Breaking change: The semantics of passing a named list to keras_model()
have changed.
Previously, keras_model()
would unname()
supplied inputs
and outputs
. Then, if a named list was passed to subsequent fit()
/evaluate()
/call()
/predict()
invocations, matching of x
and y
was done to the model’s input and outpt tensor$name
’s. Now, matching is done to names()
of inputs
and/or outputs
supplied to keras_model()
. Call unname()
on inputs
and outputs
to restore the old behavior, e.g.: keras_model(unname(inputs), unname(outputs))
keras_model()
can now accept a named list for multi-input and/or multi-output models. The named list is converted to a dict
in python. (Requires Tensorflow >= 2.4, Python >= 3.7).
If inputs
is a named list:
call()
, fit()
, evaluate()
, and predict()
methods can also accept a named list for x
, with names matching to the names of inputs
when the model was constructed. Positional matching of x
is still also supported (requires python 3.7+).If outputs
is a named list:
fit()
and evaluate()
methods can only accept a named list for y
, with names matching to the names of outputs
when the model was constructed.New layer layer_depthwise_conv_1d()
.
Models gain format()
and print()
S3 methods for compatibility with the latest reticulate. Both are powered by model$summary()
.
summary()
method for Models gains arguments expand_nested
and show_trainable
, both default to FALSE
.
keras_model_custom()
is soft deprecated. Please define custom models by subclassing keras$Model
directly using %py_class%
or R6::R6Class()
.
Fixed warning issued by k_random_binomial()
.
Fixed error raised when k_random_binomial()
was passed a non-floating dtype.
Added k_random_bernouli()
as an alias for k_random_binomial()
.
image_load()
gains a color_mode
argument.
Fixed issue where create_layer_wrapper()
would not include arguments with a NULL
default value in the returned wrapper.
Fixed issue in r_to_py.R6ClassGenerator
(and %py_class%
) where single-expression initialize
functions defined without {
would error.
Deprecated functions are no longer included in the package documentation index.
Default Tensorflow + Keras version is now 2.7.
New API for constructing RNN (Recurrent Neural Network) layers. This is a flexible interface that complements the existing RNN layers. It is primarily intended for advanced / research applications, e.g, prototyping novel architectures. It allows you to compose a RNN with a custom “cell”, a Keras layer that processes one step of a sequence. New symbols:
layer_rnn()
, which can compose with builtin cells:layer_gru_cell()
layer_lstm_cell()
layer_simple_rnn_cell()
layer_stacked_rnn_cells()
To learn more, including how to make a custom cell layer, see the new vignette: “Working with RNNs”.New dataset functions:
text_dataset_from_directory()
timeseries_dataset_from_array()
New layers:
layer_additive_attention()
layer_conv_lstm_1d()
layer_conv_lstm_3d()
layer_cudnn_gru()
and layer_cudnn_lstm()
are deprecated. layer_gru()
and layer_lstm()
will automatically use CuDNN if it is available.
layer_lstm()
and layer_gru()
: default value for recurrent_activation
changed from "hard_sigmoid"
to "sigmoid"
.
layer_gru()
: default value reset_after
changed from FALSE
to TRUE
New vignette: “Transfer learning and fine-tuning”.
New applications:
application_mobilenet_v3_large()
, application_mobilenet_v3_small()
application_resnet101()
, application_resnet152()
, resnet_preprocess_input()
application_resnet50_v2()
, application_resnet101_v2()
, application_resnet152_v2()
and resnet_v2_preprocess_input()
application_efficientnet_b{0,1,2,3,4,5,6,7}()
Many existing application_*()
’s gain argument classifier_activation
, with default 'softmax'
. Affected: application_{xception, inception_resnet_v2, inception_v3, mobilenet, vgg16, vgg19}()
New function %<-active%
, a ergonomic wrapper around makeActiveBinding()
for constructing Python @property
decorated methods in %py_class%
.
bidirectional()
sequence processing layer wrapper gains a backwards_layer
arguments.
Global pooling layers layer_global_{max,average}_pooling_{1,2,3}d()
gain a keepdims
argument with default value FALSE
.
Signatures for layer functions are in the process of being simplified. Standard layer arguments are moving to ...
where appropriate (and will need to be provided as named arguments). Standard layer arguments include: input_shape
, batch_input_shape
, batch_size
, dtype
, name
, trainable
, weights
. Layers updated: layer_global_{max,average}_pooling_{1,2,3}d()
, time_distributed()
, bidirectional()
, layer_gru()
, layer_lstm()
, layer_simple_rnn()
All the backend function with a shape argument k_*(shape =)
that now accept a a mix of integer tensors and R numerics in the supplied list.
