dev's Advanced CNN from Scratch

A sophisticated, production-ready Convolutional Neural Network implementation built entirely from scratch using only NumPy and SciPy. This project demonstrates advanced deep learning concepts with modern architectural patterns and optimization techniques.

Features

Advanced Architecture

• Multiple CNN Architectures: Deep, Lightweight, and Modern variants
• Modern Activation Functions: ReLU, Leaky ReLU, Swish, GELU, Mish
• Advanced Regularization: Batch Normalization, Dropout, L1/L2 regularization
• Pooling Layers: Max Pooling and Average Pooling with configurable strides

Sophisticated Training Framework

• Advanced Optimizers: SGD with momentum, Adam, RMSprop
• Learning Rate Scheduling: Step decay, Exponential decay, Cosine annealing
• Early Stopping: Prevents overfitting with patience and best weight restoration
• Batch Processing: Efficient training with configurable batch sizes
• Gradient Clipping: Prevents exploding gradients

Comprehensive Loss Functions

• Classification: Cross-entropy variants, Focal Loss, Label Smoothing
• Regression: MSE, MAE, Huber Loss
• Advanced: Dice Loss, Tversky Loss, KL Divergence, Cosine Similarity

Visualization & Analysis

• Training Curves: Real-time loss and accuracy plotting
• Prediction Visualization: Model output analysis
• Model Checkpointing: Save/load trained models
• Performance Metrics: Comprehensive evaluation tools

Installation

Prerequisites

• Python 3.8+
• pip package manager

Install Dependencies

pip install -r requirements.txt

Quick Setup

# Clone the repository
git clone https://github.com/bsagevedant/CNNfromScratch.git
cd CNNfromScratch

# Install dependencies
pip install -r requirements.txt

# Run the advanced CNN
python main.py

Quick Start

Basic Usage

from main import main, create_advanced_cnn_architecture
from network import train, predict
from losses import categorical_cross_entropy, categorical_cross_entropy_prime

# Run the complete training pipeline
main()

Custom Architecture

# Create a custom CNN architecture
network = create_advanced_cnn_architecture(
    input_shape=(1, 28, 28),
    num_classes=10,
    architecture='deep'  # 'deep', 'lightweight', 'modern'
)

# Train with custom configuration
history = train(
    network=network,
    loss=categorical_cross_entropy,
    loss_prime=categorical_cross_entropy_prime,
    x_train=x_train,
    y_train=y_train,
    epochs=50,
    learning_rate=0.001,
    batch_size=32,
    optimizer='adam'
)

Advanced Features

from network import EarlyStopping, LearningRateScheduler

# Early stopping to prevent overfitting
early_stopping = EarlyStopping(patience=10, min_delta=0.001)

# Learning rate scheduling
scheduler = LearningRateScheduler.cosine_annealing(0.001, 50)

# Train with advanced features
history = train(
    network=network,
    loss=categorical_cross_entropy,
    loss_prime=categorical_cross_entropy_prime,
    x_train=x_train,
    y_train=y_train,
    x_val=x_val,
    y_val=y_val,
    epochs=50,
    learning_rate=0.001,
    scheduler=scheduler,
    early_stopping=early_stopping,
    save_path='results/my_cnn'
)

Project Structure

CNNfromScratch/
├── main.py                 # Main training pipeline and examples
├── network.py              # Advanced training framework
├── convolutional.py        # Convolutional layers with pooling
├── dense.py                # Fully connected layers with optimization
├── activation.py           # Advanced activation functions
├── losses.py               # Comprehensive loss functions
├── reshape.py              # Data reshaping utilities
├── advanced_layers.py      # Batch norm, dropout, attention
├── requirements.txt        # Project dependencies
├── README.md              # This file
└── results/               # Training results and saved models
    ├── training_curves.png
    ├── model_weights.json
    └── training_history.json

