Introducing Keras 3 for R

We’re thrilled to introduce keras3, the following model of the Keras R
package deal. keras3 is a ground-up rebuild of {keras}, sustaining the
beloved options of the unique whereas refining and simplifying the API
based mostly on beneficial insights gathered over the previous few years.

Keras gives an entire toolkit for constructing deep studying fashions in
R—it’s by no means been simpler to construct, prepare, consider, and deploy deep
studying fashions.

Set up

To put in Keras 3:

set up.packages("keras3")
library(keras3)
install_keras()

Keras 3 comes with a full refresh of the web site:
https://keras.posit.co. There, you will discover guides, tutorials,
reference pages with rendered examples, and a brand new examples gallery. All
the reference pages and guides are additionally out there by way of R’s built-in assist
system.

In a fast-paced ecosystem like deep studying, creating nice
documentation and wrappers as soon as shouldn’t be sufficient. There additionally must be
workflows that make sure the documentation is up-to-date with upstream
dependencies. To perform this, {keras3} contains two new maintainer
options that make sure the R documentation and performance wrappers will keep
up-to-date:

• We now take snapshots of the upstream documentation and API floor.
  With every launch, all R documentation is rebased on upstream
  updates. This workflow ensures that each one R documentation (guides,
  examples, vignettes, and reference pages) and R operate signatures
  keep up-to-date with upstream. This snapshot-and-rebase
  performance is applied in a brand new standalone R package deal,
  {doctether}, which can
  be helpful for R package deal maintainers needing to maintain documentation in
  parity with dependencies.

• All examples and vignettes can now be evaluated and rendered throughout
  a package deal construct. This ensures that no stale or damaged instance code
  makes it right into a launch. It additionally means all consumer going through instance code
  now moreover serves as an prolonged suite of snapshot unit and
  integration checks.

  Evaluating code in vignettes and examples remains to be not permitted
  based on CRAN restrictions. We work across the CRAN restriction
  by including further package deal construct steps that pre-render
  examples
  and
  vignettes.

Mixed, these two options will make it considerably simpler for Keras
in R to take care of characteristic parity and up-to-date documentation with the
Python API to Keras.

Multi-backend assist

Quickly after its launch in 2015, Keras featured assist for hottest
deep studying frameworks: TensorFlow, Theano, MXNet, and CNTK. Over
time, the panorama shifted; Theano, MXNet, and CNTK had been retired, and
TensorFlow surged in reputation. In 2021, three years in the past, TensorFlow
turned the premier and solely supported Keras backend. Now, the panorama
has shifted once more.

Keras 3 brings the return of multi-backend assist. Select a backend by
calling:

use_backend("jax") # or "tensorflow", "torch", "numpy"

{keras3} introduces a brand new “Operations” household of operate. The Ops
household, at the moment with over 200
features,
gives a complete suite of operations usually wanted when
working on nd-arrays for deep studying. The Operation household
supersedes and significantly expands on the previous household of backend features
prefixed with k_ within the {keras} package deal.

The Ops features allow you to write backend-agnostic code. They supply a
uniform API, no matter for those who’re working with TensorFlow Tensors,
Jax Arrays, Torch Tensors, Keras Symbolic Tensors, NumPy arrays, or R
arrays.

The Ops features:

• all begin with prefix op_ (e.g., op_stack())
• all are pure features (they produce no side-effects)
• all use constant 1-based indexing, and coerce doubles to integers
  as wanted
• all are secure to make use of with any backend (tensorflow, jax, torch, numpy)
• all are secure to make use of in each keen and graph/jit/tracing modes

The Ops API contains:

• Everything of the NumPy API (numpy.*)
• The TensorFlow NN API (tf.nn.*)
• Frequent linear algebra features (A subset of scipy.linalg.*)
• A subfamily of picture transformers
• A complete set of loss features
• And extra!

Ingest tabular knowledge with layer_feature_space()

keras3 gives a brand new set of features for constructing fashions that ingest
tabular knowledge: layer_feature_space() and a household of characteristic
transformer features (prefix, feature_) for constructing keras fashions
that may work with tabular knowledge, both as inputs to a keras mannequin, or
as preprocessing steps in a knowledge loading pipeline (e.g., a
tfdatasets::dataset_map()).

See the reference
web page and an
instance utilization in a full end-to-end
instance
to study extra.

