I am wondering why MultiLayerPerceptron http://apache.github.io/mahout/0.10.1/docs/mahout-mr/index.html?org/apache/mahout/classifier/mlp/MultilayerPerceptron.html and all classes within MLP Package of Apache Mahout were deprecated. This was done as part of https://issues.apache.org/jira/browse/MAHOUT-1676. The JIRA issue doesn't mention about any reasons for doing so. I was planning to use this classifier in my project but since it is deprecated I am now hesitant to use it. I don't see any alternative version of classes to be used as mentioned in the Javadoc . Does anyone using Mahout for MLP has any idea?
I'm not sure how to answer this because it's basically off-topic. The mahout team considers that the MLP isn't used much by the community and it's painful to maintain so far. Thus they've decided to remove it since there exist other good implementations of it in other frameworks.
Related
Are there any tools to monitor network's training in PyTorch? Like tensorboard in tensorflow.
PyTorch 1.1.0 supports TensorBoard natively with torch.utils.tensorboard. The API is very similar to tensorboardX. See the documentation for more details.
I am using tensorboardX. It supports most (if not all) of the features of TensorBoard. I am using the Scalar, Images, Distributions, Histograms and Text. I haven't tried the rest, like audio and graph, but the repo also contains examples for those use cases. The installation can be done easily with pip. It's all explained in the README file of the repo.
There are also other github repos which implement a wrapper for PyTorch (and other languages/frameworks) to tensorboard. As far as I know they support fewer functionalities. But have a look at:
Crayon
Tensorboard-Logger
I have asked this question before in the forums. Tensorboard seems very convenient for Tensorflow and it is also made part of the library/framework itself. However, PyTorch wouldn't take the same approach. But there is a library called visdom here that is released by Facebook, that helps you log the training information. This gives you the flexibility of logging information the way you want. While this means a lot of flexibility, it also means you need to write some extra code to make things work.
Following up on blckbird's answer, I'm also a big fan of Tensorboard-PyTorch. However I also found that its API is relatively low level and I was writing a lot of similar code over and over to do the logging. So (shameless plug) I've written a small package on top of it to automate monitoring network training experiments with minimal code. Hopefully someone else finds it helpful. pytorch-monitor
Minetorch helps me a lot at the past 2 Kaggle competitions. I think it's ready for others to use. It has built-in tensorboard or matplotlib supported. And many other features which make the work easy, includes:
Logger
Tensorboard supported
Matplotlib (to generate png to file)
Auto resume training
Auto best model saving
Hook points for customize
...
It's still in developing so any issues or PRs are very welcomed : )
Is there any thing that can generate a sequence diagram between actors in akka?
Or any kind of diagram in akka?
I use sbt 0.13.13 with scala 2.11.8, akka 2.4.16
The idea of general visualization of akka actors (mailboxes) is quite fresh, so there is not much in terms of existing frameworks for it. The one I came around and seemed closest to kind of general solution was akka-visualmailbox.
They also have sample project and comprehensive installation instructions in README.
The best part of this one is that it does not require code changes.
Another one that does is akka-message-visualization however I did not see any simple setup guides out there for it.
Use Lightbend Telemetry Visualization tools
Specially look into Vizceral Visualization tool
I'm currently building one called Muse, it's still in early stages, but I have already begun using it at the office. You can select one or more messages and generate a sequence diagram from them.
Several links show that https://code.google.com/p/concurrentlinkedhashmap/ has been ported to Guava, but I can not find the corresponding API in Guava.
Can anybody show me an example to build a ConcurrentLinkedHashmap using Guava ?
Thanks!
ConcurrentLinkedHashmap API wasn't ported to Guava 1:1, but some solutions from CLHM were used in MapMaker and Cache (with CacheBuilder).
ConcurrentLinkedHashmap's author Ben Manes answers a question which may clarify your doubts: What does it mean that ConcurrentLinkedHashMap has been integrated into Guava?:
Guava is the long term replacement and most of the time you should use it. The history is that ConcurrentLinkedHashMap figured out the algorithms, Guava subsumed it, and then focused on adding features.
I think it's also important to note that he's currently writing another library which may suit your needs:
Caffeine is a Java 8 rewrite of Guava's cache. It tries to provide the best of ConcurrentLinkedHashMap and Guava, modernized with Java 8, and adopting the techniques that I've learned since those previous projects.
For a project I'm using both Scala and Riak (two things I have never worked with before ;) ).
Google searches seem to suggest using Riakki. However, it seems like that particular library hasn't been maintained since 2009 and doesn't even compile on my system. There is a more up-to-date fork on GitHub that does seem to work with more recent Scala versions. But Riakki seems to depend on Jiak, which has been deprecated since february of last year.
Seems like the only reasonable choice would be to use the official Riak Java-library from Scala. That's certainly possible, but I'd like to do things the Scala-way as I'm trying to learn the language. Having to interface with a Java-style API might ruin a bit of the fun. Writing my own wrapper sounds like it will be too much work.
tl;dr: I want to use Riak from Scala. What are other people using?
edit: just found Ryu (can't link to it - annoying limit on amount of hyperlinks per question for new users). Doesn't seem all that mature though.
Stackmob recently opensourced Scalariak.
Scaliak is a scala-ified version of the High-Level Riak Java Client w/
a Functional Twist. It is currently being used in production at
StackMob.
Scaliak is currently feature incomplete vs. the original High-Level
Riak Java Client. What is currently supported are mostly features
being used in production (there have been a few features implemented
and subsequently not used).
There is also Raiku which states that it is async.
I'm in the same bucket - excuse the bad pun - although I have some experience with Scala. I'm thinking of using the official Java client.
When you are toiling up a steep learning curve, you don't need to be dealing with incomplete and potentially wobbly API's. In my experience, using Java API's from Scala is minimally painful.
I think there'll be enough delight in playing with our new Raik toy that we'll forget about whatever un-Scala-ish foibles the Java API inflicts upon us. All the best.
I'm the author of yet another Scala Riak client, simply called riak-scala-client. It is based on Akka and Spray, it is not built on top of the existing Java client, and most importantly it is completely non-blocking.
Check it out at http://riak.scalapenos.com and let me know what you think.
Firstly, I am a beginner in artificial neural networks and I need a special library for training the artificial neural networks, but I very confused in the selection of the library, and since I didn't have the experience I wanted to consult you.
I have read about three libraries:
FANN, Flood, and Neuro Fusion libraries.
So, what are you think about the easiest and Least problems library for using it with VC++.6?
I just started using FANN, and it seems to be very well documented, with great examples and fast.
It operates with floats/doubles/integers and implements the Cascade2 training method, which is really great if you are unsure about the architecture of your NN.
It is not as rich as Encog (didn't use it), but if FANN implements all the functionalities you need, I think you should go with it.
Edit: I just realized that Encog is only available for .NET C# (besides Java)