keras 2.6.1

• New family of preprocessing layers. These are the spiritual successor to the tfdatasets::step_* family of data transformers (to be deprecated in a future release). See the new vignette "Working with Preprocessing Layers" for details.
  New functions:

  Image preprocessing:

  • layer_resizing()
  • layer_rescaling()
  • layer_center_crop()

  Image augmentation:

  • layer_random_crop()
  • layer_random_flip()
  • layer_random_translation()
  • layer_random_rotation()
  • layer_random_zoom()
  • layer_random_contrast()
  • layer_random_height()
  • layer_random_width()

  Categorical features preprocessing:

  • layer_category_encoding()
  • layer_hashing()
  • layer_integer_lookup()
  • layer_string_lookup()

  Numerical features preprocessing:

  • layer_normalization()
  • layer_discretization()

  These join the previous set of text preprocessing functions, each of which have some minor changes:

  • layer_text_vectorization() (changed arguments)
  • get_vocabulary()
  • set_vocabulary()
  • adapt()

• adapt() changes:

  • Now accepts all features preprocessing layers, previously
    only layer_text_vectorization() instances were valid.
  • reset_state argument is removed. It only ever accepted the default value of TRUE.
  • New arguments batch_size and steps.
  • Now returns the adapted layer invisibly for composability with %>% (previously returned NULL)

• get_vocabulary() gains a include_special_tokens argument.

• set_vocabulary():

  • Now returns the adapted layer invisibly for composability with %>% (previously returned NULL)
  • Signature simplified. Deprecated arguments (df_data oov_df_value) are now subsumed in ....

• layer_text_vectorization():

  • valid values for argument output_mode change: "binary" is renamed to "multi_hot" and
    "tf-idf" is renamed to "tf_idf" (backwards compatibility is preserved).
  • Fixed an issue where valid values of output_mode = "int" would incorrectly
    return a ragged tensor output shape.

• Existing layer instances gain the ability to be added to sequential models via a call. E.g.:

  layer <- layer_dense(units = 10)
  model <- keras_model_sequential(input_shape = c(1,2,3)) %>%
    layer()

• Functions in the merging layer family gain the ability to return a layer instance if
  the first argument inputs is missing. (affected: layer_concatenate(), layer_add(),
  layer_subtract(), layer_multiply(), layer_average(), layer_maximum(),
  layer_minimum() , layer_dot())

• %py_class% gains the ability to delay initializing the Python session until first use.
  It is now safe to implement and export %py_class% objects in an R package.

• Fixed an issue in layer_input() where passing a tensorflow DType objects to argument dtype would throw an error.

• Fixed an issue in compile() where passing an R function via an in-line
  call would result in an error from subsequent fit() calls.
  (e.g., compile(loss = function(y_true, y_pred) my_loss(y_true, y_pred))
  now succeeds)

• clone_model() gains a clone_function argument that allows you to customize each layer as it is cloned.

• Bumped minimum R version to 3.4. Expanded CI to test on all supported R version. Fixed regression that prevented package installation on R <= 3.4