keras3 1.3.0

• Keras now uses reticulate::py_require() to resolve Python dependencies.
  Calling install_keras() is no longer required (but is still supported).

• use_backend() gains a gpu argument, to specify if a GPU-capable set of
  dependencies should be resolved by py_require().

• The progress bar in fit(), evaluate() and predict() now
  defaults to not presenting during testthat tests.

• dotty::. is now reexported.

• %*% now dispatches to op_matmul() for tensorflow tensors, which
  has relaxed shape constraints compared to tf$matmul().

• Fixed an issue where calling a Metric and Loss object
  with unnamed arguments would error.

Added compatibility with Keras v3.8.0. User-facing changes:

• New symbols:

  • activation_sparse_plus()
  • activation_sparsemax()
  • activation_threshold()
  • layer_equalization()
  • layer_mix_up()
  • layer_rand_augment()
  • layer_random_color_degeneration()
  • layer_random_color_jitter()
  • layer_random_grayscale()
  • layer_random_hue()
  • layer_random_posterization()
  • layer_random_saturation()
  • layer_random_sharpness()
  • layer_random_shear()
  • op_diagflat()
  • op_sparse_plus()
  • op_sparsemax()
  • op_threshold()
  • op_unravel_index()

• Add argument axis to tversky loss

• New: ONNX model export with export_savedmodel()

• Doc improvements and bug fixes.

• JAX specific changes: Add support for JAX named scope

• TensorFlow specific changes: Make random_shuffle() XLA compilable

Added compatibility with Keras v3.7.0. User-facing changes:

New functions

Activations

• activation_celu()
• activation_glu()
• activation_hard_shrink()
• activation_hard_tanh()
• activation_log_sigmoid()
• activation_soft_shrink()
• activation_squareplus()
• activation_tanh_shrink()

Configuration

• config_disable_flash_attention()
• config_enable_flash_attention()
• config_is_flash_attention_enabled()

Layers and Initializers

• initializer_stft()
• layer_max_num_bounding_boxes()
• layer_stft_spectrogram()

Losses and Metrics

• loss_circle()
• metric_concordance_correlation()
• metric_pearson_correlation()

Operations

• op_celu()
• op_exp2()
• op_glu()
• op_hard_shrink()
• op_hard_tanh()
• op_ifft2()
• op_inner()
• op_soft_shrink()
• op_squareplus()
• op_tanh_shrink()

New arguments

• callback_backup_and_restore(): Added double_checkpoint argument to save a fallback checkpoint
• callback_tensorboard(): Added support for profile_batch argument
• layer_group_query_attention(): Added flash_attention and seed arguments
• layer_multi_head_attention(): Added flash_attention argument
• metric_sparse_top_k_categorical_accuracy(): Added from_sorted_ids argument

Performance improvements

• Added native Flash Attention support for GPU (via cuDNN) and TPU (via Pallas kernel) in JAX backend
• Added opt-in native Flash Attention support for GPU in PyTorch backend
• Enabled additional kernel fusion via bias_add in TensorFlow backend
• Added support for Intel XPU devices in PyTorch backend

• 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.

keras 2.15.0

• Default TensorFlow/Keras version installed by install_keras() is now 2.15.
  This is the last Tensorflow version where where Keras 2 is the default.
  To use Keras with Tensorflow v2.16 and up, use the new {keras3} R package.

• Updates to allow both R packages {keras} and {keras3} to be loaded.

• Updates for R-devel (4.4).

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 for an overview
  and https://keras.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())

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)