a-simple-neural-network-for-classification

Introduction

This repository contains an R script for setting up and training a simple neural network. The code is designed for classification tasks and uses stochastic gradient descent as the optimization method.

Overview

Script Structure

• Initialization Function (netup)

  • Initializes a neural network with random weights and biases.
  • Parameters include a vector specifying the number of nodes in each layer.

• Forward Propagation Function (forward)

  • Performs a forward pass through the neural network.
  • Computes the values of the nodes given the input.

• Backward Propagation Function (backward)

  • Executes a backward pass through the neural network.

• Calculates gradients for loss optimization. Training Function (train)

  • Implements mini-batch gradient descent for network training.
  • Parameters include the neural network, input data, target labels, learning rate, mini-batch size, and number of training iterations.

• Prediction Function (predict.nn)

  • Predicts class labels for input samples using the trained neural network.

• Test Function (test_func)

  • Demonstrates the creation, training, and evaluation of the network using the Iris dataset.
  • Calculates and prints the misclassification rate on the test set.

Usage Instructions

1. Setup
   • Ensure that R is installed on your system. Clone this repository to your local machine.
2. Running the Script
   • Open the script in an R environment (like RStudio).
   • Load the script into your R environment.
   • Call test_func() to run the test procedure. This will create a neural network, train it using the Iris dataset, and output the misclassification rate.
3. Customization
   • Modify the layer_sizes in the test_func to experiment with different network structures.
   • Change the dataset or modify the neural network configuration as needed for different classification tasks.

Important Notes

• This script is designed for educational purposes and to demonstrate basic concepts in neural network implementation.
• The Iris dataset is used as a sample dataset for testing.
• To compute the misclassification rate on the test set, ensure the last line in test_func() is uncommented and run.

Dependencies

• R environment for running the script.
• No additional libraries are required as the script uses basic R functions.

License

This project is licensed under the MIT License - see the LICENSE file for details.