Mercurial > pylearn

annotate algorithms/tests/test_stacker.py @ 476:8fcd0f3d9a17

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added a few algorithms
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Mon, 27 Oct 2008 17:26:00 -0400
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476
8fcd0f3d9a17 added a few algorithms
Olivier Breuleux <breuleuo@iro.umontreal.ca>
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1
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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2 import models
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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3 import theano
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4 import numpy
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5 import time
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6
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7
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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8 def test_train(mode = theano.Mode('c|py', 'fast_run')):
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9
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10 reg = models.Stacker([(models.BinRegressor, 'output'), (models.BinRegressor, 'output')],
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11 regularize = False)
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12 #print reg.global_update[1].pretty(mode = mode.excluding('inplace'))
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13
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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14 model = reg.make([100, 200, 1],
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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15 lr = 0.01,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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16 mode = mode,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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17 seed = 10)
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18
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19 R = numpy.random.RandomState(100)
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20 t1 = time.time()
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21 for i in xrange(1001):
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22 data = R.random_integers(0, 1, size = (10, 100))
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23 targets = data[:, 6].reshape((10, 1))
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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24 cost = model.update(data, targets)
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25 if i % 100 == 0:
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26 print i, '\t', cost, '\t', 1*(targets.T == model.classify(data).T)
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27 t2 = time.time()
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28 return t2 - t1
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29
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30 if __name__ == '__main__':
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31 print 'optimized:'
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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32 t1 = test_train(theano.Mode('c|py', 'fast_run'))
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33 print 'time:',t1
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34 print
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35
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36 print 'not optimized:'
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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37 t2 = test_train(theano.Mode('c|py', 'fast_compile'))
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38 print 'time:',t2
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39
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40
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41
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42
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43