I have been clinging to a problem on multi class classification with a large number of classes (500) and a one vs rest classifier seems to be a good approach to handle it. Is there any support for the same in pytorch itself?
All that I could find is this post which doesn't have answers either. I understand that scikit-learn does offer this feature but I wish to stick to pytorch because of the numerous familiar customisations that can be tweaked with in this framework. I've tried exploring skorch but even it's overly simplified and doesn't have a good documentation to allow modifications which my use case needs
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I am doing a meta learning research and am using the MAML optimization provided by learn2learn. However as one of the baseline, I would like to test a non-meta-learning approach, i.e. the traditional training + testing.
Due to the lightning's internal usage of optimizer it seems that it is difficult to make the MAML work with learn2learn in lightning, so I couldn't use lightning in my meta-learning setup, however for my baseline, I really like to use lightning in that it provides many handy functionalities like deepspeed or ddp out of the box.
Here is my question, other than setting up two separate folders/repos, how could I mix the vanilia pytorch (learn2learn) with pytorch lightning (baseline)? What is the best practice?
Thanks!
Decided to answer my question. So I ended up using the torch lightning's manual optimization so that I can customize the optimization step. This would make both approaches using the same framework, and I think is better than maintaining 2 separate repos.
So I'm trying to use a pretrained Doc2vec for my semantic search project. I tried with this one https://github.com/jhlau/doc2vec (English Wikipedia DBOW) and with the forked version of Gensim (0.12.4) and python 2.7
It works fine when I use most_similar but when i try to use infer_vector I get this error:
AttributeError: 'Doc2Vec' object has no attribute 'neg_labels'
what can i do to make this work?
For reasons given in this other answer, I'd recommend against using a many-years-old custom fork of Gensim, and also find those particular pre-trained models a little fishy in their sizes to actually contain all the purported per-article vectors.
But also: that error resembles a very-old bug which only showed up if Gensim was not fully installed to have the necessary Cython-optimized routines for fast training/inference operations. (That caused some older, seldom-run code to be run that had a dependency on the missing neg_labels. Newer versions of Gensim have eliminated that slow code-path entirely.)
My comment on an old Gensim issue has more details, and a workaround that might help - but really, the much better thing to do for quality results & speedy code is to use a current Gensim, & train your own model.
I am working at timeseries forecasting, and I've using the PyTorch lib pytorch-forecasting lately. If you didn't know it, try it. It's great.
I am interested in SELU activation function for Self-Normalizing-Networks (SNNs, see, e.g., the docs). As I didn't find any N-Beats corrected to use SELU and its requirements (i.e. AlphaDropout, proper weights init), I made an implementation myself.
It would be great if any of you with experience with these concepts -NBeats architecture, pytorch-forecasting, or SELU()- could review whether everything is right in my implementation.
My implementation here: https://gist.github.com/pnmartinez/fef1f488497fa85a2cc1626af2a5b4bd
I am working on a project that requires inversion of multiple large dense matrix. I would like to know if ojAlgo supports multi-threading.
I do notice the github page for ojAlgo is tagged with multi-threading, however I am unable to find any documentation about it.
Thanks
My project is to extract events automatically from a given text. The events can be written specifically, or just mentioned in a sentence, or not be there at all.
What is the library or technique that I should use for this? I tried the Stanford NER demo, but it gave bad results. I have enough time to explore and learn a library, so complexity is not a problem. Accuracy is a priority.
The task itself is a complex one, it is still not solve now(2018). But recently a very useful python library for nlp is emerging. It is Spacy, this lib has a relative higher performance than its competitors. With the library you can do things like tokenize，POS tagging，NER and sentence similarity。But you still need to utilize these features and extract events based on your specific rule.
Deeplearning may also be a way to try, but even though it works fine on other nlp tasks, it is still immature on event extraction.