Neural Network Backpropagation Visualizer

An interactive visualization tool that demonstrates backpropagation in a simple 2-layer neural network. This tool helps understand how neural networks learn by showing the forward and backward passes of computation, along with real-time parameter updates.

Features

Interactive visualization of a 2-layer neural network
Real-time parameter adjustment through sliders
Live computation of forward and backward passes
Visualization of gradients and activations
Adjustable learning rate
Reset parameters functionality
Step-by-step training capability

Architecture

The neural network consists of:

Input layer (1 neuron)
Hidden layer (2 neurons)
Output layer (1 neuron)
Sigmoid activation function
Mean Squared Error (MSE) loss function

Requirements

Python 3.x
NumPy
Tkinter (usually comes with Python)

Installation

Clone this repository:

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

Install the required dependencies:

pip install numpy

Usage

Run the visualization tool:

python backprop.py

Controls

Input x: Set the input value (default: 0.5)
Target y: Set the target output value (default: 0.8)
LR (α): Set the learning rate for training
Reset Params: Randomize all network parameters
Train (1 Step): Perform one step of gradient descent

Interactive Features

Adjust network parameters (weights and biases) using sliders
View real-time updates of:
- Network activations
- Parameter values
- Gradients
- Loss value
- Predicted output

How It Works

Forward Pass:
- Input is multiplied by weights and added to biases
- Sigmoid activation is applied
- Final output is computed
- Loss is calculated
Backward Pass:
- Gradients are computed for all parameters
- Parameters are updated using gradient descent
- Updates are reflected in the visualization

Contributing

Feel free to submit issues and enhancement requests!

License

This project is open source and available under the MIT License.

gitced/neural_network_backprop

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