multilayer-perceptron-from-scratch 0.1.1

A clean and educational implementation of a Multilayer Perceptron from scratch using NumPy

Multilayer Perceptron (MLP) from Scratch 🧠

A lightweight, educational, and high-performance implementation of a Multilayer Perceptron neural network, built entirely from scratch using NumPy.

This project implements the fundamental mathematics of deep learning—including Backpropagation and Stochastic Gradient Descent (SGD) with data shuffling—without the overhead of heavy frameworks like PyTorch or TensorFlow.

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✨ Key Features

• 🎯 Pure NumPy: Zero dependencies for the core engine (only numpy).

• 🔄 Stochastic Gradient Descent (SGD): Includes automatic data shuffling every epoch for better convergence.

• 🏗️ Flexible Architecture: Custom-define any number of hidden layers and neurons.

• 📉 Matrix-Based Backpropagation: Efficient implementation using the four fundamental equations of backpropagation.

• 🎓 Educational Design: Clean, commented code ideal for learning how neural networks actually work "under the hood."

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📦 Installation

You can install the package directly from PyPI:

pip install multilayer-perceptron-from-scratch

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🚀 Quick Start (XOR Example)

The following example demonstrates how to solve the classic XOR problem using the Network class.

import numpy as np
from multilayer_perceptron import Network

# 1. Define Architecture: [Input (2), Hidden (20), Output (1)]
net = Network([2, 20, 1])

# 2. Prepare Training Data (XOR)
# Format: List of tuples (input_vector, target_vector)
training_data = [
    (np.array([[0], [0]]), np.array([[0]])),
    (np.array([[0], [1]]), np.array([[1]])),
    (np.array([[1], [0]]), np.array([[1]])),
    (np.array([[1], [1]]), np.array([[0]]))
]

# 3. Train using SGD
# Parameters: data, epochs, learning_rate
print("Starting Training...")
net.SGD(training_data, epochs=3000, learning_rate=0.2)

# 4. Test the model
print("\nResults:")
for x, y in training_data:
    output, _, _ = net.feedforward(x)
    prediction = 1 if output >= 0.5 else 0
    print(f"Input: {x.flatten()} | Target: {int(y[0][0])} | Predicted: {prediction}")

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📜 License

Distributed under the MIT License. See LICENSE for more information.

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Created with ❤️ by Nehorai Yosef