After reading the article “How to Learn to Add Numbers with seq2seq Recurrent Neural Networks” by Jason Brownlee (that I suggest reading before going on), I’ve decided to try an experiment with more complex expressions like: -(10+5) or 4+ -2, etc. The code (with some extra information) is published on the GIST: https://goo.gl/ZmH6Tf, where there are also some test results. Unfortunately, the results are not extraordinary and there are still many errors, however, I think it depends on the size of the dataset and on the limited ability to generalize that Seq2Seq networks show. I’m working on an enhanced version, to allow a bit more generalization. Complete Python script (Keras 2 with Theano/Tensorflow is needed, moreover I’ve used Scikit-Learn for binarization): View the code on Gist. See also: Hopfield Networks addendum: Brain-State-in-a-Box model – Giuseppe Bonaccorso The Brain-State-in-a-Box is neural model proposed by Anderson, Silverstein, Ritz and Jones in 1977, that […]
OpenAI-Gym evaluation page: https://gym.openai.com/evaluations/eval_JxPKNwd1QjaofWkaE4aLfQ. Below there’s the whole Gist page containing the full Python code. It has been developed and tested with Theano/GPU support, but it can easily work with CPU-only support. Any comment or suggestion is welcome! View the code on Gist. See also: CIFAR-10 image classification with Keras ConvNet – Giuseppe Bonaccorso CIFAR-10 is a small image (32 x 32) dataset made up of 60000 images subdivided into 10 main categories. Check the web page in the reference list in order to have further information about it and download the whole set.
Fork Reuters-21578 is a collection of about 20K news-lines (see reference for more information, downloads and copyright notice), structured using SGML and categorized with 672 labels. They are diveded into five main categories: Topics Places People Organizations Exchanges However, most of them are unused and, looking at the distribution, it’s possible to notice a complete lack of homogeneity. These are the 20 top categories (the prefix is made with the two initial letter of each main category) with the number of related news-lines: ID Name Category Newslines 161 pl_usa Places 12542 533 to_earn Topics 3987 498 to_acq Topics 2448 158 pl_uk Places 1489 84 pl_japan Places 1138 31 pl_canada Places 1104 571 to_money-fx Topics 801 526 to_crude Topics 634 543 to_grain Topics 628 167 pl_west-germany Places 567 624 to_trade Topics 552 553 to_interest Topics 513 56 pl_france Places 469 185 or_ec Organizations 349 23 pl_brazil Places 332 628 to_wheat Topics […]