Changelog

Source: NEWS.md

keras3 (development version)

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

CRAN release: 2024-04-18

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

CRAN release: 2024-02-17

• 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.