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61 lines
1.9 KiB
61 lines
1.9 KiB
8 years ago
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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class MNISTloader():
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def __init__(self,
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data_path="./data/mnist_data/",
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batch_size=60,
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process='train'):
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self.batch_size = batch_size
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self.data_path = data_path
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self._pointer = 0
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self.image_batches = np.array([])
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self.process = process
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def _extract_images(self, filename, n):
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f = open(filename, 'rb')
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f.read(16)
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data = np.fromfile(f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28))
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#Mapping data into [-1, 1]
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data = data / 255. * 2. - 1
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data_batches = np.split(data, 60000 / self.batch_size, 0)
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f.close()
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return data_batches
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@property
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def pointer(self):
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return self._pointer
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def load_data(self):
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TRAIN_IMAGES = '%s/train-images-idx3-ubyte' % self.data_path
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TEST_IMAGES = '%s/t10k-images-idx3-ubyte' % self.data_path
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if self.process == 'train':
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self.image_batches = self._extract_images(TRAIN_IMAGES, 60000)
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else:
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self.image_batches = self._extract_images(TEST_IMAGES, 10000)
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def next_batch(self):
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batch = self.image_batches[self._pointer]
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self._pointer = (self._pointer + 1) % (60000 / self.batch_size)
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return np.array(batch)
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def reset_pointer(self):
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self._pointer = 0
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