mayini-framework 0.1.9

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
• Learning Rate Schedulers: Step, Exponential, and Cosine Annealing schedulers

Training Infrastructure

• DataLoader: Efficient batch processing with shuffling
• Trainer: Complete training framework with logging and checkpointing
• Metrics: Comprehensive evaluation (accuracy, precision, recall, F1)
• Early Stopping: Prevent overfitting with validation monitoring
• Checkpointing: Save and restore model states

📦 Installation

From PyPI

pip install mayini-framework

From Source

git clone https://github.com/907-bot-collab/mayini.git
cd mayini
pip install -e .

Development Installation

git clone https://github.com/907-bot-collab/mayini.git
cd mayini
pip install -e ".[dev]"

---

📚 Complete Documentation

1. Core Components

1.1 Tensor Operations

The Tensor class is the fundamental building block with automatic differentiation support.

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)

# Tensor operations
z = x.matmul(y)      # Matrix multiplication
w = x + y            # Element-wise addition
v = x * 2            # Scalar multiplication
u = x ** 2           # Power operation

# Reduction operations
sum_val = x.sum()             # Sum all elements
mean_val = x.mean()           # Mean of all elements
sum_axis = x.sum(axis=0)      # Sum along axis

# Shape operations
reshaped = x.reshape((4, 1))  # Reshape tensor
transposed = x.transpose()    # Transpose tensor

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

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
• detach(): Detach from computation graph
• numpy(): Convert to numpy array
• item(): Get scalar value

---

2. Neural Network Layers

2.1 Linear (Fully Connected) Layer

Dense layer with multiple weight initialization methods.

from mayini.nn import Linear

# Create linear layers with different initializations
layer1 = Linear(784, 256, init_method='xavier')  # Xavier initialization
layer2 = Linear(256, 128, init_method='he')      # He initialization  
layer3 = Linear(128, 10, init_method='normal')   # Normal initialization

# Forward pass
x = mn.Tensor(np.random.randn(32, 784))
output = layer1(x)
print(f"Output shape: {output.shape}")  # (32, 256)

Parameters:

• in_features: Number of input features
• out_features: Number of output features
• bias: Whether to include bias term (default: True)
• init_method: Weight initialization ('xavier', 'he', 'normal')

2.2 Convolutional Layer (Conv2D)

2D convolutional layer with im2col optimization for efficiency.

from mayini.nn import Conv2D

# Create convolutional layer
conv = Conv2D(
    in_channels=3,      # RGB input
    out_channels=64,    # 64 filters
    kernel_size=3,      # 3x3 kernel
    stride=1,           # Stride of 1
    padding=1,          # Padding of 1
    bias=True           # Include bias
)

# Forward pass
x = mn.Tensor(np.random.randn(32, 3, 28, 28))  # (batch, channels, height, width)
output = conv(x)
print(f"Output shape: {output.shape}")  # (32, 64, 28, 28)

Parameters:

• in_channels: Number of input channels
• out_channels: Number of output channels (filters)
• kernel_size: Size of convolutional kernel
• stride: Stride of convolution (default: 1)
• padding: Zero padding (default: 0)
• bias: Whether to include bias (default: True)

2.3 Pooling Layers

MaxPool2D

from mayini.nn import MaxPool2D

pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
x = mn.Tensor(np.random.randn(32, 64, 28, 28))
output = pool(x)
print(f"Output shape: {output.shape}")  # (32, 64, 14, 14)

AvgPool2D

from mayini.nn import AvgPool2D

pool = AvgPool2D(kernel_size=2, stride=2, padding=0)
x = mn.Tensor(np.random.randn(32, 64, 28, 28))
output = pool(x)
print(f"Output shape: {output.shape}")  # (32, 64, 14, 14)

2.4 Normalization Layers

Batch Normalization

from mayini.nn import BatchNorm1d

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

# Training mode
bn.train()
x = mn.Tensor(np.random.randn(32, 256))
output = bn(x)

# Evaluation mode
bn.eval()
x_test = mn.Tensor(np.random.randn(16, 256))
output_test = bn(x_test)

Parameters:

• num_features: Number of features/channels
• eps: Small constant for numerical stability (default: 1e-5)
• momentum: Momentum for running statistics (default: 0.1)

2.5 Regularization

Dropout

from mayini.nn import Dropout

dropout = Dropout(p=0.5)

# Training mode - drops 50% of neurons
dropout.train()
x = mn.Tensor(np.random.randn(32, 256))
output = dropout(x)

# Evaluation mode - no dropout
dropout.eval()
output_test = dropout(x)

2.6 Utility Layers

Flatten

from mayini.nn import Flatten

flatten = Flatten(start_dim=1)
x = mn.Tensor(np.random.randn(32, 64, 7, 7))
output = flatten(x)
print(f"Output shape: {output.shape}")  # (32, 3136)

---

3. Activation Functions

3.1 ReLU (Rectified Linear Unit)

from mayini.nn import ReLU

relu = ReLU()
x = mn.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
output = relu(x)
print(output.data)  # [[0. 0. 0. 1. 2.]]

