![]() Here are the relevant (for our investigation) frames of the backtrace: Thread 1 "R" hit Breakpoint 3, 0x00007fffe48315d0 in py_to_r(libpython::_object*, () from /home/key/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/libs/reticulate.so Thread 1 "R" hit Breakpoint 1, 0x00007fffe48315d0 in py_to_r(libpython::_object*, () from /home/key/R/x86_64-redhat-linux-gnu-library/3.6/reticulate/libs/reticulate.so Now what are we going to see when we access that x? > py$x Now, use Ctrl-C to “escape” to gdb, set a breakpoint in py_to_r and type c to get back to R: (gdb) b py_to_rīreakpoint 1 at 0x7fffe48315d0 (2 locations) So that set up our scenario, the Python object (named x) we want to convert to R. Let’s assume we have created a Python object in the global namespace, like this: Finally, we go the other way round: We ask how we can pass an R function to Python for later usage. Then, we’ll investigate how we can create a Python object, calling its constructor. ![]() We’ll have three scenarios.įirst, we assume we already have a Python object (that has been constructed in whatever way) and need to convert that to R. Instead of exposing the topic top-down, we follow a by-example approach, building up complexity as we go. Now that we know what each of the packages is “for”, let’s dig deeper into what makes this possible. Essential internal functionality includes, e.g., implementations of S3 methods, like print, [ or +, on Tensors, so you can operate on them like on R vectors. What about the tensorflow package? As a user you only need it when you have to do advanced stuff, like configure TensorFlow device usage or (in TF 1.x) access elements of the Graph or the Session. Here, keras will make sure an actual Python object is created which subclasses tf.keras.Model and when called, runs the above anonymous function(). As an example for the first two, consider layer_dense which, among others, converts its units argument to an integer, and takes arguments in an order that allow it to be “pipe-added” to a model: Instead of 5įunctionality provided by keras spans the whole range between boilerplate-avoidance over enabling elegant, R-like idioms to providing means of advanced feature usage. keras is where the TensorFlow-specific usability, re-usability, and convenience features live. Plus, as a user you’d have to know the complete Python-side module structure (now where do optimizers live, currently: tf.keras.optimizers, tf.optimizers …?), and keep up with all path and name changes in the Python API. M $ add ( tf $ keras $ layers $ Dense ( 32, "relu" ) ) m $ add ( tf $ keras $ layers $ Dense ( 1 ) ) m $ layers ]īut who would want to? If this were the only way, it’d be less cumbersome to directly write Python instead. If we absolutely wanted, we could construct a Keras model like this: 3 reticulate is an R package designed to allow seemless interoperability between R and Python. So keras, tensorflow, reticulate, what are they for?įirstly, nothing of this would be possible without reticulate. Of course, this makes Python Keras a subset of Python TensorFlow, but all examples in this post will use that subset so we can use both to refer to the same thing. Standalone Keras does still exist, but recent work has been, and is being, done in tf.keras. Historically, these have been different, and TensorFlow was commonly thought of as one possible backend to run Keras on, besides the pioneering, now discontinued Theano, and CNTK. For Python we are going to use TensorFlow and Keras interchangeably. ![]() One note on terminology before we jump in: On the R side, we’re making a clear distinction between the packages keras and tensorflow. We then go deeper into what happens in the background. This post first elaborates a bit on the short answer. ![]() ![]() On that basis, tensorflow and keras then wrap the respective Python libraries 2 and let you write R code that, in fact, looks like R. A single process means a single address space: The same objects exist, and can be operated upon, regardless of whether they’re seen by R or by Python. Reticulate embeds a Python session within the R process. The short answer is, you have keras, tensorflow and reticulate installed. (nice with %>%s and all!) – then train and evaluate it, get predictions and plot them, all that without ever leaving R? Library ( keras ) model % layer_dense (units = 32, activation = "relu" ) %>% layer_dense (units = 1 ) ![]()
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