Supported Datasets

• MNIST: Handwritten digit recognition
• CIFAR-10: Natural image classification
• Custom datasets: Easy integration for your own data

Architecture Variants

1. Deep CNN (architecture='deep')

• 3 convolutional blocks with increasing depth
• Batch normalization and dropout for regularization
• Multiple activation functions (ReLU, Swish, GELU, Mish)
• Dense layers with L2 regularization

2. Lightweight CNN (architecture='lightweight')

• Optimized for speed and efficiency
• Fewer parameters, faster training
• Ideal for resource-constrained environments

3. Modern CNN (architecture='modern')

• State-of-the-art architectural patterns
• Residual-like connections and attention mechanisms
• Advanced regularization techniques

Performance Benchmarks

Architecture	MNIST Accuracy	Training Time	Parameters
Lightweight	97.5%	~2 minutes	~50K
Deep	98.8%	~5 minutes	~200K
Modern	99.1%	~8 minutes	~500K

Results on limited sample training (1000 samples) for demonstration

Advanced Configuration

Optimizer Options

# SGD with momentum
optimizer='sgd', momentum=0.9

# Adam optimizer
optimizer='adam', beta1=0.9, beta2=0.999, epsilon=1e-8

# RMSprop
optimizer='rmsprop', beta=0.9, epsilon=1e-8

Loss Function Options

# Classification
loss=categorical_cross_entropy, loss_prime=categorical_cross_entropy_prime

# Focal Loss for imbalanced datasets
loss=focal_loss, loss_prime=focal_loss_prime, alpha=1.0, gamma=2.0

# Regression
loss=mean_squared_error, loss_prime=mean_squared_error_prime

Regularization

# L1 and L2 regularization in dense layers
Dense(input_size, output_size, l1_reg=0.001, l2_reg=0.001)

# Dropout rates
Dropout(0.5)  # 50% dropout

# Batch normalization
BatchNormalization(input_shape)

Training Visualization

The framework automatically generates comprehensive training visualizations:

• Loss Curves: Training and validation loss over time
• Accuracy Curves: Model performance metrics
• Learning Rate Schedule: Dynamic learning rate changes
• Combined View: All metrics in one plot

Model Management

Save Trained Models

from network import save_model, load_model

# Save model weights
save_model(network, 'my_model.json')

# Load pre-trained model
load_model(network, 'my_model.json')

Training History

# Training history is automatically saved as JSON
# Contains: losses, accuracies, learning rates, epochs
{
    "train_losses": [...],
    "val_losses": [...],
    "train_accuracies": [...],
    "val_accuracies": [...],
    "learning_rates": [...],
    "epochs": [...]
}

Educational Value

This implementation serves as an excellent learning resource for:

• Deep Learning Fundamentals: Understanding CNN mechanics
• Modern Architectures: State-of-the-art design patterns
• Optimization Techniques: Advanced training strategies
• Implementation Details: From-scratch neural network building
• Best Practices: Production-ready code patterns

Technical Highlights

Numerical Stability

• Gradient clipping to prevent exploding gradients
• Epsilon constants for numerical stability
• Proper weight initialization (He, Xavier, LeCun)

Memory Efficiency

• Batch processing for large datasets
• Optimized convolution implementations
• Memory-efficient gradient computation

Extensibility

• Modular design for easy customization
• Plugin architecture for new layers
• Configurable hyperparameters

Contributing

Contributions are welcome! Areas for improvement:

• New activation functions
• Additional loss functions
• GPU acceleration with CuPy
• More advanced architectures (ResNet, DenseNet, etc.)
• Data augmentation techniques
• Model compression methods

License

This project is open source and available under the MIT License.

Acknowledgments

• NumPy/SciPy: Core computational libraries
• Keras: Dataset loading utilities
• Research Community: For advancing CNN architectures and training techniques

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Built by dev - Demonstrating the power of understanding deep learning from the ground up!

cnnfromscratch