RStudio AI Weblog: TensorFlow and Keras 2.9

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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 significantly 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] flooring       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 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 way in which 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() continues to be out there 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 training charge 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 complete 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 of many main advantages of this design is that R customers have full entry to every part in each R and Python. In different phrases, the R interface all the time has function parity with the Python interface—something you are 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!

    Picture by Raphael Wild on Unsplash

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    Quotation

    For attribution, please cite this work as

    Kalinowski (2022, June 9). RStudio 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 = {RStudio AI Weblog: TensorFlow and Keras 2.9},
      url = {https://blogs.rstudio.com/tensorflow/posts/2022-06-09-tf-2-9/},
      12 months = {2022}
    }

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