Changelog

Source: NEWS.md

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

CRAN release: 2024-07-17

• 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

CRAN release: 2024-05-21

• 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

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 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.