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58 lines
1.6 KiB
58 lines
1.6 KiB
9 years ago
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# Copyright (c) 2016 Baidu, Inc. 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 random
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import numpy as np
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from py_paddle import swig_paddle
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def doubleEqual(a, b):
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return abs(a - b) < 1e-5
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def __readFromFile():
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for i in xrange(10002):
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yield np.random.rand(784), random.randint(0, 9)
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def loadMNISTTrainData(batch_size=100):
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if not hasattr(loadMNISTTrainData, "gen"):
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generator = __readFromFile()
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loadMNISTTrainData.gen = generator
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else:
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generator = loadMNISTTrainData.gen
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args = swig_paddle.Arguments.createArguments(2)
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# batch_size = 100
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dense_slot = []
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id_slot = []
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atEnd = False
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for _ in xrange(batch_size):
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try:
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result = generator.next()
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dense_slot.extend(result[0])
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id_slot.append(result[1])
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except StopIteration:
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atEnd = True
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del loadMNISTTrainData.gen
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break
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dense_slot = swig_paddle.Matrix.createDense(dense_slot, batch_size, 784)
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id_slot = swig_paddle.IVector.create(id_slot)
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args.setSlotValue(0, dense_slot)
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args.setSlotIds(1, id_slot)
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return args, atEnd
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