install_tensorflow() installs TensorFlow v2.16 by default.install_tensorflow() detects a GPU on Linux, it will automatically
install the cuda package and configure required symlinks for cudnn and ptxax.install_tensorflow() installs TensorFlow v2.15 by defaultinstall_tensorflow() changes:
install_tensorflow(cuda = FALSE). Aside from the Nvidia driver, no other
pre-existing Nvidia CUDA packages are now necessary.configure_cudnn argument is now superseded by the new argument cuda.metal, for specifying if the tensorflow-metal pip package
should be installed on Arm Macs. Defaults to TRUE on Arm Macs.Fixed an issue where as.array() and other methods might fail if the tensor
had conversion disabled via r_to_py() or convert = FALSE.
Fixed an issue where Ops group generic dispatch would error one object was a tensor and the other was a non-tensor Python object (e.g., a numpy array).
Removed long deprecated symbols:
install_tensorflow_extras(), tfe_enable_eager_execution()
tfestimator generics train() and train_and_evaluate() now warn about
their deprecation status when called. The will be removed in a future release.
install_tensorflow() changes:
envname argument new default is "r-tensorflow". This means that
unless the envname argument supplied, install_tensorflow() will now
install into the "r-tensorflow" environment, bootstrapping a venv of
that name if necessary.new_env argument. If TRUE, any existing environment
specified by envname is deleted and created anew. Defaults to TRUE if
envname is "r-tensorflow", FALSE otherwise.configure_cudnn = FALSE to disable.pip_ignore_installed default is now FALSE again.tensorflow-macos and tensorflow-metal.New pillar:type_sum() method for Tensors, giving a
more informative printout of Tensors in R tracebacks and tibbles.
install_tensorflow() now installs TF v2.11 by default.
as_tensor() now coerces bare R atomic vectors to R arrays before conversion.
As a consequence, by default, R atomic double vectors now coerce to
'float64' dtype tensors instead of 'float32'.
shape() gains the ability to accept vectors of length > 1 in ...,
including other tf.TensorShapes. Shapes are automatically flattened.
Fixed an issue where a ListWrapper object of trackable keras layers
(e.g., as part of a keras model) would not convert to an R list.
Generic method updates:
^ will now invoke tf.square() or tf.sqrt() directly when appropriate|, &, and ! now cast arguments to 'bool' dtype.print() now shows 1d shapes without a trailing commas.str() method for tensors now returns only a single compact line;
str() on a list of tensors now does something sensible.install_tensorflow() now install TensorFlow 2.9 by default.
install_tensorflow() no longer requires conda on Windows, now works in a regular venv.
Comparing two partially-defined TensorShape now returns TRUE if each dimension matches.
e.g.: shape(NA, 4) == shape(NA, 4) now returns TRUE, previously FALSE.
Tensors with dtype 'string' now convert to R character vectors by methods
as.array() and as.matrix(). (previously they converted to python.builtin.bytes,
or an R list of python.builtin.bytes objects)
as_tensor():
tf$dtypes$saturate_cast() instead of tf$cast().shape argument now accepts a tensor.shape provided as a tensor would raise an error.tf.SparseTensor objects now inherit from "tensorflow.tensor".
Updated default Tensorflow version installed by install_tensorflow() to 2.8.
as_tensor() gains a shape argument, can be used to fill or reshape tensors.
Scalars can be recycled to a tensor of arbitrary shape, otherwise
supplied objects are reshaped using row-major (C-style) semantics.
install_tensorflow() now provides experimental support for Arm Macs,
with the following restrictions:
install_tensorflow() default conda_python_version changes from 3.7 to NULL.
tf.TensorShape()'s gain format() and print() S3 methods.
[ method for slicing tensors now accepts NA as a synonym for a missing or NULL spec.
For example x[NA:3] is now valid, equivalent to x[:3] in Python.
Default Tensorflow version installed by install_tensorflow() updated to 2.7
Breaking changes:
shape() now returns a tf.TensorShape() object
(Previously an R-list of NULLs or integers).[ method for tf.TensorShape() objects also now returns a tf.TensorShape().
Use [[, as.numeric, as.integer, and/or as.list to convert to R objects.length() method for tensorflow.tensor now returns NA_integer_ for
tensors with not fully defined shapes. (previously a zero length integer vector).dim() method for tensorflow.tensor now returns an R integer vector
with NA for dimensions that are undefined.
(previously an R list with NULL for undefined dimension)New S3 generics for tf.TensorShape()'s:
c, length, [<-, [[<-, merge, ==, !=, as_tensor(),
as.list, as.integer, as.numeric, as.double, py_str
(joining previous generics [ and [[).
See ?shape for extended examples.
Ops S3 generics for tensorflow.tensors that take two arguments now
automatically cast a supplied non-tensor to the dtype of the supplied tensor
that triggered the S3 dispatch. Casting is done via as_tensor().
e.g., this now works:
as_tensor(5L) - 2 # now returns tf.Tensor(3, shape=(), dtype=int32)
previously it would raise an error:
TypeError: `x` and `y` must have the same dtype, got tf.int32 != tf.float32
Generics that now do autocasting: +, -, *, /, %/%, %%, ^, &, |, ==, !=, <, <=, >, >=
install_tensorflow(): new argument with default pip_ignore_installed = TRUE.
This ensures that all Tensorflow dependencies like Numpy are installed by pip
rather than conda.
A message with the Tensorflow version is now shown when the python module is loaded, e.g: "Loaded Tensorflow version 2.6.0"
Updated default Tensorflow version to 2.6.
Changed default in tf_function() to autograph=TRUE.
Added S3 generic as_tensor().
tfautograph added to Imports
jsonlite removed from Imports, tfestimators removed from Suggests
Refactored install_tensorflow().
install_tensorflow(version="2.4") will install "2.4.2". Previously it would install "2.4.0")Removed "Config/reticulate" declaration from DESCRIPTION.
RETICULATE_AUTOCONFIGURE=FALSE environment variable when using non-default tensorflow installations (e.g., 'tensorflow-cpu') no longer required.install_tensorflow() for automatic installation.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.
Fixed an issue where extra packages with version constraints like
install_tensorflow(extra_packages = "Pillow<8.3") were not quoted properly.
Fixed an issue where valid tensor-like objects supplied to
log(x, base), cospi(), tanpi(), and sinpi() would raise an error.
tf_function() (e.g., jit_compile)expm1 S3 generic.tfe_enable_eager_execution is deprecated. Eager mode has been the default since TF version 2.0.tf_config() on unsuccessful installation.use_session_with_seed (#428)set_random_seed function that makes more sense for TensorFlow >= 2.0 (#442)Upgraded default installed version to 2.0.0.
Tensorboard log directory path fixes (#360).
Allow for v1 and v2 compat (#358).
install_tensorflow now does not installs tfprobability, tfhub and other
related packages.
Upgraded default installed version to 1.14.0
Refactored the install_tensorflow code delegating to reticulate (#333, #341): We completely delegate to installation to reticulate::py_install, the main difference is that now the default environment name to install is r-reticulate and not r-tensorflow.
added option to silence TF CPP info output
tf_gpu_configured function to check if GPU was correctly