All layer functions now accept NA
as a synonym for NULL
in arguments that specify shape as a vector of dimension values, e.g., input_shape
, batch_input_shape
.
k_random_uniform()
now automatically casts minval
and maxval
to the output dtype.
install_keras()
gains arg with default pip_ignore_installed = TRUE
.
New family of preprocessing layers. These are the spiritual successor to the tfdatasets::step_*
family of data transformers (to be deprecated in a future release). Added a new vignette: “Working with Preprocessing Layers”. New functions:
Image preprocessing:
layer_resizing()
layer_rescaling()
layer_center_crop()
Image augmentation:
layer_random_crop()
layer_random_flip()
layer_random_translation()
layer_random_rotation()
layer_random_zoom()
layer_random_contrast()
layer_random_height()
layer_random_width()
Categorical features preprocessing:
layer_category_encoding()
layer_hashing()
layer_integer_lookup()
layer_string_lookup()
Numerical features preprocessing:
layer_normalization()
layer_discretization()
These join the previous set of text preprocessing functions, each of which have some minor changes:
layer_text_vectorization()
(changed arguments)get_vocabulary()
set_vocabulary()
adapt()
adapt()
changes:
layer_text_vectorization()
instances were valid.reset_state
argument is removed. It only ever accepted the default value of TRUE
.batch_size
and steps
.%>%
(previously returned NULL
)get_vocabulary()
gains a include_special_tokens
argument.
set_vocabulary()
:
%>%
(previously returned NULL
)df_data
oov_df_value
) are now subsumed in ...
.layer_text_vectorization()
:
output_mode
change: "binary"
is renamed to "multi_hot"
and "tf-idf"
is renamed to "tf_idf"
(backwards compatibility is preserved).output_mode = "int"
would incorrectly return a ragged tensor output shape.Existing layer instances gain the ability to be added to sequential models via a call. E.g.:
Functions in the merging layer family gain the ability to return a layer instance if the first argument inputs
is missing. (affected: layer_concatenate()
, layer_add()
, layer_subtract()
, layer_multiply()
, layer_average()
, layer_maximum()
, layer_minimum()
, layer_dot()
)
%py_class%
gains the ability to delay initializing the Python session until first use. It is now safe to implement and export %py_class%
objects in an R package.
Fixed an issue in layer_input()
where passing a tensorflow DType
objects to argument dtype
would throw an error.
Fixed an issue in compile()
where passing an R function via an in-line call would result in an error from subsequent fit()
calls. (e.g., compile(loss = function(y_true, y_pred) my_loss(y_true, y_pred))
now succeeds)
clone_model()
gains a clone_function
argument that allows you to customize each layer as it is cloned.
Bumped minimum R version to 3.4. Expanded CI to test on all supported R version. Fixed regression that prevented package installation on R <= 3.4
Breaking changes (Tensorflow 2.6): - Note: The following breaking changes are specific to Tensorflow version 2.6.0. However, the keras R package maintains compatibility with multiple versions of Tensorflow/Keras. You can upgrade the R package and still preserve the previous behavior by installing a specific version of Tensorflow: keras::install_keras(tensorflow="2.4.0")
predict_proba()
and predict_classes()
were removed.model_to_yaml()
and model_from_yaml()
were removed.layer_text_vectorization(pad_to_max_tokens=FALSE)
set_vocabulary()
arguments df_data
and oov_df_value
are removed. They are replaced by the new argument idf_weights
.New Features:
Default Tensorflow/Keras version is now 2.6
Introduced %py_class%
, an R-language constructor for Python classes.
New vignettes:
%py_class%
.The keras
Python module is exported
Major changes to the underlying handling of custom R6 layer classes.
r_to_py()
method is provided for R6ClassGenerator
objects.r_to_py()
, without going through create_layer()
.KerasLayer
is deprecated (new classes should inherit directly from keras$layers$Layer
).KerasWrapper
is deprecated (new classes should inherit directly from keras$layers$Wrapper
).create_wrapper()
is deprecated (no longer needed, use create_layer()
directly).super
in scope that resolves to the Python super class object.super
can be accessed in the 3 common ways:
super()$"__init__"()
super(ClassName, self)$"__init__"()
super$initialize()
super()$`__init__`(...)
if appropriate.supports_masking = TRUE
attribute is now supportedcompute_mask()
user defined method is now supportedcall()
methods now support a training
argument, as well as any additional arbitrary user-defined argumentsLayer()
custom layer constructor is now lazy about initializing the Python session and safe to use on the top level of an R package (#1229).
New function create_layer_wrapper()
that can create a composing R function wrapper around a custom layer class.
Refactored install_keras()
(along with tensorflow::install_tensorflow()
). Installation should be more reliable for more users now. If you encounter installation issues, please file an issue: https://github.com/rstudio/keras/issues/new
Potentially breaking change: numeric versions supplied without a patchlevel now automatically pull the latest patch release. (e.g. install_keras(tensorflow="2.4")
will install tensorflow version “2.4.2”. Previously it would install “2.4.0”)
pandas is now a default extra packages installed by install_keras()
pyyaml is no longer a installed by default if the Tensorflow version >= 2.6.
