keras3 (development version)

install_keras() changes: if a GPU is available, the default is now to install a CPU build of TensorFlow and a GPU build of JAX. To use a GPU in the current session, call use_backend("jax").

Added compatibility with Keras v3.6.0. User-facing changes:

Breaking changes:

When using get_file() with extract = TRUE or untar = TRUE, the return value is now the path of the extracted directory, rather than the path of the archive.

Other changes and additions:

Logging is now asynchronous in fit(), evaluate(), and predict(). This enables 100% compact stacking of train_step calls on accelerators (e.g. when running small models on TPU).
- If you are using custom callbacks that rely on on_batch_end, this will disable async logging. You can re-enable it by adding self$async_safe <- TRUE to your callbacks. Note that the TensorBoard callback is not considered async-safe by default. Default callbacks like the progress bar are async-safe.
New bitwise operations:
- op_bitwise_and()
- op_bitwise_invert()
- op_bitwise_left_shift()
- op_bitwise_not()
- op_bitwise_or()
- op_bitwise_right_shift()
- op_bitwise_xor()
New math operations:
- op_logdet()
- op_trunc()
- op_histogram()
New neural network operation: op_dot_product_attention()
New image preprocessing layers:
- layer_auto_contrast()
- layer_solarization()
New Model functions get_state_tree() and set_state_tree(), for retrieving all model variables, including trainable, non-trainable, optimizer variables, and metric variables.
New layer_pipeline() for composing a sequence of layers. This class is useful for building a preprocessing pipeline. Compared to a keras_model_sequential(), layer_pipeline() has a few key differences:
- It's not a Model, just a plain layer.
- When the layers in the pipeline are compatible with tf.data, the pipeline will also remain tf.data compatible, regardless of the backend you use.
New argument: export_savedmodel(verbose = )
New argument: op_normalize(epsilon = )
Various documentation improvements and bug fixes.

keras3 1.2.0

Added compatibility with Keras v3.5.0. User facing changes:
- New functions:
  - op_associative_scan()
  - op_searchsorted()
  - optimizer_lamb()
- keras$DTypePolicy instances can now be supplied to dtype argument for losses, metrics, and layers.
- Add integration with the Hugging Face Hub. You can now save models to Hugging Face Hub directly save_model() and load .keras models directly from Hugging Face Hub with load_model().
- Added compatibility with NumPy 2.0.
- Improved keras$distribution API support for very large models.
- Bug fixes and performance improvements.
- Add data_format argument to layer_zero_padding_1d() layer.
- Miscellaneous documentation improvements.
- Bug fixes and performance improvements.

keras3 1.1.0

Fixed issue where GPUs would not be found when running on Windows under WSL Linux. (reported in #1456, fixed in #1459)
keras_shape objects (as returned by keras3::shape()) gain == and != methods.
Fixed warning from tfruns::training_run() being unable to log optimizer learning rate.
Added compatibility with Keras v3.4.1 (no R user facing changes).
Added compatibility with Keras v3.4.0. User facing changes:
- New functions:
  - op_argpartition()
  - op_map()
  - op_scan()
  - op_switch()
  - op_dtype()
  - op_lstsq()
  - op_image_hsv_to_rgb()
  - op_image_rgb_to_hsv()
- Changes:
  - Added support for arbitrary, deeply nested input/output structures in Functional models (e.g. lists of lists of lists of inputs or outputs...)
  - Add support for optional Functional inputs.
    - keras_input() gains an optional argument.
    - keras_model_sequential() gains a input_optional argument.
  - Add support for float8 inference for Dense and EinsumDense layers.
  - Enable layer_feature_space() to be used in a {tfdatasets} pipeline even when the backend isn't TensorFlow.
  - layer_string_lookup() can now take tf$SparseTensor() as input.
  - layer_string_lookup() returns "int64" dtype by default in more modes now.
  - Layer() instances gain attributes path and quantization_mode.
  - Metric()$variables is now recursive.
  - Add training argument to Model$compute_loss().
  - split_dataset() now supports nested structures in dataset.
  - All applications gain a name argument, accept a custom name.
  - layer_multi_head_attention() gains a seed argument.
  - All losses gain a dtype argument.
  - loss_dice() gains an axis argument.
  - op_ctc_decode(), new default for mask_index = 0
  - All op_image_* functions now use default data_format value to config_image_data_format()
  - op_isclose() gains arguments rtol, atol, equal_nan.
  - save_model() gains argument zipped.
  - Bugs fixes and performance improvements.

