tf_function

A possibly nested sequence of <code>tf$TensorSpec</code> objects
specifying the shapes and dtypes of the tensors that will be supplied to
this function. If <code>NULL</code>, a separate function is instantiated for each
inferred input signature. If <code>input_signature</code> is specified, every input to
<code>f</code> must be a tensor.

input_signature

Whether autograph should be applied on <code>f</code> before tracing a
graph. This allows for dynamic control flow (if's, loops etc.) in the
traced graph. See https://www.tensorflow.org/guide/autograph for more
information. Note: We set the default to <code>FALSE</code> until this functionality
is available from R.

autograph

additional arguments passed on to <code>tf.function</code> (vary based on
Tensorflow version). See
https://www.tensorflow.org/api_docs/python/tf/function for details.

<code>tf_function</code> constructs a callable that executes a TensorFlow graph created
by tracing the TensorFlow operations in <code>f</code>. This allows the TensorFlow
runtime to apply optimizations and exploit parallelism in the computation
defined by <code>f</code>.

Interface to 'TensorFlow' <https://www.tensorflow.org/>,
an open source software library for numerical computation using data
flow graphs. Nodes in the graph represent mathematical operations,
while the graph edges represent the multidimensional data arrays
(tensors) communicated between them. The flexible architecture allows
you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,
server, or mobile device with a single 'API'. 'TensorFlow' was originally
developed by researchers and engineers working on the Google Brain Team
within Google's Machine Intelligence research organization for the
purposes of conducting machine learning and deep neural networks research,
but the system is general enough to be applicable in a wide variety
of other domains as well.

tf_function: Creates a callable TensorFlow graph from an R function.

Description

Usage

Arguments