mayini-framework 0.1.7

A comprehensive deep learning framework built from scratch in Python with PyTorch-like API

MAYINI Deep Learning Framework

MAYINI is a comprehensive deep learning framework built from scratch in Python, featuring automatic differentiation, neural network components, and complete training infrastructure. It's designed for educational purposes and research, providing a PyTorch-like API with full transparency into the underlying mechanics.

🚀 Key Features

Core Engine

• Tensor Operations: Complete tensor class with automatic differentiation
• Computational Graph: Cycle detection and gradient computation
• Broadcasting Support: NumPy-style broadcasting for operations

Neural Network Components

• Linear Layers: Dense layers with multiple initialization methods (Xavier, He, Normal)
• Convolutional Layers: 2D convolution with im2col optimization
• Pooling Layers: Max and Average pooling with stride and padding support
• Normalization: Batch Normalization for improved training
• Regularization: Dropout with inverted dropout implementation

Activation Functions

• Standard Functions: ReLU, Sigmoid, Tanh, Softmax
• Modern Activations: GELU, Leaky ReLU
• Numerical Stability: Implemented with overflow/underflow protection

Recurrent Neural Networks

• RNN Cells: Vanilla RNN with configurable activations
• LSTM Cells: Long Short-Term Memory with proper gate mechanisms
• GRU Cells: Gated Recurrent Units for efficient sequence modeling
• Multi-layer Support: Stack multiple RNN layers with dropout

Loss Functions

• Regression: MSE Loss, MAE Loss, Huber Loss
• Classification: Cross-Entropy Loss, Binary Cross-Entropy Loss
• Flexible Reduction: Support for mean, sum, and none reduction modes

Optimization Algorithms

• SGD: Stochastic Gradient Descent with momentum and weight decay
• Adam: Adaptive moment estimation with bias correction
• AdamW: Adam with decoupled weight decay
• RMSprop: Root Mean Square Propagation

Training Infrastructure

• DataLoader: Efficient batch processing with shuffling
• Metrics: Comprehensive evaluation (accuracy, precision, recall, F1)
• Early Stopping: Prevent overfitting with validation monitoring
• Learning Rate Scheduling: Step, exponential, and cosine annealing schedulers
• Checkpointing: Save and restore model states

📦 Installation

From PyPI

pip install mayini-framework

From Source

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e .

Development Installation

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e ".[dev]"

🏃 Quick Start

Basic Tensor Operations

import mayini as mn

# Create tensors with automatic differentiation
x = mn.Tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
y = mn.Tensor([[2.0, 1.0], [1.0, 2.0]], requires_grad=True)

# Perform operations
z = x.matmul(y) + x * 2
loss = z.sum()

# Automatic differentiation
loss.backward()
print(f"Gradient of x: {x.grad}")
print(f"Gradient of y: {y.grad}")

Building Neural Networks

from mayini.nn import Sequential, Linear, ReLU, Softmax

# Create a simple neural network
model = Sequential(
    Linear(784, 256, init_method='he'),
    ReLU(),
    Linear(256, 128, init_method='he'), 
    ReLU(),
    Linear(128, 10),
    Softmax(dim=1)
)

# Forward pass
x = mn.Tensor(np.random.randn(32, 784))
output = model(x)
print(f"Output shape: {output.shape}")

Training a Model

from mayini.optim import Adam
from mayini.nn import CrossEntropyLoss
from mayini.data import DataLoader
from mayini.training import Trainer

# Setup training components
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
train_loader = DataLoader(X_train, y_train, batch_size=64, shuffle=True)

# Create trainer and train
trainer = Trainer(model, optimizer, criterion)
history = trainer.fit(train_loader, epochs=10, verbose=True)

Convolutional Neural Networks

from mayini.nn import Conv2D, MaxPool2D, Flatten

# CNN for image classification
cnn_model = Sequential(
    Conv2D(1, 32, kernel_size=3, padding=1),
    ReLU(),
    MaxPool2D(kernel_size=2),
    Conv2D(32, 64, kernel_size=3, padding=1),
    ReLU(), 
    MaxPool2D(kernel_size=2),
    Flatten(),
    Linear(64 * 7 * 7, 128),
    ReLU(),
    Linear(128, 10),
    Softmax(dim=1)
)

Recurrent Neural Networks

from mayini.nn import RNN, LSTMCell

# LSTM for sequence modeling
lstm_model = RNN(
    input_size=100,
    hidden_size=128, 
    num_layers=2,
    cell_type='lstm',
    dropout=0.2,
    batch_first=True
)

# Process sequences
x_seq = mn.Tensor(np.random.randn(32, 50, 100))  # (batch, seq_len, features)
output, hidden_states = lstm_model(x_seq)

📚 Documentation

API Reference

Core Components

• Tensor: Core tensor class with automatic differentiation
• Module: Base class for all neural network modules
• Sequential: Container for chaining modules

