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mayini-framework

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mayini-framework

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

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Version: 0.7.3

Maintainers: 1

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

Complete Tensor Engine with automatic differentiation
Neural Network Layers: Linear, Conv2D, Pooling, BatchNorm, Dropout
Activation Functions: ReLU, Sigmoid, Tanh, Softmax, GELU, LeakyReLU
RNN Components: Vanilla RNN, LSTM, GRU with multi-layer support
Loss Functions: MSE, MAE, CrossEntropy, BCE, Huber
Optimizers: SGD, Adam, AdamW, RMSprop
Learning Rate Schedulers: StepLR, ExponentialLR, CosineAnnealingLR
Training Infrastructure: DataLoader, Trainer, Metrics, Early Stopping
Educational Focus: Clear implementations with mathematical formulas

📦 Installation

pip install mayini-framework

🎓 Try It Now

Interactive Colab Notebook: Open in Google Colab

The notebook contains 38 working examples demonstrating all framework features!

📚 Quick Start Guide

1. Tensor Operations with Autograd

import mayini as mn
import numpy as np

# Create tensors with gradient tracking
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)      # Matrix multiplication
w = x + y            # Element-wise addition
loss = z.sum()

# Automatic differentiation
loss.backward()
print(f"Gradient of x: {x.grad}")
# Output: [[3. 3.] [3. 3.]]

2. Building Neural Networks

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

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}")  # (32, 10)

3. Complete Training Example

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

# Prepare data
X_train = np.random.randn(1000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 1000)
X_val = np.random.randn(200, 784).astype(np.float32)
y_val = np.random.randint(0, 10, 200)

train_loader = DataLoader(X_train, y_train, batch_size=64, shuffle=True)
val_loader = DataLoader(X_val, y_val, batch_size=64, shuffle=False)

# Setup training
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
trainer = Trainer(model, optimizer, criterion)

# Train
history = trainer.fit(
    train_loader,
    epochs=10,
    val_loader=val_loader,
    verbose=True
)

print(f"Final training accuracy: {history['train_acc'][-1]:.4f}")
print(f"Final validation accuracy: {history['val_acc'][-1]:.4f}")

📖 Complete API Reference

Core Components

Tensor

Core tensor class with automatic differentiation.

Key Methods:

matmul(other) - Matrix multiplication
sum(axis=None, keepdims=False) - Sum reduction
mean(axis=None, keepdims=False) - Mean reduction
reshape(shape) - Reshape tensor
transpose(axes=None) - Transpose dimensions
backward(gradient=None) - Compute gradients
zero_grad() - Reset gradients

# Example
x = mn.Tensor([[1, 2], [3, 4]], requires_grad=True)
y = x.matmul(x.transpose())
y.sum().backward()

Neural Network Layers

Linear (Fully Connected)

from mayini.nn import Linear

layer = Linear(
    in_features=784,
    out_features=256,
    bias=True,
    init_method='xavier'  # 'xavier', 'he', or 'normal'
)

Conv2D (2D Convolution)

from mayini.nn import Conv2D

conv = Conv2D(
    in_channels=3,
    out_channels=64,
    kernel_size=3,
    stride=1,
    padding=1,
    bias=True
)

Pooling Layers

from mayini.nn import MaxPool2D, AvgPool2D

max_pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
avg_pool = AvgPool2D(kernel_size=2, stride=2, padding=0)

Batch Normalization

from mayini.nn import BatchNorm1d

bn = BatchNorm1d(num_features=256, eps=1e-5, momentum=0.1)

Dropout

from mayini.nn import Dropout

dropout = Dropout(p=0.5)
dropout.train()  # Enable dropout
dropout.eval()   # Disable dropout

Flatten

from mayini.nn import Flatten

flatten = Flatten(start_dim=1)

Activation Functions

All activation functions with mathematical formulas and use cases:

ReLU

Formula: f(x) = max(0, x)
Use case: Most common for hidden layers

from mayini.nn import ReLU
relu = ReLU()

