🧠 Simple Neuron Trainer (by Renamekk)

A simple and lightweight neuron simulator written in Python. It allows training a single neuron to approximate a given input-output relationship using a basic error correction method. Currently works with one input and one output. Multi-neuron support is planned for future versions.

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🚀 Getting Started

1. Installation

1. Clone or download this repository.
2. Make sure you have Python 3.10+ installed.
3. Install all dependencies by running:

python main.py

2. Neuron Overview

The Neuron class provides the following features:

• Weight = The current weight of the neuron.
• Last Error = The last error calculated during training.
• Smoothing = A small factor to control weight adjustments.
• Calculate Output = Computes the neuron output for a given input.
• Train = Adjusts the neuron weight to minimize the error.
• Check Training Status = Determines if further training is required.

Method	Description
neuron_calculate_value(input_data)	Returns the neuron's output for a given input.
train(input, expected_result)	Trains the neuron based on the input and expected output.
train_result()	Checks if training should continue based on the last error.
get_weight()	Returns the current weight of the neuron.
get_last_error()	Returns the last error value.
get_smothing()	Returns the smoothing factor.

3. Example Usage

💻 Training a Neuron for Currency Conversion

neuron = Neuron()

input_data = 200        # Dollar
exspected_result = 170.42  # Euro
print(neuron.neuron_calculate_value(input_data))

iteration = 1

while neuron.train_result():
    neuron.train(input_data, exspected_result)
    iteration += 1

print('--- Successfully Trained ---')
print(neuron.get_weight())
print(neuron.neuron_calculate_value(input_data))

Output:

• Initial output before training.
• Final trained weight.
• Predicted output close to the expected result.

👤 Author

Developed by Renamekk

📝 License

This project is licensed under the MIT License.

You are free to:

• Use the code for personal or commercial projects

• Modify it as you see fit

• Distribute your own versions

However:

• Any modifications or forks are not affiliated with or endorsed by the original author. (Renamekk)