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annotate algorithms/_test_logistic_regression.py @ 507:b8e6de17eaa6

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modifs to smallNorb
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 29 Oct 2008 18:06:49 -0400
parents c7ce66b4e8f4
children
rev   line source
470
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
1 from logistic_regression import *
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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2 import sys, time
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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3
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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4 if __name__ == '__main__':
503
c7ce66b4e8f4 Extensions to algorithms, and some cleanup (by defining linear_output result).
Joseph Turian <turian@gmail.com>
parents: 470
diff changeset
5 pprint.assign(nnet.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
c7ce66b4e8f4 Extensions to algorithms, and some cleanup (by defining linear_output result).
Joseph Turian <turian@gmail.com>
parents: 470
diff changeset
6 pprint.assign(nnet.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
470
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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7 if 1:
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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8 lrc = Module_Nclass()
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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9
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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10 print '================'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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11 print lrc.update.pretty()
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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12 print '================'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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13 print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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14 print '================'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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15 # print lrc.update.pretty(mode = compile.FAST_RUN.excluding('inplace'))
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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16 # print '================'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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17
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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18 # sys.exit(0)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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19
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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20 lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'))
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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21 #lr = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'))
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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22 #lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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23
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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24 data_x = N.random.randn(5, 10)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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25 data_y = (N.random.randn(5) > 0)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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26
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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27 t = time.time()
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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28 for i in xrange(10000):
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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29 lr.lr = 0.02
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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30 xe = lr.update(data_x, data_y)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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31 #if i % 100 == 0:
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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32 # print i, xe
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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33
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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34 print 'training time:', time.time() - t
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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35 print 'final error', xe
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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36
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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37 #print
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
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38 #print 'TRAINED MODEL:'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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39 #print lr
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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40
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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41 if 0:
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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42 lrc = Module()
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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43
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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44 lr = lrc.make(10, mode=theano.Mode('c|py', 'merge')) #'FAST_RUN')
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
45
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
diff changeset
46 data_x = N.random.randn(5, 10)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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47 data_y = (N.random.randn(5, 1) > 0)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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48
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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49 for i in xrange(10000):
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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50 xe = lr.update(data_x, data_y)
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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51 if i % 100 == 0:
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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52 print i, xe
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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53
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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54 print
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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55 print 'TRAINED MODEL:'
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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56 print lr
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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57
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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58
bd937e845bbb new stuff: algorithms/logistic_regression, datasets/MNIST
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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59
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James Bergstra <bergstrj@iro.umontreal.ca>
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60