kotlindl - A High-Level Deep Learning API Built with Kotlin, Using TensorFlow and ONNX for JVM and Android Integration

KotlinDL: A Comprehensive Overview of Kotlin's Deep Learning API

KotlinDL is a powerful and high-level Deep Learning API built using the Kotlin programming language. Designed to simplify the development process for JVM and Android developers, KotlinDL draws inspiration from the popular deep learning library Keras. It utilizes the TensorFlow Java API and ONNX Runtime API for Java to offer a smooth experience in creating, training, and deploying deep learning models.

Key Features of KotlinDL

Ease of Use: KotlinDL empowers users to easily train deep learning models from scratch. It also supports the importation of pre-existing Keras and ONNX models, making inference tasks straightforward and accessible.
Transfer Learning: Users can leverage transfer learning to customize pre-trained models for specific tasks, significantly reducing the amount of data and time required to train a model.
Production Deployment: KotlinDL simplifies the process of deploying models in production environments, making it a versatile tool for real-world applications.

Example of a Classic Convolutional Neural Network

KotlinDL enables the definition of models in a concise and readable manner. A classic example is the LeNet, a convolutional neural network. Using KotlinDL, you can create a LeNet model with just a few lines of code, demonstrating the straightforward syntax and expressive power of the API.

private val lenet5Classic = Sequential.of(
    Input(28L, 28L, 1L),
    Conv2D(filters = 6, kernelSize = intArrayOf(5, 5), strides = intArrayOf(1, 1, 1, 1), activation = Activations.Tanh),
    AvgPool2D(poolSize = intArrayOf(1, 2, 2, 1), strides = intArrayOf(1, 2, 2, 1)),
    Conv2D(filters = 16, kernelSize = intArrayOf(5, 5), strides = intArrayOf(1, 1, 1, 1), activation = Activations.Tanh),
    AvgPool2D(poolSize = intArrayOf(1, 2, 2, 1), strides = intArrayOf(1, 2, 2, 1)),
    Flatten(),
    Dense(outputSize = 120, activation = Activations.Tanh),
    Dense(outputSize = 84, activation = Activations.Tanh),
    Dense(outputSize = 10, activation = Activations.Linear)
)

Library Structure

KotlinDL is organized into several modules, each catering to different aspects and functionalities:

API Interfaces and Classes: For defining models and layers.
Implementation Classes and Utilities: For implementing intricate functionalities.
ONNX Runtime: For model inference using ONNX.
TensorFlow Integration: For learning and inference with TensorFlow.
Visualization Utilities: For visualizing model outputs and training progress.
Dataset Classes: For handling and preparing data inputs.

Compatibility and Configuration

KotlinDL integrates well with multiple environments:

Android Projects: Native support for ONNX model inference.
Jupyter Notebooks: Interactive development using Kotlin with ease of dependency management.
Java Versions: Compatible across various Java versions from 8 upwards, depending on the KotlinDL release.

Documentation and Support

The KotlinDL community provides extensive documentation and learning materials, including a Quick Start Guide, examples, and tutorials. These resources make it accessible even for those new to Deep Learning.

For community support, developers can participate in discussions and seek help in the dedicated KotlinDL Slack channel.

Contribution and Open Source

KotlinDL is an open-source project, licensed under the Apache 2.0 License. Developers are encouraged to contribute, with detailed guidelines provided to maintain the quality and coherence of contributions.

Conclusion

KotlinDL stands as a potent tool for developers looking to explore and utilize deep learning within the Kotlin ecosystem. Its high-level API, inspired design, and comprehensive support make it an ideal choice for both development and production deployment of deep learning models.