Formula: ( f(x) = \max(0, x) )

Use cases: Most common activation for hidden layers in deep networks

3.2 Sigmoid

from mayini.nn import Sigmoid

sigmoid = Sigmoid()
x = mn.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
output = sigmoid(x)
print(output.data)  # [[0.119 0.269 0.5 0.731 0.881]]

Formula: ( f(x) = \frac{1}{1 + e^{-x}} )

Use cases: Binary classification output layer, gate activations in LSTM

3.3 Tanh (Hyperbolic Tangent)

from mayini.nn import Tanh

tanh = Tanh()
x = mn.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
output = tanh(x)
print(output.data)  # [[-0.964 -0.762 0. 0.762 0.964]]

Formula: ( f(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}} )

Use cases: Hidden layers in RNNs, when zero-centered activations are needed

3.4 Softmax

from mayini.nn import Softmax

softmax = Softmax(dim=1)
x = mn.Tensor([[1.0, 2.0, 3.0], [1.0, 1.0, 1.0]])
output = softmax(x)
print(output.data)  
# [[0.090 0.245 0.665]
#  [0.333 0.333 0.333]]

Formula: ( f(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}} )

Use cases: Multi-class classification output layer

3.5 GELU (Gaussian Error Linear Unit)

from mayini.nn import GELU

gelu = GELU()
x = mn.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
output = gelu(x)

Formula: ( f(x) = 0.5x(1 + \tanh(\sqrt{2/\pi}(x + 0.044715x^3))) )

Use cases: Modern transformer models, BERT, GPT

3.6 Leaky ReLU

from mayini.nn import LeakyReLU

leaky_relu = LeakyReLU(negative_slope=0.01)
x = mn.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
output = leaky_relu(x)
print(output.data)  # [[-0.02 -0.01 0. 1. 2.]]

Formula: ( f(x) = \max(\alpha x, x) ) where ( \alpha = 0.01 )

Use cases: Alternative to ReLU to prevent dead neurons

---

4. Recurrent Neural Networks

4.1 RNN Cell (Vanilla RNN)

from mayini.nn import RNNCell

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

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

h_next = rnn_cell(x_t, h_t)
print(f"Next hidden state: {h_next.shape}")  # (32, 128)

Formula: ( h_t = \tanh(W_{ih}x_t + b_{ih} + W_{hh}h_{t-1} + b_{hh}) )

Parameters:

• input_size: Size of input features
• hidden_size: Size of hidden state
• bias: Whether to include bias (default: True)

4.2 LSTM Cell (Long Short-Term Memory)

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))

h_next, c_next = lstm_cell(x_t, (h_t, c_t))
print(f"Next hidden: {h_next.shape}, Next cell: {c_next.shape}")

Gates:

• Forget gate: ( f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f) )
• Input gate: ( i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i) )
• Output gate: ( o_t = \sigma(W_o \cdot [h_{t-1}, x_t] + b_o) )
• Cell candidate: ( \tilde{C}t = \tanh(W_C \cdot [h{t-1}, x_t] + b_C) )
• Cell state: ( C_t = f_t \odot C_{t-1} + i_t \odot \tilde{C}_t )
• Hidden state: ( h_t = o_t \odot \tanh(C_t) )

Use cases: Long sequence modeling, machine translation, speech recognition

4.3 GRU Cell (Gated Recurrent Unit)

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))

h_next = gru_cell(x_t, h_t)
print(f"Next hidden state: {h_next.shape}")  # (32, 128)

Gates:

• Reset gate: ( r_t = \sigma(W_r \cdot [h_{t-1}, x_t]) )
• Update gate: ( z_t = \sigma(W_z \cdot [h_{t-1}, x_t]) )
• New gate: ( \tilde{h}t = \tanh(W \cdot [r_t \odot h{t-1}, x_t]) )
• Hidden state: ( h_t = (1 - z_t) \odot \tilde{h}t + z_t \odot h{t-1} )

