Keras 2.1.5 (development)

Install the development version with: install_github("rstudio/keras")

  • Support for custom constraints from R

Keras 2.1.4 (CRAN)

Keras 2.1.3

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

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