I have used the Encog C# framework to create a neural network. The neural network is fully trained. So I would like to use the neural network in my main application to solve a subproblem.
Is there a way to export the neural network into my main application which is written in C++?
If you have such wish, then most straightforward way would be to convert C# code to C++ by yourself. That way actually isn't practical or achievable easy. So I propose you another way. From your C++ app use encog as .Net assembly. There are plenty of articles which explain how to call .Net assembly from C++.
Related
I love the MPC module in Drake, providing numerous out-of-the-box cost and constraint functions. The three system building methods I know are importing from a urdf file, LinearSystem, and SymbolicVectorSystem. However, it's hard to include a neural network into the dynamics through them. Is there any suggestion here?
You could use other framework ,like pytorch, to add a neural network to your control system written by drake. They don't conflict.
I have started using Julia.I read that it is faster than C.
So far I have seen some libraries like KNET and Flux, but both are for Deep Learning.
also there is a command "Pycall" tu use Python inside Julia.
But I am interested in Machine Learning too. So I would like to use SVM, Random Forest, KNN, XGBoost, etc but in Julia.
Is there a native library written in Julia for Machine Learning?
Thank you
A lot of algorithms are just plain available using dedicated packages. Like BayesNets.jl
For "classical machine learning" MLJ.jl which is a pure Julia Machine Learning framework, it's written by the Alan Turing Institute with very active development.
For Neural Networks Flux.jl is the way to go in Julia. Also very active, GPU-ready and allow all the exotics combinations that exist in the Julia ecosystem like DiffEqFlux.jl a package that combines Flux.jl and DifferentialEquations.jl.
Just wait for Zygote.jl a source-to-source automatic differentiation package that will be some sort of backend for Flux.jl
Of course, if you're more confident with Python ML tools you still have TensorFlow.jl and ScikitLearn.jl, but OP asked for pure Julia packages and those are just Julia wrappers of Python packages.
Have a look at this kNN implementation and this for XGboost.
There are SVM implementations, but outdated an unmaintained (search for SVM .jl). But, really, think about other algorithms for much better prediction qualities and model construction performance. Have a look at the OLS (orthogonal least squares) and OFR (orthogonal forward regression) algorithm family. You will easily find detailed algorithm descriptions, easy to code in any suitable language. However, there is currently no Julia implementation I am aware of. I found only Matlab implementations and made my own java implementation, some years ago. I have plans to port it to julia, but that has currently no priority and may last some years. Meanwhile - why not coding by yourself? You won't find any other language making it easier to code a prototype and turn it into a highly efficient production algorithm running heavy load on a CUDA enabled GPGPU.
I recommend this quite new publication, to start with: Nonlinear identification using orthogonal forward regression with nested optimal regularization
I wonder If I can use libsvm in cuda.
I look for best parameters by cross validation, so I have to run same code around 4000 with different parameters.
I wonder If I can run the cross validation in parallel with cuda,
instead of using:
for i in range(4000):
predict(parameter)
find_best_parameter()
In the official webpage of the libsvm software you can find this sentence:
Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available.
And there is a link to a GPU implementation:
http://mklab.iti.gr/project/GPU-LIBSVM
I am looking for a very lightweight neural network package to solve the following problem:
2 input units, 4 hidden units, 2 output units
different activation functions for different connections
different cost (error) functions for the two output units
Could somebody, perhaps with more experience, please help?
I can recommend Weka, where you have MultilayerPerceptron, which is an out-of-the-box neural network classifier. But this is probably not suitable for your problem, so you can use the basic class NeuralNode, which is used by the MultilayerPerceptron to build it's neural network.
If you manage to separate all the needed code in and pack it into your own custom classes, you will end with a very lightweight solution - not counting the JVM needed to run it.
In which programming language or environment do you plan to implement this? If you already use Java, then the JVM will not be an overhead, since you already use it.
I'm not looking for a Neural Networks library, since I'm creating new kinds of networks. For that I need a good "dataflow" language.
Of course you can do this in C, C++, Java and co. but dealing from scratch with the multithreading etc. would be a nightmare.
At the other extremity, languages like Oz or Erlang seem more adapted, but they don't have many libraries, and they are harder to master (it's easy to play with them, but is it OK to create complete software ?).
What would you suggest ?
I watched an interesting conference presentation about using Erlang for Neural Networks. You might want to check it out:
From Telecom Networks to Neural Networks; Erlang, as the unintentional Neural Network Programming Language
I also know that the presented system is going to be open-sourced any day now according the authors tweet.
Erlang is very well suited for NN.
Neurons can be modeled by processes (no problem with having millions of them)
Connections/synapses can be represented by PIDs of target neuron. It is very easy to initialize such a network as part of standard init procedure in OTP. Communication would be realized by message passing.
Maybe it would be good to have global address space in ETS/mnesia (build in datastores) in order to do dynamic reconfiguration of network structure.
Pattern matching in receive block can determine what kind of signal neuron receives and modify it on the fly.
It would be very easy to monitor such a network.
Also consider that Erlang NN would be 'live' all the time. You would be able to query neurons, layers, routers etc any time.
In C/C++ you just read current state of arrays/data structure.
Regarding performance, we all know that C/C++ is orders of magnitude faster than Erlang,
however NN topic is tricky.
If the network would hold very few neurons, in very wide address space, in regular array,
iterating over it again and again could be costly (in C). Equivalent situation in Erlang would be solved by single query to root/roots (input layer) neurons, which would propagate query directly to well addressed neighborhs.
DXNN1, and DXNN2 which was built and introduced in the textbook: Handbook of Neuroevolution Through Erlang: http://www.amazon.com/Handbook-Neuroevolution-Through-Erlang-Gene/dp/1461444624/ref=zg_bs_760204_22
Are open source and available at: https://github.com/CorticalComputer
If you are interested in data flow programming and multi-threading then I would suggest National Instruments LabVIEW. In this case you don't need to bother about multi-threading since its already there and you can also use OOP since now OOP is also native with LabVIEW. LabVIEW OOP is also purely based on data flow programming paradigm.
If you have any Java experience, then use Scala which is a JVM language that is based on the same concept of "actors" as Erlang. But it is less strict than Erlang and can easily use any existing Java libraries.
Then, when you find a computationally expensive task that would work better in Erlang, you can use Erlang's jinterface library to communicate between your Scala code and your distributed Erlang nodes.
Using Java does not mean dealing from scratch with multithreading - just use one of numerous Java Actor Libraries.
It's not a language in and of itself, but Emergent is very powerful and can be highly customized (it has a full scripting language).
It's open source, too, which could be helpful as a guide if you need to make your own version for your novel architectures.
Why reinvent the wheel? Try PyBrain. It's free and very comprehensive:
Quickstart
Another big plus for Erlang is full integration with Drakon
http://drakon-editor.sourceforge.net/drakon-erlang/intro.html
It all depends on your application. C++, Python are some good programming languages for machine learning