Use cases: More efficient than LSTM, good for shorter sequences

4.4 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',      # 'rnn', 'lstm', or 'gru'
    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)

print(f"Output shape: {output.shape}")  # (32, 50, 128)
print(f"Number of hidden states: {len(hidden_states)}")  # 2 (one per layer)

Parameters:

• input_size: Size of input features
• hidden_size: Size of hidden state
• num_layers: Number of stacked RNN layers
• cell_type: Type of RNN cell ('rnn', 'lstm', or 'gru')
• dropout: Dropout between RNN layers (default: 0.0)
• batch_first: If True, input shape is (batch, seq, features)

---

5. Loss Functions

5.1 MSE Loss (Mean Squared Error)

from mayini.nn import MSELoss

criterion = MSELoss(reduction='mean')  # 'mean', 'sum', or 'none'

predictions = mn.Tensor([[1.0, 2.0], [3.0, 4.0]])
targets = mn.Tensor([[1.5, 2.5], [3.5, 4.5]])

loss = criterion(predictions, targets)
print(f"MSE Loss: {loss.item()}")  # 0.25

Formula: ( L = \frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2 )

Use cases: Regression tasks, predicting continuous values

5.2 MAE Loss (Mean Absolute Error)

from mayini.nn import MAELoss

criterion = MAELoss(reduction='mean')

predictions = mn.Tensor([[1.0, 2.0], [3.0, 4.0]])
targets = mn.Tensor([[1.5, 2.5], [3.5, 4.5]])

loss = criterion(predictions, targets)
print(f"MAE Loss: {loss.item()}")  # 0.5

Formula: ( L = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i| )

Use cases: Regression with outliers, robust to outliers than MSE

5.3 Cross-Entropy Loss

from mayini.nn import CrossEntropyLoss

criterion = CrossEntropyLoss(reduction='mean')

# Predictions (before softmax): (batch_size, num_classes)
predictions = mn.Tensor([[2.0, 1.0, 0.5], [0.5, 2.0, 1.0]])
# Targets (class indices): (batch_size,)
targets = mn.Tensor([0, 1])  # Class 0 and Class 1

loss = criterion(predictions, targets)
print(f"Cross-Entropy Loss: {loss.item()}")

Formula: ( L = -\frac{1}{n}\sum_{i=1}^{n}\log(\frac{e^{f_{y_i}}}{\sum_j e^{f_j}}) )

Use cases: Multi-class classification

5.4 Binary Cross-Entropy Loss

from mayini.nn import BCELoss

criterion = BCELoss(reduction='mean')

# Predictions (after sigmoid): (batch_size,)
predictions = mn.Tensor([0.8, 0.3, 0.6])
# Targets (0 or 1): (batch_size,)
targets = mn.Tensor([1.0, 0.0, 1.0])

loss = criterion(predictions, targets)
print(f"BCE Loss: {loss.item()}")

Formula: ( L = -\frac{1}{n}\sum_{i=1}^{n}[y_i\log(\hat{y}_i) + (1-y_i)\log(1-\hat{y}_i)] )

Use cases: Binary classification

5.5 Huber Loss

from mayini.nn import HuberLoss

criterion = HuberLoss(delta=1.0, reduction='mean')

predictions = mn.Tensor([[1.0, 2.0], [3.0, 4.0]])
targets = mn.Tensor([[1.5, 2.5], [5.0, 6.0]])

loss = criterion(predictions, targets)
print(f"Huber Loss: {loss.item()}")

Formula: [ L_\delta(y, \hat{y}) = \begin{cases} \frac{1}{2}(y - \hat{y})^2 & \text{if } |y - \hat{y}| \leq \delta \ \delta(|y - \hat{y}| - \frac{1}{2}\delta) & \text{otherwise} \end{cases} ]

Use cases: Robust regression, less sensitive to outliers than MSE

---

6. Optimizers

6.1 SGD (Stochastic Gradient Descent)

from mayini.optim import SGD

optimizer = SGD(
    model.parameters(), 
    lr=0.01,              # Learning rate
    momentum=0.9,         # Momentum factor
    weight_decay=1e-4     # L2 regularization
)

# Training loop
for epoch in range(epochs):
    for batch_X, batch_y in train_loader:
        predictions = model(batch_X)
        loss = criterion(predictions, batch_y)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

Update rule: [ v_t = \beta v_{t-1} + g_t \ \theta_t = \theta_{t-1} - \eta v_t ]

Parameters:

• lr: Learning rate
• momentum: Momentum factor (default: 0.0)
• weight_decay: L2 penalty (default: 0.0)

