🧠 ANN Perceptron Visualization Example

A simple Artificial Neural Network (ANN) Perceptron implementation with comprehensive visualizations for educational purposes.

📝 Description

This repository contains a straightforward implementation of a neural network perceptron using Keras, designed to demonstrate fundamental concepts of neural networks with visual aids. The example uses a simple 2D dataset to show how a basic neural network can be trained and evaluated, with detailed visualizations of the data, model architecture, training process, and performance metrics.

✨ Features

• 📊 Visualization of 2D input data with target classifications
• 🔄 Implementation of a simple neural network with one hidden layer
• 📈 Training process visualization (accuracy and loss over epochs)
• 📉 Comprehensive model evaluation with confusion matrix
• 🔍 Detailed display of network weights and biases
• 📋 Calculation and display of various performance metrics (precision, recall, etc.)

🛠️ Prerequisites

• Python 3.x
• TensorFlow/Keras
• pandas
• scikit-learn
• seaborn
• matplotlib

🚀 Usage

1. Clone the repository:

git clone https://github.com/yourusername/ANNPerceptronVizExample.git
cd ANNPerceptronVizExample

2. Install the required dependencies:

pip install tensorflow pandas scikit-learn seaborn matplotlib

3. Run the example:

python ann_perceptron_viz.py

4. Observe the visualizations:
   • Initial data scatter plot
   • Model architecture diagram (saved as model.png)
   • Training accuracy and loss graphs
   • Confusion matrix heatmap

📊 Example Output

When you run the script, you'll see:

1. A scatter plot of the input data showing the distribution of the two classes
2. The model summary in the console
3. A plot of the model accuracy over training epochs
4. A plot of the model loss over training epochs
5. Detailed network weights and biases
6. A confusion matrix and various performance metrics including:
   • True/False Positive/Negative counts
   • Precision, Recall, Accuracy
   • True Positive Rate, True Negative Rate
   • And other classification metrics

📄 License

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

🔗 Resources

For more information on the concepts demonstrated in this example:

• Keras Documentation
• Neural Networks Introduction
• Confusion Matrix Explained