How do you run deep learning models with parsnip? - deep-learning

I have explored the models offered with the R parsnip package listed at https://www.tidymodels.org/find/parsnip/ but I cannot find how to execute a generic deep learning model (by which I mean a deep-layered neural network). The closest I can find are mlp and bag_mlp.
By contrast, I know that the caret package supports at least two deep learning packages: https://topepo.github.io/caret/available-models.html.
Does parsnip not offer deep learning (yet) or am I missing something?


We don't have it via parsnip (at least not yet) since it is difficult to write a static set of tuning parameters for a network of arbitrary size and architecture.
If you want a tidy way to fit basic neural networks with multiple simple layers, the brulee package is helpful. brulee_mlp() can fit those (via torch) and has a recipes interface for easier preprocessing and feature engineering.
Also, though not part of tidymodels, there is the excellent luz package by Daniel Falbel. It's a nice interface to general deep learning models (also via torch).

Related

Pretraining a language model on a small custom corpus

I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text.
For example, having a pre-trained BERT model and a small corpus of medical (or any "type") text, make a language model that is able to generate medical text. The assumption is that you do not have a huge amount of "medical texts" and that is why you have to use transfer learning.
Putting it as a pipeline, I would describe this as:
Using a pre-trained BERT tokenizer.
Obtaining new tokens from my new text and adding them to the existing pre-trained language model (i.e., vanilla BERT).
Re-training the pre-trained BERT model on the custom corpus with the combined tokenizer.
Generating text that resembles the text within the small custom corpus.
Does this sound familiar? Is it possible with hugging-face?

I have not heard of the pipeline you just mentioned. In order to construct an LM for your use-case, you have basically two options:
Further training BERT (-base/-large) model on your own corpus. This process is called domain-adaption as also described in this recent paper. This will adapt the learned parameters of BERT model to your specific domain (Bio/Medical text). Nonetheless, for this setting, you will need quite a large corpus to help BERT model better update its parameters.
Using a pre-trained language model that is pre-trained on a large amount of domain-specific text either from the scratch or fine-tuned on vanilla BERT model. As you might know, the vanilla BERT model released by Google has been trained on Wikipedia and BookCorpus text. After the vanilla BERT, researchers have tried to train the BERT architecture on other domains besides the initial data collections. You may be able to use these pre-trained models which have a deep understanding of domain-specific language. For your case, there are some models such as: BioBERT, BlueBERT, and SciBERT.
Is it possible with hugging-face?
I am not sure if huggingface developers have developed a robust approach for pre-training BERT model on custom corpora as claimed their code is still in progress, but if you are interested in doing this step, I suggest using Google research's bert code which has been written in Tensorflow and is totally robust (released by BERT's authors). In their readme and under Pre-training with BERT section, the exact process has been declared. This will provide you with Tensorflow checkpoint, which can be easily converted to Pytorch checkpoint if you'd like to work with Pytorch/Transformers.

It is entirely possible to both pre-train and further pre-train BERT (or almost any other model that is available in the huggingface library).
Regarding the tokenizer - if you are pre-training on a a small custom corpus (and therefore using a trained bert checkpoint), then you have to use the tokenizer that was used to train Bert. Otherwise, you will just confuse the model.
If your use case is text generation (from some initial sentence/part of sentence), then I can advise you to check gpt-2 (https://huggingface.co/gpt2). I haven't used GPT-2, but with some basic research I think you can do:
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
and follow this tutorial: https://towardsdatascience.com/train-gpt-2-in-your-own-language-fc6ad4d60171 on how to train the gpt-2 model.
Note: I am not sure if DeBERTa-V3, for example, can be pre-trained as usual. I have checked their github repo and it seems that for V3 there is no official pre-training code (https://github.com/microsoft/DeBERTa/issues/71). However, I think that using huggingface we can actually do it. Once I have time I will run a pre-training script and verify this.

