Mercurial > pylearn

annotate algorithms/tests/test_aa.py @ 491:180d125dc7e2

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made logistic_regression classes compatible with stacker
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Tue, 28 Oct 2008 11:39:27 -0400
parents 267ec8baef9f
children
rev   line source
481
267ec8baef9f added import to make test_aa run
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 476
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1 #from __future__ import absolute_imports
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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2
481
267ec8baef9f added import to make test_aa run
James Bergstra <bergstrj@iro.umontreal.ca>
parents: 476
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3 from pylearn import algorithms as models
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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4 import theano
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5 import numpy
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6 import time
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7
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8
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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9 def test_train(mode = theano.Mode('c|py', 'fast_run')):
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10
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11 aa = models.SigmoidXEAutoEncoder(regularize = False)
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12 # print aa.update.pretty(mode = theano.Mode('py', 'fast_run').excluding('inplace'))
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13
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14 model = aa.make(lr = 0.01,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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15 input_size = 100,
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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16 hidden_size = 1000,
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17 mode = mode)
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18
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19 data = [[0, 1, 0, 0, 1, 1, 1, 0, 1, 0]*10]*10
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20 #data = numpy.random.rand(10, 100)
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21
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22 t1 = time.time()
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23 for i in xrange(1001):
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24 cost = model.update(data)
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25 if i % 100 == 0:
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26 print i, cost
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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 numpy.random.seed(10)
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32 print 'optimized:'
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33 t1 = test_train(theano.Mode('c|py', 'fast_run'))
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34 print 'time:',t1
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Olivier Breuleux <breuleuo@iro.umontreal.ca>
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35 print
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36
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37 numpy.random.seed(10)
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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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43