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comparison algorithms/_test_logistic_regression.py @ 470:bd937e845bbb

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new stuff: algorithms/logistic_regression, datasets/MNIST
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 22 Oct 2008 15:56:53 -0400
parents
children c7ce66b4e8f4
comparison
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469:4335309f4924 470:bd937e845bbb
1 from logistic_regression import *
2 import sys, time
3
4 if __name__ == '__main__':
5 pprint.assign(nnet_ops.crossentropy_softmax_1hot_with_bias_dx, printing.FunctionPrinter('xsoftmaxdx'))
6 pprint.assign(nnet_ops.crossentropy_softmax_argmax_1hot_with_bias, printing.FunctionPrinter('nll', 'softmax', 'argmax'))
7 if 1:
8 lrc = Module_Nclass()
9
10 print '================'
11 print lrc.update.pretty()
12 print '================'
13 print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
14 print '================'
15 # print lrc.update.pretty(mode = compile.FAST_RUN.excluding('inplace'))
16 # print '================'
17
18 # sys.exit(0)
19
20 lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'fast_run'))
21 #lr = lrc.make(10, 2, mode=compile.FAST_RUN.excluding('fast_run'))
22 #lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
23
24 data_x = N.random.randn(5, 10)
25 data_y = (N.random.randn(5) > 0)
26
27 t = time.time()
28 for i in xrange(10000):
29 lr.lr = 0.02
30 xe = lr.update(data_x, data_y)
31 #if i % 100 == 0:
32 # print i, xe
33
34 print 'training time:', time.time() - t
35 print 'final error', xe
36
37 #print
38 #print 'TRAINED MODEL:'
39 #print lr
40
41 if 0:
42 lrc = Module()
43
44 lr = lrc.make(10, mode=theano.Mode('c|py', 'merge')) #'FAST_RUN')
45
46 data_x = N.random.randn(5, 10)
47 data_y = (N.random.randn(5, 1) > 0)
48
49 for i in xrange(10000):
50 xe = lr.update(data_x, data_y)
51 if i % 100 == 0:
52 print i, xe
53
54 print
55 print 'TRAINED MODEL:'
56 print lr
57
58
59
60