Mxnet C++ prediction vs Python

We are mainly using C++ and want to use Mxnet. I found some discussion that C++ prediction or future extraction slower than Python version ?
Is there any experienced Mxnet C++ engineers to expedite this subject including the a decent way to using Python generated Mxnet model in C++?
prediction.cpp in Mxnet is not so user friendly.

MXNet was built with several frontend languages in mind, and I see no reason why prediction made using Python should be faster than predictions made using C++.
There is a gap in documentation on how to use MXNet with C++ at the moment mostly because the majority of the MXNet community is using Python (same holds true for the deep learning/machine learning field in general). One relevant C++ example you could look into is here.
If you would like to contribute more examples on how to work with MXNet using C++, you are more than welcome to submit Pull requests.

How can I start writing the code for my layer?

I have seen that researchers are adding some functionalities to the original version of Caffe and use those layers and functionalities according to what they need and then these versions are shared through Github. If I am not mistaken, there are two ways: 1) by recompiling Caffe after adding c++ and Cuda versions of layers. 2) writing a python code for the functionality and call it as python layer in Caffe.
I want to add a new layer to Caffe based on my research problem. I really do not from which point should I start writing the new layer and which steps I should consider.
My questions are:
1) Is there any documentation or any learning materials that I can use it for writing the layer?
2) Which way of above-mentioned methods of adding a new layer is preferred?
I really appreciate any help and guidance
Thanks a lot

For research purposes, for "playing around", it is usually more convenient to write a python layer: saves you the hustle of compiling etc.
You can find a short tutorial on "Python" layer here.
On the other hand, if you want better performance you should write a native c++ code for your layer.
You can find a short explanation about it here.

what is the type of deep learning algorithm in Rapidminer?

I use rapid-miner's deep learning operator for classification. But I can not find the type of deep learning algorithms.
Does Rapidminer use Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) or others?
Please help me.

RapidMiner provides an H2O integration for multi-layer feed-forward artificial neural networks.
There is also a new Kerras extension available which integrates an interface to the python library.
Regarding the DL4J project, you need to build the extension with gradle and then put the compiled *.jar file into the extension folder in your '.RapidMiner' folder.
Also feel free to ask further, or re-post, questions in the RapidMiner community forum.

This is just a regular MLP (many layers of fully connected neurons), as specified in the documentation, no convolutions, recurrence or anything more complex, just good old MLP.

Framework vs. Toolkit vs. Library [duplicate]

This question already has answers here:
What is the difference between a framework and a library? [closed]
(22 answers)
Closed 6 years ago.
What is the difference between a Framework, a Toolkit and a Library?

The most important difference, and in fact the defining difference between a library and a framework is Inversion of Control.
What does this mean? Well, it means that when you call a library, you are in control. But with a framework, the control is inverted: the framework calls you. (This is called the Hollywood Principle: Don't call Us, We'll call You.) This is pretty much the definition of a framework. If it doesn't have Inversion of Control, it's not a framework. (I'm looking at you, .NET!)
Basically, all the control flow is already in the framework, and there's just a bunch of predefined white spots that you can fill out with your code.
A library on the other hand is a collection of functionality that you can call.
I don't know if the term toolkit is really well defined. Just the word "kit" seems to suggest some kind of modularity, i.e. a set of independent libraries that you can pick and choose from. What, then, makes a toolkit different from just a bunch of independent libraries? Integration: if you just have a bunch of independent libraries, there is no guarantee that they will work well together, whereas the libraries in a toolkit have been designed to work well together – you just don't have to use all of them.
But that's really just my interpretation of the term. Unlike library and framework, which are well-defined, I don't think that there is a widely accepted definition of toolkit.

Martin Fowler discusses the difference between a library and a framework in his article on Inversion of Control:
Inversion of Control is a key part of
what makes a framework different to a
library. A library is essentially a
set of functions that you can call,
these days usually organized into
classes. Each call does some work and
returns control to the client.
A framework embodies some abstract
design, with more behavior built in.
In order to use it you need to insert
your behavior into various places in
the framework either by subclassing or
by plugging in your own classes. The
framework's code then calls your code
at these points.
To summarize: your code calls a library but a framework calls your code.

