R Interface to 'Keras'

Interface to 'Keras' < https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.


News

Keras 2.1.3 (CRAN)

  • 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

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

  • 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

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

Reference manual

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install.packages("keras")

2.1.6 by JJ Allaire, 2 months ago


https://keras.rstudio.com


Report a bug at https://github.com/rstudio/keras/issues


Browse source code at https://github.com/cran/keras


Authors: JJ Allaire [aut, cre], Fran├žois Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Daniel Falbel [ctb, cph], Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph]


Documentation:   PDF Manual  


Task views: High-Performance and Parallel Computing with R, Model Deployment with R


MIT + file LICENSE license


Imports reticulate, tensorflow, tfruns, magrittr, zeallot, methods, R6

Suggests ggplot2, testthat, knitr, rmarkdown

System requirements: Keras >= 2.0 (https://keras.io)


Imported by ruta.

Depended on by kerasformula.

Suggested by OSTSC, bamlss, cloudml, lime, reinforcelearn, tensorflow, tfdatasets.


See at CRAN