Download the PHP package golivehost/brain without Composer

GoLiveHost Brain

A comprehensive PHP neural network library for machine learning and artificial intelligence applications.

Developed by: Go Live Web Solutions (golive.host)
Author: Shubhdeep Singh (GitHub.com/shubhdeepdev)

⚠️ IMPORTANT NOTE
The examples provided are for demonstration purposes only and are trained on very limited datasets.
The outputs may not be accurate or reliable for real-world applications.
For production use, you must train models on larger, representative datasets and thoroughly validate their performance before deployment.

Table of Contents

• Installation
• Features
• Quick Start
• Neural Network Types
• Advanced Features
• Configuration Options
• Examples
• API Reference
• Contributing
• License

Installation

Install via Composer:

Features

Neural Network Architectures

• Feedforward Neural Network - Traditional multi-layer perceptron
• Recurrent Neural Network (RNN) - For sequential data processing
• Long Short-Term Memory (LSTM) - Advanced RNN with memory cells
• Gated Recurrent Unit (GRU) - Simplified LSTM variant
• Liquid State Machine (LSM) - Reservoir computing for complex temporal patterns

Training Features

• Multiple Optimization Algorithms
  • Stochastic Gradient Descent (SGD) with momentum
  • Adam optimizer
  • RMSprop optimizer
  • AdaGrad optimizer
• Advanced Training Options
  • Batch training with configurable batch sizes
  • Learning rate decay
  • Dropout regularization
  • Early stopping with patience
  • Gradient clipping
  • Model checkpointing
• Activation Functions
  • Sigmoid
  • Tanh
  • ReLU
  • Leaky ReLU
  • Softmax
  • Linear

Data Processing

• Preprocessing Utilities
  • Data normalization
  • Data formatting for sequences
  • One-hot encoding
• Evaluation Tools
  • Cross-validation (k-fold, stratified k-fold, leave-one-out)
  • Train-test split
  • Multiple evaluation metrics (MSE, RMSE, R², accuracy, precision, recall, F1)
• Model Management
  • Model serialization (JSON)
  • Model export to standalone PHP
  • Model checkpointing during training

Additional Features

• Batch Normalization - For improved training stability
• Matrix and Tensor Operations - Comprehensive mathematical utilities
• Model Validation - Input validation and error handling
• GPU Support Consideration - Architecture designed for future GPU acceleration
• PHP 8.0+ Compatibility - Modern PHP features and type safety

Quick Start

Basic Neural Network (XOR Problem)

Neural Network Types

Using the Brain Factory

The Brain class provides a convenient factory for creating different types of neural networks:

LSTM for Time Series Prediction

GRU for Sequential Data

Liquid State Machine for Complex Patterns

Advanced Features

Cross-Validation

Model Checkpointing

Batch Normalization

Custom Optimizers

Data Preprocessing

Model Validation

Configuration Options

Neural Network Options

LSTM Options

Liquid State Machine Options

Examples

The library includes comprehensive examples in the examples directory:

• basic.php - Simple XOR problem demonstration
• advanced.php - Character recognition and time series prediction
• advanced-examples.php - Non-linear regression, LSTM time series, and LSM classification
• image-recognition.php - Simple shape recognition with neural networks
• text-generation.php - Character-level text generation using LSTM
• stock-prediction.php - Time series prediction for financial data
• cross-validation-example.php - Demonstration of cross-validation techniques
• model-checkpoint-example.php - Model checkpointing during training
• batch-normalization-example.php - Using batch normalization layers
• optimizer-example.php - Comparing different optimization algorithms

API Reference

Matrix Operations

Tensor Operations

Model Export

Error Handling

The library uses custom exceptions for better error handling:

Performance Considerations

1. Memory Usage: The library is optimized for memory efficiency, especially in LSTM implementations
2. Batch Processing: Use batch training for better performance with large datasets
3. Learning Rate Decay: Helps achieve better convergence
4. Early Stopping: Prevents overfitting and reduces training time
5. Model Checkpointing: Save progress during long training sessions

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

License

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

Support

• Issues: GitHub Issues
• Documentation: Wiki
• Examples: See the examples directory

Credits

Developed by Go Live Web Solutions (golive.host)
Author: Shubhdeep Singh (GitHub.com/shubhdeepdev)

Building intelligent PHP applications with neural networks made simple.