MNIST Neural Network

This project implements a simple neural network to classify handwritten digits from the MNIST dataset. There are three implementations of the neural network:

1. Single Layer Neural Network: Implemented in NN1Layer.py.
2. Two Layer Neural Network: Implemented in NN2Layer.py.
3. Three Layer Neural Network: Implemented in NN3Layer.py.

The trained model provided in the trained_model.pkl file is based on the two-layer neural network implementation.

Files

• NN1Layer.py: Script containing the single layer neural network implementation.
• NN2Layer.py: Script containing the two layer neural network implementation.
• NN3Layer.py: Script containing the three layer neural network implementation.
• NuronalNetwork.py: Main script for loading the trained model and making predictions.
• mnist_train.csv: Training dataset.
• mnist_test.csv: Test dataset.
• trained_model.pkl: Pre-trained model using the two-layer neural network.
• tests/: Folder containing test images for prediction.
• archive.zip: Contains mnist_train.csv and mnist_test.csv.

Usage

1. Ensure that the mnist_train.csv and mnist_test.csv files are in the same directory as the scripts.
2. To train and evaluate the neural network, run the respective script:
   • For single layer neural network: python NN1Layer.py
   • For two layer neural network: python NN2Layer.py
   • For three layer neural network: python NN3Layer.py
3. To make predictions using the pre-trained model, run the NuronalNetwork.py script:

   python NuronalNetwork.py

Requirements

• numpy
• pillow

Install the required packages using:

pip install numpy pillow

Detailed Explanation

Single Layer Neural Network

The single layer neural network is implemented in NN1Layer.py. It consists of one hidden layer and an output layer. The script includes functions for forward propagation, backward propagation, and parameter updates.

Two Layer Neural Network

The two layer neural network is implemented in NN2Layer.py. It consists of two hidden layers and an output layer. The script includes functions for forward propagation, backward propagation, parameter updates, and mini-batch gradient descent. The pre-trained model provided in trained_model.pkl is based on this implementation.

Three Layer Neural Network

The three layer neural network is implemented in NN3Layer.py. It consists of three hidden layers and an output layer. The script includes functions for forward propagation, backward propagation, parameter updates, and mini-batch gradient descent.

Pre-trained Model

The pre-trained model provided in trained_model.pkl was trained using the two layer neural network implementation. The model includes the learned parameters and normalization values (mean and standard deviation) used during training. To use the pre-trained model for making predictions, run the NuronalNetwork.py script.

Loading and Preprocessing Images

The NuronalNetwork.py script includes functions for loading and preprocessing images from the tests/ folder. The images are resized to 28x28 pixels, converted to grayscale, normalized, and standardized using the mean and standard deviation from the training set.

Making Predictions

To make predictions on new images using the pre-trained model, place the images in the tests/ folder and run the NuronalNetwork.py script. The script will load the images, preprocess them, and make predictions using the pre-trained model.