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Deep Learning with KotlinDL

Deep Learning with KotlinDL

I think that many of you know the Keras library, which runs on top of Tensorflow. Tensorflow itself has stable C API, and therefore there are several high-level deep learning frameworks in other languages ​​inspired by Keras, such as TFJS, Keras.NET, Tensorflow in Go.

Today I want to introduce you to a Keras-like machine learning framework written in Kotlin.

Currently, the KotlinDL framework can boast that this is the only way to construct and train complex neural networks on JVM, such as VGG, ResNet, or MobileNet. There is also support for transfer learning for the popular models trained in Keras (or available in Keras. applications).

For image preprocessing, several functions are available that allow you to avoid complex and routine work on the JVM.

Finally, I’ll show you how you can build, train, and use a convolutional neural network (CNN) entirely in Kotlin with the help of KotlinDL, and we discuss how to use KotlinDL models in production.

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Alexey Zinoviev

April 20, 2021
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  1. Deep Learning with KotlinDL Alexey Zinovyev, ML Engineer, Apache Ignite

    PMC JetBrains
  2. Bio 1. Java & Kotlin developer 2. Distributed ML enthusiast

    3. Apache Ignite PMC 4. TensorFlow Contributor 5. ML engineer at JetBrains 6. Happy father and husband 7. https://github.com/zaleslaw 8. https://twitter.com/zaleslaw
  3. Motivation 1. Kotlin took a course to become a convenient

    language for data science 2. No modern data science without Neural Networks 3. All deep learning frameworks are good enough at image recognition 4. Convolutional neural networks (CNNs) are the gold standard for image recognition 5. Training, Transfer Learning, and Inference are now available for different CNN architectures on Kotlin with KotlinDL library
  4. Kotlin DL = Keras - Python

  5. Kotlin DL = Keras - Python

  6. How it works?

  7. How it works?

  8. How it works?

  9. How it works?

  10. The possible future

  11. Kotlin for Data Science

  12. Kotlin Data Science libraries 1. Multik - Multidimensional array library

    for Kotlin 2. Kotlin for Apache Spark 3. KMath - multiplatform math library 4. DataFrame library is in progress 5. Lets-Plot for visualisation
  13. Jupyter Kotlin kernel

  14. Keras ideology

  15. The Keras API philosophy Progressive disclosure of complexity

  16. Model building: from simple to arbitrarily flexible

  17. Model building: from simple to arbitrarily flexible

  18. Model training: from simple to arbitrarily flexible

  19. Model training: from simple to arbitrarily flexible

  20. Low-level Tensor API 1. Java API 1.15 2. Low-level LeNet-5

    on Kotlin 3. Java API 2.x 4. Low-level LeNet-5 examples on Java
  21. Kotlin DL features

  22. 0.1 release features 1. Sequential API 2. VGG-like Sequential models

    for CV domain 3. Training on TensorFlow 4. Transfer Learning for VGG’16 and VGG’19 models exported from Keras 5. Loading Keras Sequential models from JSON config + weights in h5 file format 6. Model Export to a few formats
  23. 0.2 release features: ModelZoo Model ZOO with the following models

    1. ResNet50 2. ResNet101 3. ResNet152 4. ResNet50v2 5. ResNet101v2 6. ResNet152v2 7. MobileNet 8. MobileNetv2
  24. 0.2 release features: Layers A lot of new layers required

    for ResNet and MobileNet architectures 1. BatchNorm 2. ActivationLayer 3. DepthwiseConv2D 4. SeparableConv2D 5. Merge (Add, Subtract, Multiply, Average, Concatenate, Maximum, Minimum) 6. GlobalAvgPool2D 7. Cropping2D 8. Reshape 9. ZeroPadding2D
  25. 0.2 release features: embedded datasets 1. Mnist 2. FashionMnist 3.

    Cifar’10 4. Dogs vs. Cats (catdogs) dataset from Kaggle 5. Sub-sample from Dogs vs. Cats dataset (1000 images per class)
  26. 0.2 release features: Image Preprocessing DSL 1. Load 2. Crop

    3. Resize 4. Rotate 5. Rescale 6. Save
  27. 0.2 release features: Datasets 1. OnHeapDataset 2. OnFlyDataset 3. CustomDataset

  28. KotlinDL Limitations 1. Now useful for Image Recognition task and

    Regression ML 2. Limited number of layers is supported 3. No Android support
  29. KotlinDL Roadmap 1. New models in the ModelZoo: Inception, DenseNet,

    EfficientNet 2. Rich Dataset API 3. GPU settings 4. Regularization for layers will be added 5. New metrics framework 6. Conversion to TFLite (for mobile devices) 7. ONNX support 8. ML algorithms 9. PyTorch Runtime for training
  30. Demo

  31. Useful links 1. https://github.com/JetBrains/KotlinDL 2. https://kotlinlang.org/docs/data-science-overview.html#kotlin-libraries 3. #deeplearning channel on

    Kotlin slack (join it, if you are not yet) 4. Feel free to join discussions on Github 5. Follow @zaleslaw and @KotlinForData on Twitter
  32. Use KotlinDL!