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493
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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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500
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8 import pylearn.algorithms.logistic_regression
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493
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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 model.local_update[0]([[0, 1, 0, 1]])
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27 model.local_update[1]([[0, 1, 0, 1]])
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28 model.local_update[2]([[0, 1, 0, 1]])
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29
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30 model.update([[0, 1, 0, 1]], [[0]])
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31 print model.classify([[0, 1, 0, 1]])
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32
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33
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500
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34 def test_train_daa2(mode = theano.Mode('c|py', 'fast_run')):
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35
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36 ndaa = 3
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37 daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'output')],
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38 regularize = False)
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39
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40 model = daa.make([4, 20, 20, 20, 1],
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41 lr = 0.01,
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42 mode = mode,
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43 seed = 10)
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44
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45 model.layers[0].noise_level = 0.3
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46 model.layers[1].noise_level = 0.3
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47 model.layers[2].noise_level = 0.3
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48
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49 # Update the first hidden layer
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50 model.local_update[0]([[0, 1, 0, 1]])
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51 model.local_update[1]([[0, 1, 0, 1]])
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52 model.local_update[2]([[0, 1, 0, 1]])
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53
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54 model.update([[0, 1, 0, 1]], [0])
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55 print model.classify([[0, 1, 0, 1]])
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56
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57
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493
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58
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59
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60
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61 if __name__ == '__main__':
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62 # print 'optimized:'
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63 # t1 = test_train_daa(theano.Mode('py', 'fast_compile'))
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64 # t1 = test_train_daa(theano.Mode('c|py', 'fast_run'))
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65 # print 'time:',t1
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66 # print
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67
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68 # print 'not optimized:'
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69 # t2 = test_train_daa(theano.Mode('c|py', 'fast_compile'))
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70 ## print 'time:',t2
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71
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72 # test_train_daa(theano.compile.Mode('c&py', 'merge'))
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73 test_train_daa(theano.compile.Mode('c|py', 'merge'))
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500
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74 test_train_daa2(theano.compile.Mode('c|py', 'merge'))
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