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

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

strategies(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 may write the identical code for TensorFlow Tensors
as you’ll for R arrays. For instance, take into account this small perform
from Chapter 11 of the guide:

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

Notice 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
manner 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 option 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
overlook to say a brand new household of R capabilities for modifying the training
charge throughout coaching, with a set 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 will discover the total launch notes for the R packages right here:

The discharge notes for the R packages inform solely half the story nonetheless.
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 all
the main 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
all the time has function parity with the Python interface—something you may
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
Wild
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Quotation

For attribution, please cite this work as

Kalinowski (2022, June 9). Posit AI Weblog: TensorFlow and Keras 2.9. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2022-06-09-tf-2-9/

BibTeX quotation

@misc{kalinowskitf29,
  creator = {Kalinowski, Tomasz},
  title = {Posit AI Weblog: TensorFlow and Keras 2.9},
  url = {https://blogs.rstudio.com/tensorflow/posts/2022-06-09-tf-2-9/},
  yr = {2022}
}