Simple Neural Network

This repository contains a basic implementation of a neural network for binary classification using TensorFlow and Keras.

Description

This project demonstrates how to build, train and evaluate a simple neural network model with TensorFlow. It includes code to:

• Generate synthetic data for a binary classification problem
• Create a neural network with one hidden layer
• Train the model
• Evaluate its performance
• Visualize the decision boundary and training metrics

The neural network architecture is deliberately kept simple to serve as an introduction to deep learning concepts.

Requirements

The code requires the following Python packages:

• numpy
• tensorflow
• scikit-learn
• matplotlib

You can install these dependencies using:

pip install -r requirements.txt

Usage

Simply run the Python script:

python simple_neural_network.py

This will:

1. Generate the sample data
2. Build and train the model
3. Display the model's performance
4. Create visualizations of the decision boundary and training history

Model Architecture

The neural network has the following structure:

• Input layer with 2 features
• Hidden layer with 10 neurons and ReLU activation
• Output layer with 1 neuron and sigmoid activation

Example Output

The script will print the model summary and test accuracy, and display two visualizations:

1. A plot showing the decision boundary of the trained model
2. Training and validation metrics over time (loss and accuracy)

License

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Contact

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