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Fix GPU usage in chainer example
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@ -1,6 +1,7 @@
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'''WIP --- Doesn't work well yet'''
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'''WIP --- Doesn't work well yet'''
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import plac
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import plac
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import random
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import random
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import six
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import pathlib
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import pathlib
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import cPickle as pickle
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import cPickle as pickle
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@ -9,7 +10,10 @@ from itertools import izip
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import spacy
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import spacy
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import cytoolz
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import cytoolz
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import numpy as np
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import cupy as xp
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import cupy.cuda
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import chainer.cuda
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import chainer.links as L
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import chainer.links as L
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import chainer.functions as F
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import chainer.functions as F
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from chainer import Chain, Variable, report
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from chainer import Chain, Variable, report
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@ -17,7 +21,7 @@ import chainer.training
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import chainer.optimizers
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import chainer.optimizers
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from chainer.training import extensions
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from chainer.training import extensions
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from chainer.iterators import SerialIterator
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from chainer.iterators import SerialIterator
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from chainer.datasets.tuple_dataset import TupleDataset
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from chainer.datasets import TupleDataset
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class SentimentAnalyser(object):
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class SentimentAnalyser(object):
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@ -79,6 +83,7 @@ class SentimentModel(Chain):
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encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
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encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
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attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
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attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
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predict=_Predict(shape['nr_hidden'], shape['nr_class']))
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predict=_Predict(shape['nr_hidden'], shape['nr_class']))
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self.to_gpu(0)
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def __call__(self, sentence):
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def __call__(self, sentence):
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return self.predict(
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return self.predict(
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@ -145,6 +150,16 @@ class SentenceDataset(TupleDataset):
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get_features(sents, max_length),
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get_features(sents, max_length),
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labels)
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labels)
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def __getitem__(self, index):
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batches = [dataset[index] for dataset in self._datasets]
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if isinstance(index, slice):
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length = len(batches[0])
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returns = [tuple([batch[i] for batch in batches])
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for i in six.moves.range(length)]
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return returns
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else:
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return tuple(batches)
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def _get_labelled_sentences(self, docs, doc_labels):
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def _get_labelled_sentences(self, docs, doc_labels):
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labels = []
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labels = []
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sentences = []
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sentences = []
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@ -152,19 +167,17 @@ class SentenceDataset(TupleDataset):
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for sent in doc.sents:
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for sent in doc.sents:
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sentences.append(sent)
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sentences.append(sent)
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labels.append(y)
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labels.append(y)
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return sentences, labels
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return sentences, xp.asarray(labels, dtype='i')
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class DocDataset(TupleDataset):
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class DocDataset(TupleDataset):
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def __init__(self, nlp, texts, labels):
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def __init__(self, nlp, texts, labels):
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self.max_length = max_length
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self.max_length = max_length
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TupleDataset.__init__(self,
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DatasetMixin.__init__(self,
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get_features(
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get_features(
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nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
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nlp.pipe(texts, batch_size=5000, n_threads=3), self.max_length),
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labels)
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labels)
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def read_data(data_dir, limit=0):
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def read_data(data_dir, limit=0):
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examples = []
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examples = []
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for subdir, label in (('pos', 1), ('neg', 0)):
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for subdir, label in (('pos', 1), ('neg', 0)):
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@ -180,7 +193,7 @@ def read_data(data_dir, limit=0):
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def get_features(docs, max_length):
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def get_features(docs, max_length):
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docs = list(docs)
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docs = list(docs)
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Xs = np.zeros((len(docs), max_length), dtype='int32')
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Xs = xp.zeros((len(docs), max_length), dtype='i')
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for i, doc in enumerate(docs):