keras3 1.0.0

Chains of layer_* calls with |> now instantiate layers in the same order as %>% pipe chains: left-hand-side first (#1440).
iterate(), iter_next() and as_iterator() are now reexported from reticulate.

User facing changes with upstream Keras v3.3.3:

new functions: op_slogdet(), op_psnr()
clone_model() gains new args: call_function, recursive Updated example usage.
op_ctc_decode() strategy argument has new default: "greedy". Updated docs.
loss_ctc() default name fixed, changed to "ctc"

User facing changes with upstream Keras v3.3.2:

new function: op_ctc_decode()
new function: op_eigh()
new function: op_select()
new function: op_vectorize()
new function: op_image_rgb_to_grayscale()
new function: loss_tversky()
new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value)
new arg: layer_embedding(weights) for providing an initial weights matrix
new args: op_nan_to_num(nan, posinf, neginf)
new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value)
new args: op_argmax(keepdims) and op_argmin(keepdims)
new arg: clear_session(free_memory) for clearing without invoking the garbage collector.
metric_kl_divergence() and loss_kl_divergence() clip inputs (y_true and y_pred) to the [0, 1] range.
new Layer() attributes: metrics, dtype_policy
Added initial support for float8 training
layer_conv_*d() layers now support LoRa
op_digitize() now supports sparse tensors.
Models and layers now return owned metrics recursively.
Add pickling support for Keras models. (e.g., via reticulate::py_save_object()) Note that pickling is not recommended, prefer using Keras saving APIs.

keras3 0.2.0

New functions:

quantize_weights(): quantize model or layer weights in-place. Currently, only Dense, EinsumDense, and Embedding layers are supported (which is enough to cover the majority of transformers today)
layer_mel_spectrogram()
layer_flax_module_wrapper()
layer_jax_model_wrapper()
loss_dice()
random_beta()
random_binomial()
config_set_backend(): change the backend after Keras has initialized.
config_dtype_policy()
config_set_dtype_policy()
New Ops
- op_custom_gradient()
- op_batch_normalization()
- op_image_crop()
- op_divide_no_nan()
- op_normalize()
- op_correlate()
- `
New family of linear algebra ops
- op_cholesky()
- op_det()
- op_eig()
- op_inv()
- op_lu_factor()
- op_norm()
- op_erfinv()
- op_solve_triangular()
- op_svd()
audio_dataset_from_directory(), image_dataset_from_directory() and text_dataset_from_directory() gain a verbose argument (default TRUE)
image_dataset_from_directory() gains pad_to_aspect_ratio argument (default FALSE)
to_categorical(), op_one_hot(), and fit() can now accept R factors, offset them to be 0-based (reported in #1055).
op_convert_to_numpy() now returns unconverted NumPy arrays.
op_array() and op_convert_to_tensor() no longer error when casting R doubles to integer types.
export_savedmodel() now works with a Jax backend.
Metric()$add_variable() method gains arg: aggregration.
Layer()$add_weight() method gains args: autocast, regularizer, aggregation.
op_bincount(), op_multi_hot(), op_one_hot(), and layer_category_encoding() now support sparse tensors.
op_custom_gradient() now supports the PyTorch backend
layer_lstm() and layer_gru() gain arg use_cudnn, default 'auto'.
Fixed an issue where application_preprocess_inputs() would error if supplied an R array as input.
Doc improvements.

keras3 0.1.0

The package has been rebuilt for Keras 3.0. Refer to https://blogs.rstudio.com/ai/posts/2024-05-21-keras3/ for an overview and https://keras3.posit.co for the current up-to-date documentation.