Sigmoid

Formula: f(x) = 1 / (1 + e^(-x))
Use case: Binary classification, LSTM gates

from mayini.nn import Sigmoid
sigmoid = Sigmoid()

Tanh

Formula: f(x) = (e^x - e^(-x)) / (e^x + e^(-x))
Use case: RNNs, zero-centered activation

from mayini.nn import Tanh
tanh = Tanh()

Softmax

Formula: f(x_i) = e^(x_i) / Σ e^(x_j)
Use case: Multi-class classification output

from mayini.nn import Softmax
softmax = Softmax(dim=1)

GELU

Use case: Transformers, BERT, GPT models

from mayini.nn import GELU
gelu = GELU()

Leaky ReLU

Formula: f(x) = max(αx, x) where α = 0.01
Use case: Prevent dead neurons

from mayini.nn import LeakyReLU
leaky_relu = LeakyReLU(negative_slope=0.01)

Recurrent Neural Networks

RNN Cell

from mayini.nn import RNNCell

rnn_cell = RNNCell(input_size=100, hidden_size=128, bias=True)
h_next = rnn_cell(x_t, h_t)

LSTM Cell

Gates: Forget, Input, Output, Cell Candidate
Formula:

Forget gate: f_t = σ(W_f · [h_{t-1}, x_t] + b_f)
Input gate: i_t = σ(W_i · [h_{t-1}, x_t] + b_i)
Output gate: o_t = σ(W_o · [h_{t-1}, x_t] + b_o)
Cell state: C_t = f_t ⊙ C_{t-1} + i_t ⊙ tanh(W_C · [h_{t-1}, x_t])

from mayini.nn import LSTMCell

lstm_cell = LSTMCell(input_size=100, hidden_size=128, bias=True)

# Single timestep
x_t = mn.Tensor(np.random.randn(32, 100))
h_t = mn.Tensor(np.random.randn(32, 128))
c_t = mn.Tensor(np.random.randn(32, 128))

# ✅ FIX: Call .forward() directly
h_next, c_next = lstm_cell.forward(x_t, (h_t, c_t))
print(f"Next hidden: {h_next.shape}, Next cell: {c_next.shape}")

GRU Cell

Gates: Reset, Update, New
Formula:

Reset gate: r_t = σ(W_r · [h_{t-1}, x_t])
Update gate: z_t = σ(W_z · [h_{t-1}, x_t])
Hidden state: h_t = (1 - z_t) ⊙ tanh(W · [r_t ⊙ h_{t-1}, x_t]) + z_t ⊙ h_{t-1}

from mayini.nn import GRUCell

gru_cell = GRUCell(input_size=100, hidden_size=128, bias=True)

# Single timestep
x_t = mn.Tensor(np.random.randn(32, 100))
h_t = mn.Tensor(np.random.randn(32, 128))

# ✅ FIX: Call .forward() directly
h_next = gru_cell.forward(x_t, h_t)
print(f"Next hidden state: {h_next.shape}")

Multi-layer RNN

from mayini.nn import RNN

# Multi-layer LSTM
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)

# ✅ FIX: This will work after you fix Module.__call__() in modules.py
# OR use this temporary workaround:
output, hidden_states = lstm_model.forward(x_seq)

print(f"Output shape: {output.shape}")
print(f"Number of hidden states: {len(hidden_states)}")

Loss Functions

MSE Loss

Formula: L = (1/n) Σ (y_i - ŷ_i)²
Use case: Regression tasks

from mayini.nn import MSELoss
criterion = MSELoss(reduction='mean')  # 'mean', 'sum', or 'none'

MAE Loss

Formula: L = (1/n) Σ |y_i - ŷ_i|
Use case: Robust regression

from mayini.nn import MAELoss
criterion = MAELoss(reduction='mean')

Cross-Entropy Loss

Formula: L = -(1/n) Σ log(e^(f_yi) / Σ e^(f_j))
Use case: Multi-class classification

from mayini.nn import CrossEntropyLoss

criterion = CrossEntropyLoss(reduction='mean')

