I am currently failing into find an easy and modular framework to link openAI gym or tensorflow or keras with omnet++ in such a way I can produce communication between each tool and have online learning.
There are tools like omnetpy and veins-gym, however one is very strict and not trustworthy (and no certainty into bridge with openAI, for example) and the other is really poor documented in such a way one person can’t taper how it is supposed to be incorporated into a project.
Being omnet so big project, how is it possible that it is so disconnected to ML world like this?
On top of that, I still will need to use federated learning, so a custom scrappy solution would be even more difficult.
I found various articles that say “we have used omnet++ and keras or tensorflow”, etc, but none of them shared their code, so it is kinda misterious how they did it.
Alternatively, I could use NS3, but as far as I know, it is very steeped to learn it. Some ML tools are well documented, apparently, for NS3. But since I didn’t tried to implement something in NS3 with those tools, I can’t know for sure. Omnet++ was easy to learn for what I need, changing to NS3 still seems a burden with no clear guarantees.
I would like to ask help in both senses:
if u have links regarding good middleware between omnetpp and openai-gym or keras or such, and you have used them, please share with me.
if u have experience with NS3 and ML using ML middleware to link NS3 with openai-gym and keras and so on, please share with me.
I will only be able to finish my POC if I manage to use Reinforcement Learning tooling online a omnet++ simulation (i.e., agent is deciding on simulation runtime which actions to take).
My project is actually complex, but the POC may be simple. I am relying in these tools because I have no sufficient experience to build a complex system translating a domain to another. So a help will be nice.
Thank You.
Related
A co-worker and I had an idea to create a little web game where a user enters a chunk of data about themselves and then the application would write for them to sound like them in certain structures. (Trying to leave the idea a little vague.) We are both new to ML and thought this could be a fun first dive.
We have a decent bit of background with PHP, JavaScript (FE and Node), Ruby a little bit of other languages, and have had interest in learning Python for ML. Curious if you can run a cost efficient ML library for text well with a web app, being most servers lack GPUs?
Perhaps you have to pay for one of the cloud based systems, but wanted to find the best entry point for this idea without racking up too much cost. (So far I have been reading about running Pytorch or TensorFlow, but it sounds like you lose a lot of efficiency running with CPUs.)
Thank you!
(My other thought is doing it via an iOS app and trying Apple's ML setup.)
It sounds like you are looking for something like Tensorflow JS
Yes, before jumping into training something with Deep Learning; (this might even be un-necessary for your purpose) try to build a nice and simple baseline for this.
Before Deep Learning (just a few yrs ago) people did similar tasks using n-gram feature based language models. https://web.stanford.edu/~jurafsky/slp3/3.pdf
Essentially you try to predict the next few words probabilistically given a small context(of n-words; typically n is small like 5 or 6)
This should be a lot of fun to work out and will certainly do quite well with a small amount of data. Also such a model will run blazingly fast; so you don't have to worry about GPUs and compute .
To improve on these results with Deep Learning, you'll need to collect a ton of data first; and it will be work to get it to be fast on a web based platform
I'm new to the Machine-Learning (AI) technology. I'm developing a messenger app for Android/IOs where I would like to recommend the users based on the texts/word/conversation a product from a relative small product portfolio.
Example 1:
In case the user of the messenger writes a sentence including the words "vine", "dinner", "date" the AI should recommend a bottle of vine to the user.
Example 2:
In case the user of the app writes that he has drunk a good coffee this morning, the AI should recommend a mug to the user.
Example 3:
In case the user writes something about a cute boy she met last day, the AI should recommend a "teddy bear" to the user.
I'm a Software Developer since almost 20 year with experience in the development of C/C++/Java based application (Android and IOs apps) as well as some experience in Google Cloud Platform. The ML/AI technology is completely new to me. Okay, I know the basics (input data is needed to train the ML/AI system etc.), but I wonder If there is already a framework which could help me to develop such a system which solves the above described uses-case.
I would appreciate it, if you could give me some hints where and how to start.
Thank you and regards
It is definitely possible to implement such an application, in case you want to do it in Google Cloud you will need some understanding of Tensorflow.
First of all, I recommend to you to do the Machine Learning Crash Course, for a good introduction to Machine Learning and to start to familiarize yourself with TensorFlow. Afterwards I recommend to take a look into Tensorflow tutorials which will give you a more practical introduction to Tensorflow, and include various examples on building/training/testing models.
Once you are famirialized with Tensorflow, you can jump into learning how to run jobs in the Machine Learning engine, you can start by following the quickstart. The documentation includes detailed guides on how to use the ml-engine, plus multiple samples and tutorials.
Since I believe that your application would fall into the Recommender System type, here you can see an example model, in Google Cloud ML Engine, on how to recommend items to users based on his previous searches. In your case, you would have to build a model in order to recommend items to users based on his previous words in the sentence.
The second option, in case you don't want to go through the hassle of building a new model from scratch, would be to use the Google Cloud Natural Language API, which you can understand as pre-trained models using Google (incredibly big) data. In your case, I believe that the Content Classifying API would help you achieve what your application intends to do, however, the outputs (which you can see here) are limited to what the model was trained to do, and might not be specific enough for your application, however it is an easy solution and you can still profit of this API in order to extract labels/information and send it as input to another model.
