alphanetworks 0.1.4

Alpha Hybrid CNN model for Image classification tasks

AlphaNetworks

AlphaNetworks is a Python package designed to train advanced image classification models using hybrid architectures like ResNet50V2 and DenseNet169. It provides a seamless interface for training, evaluating, and deploying deep learning models.

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Table of Contents

• Introduction
• Features
• Installation
  • Prerequisites
  • Installation via Pip
• Usage
  • Command-Line Interface
    • Basic Usage
    • Options
    • Help
  • Examples
  • Programmatic Usage
• Troubleshooting
• Project Structure
• Documentation
• Contributing
  • Reporting Issues
• License
• Contact
• Acknowledgments

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Introduction

AlphaNetworks combines state-of-the-art architectures to offer superior performance in image classification tasks. By leveraging pre-trained weights and advanced optimization techniques, it ensures robust feature extraction and better generalization.

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Features

• Hybrid Architecture: Integrates ResNet50V2 and DenseNet169.
• Data Augmentation: Enhances model robustness.
• CLI Support: Train and configure models via the command line.
• Customizable Hyperparameters: Adjust learning rate, batch size, etc.
• Download Pretrained Weights: Automatically fetch required weights.

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Installation

Prerequisites

• Python: Version 3.6 or higher.
• TensorFlow: Version 2.x or later.
• pip: Latest version recommended.

Installation via Pip

Install AlphaNetworks directly from PyPI:

pip install alphanetworks

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Usage

Command-Line Interface

Basic Usage

alphanetworks --train TRAIN --val VAL [OPTIONS]

Options

• --train or -t: Path to the training dataset.
• --val or -v: Path to the validation dataset.
• --epochs or -e: Number of training epochs (default: 30).
• --batch_size or -b: Batch size for training and validation (default: 32).
• --lr or -l: Initial learning rate for the Adam optimizer (default: 0.001).
• --output_dir or -o: Directory to save model weights and reports.
• --nc: Number of target classes for classification (default: inferred from data).

Help

For a detailed list of options, run:

alphanetworks --help

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Examples

Example 1: Basic Training

alphanetworks --train ./data/train --val ./data/val --epochs 20

Example 2: Custom Parameters

alphanetworks --train ./data/train --val ./data/val --epochs 50 --batch_size 64 --lr 0.0005 --nc 10

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Programmatic Usage

from alphanetworks import alphanet

# Build the model
model = alphanet(input_shape=(224, 224, 3), num_classes=10)

# Compile the model
model.compile(
    optimizer="adam",
    loss="categorical_crossentropy",
    metrics=["accuracy"]
)

# Train the model
model.fit(train_data, validation_data=val_data, epochs=30)

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Troubleshooting

Download Errors

During runtime, pre-trained weights for ResNet50V2 or DenseNet169 are automatically downloaded. Ensure you have a stable internet connection.

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Project Structure

alphanetworks/
├── alphanet.py
├── utils.py
├── scripts/
│   └── train.py
├── setup.py
└── README.md

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Documentation

For detailed documentation, visit the GitHub repository.

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Contributing

Contributions are welcome! Please submit a pull request or open an issue.

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License

This project is licensed under the MIT License.

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Contact

For any queries, reach out to the author at mail.

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Acknowledgments

• TensorFlow and Keras Teams
• Researchers of ResNet and DenseNet