Loss functions:
All the loss functions gain the ability to return a callable (a keras$losses$Loss
instance) if y_true
and y_pred
arguments are missing.
New builtin loss functions:
loss_huber()
loss_kl_divergence()
Metric functions:
All the metric functions gain the ability to return a keras$metrics$Metric
instance if called without y_true
and y_pred
Each metric function is now documented separately, with a common ?Metric
topic demonstrating example usage.
New built-in metrics:
metric_true_negatives()
metric_true_positives()
metric_false_negatives()
metric_false_positives()
metric_specificity_at_sensitivity()
metric_sensitivity_at_specificity()
metric_precision()
metric_precision_at_recall()
metric_sum()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sparse_categorical_accuracy()
metric_mean_tensor()
metric_mean_wrapper()
metric_mean_iou()
metric_mean_relative_error()
metric_logcosh_error()
metric_mean()
metric_cosine_similarity()
metric_categorical_hinge()
metric_accuracy()
metric_auc()
keras_model_sequential()
gains the ability to accept arguments that define the input layer like input_shape
and dtype
. See ?keras_model_sequential
for details and examples.
Many layers gained new arguments, coming to parity with the interface available in the latest Python version:
layer name | new argument |
---|---|
layer_gru |
time_major |
layer_lstm |
time_major |
layer_max_pooling_1d |
data_format |
layer_conv_lstm_2d |
return_state |
layer_depthwise_conv_2d |
dilation_rate |
layer_conv_3d_transpose |
dilation_rate |
layer_conv_1d |
groups |
layer_conv_2d |
groups |
layer_conv_3d |
groups |
layer_locally_connected_1d |
implementation |
layer_locally_connected_2d |
implementation |
layer_text_vectorization |
vocabulary |
The compile()
method for keras models has been updated:
optimizer
is now an optional argument. It defaults to "rmsprop"
for regular keras models. Custom models can specify their own default optimizer.loss
is now an optional argument.run_eagerly
, steps_per_execution
.target_tensors
and sample_weight_mode
must now be supplied as named arguments.Added activation functions swish and gelu. (#1226)
set_vocabulary()
gains a idf_weights
argument.
All optimizer had argument lr
renamed to learning_rate
. (backwards compatibility is preserved, an R warning is now issued).
The glue package was added to Imports
Refactored automated tests to closer match the default installation procedure and compute environment of most user.
Expanded CI test coverage to include R devel, oldrel and 3.6.
set_session
and get_session
. (#1046)keras_model
eg name
. (#1045)layer_text_vectorization
with TensorFlow >= 2.3 (#1131)text
to input_text
in text_one_hot
(#1133)text_hashing_trick
with missing values (@topepo #1048)k_logsumexp
as it was removed from Keras (#1137)install_keras
now installs a fixed version of h5py, because newer versions are backward incompatible. (#1142)helper-*
file. (#1173)hdf5_matrix
if using TF >= 2.4 (#1175)untar
argument to get_file
as it seems to be slightly different from extract
(#1179)layer_layer_normalization
(#1183)layer_multihead_attention
(#1184)image_dataset_from_directory
(#1185)ragged
argument to layer_input
. (#1193)*_generator
deadlocks with recent versions of TensorFlow (#1197)layer_attention
(#1000) by @atroiano.Added layer_dense_features
.
Added on_test_*
, on_test_batch_*
, on_predict_*
and on_predict_*
to callback options.
Search for the right optimizers and initializers on TensorFlow 2.0
Fixed bug in function generators when using models with multiple inputs. (#740)
Added export_savedmodel
support for TensorFlow 2.0 (#773)
Fixed bug when using metric_
functions. (#804)
Allow users to pass additional arguments to install_keras
(#808)
Enabled calling Keras models with R arrays. (#806)
Allow passing data.frames
as inputs to Keras models. (#822)
Fixed bug when passing a fixed validation set to fit_generator
(#837)
Fixed bug when passing a TensorFlow dataset to fit
within a tf$distribute
scope. (#856)
install_keras
will now install Keras dependencies (#856). It won’t re-install TensorFlow if it’s already installed.
Fixed deprecation messages showed with TensorFlow v1.14.
Largely reduced tests verbosity.
Use tf.keras
as default implementation module.
Added AppVeyor to test on Windows.
Added flow_images_from_dataframe
function (#658).
Allow for unknown input_shape
in application_*
functions.
Added save_model_tf
and load_model_tf
to save/load models in the TensorFlow’s SavedModel format.
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()
Added layer_activation_selu()
function.
Added KerasWrapper
class and corresponding create_wrapper
function.
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