Posit AI Weblog: TensorFlow and Keras 2.9

Posit AI Weblog: TensorFlow and Keras 2.9

The discharge of Deep Learning with R, 2nd
coincides with new releases of
TensorFlow and Keras. These releases deliver many refinements that permit
for extra idiomatic and concise R code.

First, the set of Tensor strategies for base R generics has significantly
expanded. The set of R generics that work with TensorFlow Tensors is now
fairly in depth:

methods(class = "tensorflow.tensor")
 [1] -           !           !=          [           [<-        
 [6] *           /           &           %/%         %%         
[11] ^           +           <           <=          ==         
[16] >           >=          |           abs         acos       
[21] all         any         aperm       Arg         asin       
[26] atan        cbind       ceiling     Conj        cos        
[31] cospi       digamma     dim         exp         expm1      
[36] ground       Im          is.finite   is.infinite is.nan     
[41] size      lgamma      log         log10       log1p      
[46] log2        max         imply        min         Mod        
[51] print       prod        vary       rbind       Re         
[56] rep         spherical       signal        sin         sinpi      
[61] kind        sqrt        str         sum         t          
[66] tan         tanpi      

Because of this usually you’ll be able to write the identical code for TensorFlow Tensors
as you’ll for R arrays. For instance, contemplate this small perform
from Chapter 11 of the e book:

reweight_distribution <-
  perform(original_distribution, temperature = 0.5) {
    original_distribution %>%
      { exp(log(.) / temperature) } %>%
      { . / sum(.) }

Word that capabilities like reweight_distribution() work with each 1D R
vectors and 1D TensorFlow Tensors, since exp(), log(), /, and
sum() are all R generics with strategies for TensorFlow Tensors.

In the identical vein, this Keras launch brings with it a refinement to the
method customized class extensions to Keras are outlined. Partially impressed by
the brand new R7 syntax, there’s a
new household of capabilities: new_layer_class(), new_model_class(),
new_metric_class(), and so forth. This new interface considerably
simplifies the quantity of boilerplate code required to outline customized
Keras extensions—a pleasing R interface that serves as a facade over
the mechanics of sub-classing Python lessons. This new interface is the
yang to the yin of %py_class%–a solution to mime the Python class
definition syntax in R. In fact, the “uncooked” API of changing an
R6Class() to Python by way of r_to_py() remains to be obtainable for customers that
require full management.

This launch additionally brings with it a cornucopia of small enhancements
all through the Keras R interface: up to date print() and plot() strategies
for fashions, enhancements to freeze_weights() and load_model_tf(),
new exported utilities like zip_lists() and %<>%. And let’s not
neglect to say a brand new household of R capabilities for modifying the educational
price throughout coaching, with a collection of built-in schedules like
learning_rate_schedule_cosine_decay(), complemented by an interface
for creating customized schedules with new_learning_rate_schedule_class().

You could find the total launch notes for the R packages right here:

The discharge notes for the R packages inform solely half the story nevertheless.
The R interfaces to Keras and TensorFlow work by embedding a full Python
course of in R (by way of the
reticulate bundle). One in every of
the key advantages of this design is that R customers have full entry to
the whole lot in each R and Python. In different phrases, the R interface
at all times has function parity with the Python interface—something you’ll be able to
do with TensorFlow in Python, you are able to do in R simply as simply. This implies
the discharge notes for the Python releases of TensorFlow are simply as
related for R customers:

Thanks for studying!

Photograph by Raphael



Textual content and figures are licensed underneath Inventive Commons Attribution CC BY 4.0. The figures which have been reused from different sources do not fall underneath this license and could be acknowledged by a be aware of their caption: “Determine from …”.


For attribution, please cite this work as

Kalinowski (2022, June 9). Posit AI Weblog: TensorFlow and Keras 2.9. Retrieved from 

BibTeX quotation

  creator = {Kalinowski, Tomasz},
  title = {Posit AI Weblog: TensorFlow and Keras 2.9},
  url = {},
  yr = {2022}

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