Diagram
If you are a more visual learner, here is a diagram that makes it clearer:
(Credits: http://tom.lokhorst.eu/2010/09/why-libraries-are-better-than-frameworks)

The answer provided by Barrass is probably the most complete. However, the explanation could easily be stated more clearly. Most people miss the fact that these are all nested concepts. So let me lay it out for you.
When writing code:
eventually you discover sections of code that you're repeating in your program, so you refactor those into Functions/Methods.
eventually, after having written a few programs, you find yourself copying functions you already made into new programs. To save yourself time you bundle those functions into Libraries.
eventually you find yourself creating the same kind of user interfaces every time you make use of certain libraries. So you refactor your work and create a Toolkit that allows you to create your UIs more easily from generic method calls.
eventually, you've written so many apps that use the same toolkits and libraries that you create a Framework that has a generic version of this boilerplate code already provided so all you need to do is design the look of the UI and handle the events that result from user interaction.
Generally speaking, this completely explains the differences between the terms.

Introduction
There are various terms relating to collections of related code, which have both historical (pre-1994/5 for the purposes of this answer) and current implications, and the reader should be aware of both, particularly when reading classic texts on computing/programming from the historic era.
Library
Both historically, and currently, a library is a collection of code relating to a specific task, or set of closely related tasks which operate at roughly the same level of abstraction. It generally lacks any purpose or intent of its own, and is intended to be used by (consumed) and integrated with client code to assist client code in executing its tasks.
Toolkit
Historically, a toolkit is a more focused library, with a defined and specific purpose. Currently, this term has fallen out of favour, and is used almost exclusively (to this author's knowledge) for graphical widgets, and GUI components in the current era. A toolkit will most often operate at a higher layer of abstraction than a library, and will often consume and use libraries itself. Unlike libraries, toolkit code will often be used to execute the task of the client code, such as building a window, resizing a window, etc. The lower levels of abstraction within a toolkit are either fixed, or can themselves be operated on by client code in a proscribed manner. (Think Window style, which can either be fixed, or which could be altered in advance by client code.)
Framework
Historically, a framework was a suite of inter-related libraries and modules which were separated into either 'General' or 'Specific' categories. General frameworks were intended to offer a comprehensive and integrated platform for building applications by offering general functionality, such as cross platform memory management, multi-threading abstractions, dynamic structures (and generic structures in general). Historical general frameworks (Without dependency injection, see below) have almost universally been superseded by polymorphic templated (parameterised) packaged language offerings in OO languages, such as the STL for C++, or in packaged libraries for non-OO languages (guaranteed Solaris C headers). General frameworks operated at differing layers of abstraction, but universally low level, and like libraries relied on the client code carrying out it's specific tasks with their assistance.
'Specific' frameworks were historically developed for single (but often sprawling) tasks, such as "Command and Control" systems for industrial systems, and early networking stacks, and operated at a high level of abstraction and like toolkits were used to carry out execution of the client codes tasks.
Currently, the definition of a framework has become more focused and taken on the "Inversion of Control" principle as mentioned elsewhere as a guiding principle, so program flow, as well as execution is carried out by the framework. Frameworks are still however targeted either towards a specific output; an application for a specific OS for example (MFC for MS Windows for example), or for more general purpose work (Spring framework for example).
SDK: "Software Development Kit"
An SDK is a collection of tools to assist the programmer to create and deploy code/content which is very specifically targeted to either run on a very particular platform or in a very particular manner. An SDK can consist of simply a set of libraries which must be used in a specific way only by the client code and which can be compiled as normal, up to a set of binary tools which create or adapt binary assets to produce its (the SDK's) output.
Engine
An Engine (In code collection terms) is a binary which will run bespoke content or process input data in some way. Game and Graphics engines are perhaps the most prevalent users of this term, and are almost universally used with an SDK to target the engine itself, such as the UDK (Unreal Development Kit) but other engines also exist, such as Search engines and RDBMS engines.
