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The discharge of *Deep Studying with R, 2nd
Version* coincides with new releases of

TensorFlow and Keras. These releases deliver many refinements that enable

for extra idiomatic and concise R code.

First, the set of Tensor strategies for base R generics has vastly

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] type sqrt str sum t
[66] tan tanpi
```

Because of this typically you may write the identical code for TensorFlow Tensors

as you’d for R arrays. For instance, think about this small perform

from Chapter 11 of the e book:

Notice that features 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

method customized class extensions to Keras are outlined. Partially impressed by

the brand new `R7`

syntax, there’s a

new household of features: `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 courses. This new interface is the

yang to the yin of `%py_class%`

–a approach 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 accessible 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 features for modifying the educational

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()`

.

Yow will discover the total 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). Certainly one of

the most important advantages of this design is that R customers have full entry to

every thing in each R *and* Python. In different phrases, the R interface

at all times 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!

Picture by Raphael

Wild

on

Unsplash

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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/}, 12 months = {2022} }

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