tf_function

<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.

Tomasz Kalinowski

tensorflow

R Interface to 'TensorFlow'

JJ Allaire

Daniel Falbel

Dirk Eddelbuettel

Yuan Tang

Nick Golding

Google Inc. 

Posit, PBC 

tf_function function

<dl><dt>f</dt>
<dd>the function to be compiled</dd>
<dt>input_signature</dt>
<dd>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.</dd>
<dt>autograph</dt>
<dd>TRUE or FALSE. If TRUE (the default), you can use tensors in
R control flow expressions <code>if</code>, <code>while</code>, <code>for</code> and <code><a href="/link/break?package=tensorflow&version=2.15.0" data-mini-rdoc="tensorflow::break">break</a></code> and they will
be traced into the tensorflow graph. A guide to getting started and
additional details can be found:
<a href="https://t-kalinowski.github.io/tfautograph/">here</a></dd>
<dt>...</dt>
<dd>additional arguments passed on to <code>tf.function</code> (vary based on
Tensorflow version). See
<a href="https://www.tensorflow.org/api_docs/python/tf/function#args_1">here</a> for
details.</dd></dl>

Arguments

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

<dl>

<dt>f</dt>
<dd>the function to be compiled</dd>


<dt>input_signature</dt>
<dd>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.</dd>


<dt>autograph</dt>
<dd>TRUE or FALSE. If TRUE (the default), you can use tensors in
R control flow expressions <code>if</code>, <code>while</code>, <code>for</code> and <code><a href='https://rdrr.io/r/base/Control.html'>break</a></code> and they will
be traced into the tensorflow graph. A guide to getting started and
additional details can be found:
<a href='https://t-kalinowski.github.io/tfautograph/'>here</a></dd>


<dt>...</dt>
<dd>additional arguments passed on to <code>tf.function</code> (vary based on
Tensorflow version). See
<a href='https://www.tensorflow.org/api_docs/python/tf/function#args_1'>here</a> for
details.</dd>

</dl>

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

Description

Usage

Arguments

Details