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@ -17,7 +17,7 @@ imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
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import paddle.v2.dataset.common
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import tarfile
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__all__ = ['train', 'test']
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__all__ = ['train', 'test', 'build_dict']
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URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz'
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MD5 = '30177ea32e27c525793142b6bf2c8e2d'
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@ -37,7 +37,9 @@ def word_count(f, word_freq=None):
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return word_freq
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def build_dict(train_filename, test_filename):
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def build_dict():
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train_filename = './simple-examples/data/ptb.train.txt'
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test_filename = './simple-examples/data/ptb.valid.txt'
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with tarfile.open(
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paddle.v2.dataset.common.download(
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paddle.v2.dataset.imikolov.URL, 'imikolov',
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@ -45,27 +47,22 @@ def build_dict(train_filename, test_filename):
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trainf = tf.extractfile(train_filename)
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testf = tf.extractfile(test_filename)
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word_freq = word_count(testf, word_count(trainf))
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if '<unk>' in word_freq:
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# remove <unk> for now, since we will set it as last index
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del word_freq['<unk>']
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TYPO_FREQ = 50
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word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
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dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
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words, _ = list(zip(*dictionary))
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word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
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words, _ = list(zip(*word_freq_sorted))
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word_idx = dict(zip(words, xrange(len(words))))
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word_idx['<unk>'] = len(words)
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return word_idx
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word_idx = {}
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def reader_creator(filename, n):
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global word_idx
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if len(word_idx) == 0:
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word_idx = build_dict('./simple-examples/data/ptb.train.txt',
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'./simple-examples/data/ptb.valid.txt')
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def reader_creator(filename, word_idx, n):
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def reader():
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with tarfile.open(
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paddle.v2.dataset.common.download(
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@ -84,9 +81,9 @@ def reader_creator(filename, n):
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return reader
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def train(n):
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return reader_creator('./simple-examples/data/ptb.train.txt', n)
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def train(word_idx, n):
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return reader_creator('./simple-examples/data/ptb.train.txt', word_idx, n)
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def test(n):
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return reader_creator('./simple-examples/data/ptb.valid.txt', n)
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def test(word_idx, n):
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return reader_creator('./simple-examples/data/ptb.valid.txt', word_idx, n)
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