segunda-feira, outubro 2, 2023

So, how come we will use TensorFlow from R?

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

So why is it you may outline a Keras mannequin as

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

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

The brief reply is, you have got keras, tensorflow and reticulate put in.
reticulate embeds a Python session inside the R course of. A single course of means a single tackle house: 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, the truth is, appears to be like like R.

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

One be aware 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 completely different, and TensorFlow was generally regarded as one potential backend to run Keras on, apart from the pioneering, now discontinued Theano, and CNTK. Standalone Keras does nonetheless exist, however latest work has been, and is being, achieved in tf.keras. After all, this makes Python Keras a subset of Python TensorFlow, however all examples on this put up will use that subset so we will use each to confer with the identical factor.

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

Firstly, nothing of this could be potential with out reticulate. reticulate is an R package deal designed to permit seemless interoperability between R and Python. If we completely wished, 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 wish to? If this had been the one method, it’d be much less cumbersome to immediately write Python as a substitute. Plus, as a person you’d should know the entire Python-side module construction (now the place do optimizers stay, presently: tf.keras.optimizers, tf.optimizers …?), and sustain with all path and identify adjustments 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 stay.
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, think about layer_dense which, amongst others, converts its items argument to an integer, and takes arguments in an order that enable it to be “pipe-added” to a mannequin: As a substitute of

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

we will simply say

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

Whereas these are good to have, there’s extra. Superior performance in (Python) Keras largely will depend on the power to subclass objects. One instance is customized callbacks. Should you had been utilizing Python, you’d should subclass tf.keras.callbacks.Callback. From R, you may create an R6 class inheriting from KerasCallback, like so

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

It is 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 educated with customized coaching loops. In R, you employ keras_model_custom to create one, for instance, like this:

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

Right here, keras will make sure that an precise Python object is created which subclasses tf.keras.Mannequin and when known as, runs the above nameless perform().

In order that’s keras. What concerning the tensorflow package deal? As a person you solely want it when you must do superior stuff, like configure TensorFlow system utilization or (in TF 1.x) entry parts of the Graph or the Session. Internally, it’s utilized by keras closely. Important inside performance consists of, e.g., implementations of S3 strategies, like print, [ or +, on Tensors, so you can operate on them like on R vectors.

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

Show me the magic: reticulate

Instead of exposing the topic top-down, we follow a by-example approach, building up complexity as we go. We’ll have three scenarios.

First, we assume we already have a Python object (that has been constructed in whatever way) and need to convert that to R. Then, we’ll investigate how we can create a Python object, calling its constructor. Finally, we go the other way round: We ask how we can pass an R function to Python for later usage.

Scenario 1: R-to-Python conversion

Let’s assume we have created a Python object in the global namespace, like this:

So: There is a variable, called x, with value 1, living in Python world. Now how do we bring this thing into R?

We know the main entry point to conversion is py_to_r, defined as a generic in conversion.R:

py_to_r <- function(x) {

… with the default implementation calling a function named py_ref_to_r:

Rcpp : You simply write your C++ perform, and Rcpp takes care of compilation and supplies the glue code essential to name this perform 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++ perform 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 under)

Extending Python Information.

In official phrases, what reticulate does it embed and lengthen Python.
Embed, as a result of it enables you to use Python from inside R. Lengthen, 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 likely be loaded when Python itself is loaded.

International Interpreter Lock, this isn’t routinely the case when different implementations are used, or C is used immediately. So call_python_function_on_main_thread makes positive that except we will execute on the primary thread, we wait.

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


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

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

Thanks for studying!

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