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for i, doc in enumerate(docs):
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j = 0
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j = 0
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for token in doc:
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for token in doc:
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@ -195,7 +208,7 @@ def get_features(docs, max_length):
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def get_embeddings(vocab, max_rank=1000):
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def get_embeddings(vocab, max_rank=1000):
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if max_rank is None:
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if max_rank is None:
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max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
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max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
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vectors = np.ndarray((max_rank+1, vocab.vectors_length), dtype='float32')
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vectors = xp.ndarray((max_rank+1, vocab.vectors_length), dtype='f')
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for lex in vocab:
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for lex in vocab:
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if lex.has_vector and lex.rank < max_rank:
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if lex.has_vector and lex.rank < max_rank:
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lex.norm = lex.rank+1
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lex.norm = lex.rank+1
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@ -208,15 +221,12 @@ def get_embeddings(vocab, max_rank=1000):
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def train(train_texts, train_labels, dev_texts, dev_labels,
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def train(train_texts, train_labels, dev_texts, dev_labels,
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lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
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lstm_shape, lstm_settings, lstm_optimizer, batch_size=100, nb_epoch=5,
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by_sentence=True):
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by_sentence=True):
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print("Loading spaCy")
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nlp = spacy.load('en', entity=False)
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nlp = spacy.load('en', entity=False)
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for lex in nlp.vocab:
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for lex in nlp.vocab:
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if lex.rank >= (lstm_shape['nr_vector'] - 1):
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if lex.rank >= (lstm_shape['nr_vector'] - 1):
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lex.norm = 0
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lex.norm = 0
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else:
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else:
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lex.norm = lex.rank+1
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lex.norm = lex.rank+1
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#print("Get embeddings")
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#embeddings = get_embeddings(nlp.vocab)
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print("Make model")
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print("Make model")
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model = Classifier(SentimentModel(lstm_shape, **lstm_settings))
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model = Classifier(SentimentModel(lstm_shape, **lstm_settings))
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print("Parsing texts...")
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print("Parsing texts...")
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@ -230,13 +240,12 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
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shuffle=True, repeat=True)
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shuffle=True, repeat=True)
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dev_iter = SerialIterator(dev_data, batch_size=batch_size,
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dev_iter = SerialIterator(dev_data, batch_size=batch_size,
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shuffle=False, repeat=False)
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shuffle=False, repeat=False)
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optimizer = chainer.optimizers.Adam()
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optimizer = chainer.optimizers.Adam()
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optimizer.setup(model)
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optimizer.setup(model)
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updater = chainer.training.StandardUpdater(train_iter, optimizer)
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updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
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trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
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trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
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trainer.extend(extensions.Evaluator(dev_iter, model))
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trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
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trainer.extend(extensions.LogReport())
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trainer.extend(extensions.LogReport())
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trainer.extend(extensions.PrintReport([
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trainer.extend(extensions.PrintReport([
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'epoch', 'main/accuracy', 'validation/main/accuracy']))
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'epoch', 'main/accuracy', 'validation/main/accuracy']))
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@ -293,14 +302,16 @@ def main(model_dir, train_dir, dev_dir,
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print("Read data")
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print("Read data")
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train_texts, train_labels = read_data(train_dir, limit=nr_examples)
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train_texts, train_labels = read_data(train_dir, limit=nr_examples)
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dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
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dev_texts, dev_labels = read_data(dev_dir, limit=nr_examples)
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train_labels = np.asarray(train_labels, dtype='int32')
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print("Using GPU 0")
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dev_labels = np.asarray(dev_labels, dtype='int32')
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#chainer.cuda.get_device(0).use()
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train_labels = xp.asarray(train_labels, dtype='i')
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dev_labels = xp.asarray(dev_labels, dtype='i')
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lstm = train(train_texts, train_labels, dev_texts, dev_labels,
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lstm = train(train_texts, train_labels, dev_texts, dev_labels,
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{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
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{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
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'nr_vector': 2000, 'nr_dim': 32},
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'nr_vector': 2000, 'nr_dim': 32},
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{'dropout': 0.5, 'lr': learn_rate},
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{'dropout': 0.5, 'lr': learn_rate},
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{},
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{},
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nb_epoch=nb_epoch, batch_size=batch_size)
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nb_epoch=nb_epoch, batch_size=batch_size)
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if __name__ == '__main__':
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if __name__ == '__main__':
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