Posit AI Weblog: TensorFlow and Keras 2.9

The discharge of Deep Learning with R, 2ndEdition coincides with new releases ofTensorFlow and Keras. These releases carry many refinements that enablefor extra idiomatic and concise R code.

First, the set of Tensor strategies for base R generics has vastlyexpanded. The set of R generics that work with TensorFlow Tensors is nowfairly intensive:

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      

Which means typically you’ll be able to write the identical code for TensorFlow Tensorsas you’ll for R arrays. For instance, think about this small performfrom Chapter 11 of the ebook:

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

Observe that features like reweight_distribution() work with each 1D Rvectors and 1D TensorFlow Tensors, since exp(), log(), /, andsum() are all R generics with strategies for TensorFlow Tensors.

In the identical vein, this Keras launch brings with it a refinement to themethod customized class extensions to Keras are outlined. Partially impressed bythe brand new R7 syntax, there’s anew household of features: new_layer_class(), new_model_class(),new_metric_class(), and so forth. This new interface considerablysimplifies the quantity of boilerplate code required to outline customizedKeras extensions—a pleasing R interface that serves as a facade overthe mechanics of sub-classing Python courses. This new interface is theyang to the yin of %py_class%–a option to mime the Python classdefinition syntax in R. After all, the “uncooked” API of changing anR6Class() to Python by way of r_to_py() continues to be accessible for customers thatrequire full management.

This launch additionally brings with it a cornucopia of small enhancementsall through the Keras R interface: up to date print() and plot() strategiesfor fashions, enhancements to freeze_weights() and load_model_tf(),new exported utilities like zip_lists() and %<>%. And let’s notneglect to say a brand new household of R features for modifying the educationalprice throughout coaching, with a set of built-in schedules likelearning_rate_schedule_cosine_decay(), complemented by an interfacefor creating customized schedules with new_learning_rate_schedule_class().

You could find the complete 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 Pythoncourse of in R (by way of thereticulate package deal). Considered one ofthe most important advantages of this design is that R customers have full entry tothe whole lot in each R and Python. In different phrases, the R interfaceall the time has function parity with the Python interface—something you’ll be able todo with TensorFlow in Python, you are able to do in R simply as simply. This impliesthe discharge notes for the Python releases of TensorFlow are simply asrelated for R customers:

Thanks for studying!

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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,
  writer = {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}
}

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