![]() ![]() +++ -38,6 +38,7 from import ndarray # NOQAįrom import negative # NOQAįrom import not_equal # NOQA +++ -393,5 +393,6 from cupy.util import clear_memo # NOQAįrom re import ElementwiseKernel # NOQAįrom re import ReductionKernel # NOQAĭiff -git a/cupy/core/_init_.py b/cupy/core/_init_.py # -ĭiff -git a/cupy/_init_.py b/cupy/_init_.py + :class:`cupy.RawKernel`, except that the ``name`` argument is + The arguments are the same as those for the resulting kernel object is cached for each argument + This function uses :func:`~` to cache the resulting + """Creates a global raw kernel function. Identity, name, raw(operation, name, **kwargs): +++ -537,6 +537,24 def reduce(in_params, out_params, map_expr, reduce_expr, post_map_expr, One method is to clone jekbradbury chainer repo, checkout raw-kernel branch and build from sources (consult with chainer docs for more information in case of problems):ĭiff -git a/chainer/cuda.py b/chainer/cuda.py Nice! We will use it!įor this implementation to work we will need some changes to be done to chainer codebase. This time wheel reinvention wasn’t needed as James Bradbury published his implementation of QRNN layer for chainer. shape ret = chunk idx += chunk_size pos = ifile. st_size with open ( path, 'rb' ) as ifile : pos = 0 idx = 0 while pos < size : chunk = np. # So to load whole matrix we read the file until it's exhausted. float32 ) # As embedding matrix could be quite big we 'stream' it into output file zeros (( size, dimensions ), dtype = np. We will use vocabulary for dataset creation and embeddings for weights initialization.ĭef load_embeddings ( path, size, dimensions ): # premature memory optimization :) For it to work one will need to pass GloVe embeddings to it (as a text file) and specify output path for both embeddings and vocabulary. I used glove_to_npy.py script that performs data conversion. Convert text samples into vectors of integers.Then we can turn text into vector of integers using create vocabulary as a reference. Data preparation and preprocessingĭata preparation includes several steps: first we need to convert dataset samples from ascii text into numbers, that is create mapping word -> number. ![]() Here you can download cased 300-dimensional embeddings from common crawl. Word vectors initialized using 300-dimensional cased GloVe embeddings To replicate paper experiment setup we will also need GloVe embeddings. As a bonus, dataset containes bag-of-words representations and vocabulary file. Samples are split into pos/ and neg/ folders. Every sample is a short description with a 10-scale rating. Basically it’s well-balanced dataset: it consists of 25k positive and 25k negative samples. ![]() Stanford offers IMDb dataset of their website. You can find whole QRNN model implementation in my repo in case you’re interested. Thanks to him! We will be using his implementation (as well as modified chainer) in our experiments. In his post James Bradbury mentioned he used chainer for his experiments and published chainer implementation of QRNN layer. This post describes how to implement QRNN network and perform sentiment analysis experiment with IMDB dataset. In a paper authors used several problems to demonstate novel networks: sentiment analysis, language modeling and character-level translation. My particular interest in QRNN was because deepvoice paper used them as “conditioning” part for synthesis model. See their blog post for some nice explanations. Some time ago MetaMind published a paper on Quasi-recurrent neural networks. The IMDB arrives through Wikidata and Freebase, not through IMDB directly.Īs for the url on the site, if it is not part of the url: in the response you will have to append it like so: + imdb_id (provided the id starts with "tt" (for movie).Ģ) Actors with birth dates, again you might have to append the url.Sergei Turukin Software engineer and enterpreneur IMDB sentiment analysis with QRNN's You can read more about the dumps and their structure here. b) a slightly smaller subset comprising of only:.You can either query for that information like shown or download one of: Get the imdb_id (generic) of a movie, given the movie's name:.Here are some queries to get the information from your example, and to show you what kind of data to expect from the dumps. 1) I would like to point you to a dataset and an API that provides IMDB information. ![]()
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