https://github.com/ranimeshehata/feed-forward-neural-network-on-mnist

A PyTorch-based project for classifying the MNIST dataset using Feed Forward Neural Networks, including training, validation, results and visualization.
https://github.com/ranimeshehata/feed-forward-neural-network-on-mnist

feedforward-neural-network matplotlib mnist python3 pytorch scikit-learn torchvision

Last synced: 6 days ago
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A PyTorch-based project for classifying the MNIST dataset using Feed Forward Neural Networks, including training, validation, results and visualization.

• Host: GitHub
• URL: https://github.com/ranimeshehata/feed-forward-neural-network-on-mnist
• Owner: ranimeshehata
• Created: 2024-12-30T13:50:14.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-01-02T08:13:41.000Z (29 days ago)
• Last Synced: 2025-01-25T14:13:33.848Z (6 days ago)
• Topics: feedforward-neural-network, matplotlib, mnist, python3, pytorch, scikit-learn, torchvision
• Language: Jupyter Notebook
• Homepage:
• Size: 22.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Feed-Forward-Neural-Network-on-MNIST

This repository contains the implementation of a Feed Forward Neural Network (FFNN) to classify handwritten digits from the MNIST dataset.

## Table of Contents

- [Introduction](#introduction)

- [Dataset](#dataset)

- [Model Architecture](#model-architecture)

- [Training](#training)

- [Evaluation](#evaluation)

- [Results](#results)

- [Usage](#usage)

- [Dependencies](#dependencies)

## Introduction

The MNIST dataset is a large database of handwritten digits commonly used for training various image processing systems. This project implements a Feed Forward Neural Network to classify the digits with high accuracy.

## Dataset

The MNIST dataset contains 60,000 training images and 10,000 test images of handwritten digits from 0 to 9. Each image is 28x28 pixels.

The dataset is automatically downloaded and preprocessed using `torchvision.datasets`.

## Model Architecture

The Feed Forward Neural Network is defined in the `FeedForwardNeuralNetwork` class. The architecture consists of:

- Input layer: 784 neurons (28x28 pixels)

- Hidden layer 1: 64 neurons

- Hidden layer 2: 32 neurons

- Output layer: 10 neurons (one for each digit class)

## Training

The training process involves the following steps:

1. **Initialize the model, loss function, and optimizer**:

    - Model: Softmax Regression

    - Loss Function: Cross-Entropy Loss

    - Optimizer: Stochastic Gradient Descent (SGD)

2. **Training Loop**:

    - For each epoch, iterate over the training dataset in batches.

    - Perform the forward pass to compute the model's predictions.

    - Compute the loss between the predictions and the ground truth labels.

    - Perform the backward pass to compute the gradients.

    - Update the model's parameters using the optimizer.

## Evaluation

The evaluation process involves:

1. **Validation**:

    - Evaluate the model on the validation dataset after each epoch.

    - Compute the validation loss and accuracy to monitor the model's performance.

2. **Testing**:

    - After training, evaluate the model on the test dataset.

    - Compute the test accuracy to assess the model's generalization performance.

## Results

The results of the training and evaluation process include:

- Training and validation loss over epochs.

- Training and validation accuracy over epochs.

- Test accuracy after training.

- Confusion matrix for the test dataset predictions.

- Plots for visualization.

- With and without L2 Regularization.

## Usage

To use this implementation, follow these steps:

1. Clone the repository:

    ```

    git clone https://github.com/ranimeshehata/Feed-Forward-Neural-Network-on-MNIST.git

    cd "Feed Forward Neural Network on MNIST"

    ```

2. Install the required dependencies:

    ```

    pip install torch torchvision scikit-learn matplotlib

    ```

3. Run the model

4. Observe the outputs and you can change batch size, learning rate or number of epochs for model tuning.

## Dependencies

- Python 3.9

- PyTorch

- torchvision

- scikit-learn

- matplotlib