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.