pypi keras 2.0.9
Keras 2.0.9

latest releases: 2.11.0, 2.11.0rc3, 2.11.0rc2...
5 years ago

Areas of improvement

  • RNN improvements:
    • Refactor RNN layers to rely on atomic RNN cells. This makes the creation of custom RNN very simple and user-friendly, via the RNN base class.
    • Add ability to create new RNN cells by stacking a list of cells, allowing for efficient stacked RNNs.
    • Add CuDNNLSTM and CuDNNGRU layers, backend by NVIDIA's cuDNN library for fast GPU training & inference.
    • Add RNN Sequence-to-sequence example script.
    • Add constants argument in RNN's call method, making RNN attention easier to implement.
  • Easier multi-GPU data parallelism via keras.utils.multi_gpu_model.
  • Bug fixes & performance improvements (in particular, native support for NCHW data layout in TensorFlow).
  • Documentation improvements and examples improvements.

API changes

  • Add "fashion mnist" dataset as keras.datasets.fashion_mnist.load_data()
  • Add Minimum merge layer as keras.layers.Minimum (class) and keras.layers.minimum(inputs) (function)
  • Add InceptionResNetV2 to keras.applications.
  • Support bool variables in TensorFlow backend.
  • Add dilation to SeparableConv2D.
  • Add support for dynamic noise_shape in Dropout
  • Add keras.layers.RNN() base class for batch-level RNNs (used to implement custom RNN layers from a cell class).
  • Add keras.layers.StackedRNNCells() layer wrapper, used to stack a list of RNN cells into a single cell.
  • Add CuDNNLSTM and CuDNNGRU layers.
  • Deprecate implementation=0 for RNN layers.
  • The Keras progbar now reports time taken for each past epoch, and average time per step.
  • Add option to specific resampling method in keras.preprocessing.image.load_img().
  • Add keras.utils.multi_gpu_model for easy multi-GPU data parallelism.
  • Add constants argument in RNN's call method, used to pass a list of constant tensors to the underlying RNN cell.

Breaking changes

  • Implementation change in keras.losses.cosine_proximity results in a different (correct) scaling behavior.
  • Implementation change for samplewise normalization in ImageDataGenerator results in a different normalization behavior.


Thanks to our 59 contributors whose commits are featured in this release!

@alok, @Danielhiversen, @Dref360, @HelgeS, @JakeBecker, @MPiecuch, @MartinXPN, @RitwikGupta, @TimZaman, @adammenges, @aeftimia, @ahojnnes, @akshaychawla, @alanyee, @aldenks, @andhus, @apbard, @aronj, @bangbangbear, @bchu, @bdwyer2, @bzamecnik, @cclauss, @colllin, @datumbox, @deltheil, @dhaval067, @durana, @ericwu09, @facaiy, @farizrahman4u, @fchollet, @flomlo, @fran6co, @grzesir, @hgaiser, @icyblade, @jsaporta, @julienr, @jussihuotari, @kashif, @lucashu1, @mangerlahn, @myutwo150, @nicolewhite, @noahstier, @nzw0301, @olalonde, @ozabluda, @patrikerdes, @podhrmic, @qin, @raelg, @roatienza, @shadiakiki1986, @smgt, @souptc, @taehoonlee, @y0z

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