Neural Network Layers

• Linear: Fully connected layer
• Conv2D: 2D convolutional layer
• MaxPool2D, AvgPool2D: Pooling layers
• BatchNorm1d: Batch normalization
• Dropout: Dropout regularization

Activation Functions

• ReLU, Sigmoid, Tanh, Softmax: Standard activations
• GELU, LeakyReLU: Modern activation functions

Loss Functions

• MSELoss: Mean squared error
• CrossEntropyLoss: Cross-entropy for classification
• BCELoss: Binary cross-entropy
• HuberLoss: Robust loss for regression

Optimizers

• SGD: Stochastic gradient descent
• Adam: Adaptive moment estimation
• AdamW: Adam with decoupled weight decay
• RMSprop: Root mean square propagation

Examples

Complete examples are available in the examples/ directory:

• MNIST Classification: Train a neural network on handwritten digits
• CIFAR-10 CNN: Convolutional neural network for image classification
• Text Classification: RNN/LSTM for sequence classification
• Time Series Prediction: Forecasting with recurrent networks

🧪 Testing

Run the test suite:

pytest tests/

Run with coverage:

pytest --cov=mayini tests/

🤝 Contributing

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

Development Setup

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e ".[dev]"
pre-commit install

Running Tests

pytest tests/
black src/
flake8 src/

📖 Educational Use

MAYINI is designed with education in mind. Each component is implemented from scratch with clear, readable code and comprehensive documentation. It's perfect for:

• Learning Deep Learning: Understand how neural networks work under the hood
• Research Projects: Prototype new architectures and algorithms
• Teaching: Demonstrate concepts with transparent implementations
• Experimentation: Quick prototyping of ideas

🔬 Comparison with Other Frameworks

Feature	MAYINI	PyTorch	TensorFlow
Educational Focus	✅	❌	❌
Transparent Implementation	✅	❌	❌
Automatic Differentiation	✅	✅	✅
GPU Support	❌	✅	✅
Production Ready	❌	✅	✅
Easy to Understand	✅	⚠️	❌

📄 License

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

🙏 Acknowledgments

• Inspired by PyTorch's design philosophy
• Built for educational purposes and research
• Thanks to the open-source community for inspiration

📞 Support

• Issues: GitHub Issues
• Documentation: Read the Docs
• Discussions: GitHub Discussions

🗺️ Roadmap

• GPU support with CUDA
• More activation functions (Swish, Mish, etc.)
• Transformer components
• Model serialization/deserialization
• Distributed training support
• Mobile deployment utilities

---

MAYINI - Making AI Neural Intelligence Intuitive 🧠✨# MAYINI Deep Learning Framework

MAYINI is a comprehensive deep learning framework built from scratch in Python, featuring automatic differentiation, neural network components, and complete training infrastructure. It's designed for educational purposes and research, providing a PyTorch-like API with full transparency into the underlying mechanics.

🚀 Key Features

Core Engine

• Tensor Operations: Complete tensor class with automatic differentiation
• Computational Graph: Cycle detection and gradient computation
• Broadcasting Support: NumPy-style broadcasting for operations

Neural Network Components

• Linear Layers: Dense layers with multiple initialization methods (Xavier, He, Normal)
• Convolutional Layers: 2D convolution with im2col optimization
• Pooling Layers: Max and Average pooling with stride and padding support
• Normalization: Batch Normalization for improved training
• Regularization: Dropout with inverted dropout implementation

Activation Functions

• Standard Functions: ReLU, Sigmoid, Tanh, Softmax
• Modern Activations: GELU, Leaky ReLU
• Numerical Stability: Implemented with overflow/underflow protection

Recurrent Neural Networks

• RNN Cells: Vanilla RNN with configurable activations
• LSTM Cells: Long Short-Term Memory with proper gate mechanisms
• GRU Cells: Gated Recurrent Units for efficient sequence modeling
• Multi-layer Support: Stack multiple RNN layers with dropout

Loss Functions

• Regression: MSE Loss, MAE Loss, Huber Loss
• Classification: Cross-Entropy Loss, Binary Cross-Entropy Loss
• Flexible Reduction: Support for mean, sum, and none reduction modes

Optimization Algorithms

• SGD: Stochastic Gradient Descent with momentum and weight decay
• Adam: Adaptive moment estimation with bias correction
• AdamW: Adam with decoupled weight decay
• RMSprop: Root Mean Square Propagation

Training Infrastructure

• DataLoader: Efficient batch processing with shuffling
• Metrics: Comprehensive evaluation (accuracy, precision, recall, F1)
• Early Stopping: Prevent overfitting with validation monitoring
• Learning Rate Scheduling: Step, exponential, and cosine annealing schedulers
• Checkpointing: Save and restore model states

📦 Installation

From PyPI

pip install mayini-framework

From Source

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e .

