rstudio · t-kalinowski · Aug 18, 2025 · Aug 18, 2025 · Aug 18, 2025 · Aug 18, 2025
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -35,19 +35,19 @@ jobs:
 
           - {os: 'ubuntu-latest' , tf: 'default', r: 'oldrel-1'}
           - {os: 'ubuntu-latest' , tf: 'default', r: 'oldrel-2'}
-          - {os: 'ubuntu-20.04'  , tf: 'default', r: '3.6'} # default R in ubuntu-20.04
+          # - {os: 'ubuntu-20.04'  , tf: 'default', r: '3.6'} # default R in ubuntu-20.04
 
           ## release keras/tf version (if different from 'default')
           # - {os: 'ubuntu-latest' , tf: 'release', r: 'release'}
           # - {os: 'windows-latest', tf: 'release', r: 'release'}
           # - {os: 'macOS-latest'  , tf: 'release', r: 'release'}
 
           ## old keras/tf versions
-          - {os: 'ubuntu-latest', tf: '2.14', r: 'release'}
-          - {os: 'ubuntu-latest', tf: '2.13', r: 'release'}
-          - {os: 'ubuntu-latest', tf: '2.12', r: 'release'}
-          - {os: 'ubuntu-latest', tf: '2.11', r: 'release'}
-          - {os: 'ubuntu-latest', tf: '2.9' , r: 'release'}
+          # - {os: 'ubuntu-latest', tf: '2.14', r: 'release'}
+          # - {os: 'ubuntu-latest', tf: '2.13', r: 'release'}
+          # - {os: 'ubuntu-latest', tf: '2.12', r: 'release'}
+          # - {os: 'ubuntu-latest', tf: '2.11', r: 'release'}
+          # - {os: 'ubuntu-latest', tf: '2.9' , r: 'release'}
 
           # these are allowed to fail
           # - {os: 'ubuntu-latest', tf: '2.14.0rc1', r: 'release'}
@@ -58,6 +58,7 @@ jobs:
     continue-on-error: ${{ matrix.tf == 'nightly' || contains(matrix.tf, 'rc') || matrix.r == 'devel' }}
     env:
       R_KEEP_PKG_SOURCE: yes
+      PY_REQUIRE_LEGACY_KERAS: true
       # R_REMOTES_NO_ERRORS_FROM_WARNINGS: 'true'
       # R_COMPILE_AND_INSTALL_PACKAGES: 'never' # commented out until CRAN builds TF 2.8
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
@@ -98,8 +99,9 @@ jobs:
           cache-version: 1
           upgrade: 'TRUE'
 
-      - name: Install Tensorflow + Keras deps
-        run: keras::install_keras(tensorflow = '${{ matrix.tf }}-cpu')
+      ## Package uses py_require() instead
+      # - name: Install Tensorflow + Keras deps
+      #   run: keras::install_keras(tensorflow = '${{ matrix.tf }}')
 
       - uses: r-lib/actions/check-r-package@v2
         with:

diff --git a/.gitignore b/.gitignore
@@ -22,3 +22,5 @@ inst/doc
 .Rprofile
 .Renviron
 revdep
+keras.Rproj
+keras2.Rproj
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: keras
 Type: Package
 Title: R Interface to 'Keras'
-Version: 2.15.0.9000
+Version: 2.16.0
 Authors@R: c(
   person("Tomasz", "Kalinowski", role = c("ctb", "cph", "cre"),
          email = "[email protected]"),
@@ -23,8 +23,8 @@ Description: Interface to 'Keras' <https://keras.io>, a high-level neural
   combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
 Encoding: UTF-8
 License: MIT + file LICENSE
-URL: https://tensorflow.rstudio.com/, https://github.com/rstudio/keras/tree/r2
-BugReports: https://github.com/rstudio/keras/issues
+URL: https://tensorflow.rstudio.com/, https://github.com/rstudio/keras3/tree/r2
+BugReports: https://github.com/rstudio/keras3/issues
 Depends:
     R (>= 3.6)
 Imports:

diff --git a/NAMESPACE b/NAMESPACE
@@ -9,6 +9,7 @@ S3method(as_generator,tensorflow.python.data.ops.dataset_ops.Dataset)
 S3method(as_generator,tensorflow.python.data.ops.dataset_ops.DatasetV2)
 S3method(compile,keras.engine.training.Model)
 S3method(compose_layer,default)
+S3method(compose_layer,keras.engine.sequential.Sequential)
 S3method(compose_layer,keras.models.Sequential)
 S3method(evaluate,keras.engine.training.Model)
 S3method(export_savedmodel,keras.engine.training.Model)
@@ -552,6 +553,7 @@ export(predict_classes)
 export(predict_generator)
 export(predict_on_batch)
 export(predict_proba)
+export(py_require_legacy_keras)
 export(regularizer_l1)
 export(regularizer_l1_l2)
 export(regularizer_l2)