6.2 Adam (Adaptive Moment Estimation)

from mayini.optim import Adam

optimizer = Adam(
    model.parameters(),
    lr=0.001,           # Learning rate
    beta1=0.9,          # Exponential decay rate for 1st moment
    beta2=0.999,        # Exponential decay rate for 2nd moment
    eps=1e-8,           # Small constant for numerical stability
    weight_decay=0.0    # L2 regularization
)

Update rule: [ m_t = \beta_1 m_{t-1} + (1-\beta_1)g_t \ v_t = \beta_2 v_{t-1} + (1-\beta_2)g_t^2 \ \hat{m}_t = \frac{m_t}{1-\beta_1^t}, \quad \hat{v}t = \frac{v_t}{1-\beta_2^t} \ \theta_t = \theta{t-1} - \eta \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon} ]

Use cases: Default choice for most deep learning tasks

6.3 AdamW (Adam with Decoupled Weight Decay)

from mayini.optim import AdamW

optimizer = AdamW(
    model.parameters(),
    lr=0.001,
    beta1=0.9,
    beta2=0.999,
    eps=1e-8,
    weight_decay=0.01   # Decoupled weight decay
)

Use cases: Better generalization than Adam, recommended for transformers

6.4 RMSprop (Root Mean Square Propagation)

from mayini.optim import RMSprop

optimizer = RMSprop(
    model.parameters(),
    lr=0.01,
    alpha=0.99,         # Smoothing constant
    eps=1e-8,
    momentum=0.0,
    weight_decay=0.0
)

Update rule: [ v_t = \alpha v_{t-1} + (1-\alpha)g_t^2 \ \theta_t = \theta_{t-1} - \eta \frac{g_t}{\sqrt{v_t} + \epsilon} ]

Use cases: Good for RNNs, online learning

---

7. Learning Rate Schedulers

7.1 StepLR

from mayini.optim import Adam, StepLR

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

for epoch in range(50):
    train(...)
    scheduler.step()  # Decay LR every 10 epochs

Schedule: LR = base_lr * gamma^(epoch // step_size)

7.2 ExponentialLR

from mayini.optim import ExponentialLR

optimizer = Adam(model.parameters(), lr=0.1)
scheduler = ExponentialLR(optimizer, gamma=0.95)

for epoch in range(50):
    train(...)
    scheduler.step()  # LR = base_lr * 0.95^epoch

7.3 CosineAnnealingLR

from mayini.optim import CosineAnnealingLR

optimizer = Adam(model.parameters(), lr=0.1)
scheduler = CosineAnnealingLR(optimizer, T_max=50, eta_min=0)

for epoch in range(50):
    train(...)
    scheduler.step()  # Cosine decay

Schedule: ( \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 + \cos(\frac{t\pi}{T_{max}})) )

---

8. Training Utilities

8.1 DataLoader

from mayini.training import DataLoader
import numpy as np

# Create dataset
X_train = np.random.randn(1000, 784).astype(np.float32)
y_train = np.random.randint(0, 10, 1000)

# Create data loader
train_loader = DataLoader(
    X_train, 
    y_train, 
    batch_size=64, 
    shuffle=True
)

# Iterate through batches
for batch_idx, (batch_X, batch_y) in enumerate(train_loader):
    print(f"Batch {batch_idx}: X shape {batch_X.shape}, y shape {batch_y.shape}")

Parameters:

• X: Input features (numpy array or Tensor)
• y: Target labels (numpy array or Tensor)
• batch_size: Number of samples per batch (default: 32)
• shuffle: Whether to shuffle data each epoch (default: True)

8.2 Trainer

Complete training framework with automatic logging, checkpointing, and validation.

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

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

trainer = Trainer(model, optimizer, criterion)

# Train the model
history = trainer.fit(
    train_loader,
    epochs=10,
    val_loader=val_loader,      # Optional validation data
    early_stopping=None,         # Optional early stopping
    verbose=True,                # Print progress
    save_best=True,              # Save best model
    checkpoint_path='model.pkl'  # Checkpoint file path
)

# Access training history
print(f"Training losses: {history['train_loss']}")
print(f"Validation losses: {history['val_loss']}")
print(f"Training accuracy: {history['train_acc']}")
print(f"Validation accuracy: {history['val_acc']}")

Methods:

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

8.3 Metrics

from mayini.training import Metrics

# Accuracy
accuracy = Metrics.accuracy(predictions, targets)

# Precision, Recall, F1-score
precision, recall, f1 = Metrics.precision_recall_f1(
    predictions, targets, num_classes=10
)

# Confusion matrix
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)

Available Metrics:

