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@t-kalinowski t-kalinowski released this 23 Aug 16:43
v2.6.0
1152a76

Breaking changes (Tensorflow 2.6):

  • Note: The following breaking changes are specific to Tensorflow version 2.6.0.
    However, the keras R package maintains compatibility with multiple versions of Tensorflow/Keras.
    You can upgrade the R package and still preserve the previous behavior by
    installing a specific version of Tensorflow: keras::install_keras(tensorflow="2.4.0")

  • predict_proba() and predict_classes() were removed.

  • model_to_yaml() and model_from_yaml() were removed.

  • default changed: layer_text_vectorization(pad_to_max_tokens=FALSE)

  • set_vocabulary() arguments df_data and oov_df_value are removed. They are replaced by the new argument idf_weights.

New Features:

  • Default Tensorflow/Keras version is now 2.6

  • Introduced %py_class%, an R-language constructor for Python classes.

  • New vignettes:

    • Subclassing Python classes: How to use %py_class%.
    • Making new layers and models via subclassing.
    • Customizing what happens in fit (example of how to define a model, like a GAN, with a custom train step).
    • Writing your own callbacks.

  • The keras Python module is exported

  • Major changes to the underlying handling of custom R6 layer classes.

    • A new r_to_py() method is provided for R6ClassGenerator objects.
    • R6 custom layers can now inherit directly from Python layer classes
      or other R6 custom layer classes.
    • Custom R6 layers can now be instantiated directly after conversion of the class generator with r_to_py(), without going through create_layer().
    • KerasLayer is deprecated (new classes should inherit directly from keras$layers$Layer).
    • KerasWrapper is deprecated (new classes should inherit directly from keras$layers$Wrapper).
    • create_wrapper() is deprecated (no longer needed, use create_layer() directly).
    • All layer class methods provided as R functions now have a super in scope that resolves to the Python super class object.
    • Methods of super can be accessed in the 3 common ways:
      • (Python 3 style): super()$"__init__"()
      • (Python 2 style): super(ClassName, self)$"__init__"()
      • (R6 style): super$initialize()
    • User defined custom classes that inherit from a Python type are responsible for calling super()$`__init__`(...) if appropriate.
    • Custom layers can now properly handle masks (#1225)
      • supports_masking = TRUE attribute is now supported
      • compute_mask() user defined method is now supported
    • call() methods now support a training argument, as well as any additional arbitrary user-defined arguments

  • Layer() custom layer constructor is now lazy about initializing the Python session and safe to use on the top level of an R package (#1229).

  • New function create_layer_wrapper() that can create a composing R function wrapper around a custom layer class.

  • Refactored install_keras() (along with tensorflow::install_tensorflow()).
    Installation should be more reliable for more users now.
    If you encounter installation issues, please file an issue: https://github.com/rstudio/keras/issues/new

    • Potentially breaking change: numeric versions supplied without a patchlevel now automatically pull the latest patch release.
      (e.g. install_keras(tensorflow="2.4") will install tensorflow version "2.4.2". Previously it would install "2.4.0")

    • pandas is now a default extra packages installed by install_keras()

    • pyyaml is no longer installed by install_keras() if TF >= 2.6.

  • Loss functions:

    • All the loss functions gain the ability to return a callable
      (a keras$losses$Loss instance) if y_true and y_pred arguments are missing.

    • New builtin loss functions:

      • loss_huber()
      • loss_kl_divergence()

  • Metric functions:

    • All the metric functions gain the ability to return a keras$metrics$Metric instance if called without y_true and y_pred

    • Each metric function is now documented separately, with a common ?Metric topic demonstrating example usage.

    • New built-in metrics:

      • metric_true_negatives()
      • metric_true_positives()
      • metric_false_negatives()
      • metric_false_positives()
      • metric_specificity_at_sensitivity()
      • metric_sensitivity_at_specificity()
      • metric_precision()
      • metric_precision_at_recall()
      • metric_sum()
      • metric_recall()
      • metric_recall_at_precision()
      • metric_root_mean_squared_error()
      • metric_sparse_categorical_accuracy()
      • metric_mean_tensor()
      • metric_mean_wrapper()
      • metric_mean_iou()
      • metric_mean_relative_error()
      • metric_logcosh_error()
      • metric_mean()
      • metric_cosine_similarity()
      • metric_categorical_hinge()
      • metric_accuracy()
      • metric_auc()

  • keras_model_sequential() gains the ability to accept arguments that
    define the input layer like input_shape and dtype.
    See ?keras_model_sequential for details and examples.

  • Many layers gained new arguments, coming to parity with the interface
    available in the latest Python version:

    layer name new argument
    layer_gru time_major
    layer_lstm time_major
    layer_max_pooling_1d data_format
    layer_conv_lstm_2d return_state
    layer_depthwise_conv_2d dilation_rate
    layer_conv_3d_transpose dilation_rate
    layer_conv_1d groups
    layer_conv_2d groups
    layer_conv_3d groups
    layer_locally_connected_1d implementation
    layer_locally_connected_2d implementation
    layer_text_vectorization vocabulary

  • The compile() method for keras models has been updated:

    • optimizer is now an optional argument. It defaults to "rmsprop" for regular keras models.
      Custom models can specify their own default optimizer.
    • loss is now an optional argument.
    • New optional arguments: run_eagerly, steps_per_execution.
    • target_tensors and sample_weight_mode must now be supplied as named arguments.

  • Added activation functions swish and gelu. (#1226)

  • set_vocabulary() gains a idf_weights argument.

  • All optimizer had argument lr renamed to learning_rate.
    (backwards compatibility is preserved, an R warning is now issued).

  • The glue package was added to Imports

  • Refactored automated tests to closer match the default installation procedure
    and compute environment of most user.

  • Expanded CI test coverage to include R devel, oldrel and 3.6.

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