Binary Cross-Entropy

Formula: L = -(1/n) Σ [y_i log(ŷ_i) + (1-y_i) log(1-ŷ_i)]
Use case: Binary classification

from mayini.nn import BCELoss
criterion = BCELoss(reduction='mean')

Huber Loss

Use case: Robust regression with outliers

from mayini.nn import HuberLoss
criterion = HuberLoss(delta=1.0, reduction='mean')

Optimizers

SGD (Stochastic Gradient Descent)

Update rule: v_t = β·v_{t-1} + g_t, θ_t = θ_{t-1} - η·v_t

from mayini.optim import SGD

optimizer = SGD(
    model.parameters(),
    lr=0.01,
    momentum=0.9,
    weight_decay=1e-4
)

Adam

Update rule: Adaptive moment estimation with bias correction

from mayini.optim import Adam

optimizer = Adam(
    model.parameters(),
    lr=0.001,
    beta1=0.9,
    beta2=0.999,
    eps=1e-8,
    weight_decay=0.0
)

AdamW

Feature: Decoupled weight decay

from mayini.optim import AdamW

optimizer = AdamW(
    model.parameters(),
    lr=0.001,
    weight_decay=0.01
)

RMSprop

from mayini.optim import RMSprop

optimizer = RMSprop(
    model.parameters(),
    lr=0.01,
    alpha=0.99,
    momentum=0.0
)

Learning Rate Schedulers

StepLR

Decays LR by gamma every step_size epochs

from mayini.optim import StepLR

scheduler = StepLR(optimizer, step_size=10, gamma=0.1)

for epoch in range(50):
    train_one_epoch()
    scheduler.step()

ExponentialLR

Exponential decay by gamma each epoch

from mayini.optim import ExponentialLR

scheduler = ExponentialLR(optimizer, gamma=0.95)

CosineAnnealingLR

Cosine annealing schedule

from mayini.optim import CosineAnnealingLR

scheduler = CosineAnnealingLR(optimizer, T_max=50, eta_min=0)

Training Utilities

DataLoader

from mayini.training import DataLoader

train_loader = DataLoader(
    X_train,
    y_train,
    batch_size=64,
    shuffle=True
)

for batch_X, batch_y in train_loader:
    # Training code
    pass

Trainer

from mayini.training import Trainer

trainer = Trainer(
    model,      # Neural network model (Module)
    optimizer,  # Optimization algorithm (Optimizer)
    criterion   # Loss function (Module)
)

Trainer Methods:

fit() - Train the model
evaluate() - Evaluate on test data
predict() - Make predictions
save_checkpoint() - Save model state
load_checkpoint() - Load model state

####fit()

history = trainer.fit(
    train_loader,              # Training data loader
    epochs=10,                 # Number of training epochs
    val_loader=None,           # Optional validation data loader
    early_stopping=None,       # Optional early stopping callback
    verbose=True,              # Print training progress
    save_best=True,            # Save best model based on validation loss
    checkpoint_path='model.pkl' # Path to save checkpoints
)

Metrics

from mayini.training import Metrics

# Classification metrics
accuracy = Metrics.accuracy(predictions, targets)
precision, recall, f1 = Metrics.precision_recall_f1(predictions, targets, num_classes=10)
cm = Metrics.confusion_matrix(predictions, targets, num_classes=10)

# Regression metrics
mse = Metrics.mse(predictions, targets)
mae = Metrics.mae(predictions, targets)
r2 = Metrics.r2_score(predictions, targets)

evaluate()

results = trainer.evaluate(
    test_loader,    # Test data loader
    detailed=True   # Compute detailed metrics
)

predict()

predictions = trainer.predict(X)  # Returns numpy array

Early Stopping

from mayini.training import EarlyStopping

early_stopping = EarlyStopping(
    patience=7,
    min_delta=0.0,
    restore_best_weights=True,
    mode='min'  # 'min' for loss, 'max' for accuracy
)

history = trainer.fit(
    train_loader,
    epochs=100,
    val_loader=val_loader,
    early_stopping=early_stopping
)

