Simple Neural Network Neuron Output Calculation in C

This repository demonstrates the basics of calculating the output of neurons in a neural network using C. Here, a single layer of three neurons processes an input vector using randomized weights and biases to simulate a forward pass in a simple neural network.

Table of Contents

• Introduction
• Requirements
• Code Explanation
• Usage
• Contributing
• License

Introduction

This project simulates a simple neural network layer with three neurons, each receiving the same input vector. Each neuron calculates an output based on its weights, the input, and a bias. This demonstration provides insight into how neural networks calculate outputs in their layers.

Requirements

This code requires a standard C compiler (like GCC). There are no external libraries, so the code is portable across most systems with basic C support.

Code Explanation

1. Input Initialization: We define a four-element input vector for each neuron to process.
2. Random Weight Initialization: Each neuron has a unique set of four weights initialized randomly.
3. Bias Initialization: Biases are set to zero, but this can be modified as desired.
4. Output Calculation: For each neuron, the weighted sum of inputs is calculated, and the bias is added to get the neuron’s output.
5. Rounded Output: The output values are printed to four decimal places.

Compilation and Execution

To compile and execute the code:

gcc neural_network.c -o neural_network
./neural_network

Example Output

After running, you’ll get an output similar to:

The output is: 3.1199 1.9801 2.4341 

Each value represents the output of a neuron after processing the input vector with its weights and bias.

License

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