Mercurial > pylearn

annotate algorithms/tests/test_daa.py @ 533:de974b4fc4ea

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Bugfix in pylearn.embeddings.length()
author Joseph Turian <turian@gmail.com>
date Tue, 18 Nov 2008 03:25:54 -0500
parents 4fb6f7320518
children
rev   line source
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1 #!/usr/bin/python
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2
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3 from pylearn import algorithms as models
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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 import pylearn.algorithms.logistic_regression
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9
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10 def test_train_daa(mode = theano.Mode('c|py', 'fast_run')):
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11
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12 ndaa = 3
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13 daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(models.BinRegressor, 'output')],
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14 regularize = False)
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15
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16 model = daa.make([4, 20, 20, 20, 1],
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17 lr = 0.01,
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18 mode = mode,
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19 seed = 10)
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20
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21 model.layers[0].noise_level = 0.3
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22 model.layers[1].noise_level = 0.3
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23 model.layers[2].noise_level = 0.3
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24
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25 # Update the first hidden layer
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26 for l in range(3):
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27 for i in range(10):
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28 model.local_update[l]([[0, 1, 0, 1]])
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29 model.local_update[l]([[1, 0, 1, 0]])
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31 for i in range(10):
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32 model.update([[0, 1, 0, 1]], [[1]])
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33 model.update([[1, 0, 1, 0]], [[0]])
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34 print model.classify([[0, 1, 0, 1]])
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35 print model.classify([[1, 0, 1, 0]])
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37
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38 def test_train_daa2(mode = theano.Mode('c|py', 'fast_run')):
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39
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40 ndaa = 3
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41 daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'pred')],
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42 regularize = False)
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43
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44 model = daa.make([4] + [20] * ndaa + [10],
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45 lr = 0.01,
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46 mode = mode,
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47 seed = 10)
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48
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49 for l in range(ndaa): model.layers[l].noise_level = 0.3
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50
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51 instances = [([[0, 1, 0, 1]], [1]), ([[1, 0, 1, 0]], [0])]
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52
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53 for l in range(ndaa):
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54 for i in range(10):
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55 for (input, output) in instances:
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56 model.local_update[l](input)
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57
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58 for i in range(10):
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59 for (input, output) in instances:
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60 # model.update(input, output)
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61 print "OLD:",
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62 print model.validate(input, output)
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63 oldloss = model.update(input, output)
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64 print oldloss
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65 print "NEW:"
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66 print model.validate(input, output)
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67 print
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68
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69 print model.apply([[0, 1, 0, 1]])
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70 print model.apply([[1, 0, 1, 0]])
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74
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75 if __name__ == '__main__':
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76 # print 'optimized:'
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77 # t1 = test_train_daa(theano.Mode('py', 'fast_compile'))
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78 # t1 = test_train_daa(theano.Mode('c|py', 'fast_run'))
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79 # print 'time:',t1
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80 # print
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81
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82 # print 'not optimized:'
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83 # t2 = test_train_daa(theano.Mode('c|py', 'fast_compile'))
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84 ## print 'time:',t2
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85
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86 # test_train_daa(theano.compile.Mode('c&py', 'merge'))
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87 # test_train_daa(theano.compile.Mode('c|py', 'merge'))
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88 test_train_daa(theano.compile.Mode('py', 'merge'))
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89
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90 test_train_daa2(theano.compile.Mode('c|py', 'merge'))