An engine will often, but not always, allow only a few of its internals to be accessible to its clients. Most often to either target a different architecture, change the presentation of the output of the engine, or for tuning purposes. Open Source Engines are by definition open to clients to change and alter as required, and some propriety engines are fixed completely. The most often used engines in the world however, are almost certainly JavaScript Engines. Embedded into every browser everywhere, there are a whole host of JavaScript engines which will take JavaScript as an input, process it, and then output to render.
API: "Application Programming Interface"
The final term I am answering is a personal bugbear of mine: API, was historically used to describe the external interface of an application or environment which, itself was capable of running independently, or at least of carrying out its tasks without any necessary client intervention after initial execution. Applications such as Databases, Word Processors and Windows systems would expose a fixed set of internal hooks or objects to the external interface which a client could then call/modify/use, etc to carry out capabilities which the original application could carry out. API's varied between how much functionality was available through the API, and also, how much of the core application was (re)used by the client code. (For example, a word processing API may require the full application to be background loaded when each instance of the client code runs, or perhaps just one of its linked libraries; whereas a running windowing system would create internal objects to be managed by itself and pass back handles to the client code to be utilised instead.
Currently, the term API has a much broader range, and is often used to describe almost every other term within this answer. Indeed, the most common definition applied to this term is that an API offers up a contracted external interface to another piece of software (Client code to the API). In practice this means that an API is language dependent, and has a concrete implementation which is provided by one of the above code collections, such as a library, toolkit, or framework.
To look at a specific area, protocols, for example, an API is different to a protocol which is a more generic term representing a set of rules, however an individual implementation of a specific protocol/protocol suite that exposes an external interface to other software would most often be called an API.
Remark
As noted above, historic and current definitions of the above terms have shifted, and this can be seen to be down to advances in scientific understanding of the underlying computing principles and paradigms, and also down to the emergence of particular patterns of software. In particular, the GUI and Windowing systems of the early nineties helped to define many of these terms, but since the effective hybridisation of OS Kernel and Windowing system for mass consumer operating systems (bar perhaps Linux), and the mass adoption of dependency injection/inversion of control as a mechanism to consume libraries and frameworks, these terms have had to change their respective meanings.
P.S. (A year later)
After thinking carefully about this subject for over a year I reject the IoC principle as the defining difference between a framework and a library. There ARE a large number of popular authors who say that it is, but there are an almost equal number of people who say that it isn't. There are simply too many 'Frameworks' out there which DO NOT use IoC to say that it is the defining principle. A search for embedded or micro controller frameworks reveals a whole plethora which do NOT use IoC and I now believe that the .NET language and CLR is an acceptable descendant of the "general" framework. To say that IoC is the defining characteristic is simply too rigid for me to accept I'm afraid, and rejects out of hand anything putting itself forward as a framework which matches the historical representation as mentioned above.
For details of non-IoC frameworks, see, as mentioned above, many embedded and micro frameworks, as well as any historical framework in a language that does not provide callback through the language (OK. Callbacks can be hacked for any device with a modern register system, but not by the average programmer), and obviously, the .NET framework.

A library is simply a collection of methods/functions wrapped up into a package that can be imported into a code project and re-used.
A framework is a robust library or collection of libraries that provides a "foundation" for your code. A framework follows the Inversion of Control pattern. For example, the .NET framework is a large collection of cohesive libraries in which you build your application on top of. You can argue there isn't a big difference between a framework and a library, but when people say "framework" it typically implies a larger, more robust suite of libraries which will play an integral part of an application.
I think of a toolkit the same way I think of an SDK. It comes with documentation, examples, libraries, wrappers, etc. Again, you can say this is the same as a framework and you would probably be right to do so.
They can almost all be used interchangeably.

very, very similar, a framework is usually a bit more developed and complete then a library, and a toolkit can simply be a collection of similar librarys and frameworks.
a really good question that is maybe even the slightest bit subjective in nature, but I believe that is about the best answer I could give.

