mlp-classifier's Introduction

MLP-Classifier

This repository explores a Multi-Layer Perceptron (MLP) classifier, comparing a handcrafted NumPy implementation with a PyTorch version. The goal is to classify shop items into categories based on measurements within a provided dataset.

Implemented Features

Handcrafted MLP classifier using only NumPy.
PyTorch MLP classifier.
Training and evaluation of the classifiers.
Visualization of the training process and evaluation results.

Datasets

Datasets are in the data folder.

Measurements of different shop items are stored in the features.txt CSV file. The data is stored in a CSV file as a 10 dimensional array.
The targets are stored in the targets.txt. The targets are the categories of the items. The categories are stored as integers from 1 to 7.
The unkown.txt file contains the measurements of the items that need to be classified.

Code Structure

src - Contains the source code.
__init__.py - The main file to run the code.
network.py - Contains the handcrafted MLP model.
- models - Contains the necessary classes for the handcrafted MLP model.
torch_model.py - Contains the PyTorch MLP model.
utils.py - Contains utility functions for data loading.
visualize.py - Contains functions for visualizing the results.
train.py - Contains functions for training the models.
data - Contains the datasets.

Usage

Run the following command to train the MLP model and visualize the results:
It will first run my handcrafted MLP model and print graphs for losses and confusion matrix and then the PyTorch MLP model.