keras 2.13.0

Default TF version installed by install_keras() is now 2.13.
Updated layers:
- layer_batch_normalization() updated signature, with changes to options for distributed training.
- layer_embedding() gains a sparse argument.
Fixed deadlock when an R generator was passed to fit(), predict(), and other endpoints.
When fit(verbose = "auto") is evaluated in the context of a knitr document (e.g., quarto or rmarkdown document being rendered), verbose will now default to 2, showing one line per epoch.

keras 2.11.1

Update S3 method formals per new CRAN requirement (r_to_py.keras_layer_wrapper())
Fixed an issue where get_file() would place incorrectly save files in the current working directory. (#1365)

keras 2.11.0

Default TensorFlow version installed by install_keras() is now 2.11.
All optimizers have been updated for keras/tensorflow version 2.11. Arguments to all the optimizers have changed. To access the previous optimizer implementations, use the constructors available at keras$optimizers$legacy. For example, use keras$optimizers$legacy$Adam() for the previous implementation of optimizer_adam().
New optimizer optimizer_frtl().
updates to layers:
- layer_attention() gains score_mode and dropout arguments.
- layer_discretization() gains output_mode and sparse arguments.
- layer_gaussian_dropout() and layer_gaussian_noise() gain a seed argument.
- layer_hashing() gains output_mode and sparse arguments.
- layer_integer_lookup() gains vocabulary_dtype and idf_weights arguments.
- layer_normalization() gains an invert argument.
- layer_string_lookup() gains an idf_weights argument.
Fixed issue where input_shape supplied to custom layers defined with new_layer_class() would result in an error (#1338)
New callback_backup_and_restore(), for resuming an interrupted fit() call.
The merging family of layers (layer_add, layer_concatenate, etc.) gain the ability to accept layers in ..., allowing for easier composition of residual blocks with the pipe %>%. e.g. something like this now works:
```
block_1_output <- ...
block_2_output <- block_1_output %>%
  layer_conv_2d(64, 3, activation = "relu", padding = "same") %>%
  layer_add(block_1_output)
```
model$get_config() method now returns an R object that can be safely serialized to rds.
keras_array() now reflects unconverted Python objects. This enables passing objects like pandas.Series() to fit() and evaluate() methods. (#1341)

