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65 lines
2.2 KiB
65 lines
2.2 KiB
import paddle.v2.dataset.common
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import subprocess
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import numpy
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__all__ = ['train', 'test']
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URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
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TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
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TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
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TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
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TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688'
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TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
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TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
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TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
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TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
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def reader_creator(image_filename, label_filename, buffer_size):
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def reader():
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# According to http://stackoverflow.com/a/38061619/724872, we
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# cannot use standard package gzip here.
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m = subprocess.Popen(["zcat", image_filename], stdout=subprocess.PIPE)
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m.stdout.read(16) # skip some magic bytes
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l = subprocess.Popen(["zcat", label_filename], stdout=subprocess.PIPE)
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l.stdout.read(8) # skip some magic bytes
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while True:
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labels = numpy.fromfile(
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l.stdout, 'ubyte', count=buffer_size).astype("int")
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if labels.size != buffer_size:
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break # numpy.fromfile returns empty slice after EOF.
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images = numpy.fromfile(
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m.stdout, 'ubyte', count=buffer_size * 28 * 28).reshape(
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(buffer_size, 28 * 28)).astype('float32')
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images = images / 255.0 * 2.0 - 1.0
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for i in xrange(buffer_size):
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yield images[i, :], labels[i]
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m.terminate()
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l.terminate()
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return reader()
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def train():
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return reader_creator(
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paddle.v2.dataset.common.download(TRAIN_IMAGE_URL, 'mnist',
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TRAIN_IMAGE_MD5),
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paddle.v2.dataset.common.download(TRAIN_LABEL_URL, 'mnist',
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TRAIN_LABEL_MD5), 100)
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def test():
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return reader_creator(
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paddle.v2.dataset.common.download(TEST_IMAGE_URL, 'mnist',
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TEST_IMAGE_MD5),
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paddle.v2.dataset.common.download(TEST_LABEL_URL, 'mnist',
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TEST_LABEL_MD5), 100)
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