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So, how come we are able to use TensorFlow from R?

Which pc language is most intently related to TensorFlow? Whereas on the TensorFlow for R weblog, we might after all like the reply to be R, likelihood is it’s Python (although TensorFlow has official bindings for C++, Swift, Javascript, Java, and Go as effectively).

So why is it you’ll be able to outline a Keras mannequin as

mannequin <- keras_model_sequential() %>%
  layer_dense(models = 32, activation = "relu") %>%
  layer_dense(models = 1)

(good with %>%s and all!) – then prepare and consider it, get predictions and plot them, all that with out ever leaving R?

The brief reply is, you will have keras, tensorflow and reticulate put in.
reticulate embeds a Python session inside the R course of. A single course of means a single deal with area: The identical objects exist, and may be operated upon, no matter whether or not they’re seen by R or by Python. On that foundation, tensorflow and keras then wrap the respective Python libraries and allow you to write R code that, in actual fact, seems to be like R.

This submit first elaborates a bit on the brief reply. We then go deeper into what occurs within the background.

One notice on terminology earlier than we soar in: On the R facet, we’re making a transparent distinction between the packages keras and tensorflow. For Python we’re going to use TensorFlow and Keras interchangeably. Traditionally, these have been totally different, and TensorFlow was generally regarded as one potential backend to run Keras on, moreover the pioneering, now discontinued Theano, and CNTK. Standalone Keras does nonetheless exist, however latest work has been, and is being, accomplished in tf.keras. In fact, this makes Python Keras a subset of Python TensorFlow, however all examples on this submit will use that subset so we are able to use each to confer with the identical factor.

So keras, tensorflow, reticulate, what are they for?

Firstly, nothing of this is able to be potential with out reticulate. reticulate is an R package deal designed to permit seemless interoperability between R and Python. If we completely needed, we might assemble a Keras mannequin like this:

<class 'tensorflow.python.keras.engine.sequential.Sequential'>

We might go on including layers …

m$add(tf$keras$layers$Dense(32, "relu"))


However who would need to? If this had been the one manner, it’d be much less cumbersome to instantly write Python as an alternative. Plus, as a person you’d need to know the entire Python-side module construction (now the place do optimizers reside, presently: tf.keras.optimizers, tf.optimizers …?), and sustain with all path and title modifications within the Python API.

That is the place keras comes into play. keras is the place the TensorFlow-specific usability, re-usability, and comfort options reside.
Performance offered by keras spans the entire vary between boilerplate-avoidance over enabling elegant, R-like idioms to offering technique of superior characteristic utilization. For instance for the primary two, contemplate layer_dense which, amongst others, converts its models argument to an integer, and takes arguments in an order that enable it to be “pipe-added” to a mannequin: As an alternative of

mannequin <- keras_model_sequential()
mannequin$add(layer_dense(models = 32L))

we are able to simply say

mannequin <- keras_model_sequential()
mannequin %>% layer_dense(models = 32)

Whereas these are good to have, there’s extra. Superior performance in (Python) Keras largely is dependent upon the power to subclass objects. One instance is customized callbacks. If you happen to had been utilizing Python, you’d need to subclass tf.keras.callbacks.Callback. From R, you’ll be able to create an R6 class inheriting from KerasCallback, like so

CustomCallback <- R6::R6Class("CustomCallback",
    inherit = KerasCallback,
    public = listing(
      on_train_begin = operate(logs) {
        # do one thing
      on_train_end = operate(logs) {
        # do one thing

It’s because keras defines an precise Python class, RCallback, and maps your R6 class’ strategies to it.
One other instance is customized fashions, launched on this weblog a few yr in the past.
These fashions may be skilled with customized coaching loops. In R, you utilize keras_model_custom to create one, for instance, like this:

m <- keras_model_custom(title = "mymodel", operate(self) {
  self$dense1 <- layer_dense(models = 32, activation = "relu")
  self$dense2 <- layer_dense(models = 10, activation = "softmax")
  operate(inputs, masks = NULL) {
    self$dense1(inputs) %>%

Right here, keras will be certain an precise Python object is created which subclasses tf.keras.Mannequin and when known as, runs the above nameless operate().

In order that’s keras. What concerning the tensorflow package deal? As a person you solely want it when you need to do superior stuff, like configure TensorFlow machine utilization or (in TF 1.x) entry components of the Graph or the Session. Internally, it’s utilized by keras closely. Important inner performance consists of, e.g., implementations of S3 strategies, like print, ( or +, on Tensors, so you’ll be able to function on them like on R vectors.

Now that we all know what every of the packages is “for”, let’s dig deeper into what makes this potential.

Present me the magic: reticulate

As an alternative of exposing the subject top-down, we observe a by-example method, increase complexity as we go. We’ll have three situations.

First, we assume we have already got a Python object (that has been constructed in no matter manner) and have to convert that to R. Then, we’ll examine how we are able to create a Python object, calling its constructor. Lastly, we go the opposite manner spherical: We ask how we are able to move an R operate to Python for later utilization.

State of affairs 1: R-to-Python conversion

Let’s assume we now have created a Python object within the world namespace, like this:

So: There’s a variable, known as x, with worth 1, residing in Python world. Now how can we convey this factor into R?

We all know the principle entry level to conversion is py_to_r, outlined as a generic in conversion.R:

py_to_r <- operate(x) {

… with the default implementation calling a operate named py_ref_to_r:

Rcpp : You simply write your C++ operate, and Rcpp takes care of compilation and gives the glue code essential to name this operate from R.

So py_ref_to_r actually is written in C++:

.Name(`_reticulate_py_ref_to_r`, x)

which lastly wraps the “actual” factor, the C++ operate py_ref_to_R we noticed above.

Through py_ref_to_r_with_convert in #1, a one-liner that extracts an object’s “convert” characteristic (see beneath)

Extending Python Information.

In official phrases, what reticulate does it embed and prolong Python.
Embed, as a result of it allows you to use Python from inside R. Prolong, as a result of to allow Python to name again into R it must wrap R capabilities in C, so Python can perceive them.

As a part of the previous, the specified Python is loaded (Py_Initialize()); as a part of the latter, two capabilities are outlined in a brand new module named rpycall, that will probably be loaded when Python itself is loaded.

World Interpreter Lock, this isn’t routinely the case when different implementations are used, or C is used instantly. So call_python_function_on_main_thread makes positive that except we are able to execute on the principle thread, we wait.

That’s it for our three “spotlights on reticulate”.


It goes with out saying that there’s quite a bit about reticulate we didn’t cowl on this article, comparable to reminiscence administration, initialization, or specifics of knowledge conversion. Nonetheless, we hope we had been in a position to shed a bit of sunshine on the magic concerned in calling TensorFlow from R.

R is a concise and stylish language, however to a excessive diploma its energy comes from its packages, together with those who let you name into, and work together with, the skin world, comparable to deep studying frameworks or distributed processing engines. On this submit, it was a particular pleasure to concentrate on a central constructing block that makes a lot of this potential: reticulate.

Thanks for studying!

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