keras 2.9.0

New functions for constructing custom keras subclasses:
- new_model_class()
- new_layer_class()
- new_callback_class()
- new_metric_class()
- new_loss_class()
- new_learning_rate_schedule_class().
Also provided is mark_active(), a decorator for indicating a class method should be an active binding (i.e., decorated with Python's @property). mark_active() can be used in the new_*_class family of class constructors as well as %py_class%.
r_to_py() method for R6 classes and %py_class% gain support for private fields and methods. Any R objects stored in private will only be available to methods, and will not be converted to Python.
New family of functions for controlling optimizer learning rates during training:
- learning_rate_schedule_cosine_decay()
- learning_rate_schedule_cosine_decay_restarts()
- learning_rate_schedule_exponential_decay()
- learning_rate_schedule_inverse_time_decay()
- learning_rate_schedule_piecewise_constant_decay()
- learning_rate_schedule_polynomial_decay()
Also, a function for constructing custom learning rate schedules: new_learning_rate_schedule_class().
New L2 unit normilization layer: layer_unit_normalization().
New regularizer_orthogonal, a regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.
New zip_lists() function for transposing lists, optionally matching by name.
New plot() S3 method for models.
pydot is now included in the packages installed by install_keras().
The png package is now listed under Suggests.
The %<>% assignment pipe from magrittr is exported.
format() method for keras models (and derivative methods print(), summary(), str(), and py_str()):
- gain a new arg compact. If TRUE (the default) white-space only lines are stripped out of model.summary().
- If any layers are marked non-trainable or frozen, the model summary now includes a "Trainable" column, indicating if a layer is frozen.
freeze_weights() and unfreeze_weights():
- gain a flexible which argument that can accept layer names (as character strings), an integer vector, a boolean vector, or a function that returns a boolean when called with a layer. (see updated examples in ?freeze_weights
- from and to arguments gain the ability to accept negative integers, to specify layers counting from the end of the layers list.
get_weights() gains a trainable argument that can accept TRUE or FALSE, allowing for returning only the unfrozen or frozen weights, respectively.
timeseries_dataset_from_array():
- R arrays are now cast to the floatx dtype ("float32" by default)
- start_index and end_index now are 1-based.
image_dataset_from_directory() gains a crop_to_aspect_ratio argument which can be used to prevent distorting images when resizing to a new aspect ratio.
Layer is deprecated, superseded by new_layer_class().
load_model_tf() argument custom_objects gains the ability to accept an unnamed list (e.g, of objects returned by new_layer_class() or similar). Appropriate names for the supplied objects are automatically inferred.
Fixed an issue where negative values less than -1 supplied to axis arguments were selecting the wrong axis.
get_layer() gains the ability to accept negative values for the index argument.
Fixed warning from create_layer_wrapper() when the custom layer didn't have an overridden initialize or __init__ method.
Backend functions:
- k_clip() min_value and max_value gain default values of NULL, can be omitted. NULL is taken as -Inf or Inf, respectively.
- k_squeeze(): axis argument can be omitted, in which case all axes of size 1 are dropped.
- k_tile(): n argument can now be supplied as a tensor.
- New function k_unstack().
KerasTensor objects (e.g, returned by layer_input()) now inherit S3 methods for "tensorflow.tensor".
plot.keras_training_history() no longer issues message `geom_smooth()` using formula 'y ~ x' when method = "ggplot2".
print and related methods for models (format, summary) now accept a width argument.
evaluate(), fit(), and predict() methods for keras Models now default to verbose = "auto", with verbosity adjusted appropriately based on calls to keras$utils$disable_interactive_logging(), and contexts like ParameterServerStrategy.
install_keras() now accepts version = "release-cpu" as a valid specification.

keras 2.8.0

Breaking change: The semantics of passing a named list to keras_model() have changed.

Previously, keras_model() would unname() supplied inputs and outputs. Then, if a named list was passed to subsequent fit()/evaluate()/call()/predict() invocations, matching of x and y was done to the model's input and outpt tensor$name's. Now, matching is done to names() of inputs and/or outputs supplied to keras_model(). Call unname() on inputs and outputs to restore the old behavior, e.g.:
```
keras_model(unname(inputs), unname(outputs))
```
keras_model() can now accept a named list for multi-input and/or multi-output models. The named list is converted to a dict in python. (Requires Tensorflow >= 2.4, Python >= 3.7).

If inputs is a named list:
- call(), fit(), evaluate(), and predict() methods can also accept a named list for x, with names matching to the names of inputs when the model was constructed. Positional matching of x is still also supported (requires python 3.7+).
If outputs is a named list:
- fit() and evaluate() methods can only accept a named list for y, with names matching to the names of outputs when the model was constructed.
New layer layer_depthwise_conv_1d().
Models gain format() and print() S3 methods for compatibility with the latest reticulate. Both are powered by model$summary().
summary() method for Models gains arguments expand_nested and show_trainable, both default to FALSE.
keras_model_custom() is soft deprecated. Please define custom models by subclassing keras$Model directly using %py_class% or R6::R6Class().
Fixed warning issued by k_random_binomial().
Fixed error raised when k_random_binomial() was passed a non-floating dtype.
Added k_random_bernouli() as an alias for k_random_binomial().
image_load() gains a color_mode argument.
Fixed issue where create_layer_wrapper() would not include arguments with a NULL default value in the returned wrapper.
Fixed issue in r_to_py.R6ClassGenerator (and %py_class%) where single-expression initialize functions defined without { would error.
Deprecated functions are no longer included in the package documentation index.