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,13 @@
-# keras (development version)
+# keras 2.16.0
+
+- Updates for usage with Legacy Keras (#1515). 
+
+  - A package startup message is now displayed encouraging users to migrate to keras3.
+  - New function `py_require_legacy_keras()`.
+  - `install_keras()` now installs legacy keras `tf-keras`.
+  - `TF_USE_LEGACY_KERAS=1` envvar is now set on package startup.
+
+- Documentation updates for CRAN (#1514)
 
 # keras 2.15.0
 

diff --git a/R/install.R b/R/install.R
@@ -1,4 +1,34 @@
-#' Install TensorFlow and Keras, including all Python dependencies
+#' Install TensorFlow and Legacy Keras, including all Python dependencies
+#'
+#' This function can be used to create a persistent virtual environment for usage
+#' with Legacy Keras. New code is recommended to use the `keras3` package which
+#' works automatically and does any special configuration like this.
+#'
+#' Note that recent versions of reticulate will automatically manage
+#' environments if dependencies are declared with `reticulate::py_require()`.
+#' Instead of using this `install_keras()` function, new users are first
+#' encouraged to use `keras3`. If that's not an option, instead of creating a
+#' persistent venv, you can this code at the start of the R session, before
+#' loading keras:
+#'
+#' ```r
+#' # declare requirements for legacy keras (tf-keras)
+#' reticulate::py_require(c("tensorflow", "tf-keras", "numpy<2"))
+#'
+#' # also declare optional dependencies
+#' reticulate::py_require(c(
+#'   "tensorflow-hub", "tensorflow-datasets", "scipy",
+#'   "requests", "Pillow", "h5py", "pandas", "pydot"
+#' ))
+#' library(keras)
+#' ```
+#'
+#' Or more simply, call `py_require_legacy_keras()`
+#'
+#' ```r
+#' library(keras)
+#' py_require_legacy_keras()
+#' ```
 #'
 #' This function will install Tensorflow and all Keras dependencies. This is a
 #' thin wrapper around [`tensorflow::install_tensorflow()`], with the only
@@ -44,7 +74,7 @@ install_keras <- function(method = c("auto", "virtualenv", "conda"),
     method = method,
     conda = conda,
     version = tensorflow,
-    extra_packages = pkgs,
+    extra_packages = c(pkgs, "tf-keras"),
     # envname = envname,
     # new_env = new_env,
     ...

diff --git a/R/layers-core.R b/R/layers-core.R
@@ -563,5 +563,6 @@ compose_layer.keras.models.Sequential <- function(object, layer, ...) {
   object
 }
 
+#' @export
 compose_layer.keras.engine.sequential.Sequential <- compose_layer.keras.models.Sequential
 # compose_layer.keras.src.engine.sequential.Sequential <- compose_layer.keras.models.Sequential
diff --git a/R/package.R b/R/package.R
@@ -94,11 +94,44 @@ use_backend <- function(backend = c("tensorflow", "cntk", "theano", "plaidml"))
 keras <- NULL
 
 
-.onLoad <- function(libname, pkgname) {
-
+.onAttach <- function(libname, pkgname) {
   packageStartupMessage(
     "The keras package is deprecated. Use the keras3 package instead."
   )
+}
+
+#' @export
+#' @rdname install_keras
+py_require_legacy_keras <- function(extra_packages = TRUE) {
+  Sys.setenv(TF_USE_LEGACY_KERAS = 1L)
+  reticulate::py_require(
+    c(
+      "tf-keras", "tensorflow", "numpy<2",
+      if (extra_packages) {
+        c(
+          "tensorflow-hub",
+          "tensorflow-datasets",
+          "scipy",
+          "requests",
+          "Pillow",
+          "h5py",
+          "pandas",
+          "pydot"
+        )
+      }
+    )
+  )
+}
+
+.onLoad <- function(libname, pkgname) {
+
+  Sys.setenv(TF_USE_LEGACY_KERAS = 1L)
+
+  ## In a future update, we will declare these dependencies by default.
+  ## We're making this opt-in for now to minimize breaking user code
+  ## and give package maintainers an opportunity to make updates
+  if (tolower(Sys.getenv("PY_REQUIRE_LEGACY_KERAS")) %in% c("1", "true", "yes"))
+    py_require_legacy_keras()
 
