Posit AI Blog: TensorFlow and Keras 2.9

The release of Deep Learning with R, 2nd
Edition coincides with new releases of
TensorFlow and Keras. These releases bring many refinements that allow
for more idiomatic and concise R code.

First, the set of Tensor methods for base R generics has greatly
expanded. The set of R generics that work with TensorFlow Tensors is now
quite extensive:

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] floor       Im          is.finite   is.infinite is.nan     
[41] length      lgamma      log         log10       log1p      
[46] log2        max         mean        min         Mod        
[51] print       prod        range       rbind       Re         
[56] rep         round       sign        sin         sinpi      
[61] sort        sqrt        str         sum         t          
[66] tan         tanpi      

This means that often you can write the same code for TensorFlow Tensors
as you would for R arrays. For example, consider this small function
from Chapter 11 of the book:

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

Note that functions like reweight_distribution() work with both 1D R
vectors and 1D TensorFlow Tensors, since exp(), log(), /, and
sum() are all R generics with methods for TensorFlow Tensors.

In the same vein, this Keras release brings with it a refinement to the
way custom class extensions to Keras are defined. Partially inspired by
the new R7 syntax, there is a
new family of functions: new_layer_class(), new_model_class(),
new_metric_class(), and so on. This new interface substantially
simplifies the amount of boilerplate code required to define custom
Keras extensions—a pleasant R interface that serves as a facade over
the mechanics of sub-classing Python classes. This new interface is the
yang to the yin of %py_class%–a way to mime the Python class
definition syntax in R. Of course, the “raw” API of converting an
R6Class() to Python via r_to_py() is still available for users that
require full control.

This release also brings with it a cornucopia of small improvements
throughout the Keras R interface: updated print() and plot() methods
for models, enhancements to freeze_weights() and load_model_tf(),
new exported utilities like zip_lists() and %<>%. And let’s not
forget to mention a new family of R functions for modifying the learning
rate during training, with a suite of built-in schedules like
learning_rate_schedule_cosine_decay(), complemented by an interface
for creating custom schedules with new_learning_rate_schedule_class().

You can find the full release notes for the R packages here:

The release notes for the R packages tell only half the story however.
The R interfaces to Keras and TensorFlow work by embedding a full Python
process in R (via the
reticulate package). One of
the major benefits of this design is that R users have full access to
everything in both R and Python. In other words, the R interface
always has feature parity with the Python interface—anything you can
do with TensorFlow in Python, you can do in R just as easily. This means
the release notes for the Python releases of TensorFlow are just as
relevant for R users:

Thanks for reading!

Photo by Raphael
Wild
on
Unsplash

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Citation

For attribution, please cite this work as

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

BibTeX citation

@misc{kalinowskitf29,
  author = {Kalinowski, Tomasz},
  title = {Posit AI Blog: TensorFlow and Keras 2.9},
  url = {},
  year = {2022}
}