Install the development version with devtools::install_github("rstudio/keras")
Improve handling of timeseries_generator() in calls to fit_generator()
Add support for input_shape argument to layer_dropout()
Improve error message for data frames passed to fit(), etc.
Use 1-based axis indices for k_gather()
Added version parameter to install_keras() for installing alternate/older versions
Added activation_exponential() function.
Added threshold parameter to activation_relu()
Added restore_best_weights parameter to callback_model_checkpoint()
Added update_freq parameter to callback_tensorboard()
Added negative_slope and threshold parameters to layer_activation_relu()
Added output_padding and dilation_rate parameters to layer_conv_2d_transpose()
Added output_padding argument to layer_conv_3d_transpose()
Added data_format argument to layer_separable_conv_1d(), layer_average_pooling_1d(), layer_global_max_pooling_1d(), and layer_global_average_pooling_1d()
Added interpolation argument to layer_upsampling_1d() and layer_upsampling_2d()
Added dtype argument to to_categorical()
Fix issue with serializing models that have constraint arguments
Fix issue with k_tile that needs an integer vector instead of a list as the n argument.
Fix issue with user-supplied output_shape in layer_lambda() not being supplied to tensorflow backends
Filter out metrics that were created for callbacks (e.g. lr)
Added application_mobilenet_v2() pre-trained model
Added sample_weight parameter to flow_images_from_data()
Use native Keras implementation (rather than SciPy) for image_array_save()
Default layer_flatten() data_format argument to NULL (which defaults to global Keras config).
Add baseline argument to callback_early_stopping() (stop training if a given baseline isn’t reached).
Add data_format argument to layer_conv_1d().
Add layer_activation_relu(), making the ReLU activation easier to configure while retaining easy serialization capabilities.
Add axis = -1 argument in backend crossentropy functions specifying the class prediction axis in the input tensor.
Handle symbolic tensors and TF datasets in calls to fit(), evaluate(), and predict()
Add embeddings_data argument to callback_tensorboard()
Support for defining custom Keras models (i.e. custom call() logic for forward pass)
Handle named list of model output names in metrics argument of compile()
New custom_metric() function for defining custom metrics in R
Provide typed wrapper for categorical custom metrics
Provide access to Python layer within R custom layers
Don’t convert custom layer output shape to tuple when shape is a list or tuple of other shapes
Re-export shape() function from tensorflow package
Re-export tuple() function from reticulate package
Indexes for get_layer() are now 1-based (for consistency w/ freeze_weights())
Accept named list for sample_weight argument to fit()
Fix issue with single-element vectors passed to text preprocessing functions
Compatibility with TensorFlow v1.7 Keras implementation
Support workers parameter for native Keras generators (e.g. flow_images_from_directory())
Accept tensor as argument to k_pow()
In callback_reduce_lr_on_plateau(), rename epsilon argument to min_delta (backwards-compatible).
Add axis parameter to k_softmax()
Add send_as_json parameter to callback_remote_monitor()
Add data_format method to layer_flatten()
In multi_gpu_model(), add arguments cpu_merge and cpu_relocation (controlling whether to force the template model’s weights to be on CPU, and whether to operate merge operations on CPU or GPU).
Record correct loss name for tfruns when custom functions are provided for loss
Support for custom constraints from R
Added timeseries_generator() utility function
New layer layer_depthwise_conv_2d()
Added brightness_range and validation_split arguments to [image_data_generator()].
Added support for remove_learning_phase in export_savedmodel() to avoid removing learning phase.
Normalize validation data to Keras array in fit() and fit_generator()
Ensure that custom layers return a tuple from compute_output_shape()
Added Nasnet and Densenet pre-trained models
New layers layer_activation_softmax() and layer_separable_conv_1d()
Added amsgrad parameter to optimizer_adam()
Fix incompatibility with Progbar.update() method in Keras 2.1.4
Models saved via export_savedmodel() that make use of learning phases can now be exported without having to manually reload the original model.
Ensure that models saved via export_savedmodel() can be served from CloudML
Run image data generators with R preprocessing functions on the main thread
Return R list from texts_to_sequences()
Various fixes for use_implementation() function
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)
Added multi_gpu_model() function.
Automatically call keras_array() on the results of generator functions.
Ensure that steps_per_epoch is passed as an integer
Import evaluate() generic from tensorflow package
Handle NULL when converting R arrays to Keras friendly arrays
Added dataset_imbd_word_index() function
Ensure that sample_weight is passed to fit() as an array.
Accept single function as metrics argument to compile()
Automatically cast input_shape argument to applications to integer
Allow Keras models to be composable within model pipelines
Added freeze_weights() and unfreeze_weights() functions.
Implement export_savedmodel() generic from TensorFlow package
Convert R arrays to row-major before image preprocessing
Use tensorflow.keras for tensorflow implementation (TF v1.4)
Added application_inception_resnet_v2() pre-trained model
Added dataset_fashion_mnist() dataset
Added layer_cudnn_gru() and layer_cudnn_lstm() (faster recurrent layers backed by CuDNN)
Added layer_minimum() function
Added interpolation parameter to image_load() function
Add save_text_tokenizer() and load_text_tokenizer() functions.
Fix for progress bar output in Keras >= 2.0.9
Remove deprecated implementation argument from recurrent layers
Support for passing generators for validation data in fit_generator()
Accept single integer arguments for kernel sizes
Add standard layer arguments to layer_flatten() and layer_separable_conv_2d()
Added image_array_resize() and image_array_save() for 3D image arrays.
Allow custom layers and lambda layers to accept list parameters.
Expose add_loss() function for custom layers
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.
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