Mercurial > pylearn
annotate algorithms/tests/test_daa.py @ 527:3eb59514b534
bugfix. fix an import bug that I don't understand the cause.
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Mon, 17 Nov 2008 13:16:33 -0500 |
| parents | 4fb6f7320518 |
| children | de974b4fc4ea |
| rev | line source |
|---|---|
| 493 | 1 #!/usr/bin/python |
| 2 | |
| 3 from pylearn import algorithms as models | |
| 4 import theano | |
| 5 import numpy | |
| 6 import time | |
| 7 | |
| 500 | 8 import pylearn.algorithms.logistic_regression |
| 493 | 9 |
| 10 def test_train_daa(mode = theano.Mode('c|py', 'fast_run')): | |
| 11 | |
| 12 ndaa = 3 | |
| 13 daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(models.BinRegressor, 'output')], | |
| 14 regularize = False) | |
| 15 | |
| 16 model = daa.make([4, 20, 20, 20, 1], | |
| 17 lr = 0.01, | |
| 18 mode = mode, | |
| 19 seed = 10) | |
| 20 | |
| 21 model.layers[0].noise_level = 0.3 | |
| 22 model.layers[1].noise_level = 0.3 | |
| 23 model.layers[2].noise_level = 0.3 | |
| 24 | |
| 25 # Update the first hidden layer | |
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N-class logistic regression top-layer works
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26 for l in range(3): |
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N-class logistic regression top-layer works
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parents:
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27 for i in range(10): |
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4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
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28 model.local_update[l]([[0, 1, 0, 1]]) |
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4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
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changeset
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29 model.local_update[l]([[1, 0, 1, 0]]) |
| 493 | 30 |
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parents:
500
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31 for i in range(1): |
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4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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32 model.update([[0, 1, 0, 1]], [[1]]) |
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4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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33 model.update([[1, 0, 1, 0]], [[0]]) |
| 493 | 34 print model.classify([[0, 1, 0, 1]]) |
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35 print model.classify([[1, 0, 1, 0]]) |
| 493 | 36 |
| 37 | |
| 500 | 38 def test_train_daa2(mode = theano.Mode('c|py', 'fast_run')): |
| 39 | |
| 40 ndaa = 3 | |
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N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
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changeset
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41 daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'pred')], |
| 500 | 42 regularize = False) |
| 43 | |
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501
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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44 model = daa.make([4, 20, 20, 20, 10], |
| 500 | 45 lr = 0.01, |
| 46 mode = mode, | |
| 47 seed = 10) | |
| 48 | |
| 49 model.layers[0].noise_level = 0.3 | |
| 50 model.layers[1].noise_level = 0.3 | |
| 51 model.layers[2].noise_level = 0.3 | |
| 52 | |
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501
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
53 for l in range(3): |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
54 for i in range(10): |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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55 model.local_update[l]([[0, 1, 0, 1]]) |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
56 model.local_update[l]([[1, 0, 1, 0]]) |
| 500 | 57 |
|
501
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
58 for i in range(1): |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
59 model.update([[0, 1, 0, 1]], [1]) |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
60 model.update([[1, 0, 1, 0]], [0]) |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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61 print model.apply([[0, 1, 0, 1]]) |
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4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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62 print model.apply([[1, 0, 1, 0]]) |
| 500 | 63 |
| 493 | 64 |
| 65 | |
| 66 | |
| 67 if __name__ == '__main__': | |
| 68 # print 'optimized:' | |
| 69 # t1 = test_train_daa(theano.Mode('py', 'fast_compile')) | |
| 70 # t1 = test_train_daa(theano.Mode('c|py', 'fast_run')) | |
| 71 # print 'time:',t1 | |
| 72 # print | |
| 73 | |
| 74 # print 'not optimized:' | |
| 75 # t2 = test_train_daa(theano.Mode('c|py', 'fast_compile')) | |
| 76 ## print 'time:',t2 | |
| 77 | |
| 78 # test_train_daa(theano.compile.Mode('c&py', 'merge')) | |
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501
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
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79 # test_train_daa(theano.compile.Mode('c|py', 'merge')) |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
80 test_train_daa(theano.compile.Mode('py', 'merge')) |
|
4fb6f7320518
N-class logistic regression top-layer works
Joseph Turian <turian@gmail.com>
parents:
500
diff
changeset
|
81 |
| 500 | 82 test_train_daa2(theano.compile.Mode('c|py', 'merge')) |