   # resolve the implementation module (might be keras proper or might be tensorflow)
   implementation_module <- resolve_implementation_module()
@@ -133,6 +166,10 @@ keras <- NULL
     },
 
     on_error = function(e) {
+      emit <- base::message
+      emit(
+        "To use legacy Keras via py_require(), call py_require_legacy_keras() at the start of the R session."
+      )
       if (is_tensorflow_implementation())
         stop(tf_config()$error_message, call. = FALSE)
       else {
@@ -156,6 +193,9 @@ keras <- NULL
     # replace "tensorflow.python.keras.*" with "keras.*"
     classes <- sub(paste0("^", module), "keras", classes)
 
+    # replace legacy keras module name: tf_keras -> keras
+    classes <- sub("^tf_keras\\.", "keras.", classes)
+
     # All python symbols moved in v2.13 under .src
     # Preserve the original symbols for compatability with keras3,
     # interleaving the back-compat class names after the originals.

diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 ![](https://s3.amazonaws.com/keras.io/img/keras-logo-2018-large-1200.png)
 
-[![R build status](https://github.com/rstudio/keras/workflows/R-CMD-check/badge.svg)](https://github.com/rstudio/keras/actions?workflow=R-CMD-check)
+[![R build status](https://github.com/rstudio/keras3/workflows/R-CMD-check/badge.svg)](https://github.com/rstudio/keras3/actions?workflow=R-CMD-check)
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/keras)](https://cran.r-project.org/package=keras)
 [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/keras-team/keras/blob/master/LICENSE)
 

diff --git a/index.Rmd b/index.Rmd
@@ -65,7 +65,7 @@ This will provide you with default CPU-based installations of Keras and TensorFl
 
 Below we walk through a simple example of using Keras to recognize handwritten digits from the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset. After getting familiar with the basics, check out the [tutorials](#tutorials) and additional [learning resources](#learning-more) available on this website.
 
-The [Deep Learning with R](https://www.amazon.com/Deep-Learning-R-Francois-Chollet/dp/161729554X) book by François Chollet (the creator of Keras) provides a more comprehensive introduction to both Keras and the concepts and practice of deep learning.
+The [Deep Learning with R](https://www.manning.com/books/deep-learning-with-r-second-edition) book by François Chollet (the creator of Keras) provides a more comprehensive introduction to both Keras and the concepts and practice of deep learning.
 
 You may also find it convenient to download the [Deep Learning with Keras](https://github.com/rstudio/cheatsheets/raw/main/keras.pdf){target="_self"} cheat sheet, a quick high-level reference to all of the capabilities of Keras.
 
@@ -248,12 +248,12 @@ The [examples](articles/examples/index.html) demonstrate more advanced models in
 
 The [function reference](reference/index.html) includes detailed information on all of the functions available in the package.
 
-[![](https://images.manning.com/720/960/resize/book/a/4e5e97f-4e8d-4d97-a715-f6c2b0eb95f5/Allaire-DLwithR-HI.png){width=125 align=right style="margin-left:10px; margin-right: 20px; margin-top: 15px; border: solid 1px #cccccc;"}](https://www.amazon.com/Deep-Learning-R-Francois-Chollet/dp/161729554X)
+[![](https://images.manning.com/264/352/resize/book/a/8f961a8-9cf7-47e8-a69b-949186e59a81/Chollet2-HI.png){width=125 align=right style="margin-left:10px; margin-right: 20px; margin-top: 15px; border: solid 1px #cccccc;"}](https://www.manning.com/books/deep-learning-with-r-second-edition)
 
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R](https://www.amazon.com/Deep-Learning-R-Francois-Chollet/dp/161729554X) book from Manning. This book is a collaboration between François Chollet, the creator of Keras, and J.J. Allaire, who wrote the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, who maintains the R interface today.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.
 

diff --git a/index.html b/index.html
diff --git a/man/install_keras.Rd b/man/install_keras.Rd
diff --git a/man/keras-package.Rd b/man/keras-package.Rd
diff --git a/vignettes/index.Rmd b/vignettes/index.Rmd
@@ -202,7 +202,7 @@ Keras provides a vocabulary for building deep learning models that is simple, el
 
 ### Deep Learning with R Book
 
-If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R](https://www.amazon.com/Deep-Learning-R-Francois-Chollet/dp/161729554X) book from Manning. This book is a collaboration between François Chollet, the creator of Keras, and J.J. Allaire, who wrote the R interface to Keras.
+If you want a more comprehensive introduction to both Keras and the concepts and practice of deep learning, we recommend the [Deep Learning with R](https://www.manning.com/books/deep-learning-with-r-second-edition) book from Manning. This book is a collaboration between François Chollet, the creator of Keras, J.J. Allaire, who wrote the original R interface to Keras, and Tomasz Kalinowski, who maintains the R interface today.
 
 The book presumes no significant knowledge of machine learning and deep learning, and goes all the way from basic theory to advanced practical applications, all using the R interface to Keras.