New Subclassing API

The subclassing API has been refined and prolonged to extra Keras
sorts.
Outline subclasses just by calling: Layer(), Loss(), Metric(),
Callback(), Constraint(), Mannequin(), and LearningRateSchedule().
Defining {R6} proxy courses is now not obligatory.

Moreover the documentation web page for every of the subclassing
features now comprises a complete itemizing of all of the out there
attributes and strategies for that sort. Take a look at
?Layer to see what’s
attainable.

Saving and Export

Keras 3 brings a brand new mannequin serialization and export API. It’s now a lot
less complicated to save lots of and restore fashions, and in addition, to export them for
serving.

• save_model()/load_model():
  A brand new high-level file format (extension: .keras) for saving and
  restoring a full mannequin.

  The file format is backend-agnostic. This implies you can convert
  educated fashions between backends, just by saving with one backend,
  after which loading with one other. For instance, prepare a mannequin utilizing Jax,
  after which convert to Tensorflow for export.

• export_savedmodel():
  Export simply the ahead go of a mannequin as a compiled artifact for
  inference with TF
  Serving or (quickly)
  Posit Join. This
  is the simplest technique to deploy a Keras mannequin for environment friendly and
  concurrent inference serving, all with none R or Python runtime
  dependency.

• Decrease stage entry factors:

  • save_model_weights() / load_model_weights():
    save simply the weights as .h5 information.
  • save_model_config() / load_model_config():
    save simply the mannequin structure as a json file.

• register_keras_serializable():
  Register customized objects to allow them to be serialized and
  deserialized.

• serialize_keras_object() / deserialize_keras_object():
  Convert any Keras object to an R checklist of straightforward sorts that’s secure
  to transform to JSON or rds.

• See the brand new Serialization and Saving
  vignette
  for extra particulars and examples.

New random household

A brand new household of random tensor
turbines.
Just like the Ops household, these work with all backends. Moreover, all of the
RNG-using strategies have assist for stateless utilization if you go in a
seed generator. This allows tracing and compilation by frameworks that
have particular assist for stateless, pure, features, like Jax. See
?random_seed_generator()
for instance utilization.

Different additions:

• New form()
  operate, one-stop utility for working with tensor shapes in all
  contexts.

• New and improved print(mannequin) and plot(mannequin) methodology. See some
  examples of output within the Purposeful API
  information

• All new match() progress bar and dwell metrics viewer output,
  together with new dark-mode assist within the RStudio IDE.

• New config
  household,
  a curated set of features for getting and setting Keras world
  configurations.

• All the different operate households have expanded with new members:

Migrating from {keras} to {keras3}

{keras3} supersedes the {keras} package deal.

If you happen to’re writing new code at present, you can begin utilizing {keras3} proper
away.

In case you have legacy code that makes use of {keras}, you might be inspired to
replace the code for {keras3}. For a lot of high-level API features, such
as layer_dense(), match(), and keras_model(), minimal to no adjustments
are required. Nevertheless there’s a lengthy tail of small adjustments that you simply
would possibly have to make when updating code that made use of the lower-level
Keras API. A few of these are documented right here:
https://keras.io/guides/migrating_to_keras_3/.

If you happen to’re working into points or have questions on updating, don’t
hesitate to ask on https://github.com/rstudio/keras/points or
https://github.com/rstudio/keras/discussions.

The {keras} and {keras3} packages will coexist whereas the neighborhood
transitions. In the course of the transition, {keras} will proceed to obtain
patch updates for compatibility with Keras v2, which continues to be
revealed to PyPi below the package deal title tf-keras. After tf-keras is
now not maintained, the {keras} package deal shall be archived.

Abstract

In abstract, {keras3} is a sturdy replace to the Keras R package deal,
incorporating new options whereas preserving the benefit of use and
performance of the unique. The brand new multi-backend assist,
complete suite of Ops features, refined mannequin serialization API,
and up to date documentation workflows allow customers to simply take
benefit of the newest developments within the deep studying neighborhood.

Whether or not you’re a seasoned Keras consumer or simply beginning your deep
studying journey, Keras 3 gives the instruments and adaptability to construct,
prepare, and deploy fashions with ease and confidence. As we transition from
Keras 2 to Keras 3, we’re dedicated to supporting the neighborhood and
making certain a clean migration. We invite you to discover the brand new options,
try the up to date documentation, and be part of the dialog on our
GitHub discussions web page. Welcome to the following chapter of deep studying in
R with Keras 3!