keras 2.7.0

Default Tensorflow + Keras version is now 2.7.
New API for constructing RNN (Recurrent Neural Network) layers. This is a flexible interface that complements the existing RNN layers. It is primarily intended for advanced / research applications, e.g, prototyping novel architectures. It allows you to compose a RNN with a custom "cell", a Keras layer that processes one step of a sequence. New symbols:
- layer_rnn(), which can compose with builtin cells:
- rnn_cell_gru()
- rnn_cell_lstm()
- rnn_cell_simple()
- rnn_cells_stack() To learn more, including how to make a custom cell layer, see the new vignette: "Working with RNNs".
New dataset functions:
- text_dataset_from_directory()
- timeseries_dataset_from_array()
New layers:
- layer_additive_attention()
- layer_conv_lstm_1d()
- layer_conv_lstm_3d()
layer_cudnn_gru() and layer_cudnn_lstm() are deprecated. layer_gru() and layer_lstm() will automatically use CuDNN if it is available.
layer_lstm() and layer_gru(): default value for recurrent_activation changed from "hard_sigmoid" to "sigmoid".
layer_gru(): default value reset_after changed from FALSE to TRUE
New vignette: "Transfer learning and fine-tuning".
New applications:
- MobileNet V3: application_mobilenet_v3_large(), application_mobilenet_v3_small()
- ResNet: application_resnet101(), application_resnet152(), resnet_preprocess_input()
- ResNet V2:application_resnet50_v2(), application_resnet101_v2(), application_resnet152_v2() and resnet_v2_preprocess_input()
- EfficientNet: application_efficientnet_b{0,1,2,3,4,5,6,7}()
Many existing application_*()'s gain argument classifier_activation, with default 'softmax'. Affected: application_{xception, inception_resnet_v2, inception_v3, mobilenet, vgg16, vgg19}()
New function %<-active%, a ergonomic wrapper around makeActiveBinding() for constructing Python @property decorated methods in %py_class%.
bidirectional() sequence processing layer wrapper gains a backwards_layer arguments.
Global pooling layers layer_global_{max,average}_pooling_{1,2,3}d() gain a keepdims argument with default value FALSE.
Signatures for layer functions are in the process of being simplified. Standard layer arguments are moving to ... where appropriate (and will need to be provided as named arguments). Standard layer arguments include: input_shape, batch_input_shape, batch_size, dtype, name, trainable, weights. Layers updated: layer_global_{max,average}_pooling_{1,2,3}d(), time_distributed(), bidirectional(), layer_gru(), layer_lstm(), layer_simple_rnn()
All the backend function with a shape argument k_*(shape =) that now accept a a mix of integer tensors and R numerics in the supplied list.
All layer functions now accept NA as a synonym for NULL in arguments that specify shape as a vector of dimension values, e.g., input_shape, batch_input_shape.
k_random_uniform() now automatically casts minval and maxval to the output dtype.
install_keras() gains arg with default pip_ignore_installed = TRUE.

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). Added a new vignette: "Working with Preprocessing Layers". 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

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: keras3::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 a installed by default if the Tensorflow version >= 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.

keras 2.4.0

Use compat module when using set_session and get_session. (#1046)
Allows passing other arguments to keras_model eg name. (#1045)
Fixed bug when serializing models with the plaidml backends.(#1084)
Install keras no longer tries to install scipy because it's already installed by tensorflow (#1081)
Fixed bug with layer_text_vectorization with TensorFlow >= 2.3 (#1131)
Handle renamed argument text to input_text in text_one_hot (#1133)
Added TensorFlow 2.3 to the CI (#1102)
Fix C stack error when using Image Data Generators and Time Series generators with TensorFlow <= 2.0.1 (#1135)
Fixed warning raised in the initial epoch (@gsteinbu #1130)
Consistent result when using text_hashing_trick with missing values (@topepo #1048)
Added a custom error message for k_logsumexp as it was removed from Keras (#1137)
Fixed bug when printing models that are not built yet. (#1138)
Fix drop_duplicates DeprecationWarning with tf 2.3 (@gsteinbu #1139 #1141)
Fixed bug when plotting the model history if the model used an early stopping callback (#1140)
install_keras now installs a fixed version of h5py, because newer versions are backward incompatible. (#1142)
Simplify testing utilities by using a helper-* file. (#1173)
Deprecated hdf5_matrix if using TF >= 2.4 (#1175)
Fixed TensorFlow nightly installation on CI (#1176)
Support for TensorFlow v2.4: just small fixes for custom classes. (#1177)
Added untar argument to get_file as it seems to be slightly different from extract (#1179)
Warn when not using the tensorflow implementation of Keras (#1181)
Added layer_layer_normalization (#1183)
Added layer_multihead_attention (#1184)
Added image_dataset_from_directory (#1185)
Fixed bug when using a custom layer with a time distributed adverb. (#1188)
Added the ragged argument to layer_input. (#1193)
Fixed *_generator deadlocks with recent versions of TensorFlow (#1197)