Metrics

from mayini.training import Metrics

accuracy()

accuracy = Metrics.accuracy(predictions, targets)
# Returns: float (0.0 to 1.0)

precision_recall_f1()

precision, recall, f1 = Metrics.precision_recall_f1(
    predictions, 
    targets, 
    num_classes=10
)
# Returns: Three numpy arrays of shape (num_classes,)

confusion_matrix()

cm = Metrics.confusion_matrix(predictions, targets, num_classes=10)
# Returns: numpy array of shape (num_classes, num_classes)

r2_score()

r2 = Metrics.r2_score(predictions, targets)

💡 Complete Examples

Example 1: Basic Training

import numpy as np
import mayini as mn
from mayini.nn import Sequential, Linear, ReLU, Softmax, CrossEntropyLoss
from mayini.optim import Adam
from mayini.training import DataLoader, Trainer

# Build model
model = Sequential(
    Linear(784, 128, init_method='he'),
    ReLU(),
    Linear(128, 10),
    Softmax(dim=1)
)

# Prepare data
X_train = np.random.randn(5000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 5000)

train_loader = DataLoader(X_train, y_train, batch_size=128, shuffle=True)

# Train
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
trainer = Trainer(model, optimizer, criterion)

history = trainer.fit(train_loader, epochs=20, verbose=True)

Example 2: MNIST Classification

import mayini as mn
import numpy as np
from mayini.nn import Sequential, Linear, ReLU, Dropout, Softmax, CrossEntropyLoss
from mayini.optim import Adam
from mayini.training import DataLoader, Trainer

# Build model
model = Sequential(
    Linear(784, 512, init_method='he'),
    ReLU(),
    Dropout(0.2),
    Linear(512, 256, init_method='he'),
    ReLU(),
    Dropout(0.2),
    Linear(256, 10),
    Softmax(dim=1)
)

# Prepare data
X_train = np.random.randn(5000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 5000)
X_val = np.random.randn(1000, 784).astype(np.float32)
y_val = np.random.randint(0, 10, 1000)

train_loader = DataLoader(X_train, y_train, batch_size=128, shuffle=True)
val_loader = DataLoader(X_val, y_val, batch_size=128, shuffle=False)

# Train
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
trainer = Trainer(model, optimizer, criterion)

history = trainer.fit(train_loader, epochs=20, val_loader=val_loader, verbose=True)

Example 3: CNN for Image Classification

from mayini.nn import Conv2D, MaxPool2D, Flatten, BatchNorm1d

cnn_model = Sequential(
    # Conv block 1
    Conv2D(1, 32, kernel_size=3, padding=1),
    ReLU(),
    MaxPool2D(kernel_size=2, stride=2),
    
    # Conv block 2
    Conv2D(32, 64, kernel_size=3, padding=1),
    ReLU(),
    MaxPool2D(kernel_size=2, stride=2),
    
    # Classifier
    Flatten(),
    Linear(64 * 7 * 7, 256),
    ReLU(),
    Dropout(0.5),
    Linear(256, 10),
    Softmax(dim=1)
)

# Train similarly to Example 1

Example 4: LSTM for Sequence Classification

from mayini.nn import RNN

lstm_model = Sequential(
    RNN(
        input_size=100,
        hidden_size=128,
        num_layers=2,
        cell_type='lstm',
        dropout=0.3,
        batch_first=True
    ),
    Linear(128, 64),
    ReLU(),
    Linear(64, 3),
    Softmax(dim=1)
)

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

Example 5: Custom Training Loop

# Manual training loop with learning rate scheduling
from mayini.optim import Adam, StepLR

optimizer = Adam(model.parameters(), lr=0.1)
scheduler = StepLR(optimizer, step_size=10, gamma=0.1)
criterion = CrossEntropyLoss()

for epoch in range(50):
    model.train()
    epoch_loss = 0
    
    for batch_X, batch_y in train_loader:
        # Forward pass
        predictions = model(batch_X)
        loss = criterion(predictions, batch_y)
        
