary files under nn_implementation_code/data/ (keep large files out of Git; prefer downloads or caching at runtime).

License

No explicit license file is present. If you plan to share or extend this work, consider adding a LICENSE (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage terms.

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For questions or contributions, please open an issue or submit a PR.