TensorFlow and Keras 2.9

TensorFlow/Keras Packages/Releases R

New TensorFlow and Keras releases bring improvements big and small.

Tomasz Kalinowski (RStudio)https://www.rstudio.com/

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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For attribution, please cite this work as

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

BibTeX citation

  author = {Kalinowski, Tomasz},
  title = {Posit AI Blog: TensorFlow and Keras 2.9},
  url = {https://blogs.rstudio.com/tensorflow/posts/2022-06-09-tf-2-9/},
  year = {2022}