keras 2.6.0

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.