Keras 2.2.3.0 (CRAN)

Added layer_attention (#1000) by @atroiano.
Fixed issue regarding the KerasMetricsCallback with TF v2.2 (#1020)

Keras 2.2.5.0 (CRAN)

Added layer_dense_features.
Added on_test_*, on_test_batch_*, on_predict_* and on_predict_* to callback options.
Search for the right optimizers and initializers on TensorFlow 2.0
Fixed bug in function generators when using models with multiple inputs. (#740)
Added export_savedmodel support for TensorFlow 2.0 (#773)
Fixed bug when using metric_ functions. (#804)
Allow users to pass additional arguments to install_keras (#808)
Enabled calling Keras models with R arrays. (#806)
Allow passing data.frames as inputs to Keras models. (#822)
Fixed bug when passing a fixed validation set to fit_generator (#837)
Fixed bug when passing a TensorFlow dataset to fit within a tf$distribute scope. (#856)
install_keras will now install Keras dependencies (#856). It won't re-install TensorFlow if it's already installed.
Fixed deprecation messages showed with TensorFlow v1.14.
Largely reduced tests verbosity.

Keras 2.2.4.1 (CRAN)

Use tf.keras as default implementation module.
Added AppVeyor to test on Windows.
Added flow_images_from_dataframe function (#658).
Allow for unknown input_shape in application_* functions.
Added save_model_tf and load_model_tf to save/load models in the TensorFlow's SavedModel format.

Keras 2.2.4 (CRAN)

Improve handling of timeseries_generator() in calls to fit_generator()
Add support for input_shape argument to layer_dropout()
Improve error message for data frames passed to fit(), etc.
Use 1-based axis indices for k_gather()
Added version parameter to install_keras() for installing alternate/older versions
Added activation_exponential() function.
Added threshold parameter to activation_relu()
Added restore_best_weights parameter to callback_model_checkpoint()
Added update_freq parameter to callback_tensorboard()
Added negative_slope and threshold parameters to layer_activation_relu()
Added output_padding and dilation_rate parameters to layer_conv_2d_transpose()
Added output_padding argument to layer_conv_3d_transpose()
Added data_format argument to layer_separable_conv_1d(), layer_average_pooling_1d(), layer_global_max_pooling_1d(), and layer_global_average_pooling_1d()
Added interpolation argument to layer_upsampling_1d() and layer_upsampling_2d()
Added dtype argument to to_categorical()
Added layer_activation_selu() function.
Added KerasWrapper class and corresponding create_wrapper function.