I hope that these links provide you with some foundations on what is possible to do with Tensorflow in the ML Engine, and are useful to you.
I want to create a application which converts 2d-images/video into a 3d model. While researching on it i found out similar application like Trnio, Scann3D, Qlone,and few others(Though few of them provide poor output 3D model). I also find out about a technology launched by the microsoft research called mobileFusion which showed the same vision i was hoping for my application but these apps were non like that.
Creating a 3D modelling app is complex task, and achieving it to a high standard requires a lot of studying. To point you in the right direction, you most likely want to perform something called Structure-from-Motion(SfM) or Simultaneous Localization and Mapping (SLAM).
If you want to program this yourself OpenCV is a good place to start if you know C++ or Python. A typical pipeline involves; feature extraction and matching, camera pose estimation, triangulation and then optimised using a bundle adjustment. All pipelines for SfM and SLAM follow these general steps (with exceptions of course). All of these steps are possible is OpenCV although Googles Ceres Solver is an excellent open-source bundle adjustment. SfM generally goes onto dense matching which is where you get very dense point clouds which are good for creating meshes. A free open-source pipeline for this is OpenSfM. Another good source for tools is OpenMVG which has all of the tools you need to make a full pipeline.
SLAM is similar to SfM, however, has more of a focus on real-time application and less on absolute accuracy. Applications for this is more centred around robotics where a robot wants to know where it is relative to its environment, but it not so concerned on absolute accuracy. The top SLAM algorithms are ORB-SLAM and LSD-SLAM. Both are open-source and free for you to implement into your own software.
So really it depends what you want... SfM for high accuracy, SLAM for real-time. If you want a good 3D model I would recommend using existing algorithms as they are very good.
The best commercial software in my opinion... Agisoft Photoscan. If you can make anything half as good as this i'd be very impressed. To answer your question what resources will you require. In my opinion, python/c++ skills, the ability to google well and a spare time to read up on photogrammetry and SfM properly.
This year Google produced 5 different packages for seq2seq:
seq2seq (claimed to be general purpose but
inactive)
nmt (active but supposed to be just
about NMT probably)
legacy_seq2seq
(clearly legacy)
contrib/seq2seq
(not complete probably)
tensor2tensor (similar purpose, also
active development)
Which package is actually worth to use for the implementation? It seems they are all different approaches but none of them stable enough.
I've had too a headache about some issue, which framework to choose? I want to implement OCR using Encoder-Decoder with attention. I've been trying to implement it using legacy_seq2seq (it was main library that time), but it was hard to understand all that process, for sure it should not be used any more.
https://github.com/google/seq2seq: for me it looks like trying to making a command line training script with not writing own code. If you want to learn Translation model, this should work but in other case it may not (like for my OCR), because there is not enough of documentation and too little number of users
https://github.com/tensorflow/tensor2tensor: this is very similar to above implementation but it is maintained and you can add more of own code for ex. reading own dataset. The basic usage is again Translation. But it also enable such task like Image Caption, which is nice. So if you want to try ready to use library and your problem is txt->txt or image->txt then you could try this. It should also work for OCR. I'm just not sure it there is enough documentation for each case (like using CNN at feature extractor)
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/seq2seq: apart from above, this is just pure library, which can be useful when you want to create a seq2seq by yourself using TF. It have a function to add Attention, Sequence Loss etc. In my case I chose that option as then I have much more freedom of choosing the each step of framework. I can choose CNN architecture, RNN cell type, Bi or Uni RNN, type of decoder etc. But then you will need to spend some time to get familiar with all the idea behind it.
https://github.com/tensorflow/nmt : another translation framework, based on tf.contrib.seq2seq library
From my perspective you have two option:
If you want to check the idea very fast and be sure that you are using very efficient code, use tensor2tensor library. It should help you to get early results or even very good final model.
If you want to make a research, not being sure how exactly the pipeline should look like or want to learn about idea of seq2seq, use library from tf.contrib.seq2seq.
I have been playing around with building some deep learning models in Python and now I have a couple of outcomes I would like to be able to show friends and family.
Unfortunately(?), most of my friends and family aren't really up to the task of installing any of the advanced frameworks that are more or less necessary to have when creating these networks, so I can't just send them my scripts in the present state and hope to have them run.
But then again, I have already created the nets, and just using the finished product is considerably less demanding than making it. We don't need advanced graph compilers or GPU compute powers for the show and tell. We just need the ability to make a few matrix multiplications.
"Just" being a weasel word, regrettably. What I would like to do is convert the the whole model (connectivity,functions and parameters) to a model expressed in e.g. regular Numpy (which, though not part of standard library, is both much easier to install and easier to bundle reliably with a script)
I fail to find any ready solutions to do this. (I find it difficult to pick specific keywords on it for a search engine). But it seems to me that I can't be the first guy who wants to use a ready-made deep learning model on a lower-spec machine operated by people who aren't necessarily inclined to spend months learning how to set the parameters in an artificial neural network.
Are there established ways of transferring a model from e.g. Theano to Numpy?
I'm not necessarily requesting those specific libraries. The main point is I want to go from a GPU-capable framework in the creation phase to one that is trivial to install or bundle in the usage phase, to alleviate or eliminate the threshold the dependencies create for users without extensive technical experience.
An interesting option for you would be to deploy your project to heroku, like explained on this page:
https://github.com/sugyan/tensorflow-mnist