        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        epoch_loss += loss.item()
    
    # Update learning rate
    scheduler.step()
    
    print(f"Epoch {epoch+1}: Loss = {epoch_loss/len(train_loader):.4f}, LR = {optimizer.lr:.6f}")

Example 6:Training with validation

import numpy as np
from mayini.nn import Sequential, Linear, ReLU, Dropout, Softmax, CrossEntropyLoss
from mayini.optim import Adam
from mayini.training import DataLoader, Trainer

# Build model with dropout
model = Sequential(
    Linear(784, 512, init_method='he'),
    ReLU(),
    Dropout(0.3),
    Linear(512, 256, init_method='he'),
    ReLU(),
    Dropout(0.3),
    Linear(256, 10),
    Softmax(dim=1)
)

# Prepare train and validation data
X_train = np.random.randn(5000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 5000)
X_val = np.random.randn(1000, 784).astype(np.float32)
y_val = np.random.randint(0, 10, 1000)

train_loader = DataLoader(X_train, y_train, batch_size=128, shuffle=True)
val_loader = DataLoader(X_val, y_val, batch_size=128, shuffle=False)

# Train with validation
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
trainer = Trainer(model, optimizer, criterion)

history = trainer.fit(
    train_loader,
    epochs=30,
    val_loader=val_loader,
    verbose=True
)

# Plot training curves (if matplotlib available)
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)
plt.plot(history['train_loss'], label='Train Loss')
plt.plot(history['val_loss'], label='Val Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.title('Training and Validation Loss')

plt.subplot(1, 2, 2)
plt.plot(history['train_acc'], label='Train Acc')
plt.plot(history['val_acc'], label='Val Acc')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.title('Training and Validation Accuracy')

plt.tight_layout()
plt.show()

Example 7: Evaluation and Testing

import numpy as np
from mayini.training import Trainer, DataLoader, Metrics

# Assume model is already trained (from previous examples)

# Prepare test data
X_test = np.random.randn(1000, 784).astype(np.float32)
y_test = np.random.randint(0, 10, 1000)
test_loader = DataLoader(X_test, y_test, batch_size=128, shuffle=False)

# Evaluate
results = trainer.evaluate(test_loader, detailed=True)

print("Test Results:")
print(f"Test Loss: {results['test_loss']:.4f}")
print(f"Test Accuracy: {results['accuracy']:.4f}")

print("\nPer-class Metrics:")
for i in range(10):
    print(f"Class {i}:")
    print(f"  Precision: {results['precision'][i]:.3f}")
    print(f"  Recall:    {results['recall'][i]:.3f}")
    print(f"  F1-Score:  {results['f1_score'][i]:.3f}")

print("\nConfusion Matrix:")
print(results['confusion_matrix'])

# Make predictions on new data
X_new = np.random.randn(10, 784).astype(np.float32)
predictions = trainer.predict(X_new)
predicted_classes = np.argmax(predictions, axis=1)
print(f"\nPredicted classes: {predicted_classes}")

Example 8: Custom Training Loop

import numpy as np
from mayini.nn import Sequential, Linear, ReLU, Softmax, CrossEntropyLoss
from mayini.optim import Adam
from mayini.training import DataLoader
import mayini as mn

# Build model
model = Sequential(
    Linear(784, 256, init_method='he'),
    ReLU(),
    Linear(256, 10),
    Softmax(dim=1)
)

# Prepare data
X_train = np.random.randn(1000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 1000)
train_loader = DataLoader(X_train, y_train, batch_size=64, shuffle=True)