Keras 2.2.0

Fix issue with serializing models that have constraint arguments
Fix issue with k_tile that needs an integer vector instead of a list as the n argument.
Fix issue with user-supplied output_shape in layer_lambda() not being supplied to tensorflow backends
Filter out metrics that were created for callbacks (e.g. lr)
Added application_mobilenet_v2() pre-trained model
Added sample_weight parameter to flow_images_from_data()
Use native Keras implementation (rather than SciPy) for image_array_save()
Default layer_flatten() data_format argument to NULL (which defaults to global Keras config).
Add baseline argument to callback_early_stopping() (stop training if a given baseline isn't reached).
Add data_format argument to layer_conv_1d().
Add layer_activation_relu(), making the ReLU activation easier to configure while retaining easy serialization capabilities.
Add axis = -1 argument in backend crossentropy functions specifying the class prediction axis in the input tensor.
Handle symbolic tensors and TF datasets in calls to fit(), evaluate(), and predict()
Add embeddings_data argument to callback_tensorboard()
Support for defining custom Keras models (i.e. custom call() logic for forward pass)
Handle named list of model output names in metrics argument of compile()
New custom_metric() function for defining custom metrics in R
Provide typed wrapper for categorical custom metrics
Provide access to Python layer within R custom layers
Don't convert custom layer output shape to tuple when shape is a list or tuple of other shapes
Re-export shape() function from tensorflow package
Re-export tuple() function from reticulate package
Indexes for get_layer() are now 1-based (for consistency w/ freeze_weights())
Accept named list for sample_weight argument to fit()

Keras 2.1.6

Fix issue with single-element vectors passed to text preprocessing functions
Compatibility with TensorFlow v1.7 Keras implementation
Support workers parameter for native Keras generators (e.g. flow_images_from_directory())
Accept tensor as argument to k_pow()
In callback_reduce_lr_on_plateau(), rename epsilon argument to min_delta (backwards-compatible).
Add axis parameter to k_softmax()
Add send_as_json parameter to callback_remote_monitor()
Add data_format method to layer_flatten()
In multi_gpu_model(), add arguments cpu_merge and cpu_relocation (controlling whether to force the template model's weights to be on CPU, and whether to operate merge operations on CPU or GPU).
Record correct loss name for tfruns when custom functions are provided for loss

Keras 2.1.5

Support for custom constraints from R
Added timeseries_generator() utility function
New layer layer_depthwise_conv_2d()
Added brightness_range and validation_split arguments to [image_data_generator()].

Keras 2.1.4

Added support for remove_learning_phase in export_savedmodel() to avoid removing learning phase.
Normalize validation data to Keras array in fit() and fit_generator()
Ensure that custom layers return a tuple from compute_output_shape()
Added Nasnet and Densenet pre-trained models
New layers layer_activation_softmax() and layer_separable_conv_1d()
Added amsgrad parameter to optimizer_adam()
Fix incompatibility with Progbar.update() method in Keras 2.1.4

Keras 2.1.3

Models saved via export_savedmodel() that make use of learning phases can now be exported without having to manually reload the original model.
Ensure that models saved via export_savedmodel() can be served from CloudML
Run image data generators with R preprocessing functions on the main thread
Return R list from texts_to_sequences()
Various fixes for use_implementation() function

Keras 2.1.2

Added theme_bw option to plot method for training history
Support TF Dataset objects as generators for fit_generator(), etc.
Added use_implementation() and use_backend() functions as alternative to setting KERAS_IMPLEMENATION and KERAS_BACKEND environment variables.
Added R wrappers for Keras backend functions (e.g. k_variable(), k_dot(), etc.)
Use 1-based axis for normalize function.
Fix issue with printing training history after early stopping.
Experimental support for using the PlaidML backend.
Correct handling for R functions specified in custom_objects
Added with_custom_object_scope() function.
Automatically provide name to loss function during compile (enables save/load of models with custom loss function)
Provide global keras.fit_verbose option (defaults to 1)