Library
I think it's unanimous that a library is code already coded that you can use so as not to have to code it again. The code must be organized in a way that allows you to look up the functionality you want and use it from your own code.
Most programming languages come with standard libraries, especially some code that implements some kind of collection. This is always for the convenience that you don't have to code these things yourself. Similarly, most programming languages have construct to allow you to look up functionality from libraries, with things like dynamic linking, namespaces, etc.
So code that finds itself often needed to be re-used is great code to be put inside a library.
Toolkit
A set of tools used for a particular purpose. This is unanimous. The question is, what is considered a tool and what isn't. I'd say there's no fixed definition, it depends on the context of the thing calling itself a toolkit. Example of tools could be libraries, widgets, scripts, programs, editors, documentation, servers, debuggers, etc.
Another thing to note is the "particular purpose". This is always true, but the scope of the purpose can easily change based on who made the toolkit. So it can easily be a programmer's toolkit, or it can be a string parsing toolkit. One is so broad, it could have tool touching everything programming related, while the other is more precise.
SDKs are generally toolkits, in that they try and bundle a set of tools (often of multiple kind) into a single package.
I think the common thread is that a tool does something for you, either completely, or it helps you do it. And a toolkit is simply a set of tools which all perform or help you perform a particular set of activities.
Framework
Frameworks aren't quite as unanimously defined. It seems to be a bit of a blanket term for anything that can frame your code. Which would mean: any structure that underlies or supports your code.
This implies that you build your code against a framework, whereas you build a library against your code.
But, it seems that sometimes the word framework is used in the same sense as toolkit or even library. The .Net Framework is mostly a toolkit, because it's composed of the FCL which is a library, and the CLR, which is a virtual machine. So you would consider it a toolkit to C# development on Windows. Mono being a toolkit for C# development on Linux. Yet they called it a framework. It makes sense to think of it this way too, since it kinds of frame your code, but a frame should more support and hold things together, then do any kind of work, so my opinion is this is not the way you should use the word.
And I think the industry is trying to move into having framework mean an already written program with missing pieces that you must provide or customize. Which I think is a good thing, since toolkit and library are great precise terms for other usages of "framework".

Framework: installed on you machine and allowing you to interact with it. without the framework you can't send programming commands to your machine
Library: aims to solve a certain problem (or several problems related to the same category)
Toolkit: a collection of many pieces of code that can solve multiple problems on multiple issues (just like a toolbox)

It's a little bit subjective I think. The toolkit is the easiest. It's just a bunch of methods, classes that can be use.
The library vs the framework question I make difference by the way to use them. I read somewhere the perfect answer a long time ago. The framework calls your code, but on the other hand your code calls the library.

In relation with the correct answer from Mittag:
a simple example. Let's say you implement the ISerializable interface (.Net) in one of your classes. You make use of the framework qualities of .Net then, rather than it's library qualities. You fill in the "white spots" (as mittag said) and you have the skeleton completed. You must know in advance how the framework is going to "react" with your code. Actually .net IS a framework, and here is where i disagree with the view of Mittag.
The full, complete answer to your question is given very lucidly in Chapter 19 (the whole chapter devoted to just this theme) of this book, which is a very good book by the way (not at all "just for Smalltalk").

Others have noted that .net may be both a framework and a library and a toolkit depending on which part you use but perhaps an example helps. Entity Framework for dealing with databases is a part of .net that does use the inversion of control pattern. You let it know your models it figures out what to do with them. As a programmer it requires you to understand "the mind of the framework", or more realistically the mind of the designer and what they are going to do with your inputs. datareader and related calls, on the other hand, are simply a tool to go get or put data to and from table/view and make it available to you. It would never understand how to take a parent child relationship and translate it from object to relational, you'd use multiple tools to do that. But you would have much more control on how that data was stored, when, transactions, etc.