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@fchollet fchollet released this Aug 22, 2019 · 173 commits to master since this release

Keras 2.2.5 is the last release of Keras that implements the 2.2.* API. It is the last release to only support TensorFlow 1 (as well as Theano and CNTK).

The next release will be 2.3.0, which makes significant API changes and add support for TensorFlow 2.0. The 2.3.0 release will be the last major release of multi-backend Keras. Multi-backend Keras is superseded by tf.keras.

At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf.keras in TensorFlow 2.0. tf.keras is better maintained and has better integration with TensorFlow features.

API Changes

  • Add new Applications: ResNet101, ResNet152, ResNet50V2, ResNet101V2, ResNet152V2.
  • Callbacks: enable callbacks to be passed in evaluate and predict.
    • Add callbacks argument (list of callback instances) in evaluate and predict.
    • Add callback methods on_train_batch_begin, on_train_batch_end, on_test_batch_begin, on_test_batch_end, on_predict_batch_begin, on_predict_batch_end, as well as on_test_begin, on_test_end, on_predict_begin, on_predict_end. Methods on_batch_begin and on_batch_end are now aliases for on_train_batch_begin and on_train_batch_end.
  • Allow file pointers in save_model and load_model (in place of the filepath)
  • Add name argument in Sequential constructor
  • Add validation_freq argument in fit, controlling the frequency of validation (e.g. setting validation_freq=3 would run validation every 3 epochs)
  • Allow Python generators (or Keras Sequence objects) to be passed in fit, evaluate, and predict, instead of having to use *_generator methods.
    • Add generator-related arguments max_queue_size, workers, use_multiprocessing to these methods.
  • Add dilation_rate argument in layer DepthwiseConv2D.
  • MaxNorm constraint: rename argument m to max_value.
  • Add dtype argument in base layer (default dtype for layer's weights).
  • Add Google Cloud Storage support for model.save_weights and model.load_weights.
  • Add JSON-serialization to the Tokenizer class.
  • Add H5Dict and model_to_dot to utils.
  • Allow default Keras path to be specified at startup via environment variable KERAS_HOME.
  • Add arguments expand_nested, dpi to plot_model.
  • Add update_sub, stack, cumsum, cumprod, foldl, foldr to CNTK backend
  • Add merge_repeated argument to ctc_decode in TensorFlow backend

Thanks to the 89 committers who contributed code to this release!

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