keras 2.0.9

Added multi_gpu_model() function.
Automatically call keras_array() on the results of generator functions.
Ensure that steps_per_epoch is passed as an integer
Import evaluate() generic from tensorflow package
Handle NULL when converting R arrays to Keras friendly arrays
Added dataset_imbd_word_index() function
Ensure that sample_weight is passed to fit() as an array.
Accept single function as metrics argument to compile()
Automatically cast input_shape argument to applications to integer
Allow Keras models to be composable within model pipelines
Added freeze_weights() and unfreeze_weights() functions.
Implement export_savedmodel() generic from TensorFlow package
Convert R arrays to row-major before image preprocessing
Use tensorflow.keras for tensorflow implementation (TF v1.4)
Added application_inception_resnet_v2() pre-trained model
Added dataset_fashion_mnist() dataset
Added layer_cudnn_gru() and layer_cudnn_lstm() (faster recurrent layers backed by CuDNN)
Added layer_minimum() function
Added interpolation parameter to image_load() function
Add save_text_tokenizer() and load_text_tokenizer() functions.
Fix for progress bar output in Keras >= 2.0.9
Remove deprecated implementation argument from recurrent layers
Support for passing generators for validation data in fit_generator()
Accept single integer arguments for kernel sizes
Add standard layer arguments to layer_flatten() and layer_separable_conv_2d()
Added image_array_resize() and image_array_save() for 3D image arrays.
Allow custom layers and lambda layers to accept list parameters.
Expose add_loss() function for custom layers

keras 2.0.8

Add use_session_with_seed() function that establishes a random seed for the Keras session. Note that this should not be used when training time is paramount, as it disables GPU computation and CPU parallelism by default for more deterministic computations.
Fix for plotting training history with early stopping callback (thanks to @JamesAllingham).
Return R training history object from fit_generator()
Rename to_numpy_array() function to keras_array() reflecting automatic use of Keras default backend float type and "C" ordering.
Add standard layer arguments (e.g. name, trainable, etc.) to merge layers
Better support for training models from data tensors in TensorFlow (e.g. Datasets, TFRecords). Add a related example script.
Add clone_model() function, enabling to construct a new model, given an existing model to use as a template. Works even in a TensorFlow graph different from that of the original model.
Add target_tensors argument in compile(), enabling to use custom tensors or placeholders as model targets.
Add steps_per_epoch argument in fit(), enabling to train a model from data tensors in a way that is consistent with training from arrays. Similarly, add steps argument in predict() and evaluate().
Add layer_subtract() layer function.
Add weighted_metrics argument in compile to specify metric functions meant to take into account sample_weight or class_weight.
Enable stateful RNNs with CNTK.

keras 2.0.6

install_keras() function which installs both TensorFlow and Keras
Use keras package as default implementation rather than tf.contrib.keras
Training metrics plotted in realtime within the RStudio Viewer during fit
serialize_model() and unserialize_model() functions for saving Keras models as 'raw' R objects.
Automatically convert 64-bit R floats to backend default float type
Ensure that arrays passed to generator functions are normalized to C-order
to_numpy_array() utility function for custom generators (enables custom generators to yield C-ordered arrays of the correct float type)
Added batch_size and write_grads arguments to callback_tensorboard()
Added return_state argument to recurrent layers.
Don't re-export install_tensorflow() and tf_config() from tensorflow package.
is_keras_available() function to probe whether the Keras Python package is available in the current environment.
as.data.frame() S3 method for Keras training history
Remove names from keras_model() inputs
Return result of evaluate() as named list
Write run metrics and evaluation data to tfruns
Provide hint to use r-tensorflow environment when importing keras

keras 2.0.5

Initial CRAN release

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keras3 (development version)

Added compatibility with Keras v3.6.0. User-facing changes:

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keras3 1.2.0

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Keras 2.1.6

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keras3 (development version)

Added compatibility with Keras v3.6.0. User-facing changes:

Breaking changes:

Other changes and additions:

keras3 1.2.0

keras3 1.1.0

keras3 1.0.0

keras3 0.2.0

keras3 0.1.0

keras 2.13.0

keras 2.11.1

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keras 2.9.0

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keras 2.6.1

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keras 2.4.0

Keras 2.2.3.0 (CRAN)

Keras 2.2.5.0 (CRAN)

Keras 2.2.4.1 (CRAN)

Keras 2.2.4 (CRAN)

Keras 2.2.0

Keras 2.1.6

Keras 2.1.5

Keras 2.1.4

Keras 2.1.3

Keras 2.1.2

keras 2.0.9

keras 2.0.8

keras 2.0.6

keras 2.0.5