Development Installation

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e ".[dev]"

🏃 Quick Start

Basic Tensor Operations

import mayini as mn

# Create tensors with automatic differentiation
x = mn.Tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True)
y = mn.Tensor([[2.0, 1.0], [1.0, 2.0]], requires_grad=True)

# Perform operations
z = x.matmul(y) + x * 2
loss = z.sum()

# Automatic differentiation
loss.backward()
print(f"Gradient of x: {x.grad}")
print(f"Gradient of y: {y.grad}")

Building Neural Networks

from mayini.nn import Sequential, Linear, ReLU, Softmax

# Create a simple neural network
model = Sequential(
    Linear(784, 256, init_method='he'),
    ReLU(),
    Linear(256, 128, init_method='he'), 
    ReLU(),
    Linear(128, 10),
    Softmax(dim=1)
)

# Forward pass
x = mn.Tensor(np.random.randn(32, 784))
output = model(x)
print(f"Output shape: {output.shape}")

Training a Model

from mayini.optim import Adam
from mayini.nn import CrossEntropyLoss
from mayini.data import DataLoader
from mayini.training import Trainer

# Setup training components
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
train_loader = DataLoader(X_train, y_train, batch_size=64, shuffle=True)

# Create trainer and train
trainer = Trainer(model, optimizer, criterion)
history = trainer.fit(train_loader, epochs=10, verbose=True)

Convolutional Neural Networks

from mayini.nn import Conv2D, MaxPool2D, Flatten

# CNN for image classification
cnn_model = Sequential(
    Conv2D(1, 32, kernel_size=3, padding=1),
    ReLU(),
    MaxPool2D(kernel_size=2),
    Conv2D(32, 64, kernel_size=3, padding=1),
    ReLU(), 
    MaxPool2D(kernel_size=2),
    Flatten(),
    Linear(64 * 7 * 7, 128),
    ReLU(),
    Linear(128, 10),
    Softmax(dim=1)
)

Recurrent Neural Networks

from mayini.nn import RNN, LSTMCell

# LSTM for sequence modeling
lstm_model = RNN(
    input_size=100,
    hidden_size=128, 
    num_layers=2,
    cell_type='lstm',
    dropout=0.2,
    batch_first=True
)

# Process sequences
x_seq = mn.Tensor(np.random.randn(32, 50, 100))  # (batch, seq_len, features)
output, hidden_states = lstm_model(x_seq)

📚 Documentation

API Reference

Core Components

• Tensor: Core tensor class with automatic differentiation
• Module: Base class for all neural network modules
• Sequential: Container for chaining modules

Neural Network Layers

• Linear: Fully connected layer
• Conv2D: 2D convolutional layer
• MaxPool2D, AvgPool2D: Pooling layers
• BatchNorm1d: Batch normalization
• Dropout: Dropout regularization

Activation Functions

• ReLU, Sigmoid, Tanh, Softmax: Standard activations
• GELU, LeakyReLU: Modern activation functions

Loss Functions

• MSELoss: Mean squared error
• CrossEntropyLoss: Cross-entropy for classification
• BCELoss: Binary cross-entropy
• HuberLoss: Robust loss for regression

Optimizers

• SGD: Stochastic gradient descent
• Adam: Adaptive moment estimation
• AdamW: Adam with decoupled weight decay
• RMSprop: Root mean square propagation

Examples

Complete examples are available in the examples/ directory:

• MNIST Classification: Train a neural network on handwritten digits
• CIFAR-10 CNN: Convolutional neural network for image classification
• Text Classification: RNN/LSTM for sequence classification
• Time Series Prediction: Forecasting with recurrent networks

🧪 Testing

Run the test suite:

pytest tests/

Run with coverage:

pytest --cov=mayini tests/

🤝 Contributing

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

Development Setup

git clone https://github.com/yourusername/mayini-framework.git
cd mayini-framework
pip install -e ".[dev]"
pre-commit install

Running Tests

pytest tests/
black src/
flake8 src/

📖 Educational Use

MAYINI is designed with education in mind. Each component is implemented from scratch with clear, readable code and comprehensive documentation. It's perfect for:

• Learning Deep Learning: Understand how neural networks work under the hood
• Research Projects: Prototype new architectures and algorithms
• Teaching: Demonstrate concepts with transparent implementations
• Experimentation: Quick prototyping of ideas

🔬 Comparison with Other Frameworks

Feature	MAYINI	PyTorch	TensorFlow
Educational Focus	✅	❌	❌
Transparent Implementation	✅	❌	❌
Automatic Differentiation	✅	✅	✅
GPU Support	❌	✅	✅
Production Ready	❌	✅	✅
Easy to Understand	✅	⚠️	❌

📄 License

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

🙏 Acknowledgments

• Inspired by PyTorch's design philosophy
• Built for educational purposes and research
• Thanks to the open-source community for inspiration

📞 Support

• Issues: GitHub Issues
• Documentation: Read the Docs
• Discussions: GitHub Discussions

🗺️ Roadmap

• GPU support with CUDA
• More activation functions (Swish, Mish, etc.)
• Transformer components
• Model serialization/deserialization
• Distributed training support
• Mobile deployment utilities

---

MAYINI - Making AI Neural Intelligence Intuitive 🧠✨