Mercurial > pylearn

annotate pylearn/algorithms/tests/test_regressor.py @ 1506:2f69c9932d9a

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Fix test in float32.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 12 Sep 2011 10:56:38 -0400
parents f7957524f76e
children
rev   line source
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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1
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Olivier Delalleau <delallea@iro>
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2 import pylearn.algorithms.regressor as models
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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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8 def test_train(mode = theano.Mode('c|py', 'fast_run')):
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9
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10 reg = models.BinRegressor(regularize = False)
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11
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12 model = reg.make(lr = 0.01,
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13 input_size = 100,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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14 mode = mode,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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15 seed = 10)
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16
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17 # data = [[0, 1, 0, 0, 1, 1, 1, 0, 1, 0]*10]*10
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18 # targets = [[1]]*10
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19 #data = numpy.random.rand(10, 100)
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20
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21 R = numpy.random.RandomState(100)
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22 t1 = time.time()
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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23 for i in xrange(1001):
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2f69c9932d9a Fix test in float32.
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24 data = R.random_integers(0, 1, size = (10, 100)).astype(theano.config.floatX)
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25 targets = data[:, 6].reshape((10, 1))
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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26 cost = model.update(data, targets)
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27 if i % 100 == 0:
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28 print i, '\t', cost, '\t', 1*(targets.T == model.classify(data).T)
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29 t2 = time.time()
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30 return t2 - t1
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31
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32 if __name__ == '__main__':
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33 print 'optimized:'
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34 t1 = test_train(theano.Mode('c|py', 'fast_run'))
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35 print 'time:',t1
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36 print
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37
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38 print 'not optimized:'
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39 t2 = test_train(theano.Mode('c|py', 'fast_compile'))
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40 print 'time:',t2
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41
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42
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45