# Setup
optimizer = Adam(model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()

# Custom training loop
history = {'train_loss': [], 'train_acc': []}

for epoch in range(20):
    model.train()
    epoch_loss = 0
    correct = 0
    total = 0
    
    for batch_X, batch_y in train_loader:
        # Forward pass
        predictions = model(batch_X)
        loss = criterion(predictions, batch_y)
        
        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        # Track metrics
        epoch_loss += loss.item()
        pred_classes = np.argmax(predictions.data, axis=1)
        correct += np.sum(pred_classes == batch_y.data.flatten())
        total += len(batch_y.data)
    
    # Calculate epoch metrics
    avg_loss = epoch_loss / len(train_loader)
    accuracy = correct / total
    
    history['train_loss'].append(avg_loss)
    history['train_acc'].append(accuracy)
    
    print(f"Epoch {epoch+1}/20 - Loss: {avg_loss:.4f}, Accuracy: {accuracy:.4f}")

📂 Module Structure

mayini/
├── __init__.py           # Main package
├── tensor.py             # Tensor with autograd
├── nn/
│   ├── modules.py        # Layers (Linear, Conv2D, etc.)
│   ├── activations.py    # Activation functions
│   ├── losses.py         # Loss functions
│   └── rnn.py            # RNN components
├── optim/
│   └── optimizers.py     # Optimizers & LR schedulers
└── training/
    └── trainer.py        # Training utilities

🎓 Educational Resources

Interactive Notebook

Open in Google Colab

The notebook includes 38 runnable examples covering:

Tensor operations and autograd
All neural network layers
All activation functions
RNN/LSTM/GRU cells
Loss functions
Optimizers and schedulers
Complete training workflows
CNN and LSTM projects

Key Concepts

Automatic Differentiation:
MAYINI implements reverse-mode automatic differentiation (backpropagation) with computational graph construction and cycle detection.

Initialization Methods:

Xavier/Glorot: Good for sigmoid/tanh activations
He: Recommended for ReLU activations
Normal: Simple normal distribution

Training Best Practices:

Use He initialization with ReLU
Apply batch normalization for deep networks
Use dropout for regularization
Start with Adam optimizer
Apply learning rate scheduling
Monitor validation metrics
Use early stopping to prevent overfitting

🧪 Testing

# Run tests
pytest tests/

# With coverage
pytest --cov=mayini tests/

🤝 Contributing

We welcome contributions! Please:

Fork the repository
Create a feature branch
Make your changes
Add tests
Submit a pull request

See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

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

📞 Support & Links

GitHub Repository: 907-bot-collab/mayini
PyPI Package: mayini-framework
Interactive Notebook: Google Colab
Report Issues: GitHub Issues
Documentation: This README

🗺️ Version History

v0.1.9 (Latest): Fixed Module.call(), exported LR schedulers, removed numpy upper bound
v0.1.8: Added comprehensive RNN support
v0.1.7: Initial public release
v0.1.6: Beta release

🎯 Comparison with Other Frameworks

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

💻 Quick Reference

Essential Imports

import mayini as mn
from mayini.nn import (
    Sequential, Linear, Conv2D, MaxPool2D, Flatten,
    ReLU, Sigmoid, Tanh, Softmax,
    RNN, LSTMCell, GRUCell,
    MSELoss, CrossEntropyLoss
)
from mayini.optim import Adam, SGD, StepLR
from mayini.training import DataLoader, Trainer, Metrics, EarlyStopping

Minimal Working Example

import mayini as mn
import numpy as np
from mayini.nn import Sequential, Linear, ReLU, Softmax, CrossEntropyLoss
from mayini.optim import Adam
from mayini.training import DataLoader, Trainer

# Model
model = Sequential(Linear(10, 5), ReLU(), Linear(5, 2), Softmax(dim=1))

# Data
X = np.random.randn(100, 10).astype(np.float32)
y = np.random.randint(0, 2, 100)
loader = DataLoader(X, y, batch_size=32)

# Train
trainer = Trainer(model, Adam(model.parameters(), lr=0.01), CrossEntropyLoss())
history = trainer.fit(loader, epochs=10)

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