• accuracy(): Classification accuracy
• precision_recall_f1(): Per-class precision, recall, F1
• confusion_matrix(): Confusion matrix
• mse(): Mean squared error
• mae(): Mean absolute error
• r2_score(): R² score for regression

8.4 Early Stopping

from mayini.training import EarlyStopping

early_stopping = EarlyStopping(
    patience=7,                  # Number of epochs to wait
    min_delta=0.001,             # Minimum improvement threshold
    restore_best_weights=True,   # Restore best weights when stopped
    mode='min'                   # 'min' for loss, 'max' for accuracy
)

# Use with trainer
history = trainer.fit(
    train_loader,
    epochs=100,
    val_loader=val_loader,
    early_stopping=early_stopping,
    verbose=True
)

---

9. Complete Examples

9.1 Simple Neural Network for MNIST

import mayini as mn
import numpy as np
from mayini.nn import Sequential, Linear, ReLU, Softmax, CrossEntropyLoss, Dropout
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)

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

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

9.2 CNN for Image Classification

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

# Build CNN
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),
    
    # Flatten and FC layers
    Flatten(),
    Linear(64 * 7 * 7, 256),
    ReLU(),
    Dropout(0.5),
    Linear(256, 10),
    Softmax(dim=1)
)

# Train (same as above)
optimizer = Adam(cnn_model.parameters(), lr=0.001)
criterion = CrossEntropyLoss()
trainer = Trainer(cnn_model, optimizer, criterion)

9.3 LSTM for Sequence Classification

from mayini.nn import RNN

# Build LSTM model
lstm_model = Sequential(
    RNN(
        input_size=100,
        hidden_size=128,
        num_layers=2,
        cell_type='lstm',
        dropout=0.3,
        batch_first=True
    ),
    # Extract last timestep output (implement custom layer or use slicing)
    Linear(128, 64),
    ReLU(),
    Linear(64, 3),  # 3 classes
    Softmax(dim=1)
)

9.4 GRU for Time Series Prediction

# Build GRU model for regression
gru_model = Sequential(
    RNN(
        input_size=10,
        hidden_size=64,
        num_layers=3,
        cell_type='gru',
        dropout=0.2,
        batch_first=True
    ),
    Linear(64, 32),
    ReLU(),
    Linear(32, 1)  # Single output for regression
)

# Use MSE loss for regression
criterion = MSELoss()
optimizer = Adam(gru_model.parameters(), lr=0.001)

---

10. Module Structure

mayini/
├── __init__.py           # Main package initialization
├── tensor.py             # Core Tensor class with autograd
├── base.py               # Base classes
├── metrics.py            # Standalone metrics utilities
├── utils.py              # Utility functions
├── nn/
│   ├── __init__.py
│   ├── modules.py        # Module, Sequential, Linear, Conv2D, etc.
│   ├── activations.py    # ReLU, Sigmoid, Tanh, Softmax, GELU, etc.
│   ├── losses.py         # MSELoss, CrossEntropyLoss, BCELoss, etc.
│   └── rnn.py            # RNNCell, LSTMCell, GRUCell, RNN
├── optim/
│   ├── __init__.py
│   └── optimizers.py     # SGD, Adam, AdamW, RMSprop, Schedulers
└── training/
    ├── __init__.py
    └── trainer.py        # DataLoader, Trainer, Metrics, EarlyStopping

---

11. Quick Reference

Imports

import mayini as mn
from mayini.nn import (
    Sequential, Module,
    Linear, Conv2D, MaxPool2D, AvgPool2D, Flatten,
    BatchNorm1d, Dropout,
    ReLU, Sigmoid, Tanh, Softmax, GELU, LeakyReLU,
    RNN, RNNCell, LSTMCell, GRUCell,
    MSELoss, MAELoss, CrossEntropyLoss, BCELoss, HuberLoss
)
from mayini.optim import SGD, Adam, AdamW, RMSprop
from mayini.optim import StepLR, ExponentialLR, CosineAnnealingLR
from mayini.training import DataLoader, Trainer, Metrics, EarlyStopping

---

🧪 Testing

pytest tests/
pytest --cov=mayini tests/

🤝 Contributing

See CONTRIBUTING.md for guidelines.

📄 License

MIT License - see LICENSE file.

🙏 Acknowledgments

• Inspired by PyTorch's design philosophy
• Built for educational purposes and research

📞 Support

• GitHub: 907-bot-collab/mayini
• PyPI: mayini-framework
• Issues: Report Issues
• Colab: Example Notebook

---

MAYINI - Making AI Neural Intelligence Intuitive 🧠✨