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keras 2.6.1
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@t-kalinowski t-kalinowski released this 30 Sep 19:20
v2.6.1
6af2391

  • 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

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