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[0.2.0] - 2021-17-05
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@zaleslaw zaleslaw released this 14 May 15:48
· 388 commits to master since this release
v0.2.0
507c2d0

Features:

  • Added support for Functional API
  • Added BatchNorm layer for inference
  • Added GlobalAveragePooling2D layer
  • Added 7 Merge layers
    (Add, Average, Concatenate, Maximum, Minimum, Multiply, Subtract)
  • Added Activation layer
  • Added ReLU layer
  • Added DepthwiseConv2D layer
  • Added SeparableConv2D layer
  • Added Reshape layer
  • Added Cropping2D layer
  • Added ZeroPadding2D layer
  • Added NoGradients interface to indicate layers whose weights cannot be updated during training due to the lack of gradients in TensorFlow
  • Added Model Zoo with the following models:
    • VGG'16
    • VGG'19
    • ResNet50
    • ResNet101
    • ResNet152
    • ResNet50V2
    • ResNet101V2
    • ResNet152V2
    • MobileNet
    • MobileNetV2
  • Added ImageNet related preprocessing for each of the ModelZoo supported models: available in ModelZoo object and as a sharpen stage in the image preprocessing DSL
  • Added model descriptions for models from ModelZoo (excluding MobileNet) designed with the Functional API in org.jetbrains.kotlinx.dl.api.core.model package
  • Added two implementations of the Dataset class: OnFlyImageDataset and OnHeapDataset
  • Added topological sort for layers as nodes in the DAG model representation
  • Added shuffle function support for both Dataset implementations
  • Added the Kotlin-idiomatic DSL for image preprocessing with the following operations:
    • Load
    • Crop
    • Resize
    • Rotate
    • Rescale
    • Sharpen
    • Save
  • Implemented label generation on the fly from the names of image folders
  • Implemented summary method for the Functional API
  • Added embedded datasets support (MNIST, FashionMNIST, Cifar'10, Cats & Dogs)

Bugs:

  • Fixed a bug with BGR and RGB preprocessing in examples
  • Fixed missed useBias field in convolutional layers

Internals improvements:

  • Refactored: both Sequential and Functional models now inherit the GraphTrainableModel class
  • Completed the Klaxon migration from 5.0.1 to 5.5
  • Removed useless labels and data transformations before sending to Tensor.create(...)

Infrastructure:

  • Loaded the weights and JSON configurations of ModelZoo models to S3 storage
  • Added a TeamCity build for the examples
  • Loaded embedded datasets to S3 storage
  • Removed dependencies from jcenter
  • Moved an artifact to the Maven Central Repository
  • Changed the groupId and artifactId
  • Reduced the size of the downloaded api artifact from 65 MB to 650 KB by cleaning up resources and migrating the model and datasets to the S3 storage

Docs:

  • Updated all the tutorials
  • Updated the Readme.md

Examples:

  • Renamed all the example's packages
  • Regrouped examples between packages
  • Added examples for training all ResNet models from scratch on the Cats & Dogs dataset
  • Tuned hyper-parameters in all examples with VGG-like architecture to achieve convergence
  • Added examples for the Image Preprocessing DSL
  • Added examples for all available ModelZoo models, including additional training on the subset of the Cats & Dogs dataset
  • Added ToyResNet examples (trained on the FashionMnist dataset)

Tests:

  • Converted all examples to integration tests by refactoring main functions

Thanks to our contributors:

Contributors

MKhalusova, zaleslaw, and 2 other contributors
Assets 4
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