Mercurial > pylearn
annotate _test_nnet_ops.py @ 492:6dfdcee64e9b
merge
author | Olivier Breuleux <breuleuo@iro.umontreal.ca> |
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date | Tue, 28 Oct 2008 11:39:47 -0400 |
parents | 9cfc2fc0f4d1 |
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rev | line source |
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24 | 1 |
2 import unittest | |
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3 import theano |
24 | 4 import theano._test_tensor as TT |
5 import numpy | |
6 | |
7 from nnet_ops import * | |
8 | |
9 class T_sigmoid(unittest.TestCase): | |
10 def setUp(self): | |
11 numpy.random.seed(9999) | |
12 def test_elemwise(self): | |
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13 TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)]) |
24 | 14 |
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15 class T_softplus(unittest.TestCase): |
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16 def setUp(self): |
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17 numpy.random.seed(9999) |
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18 def test_elemwise(self): |
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19 TT.verify_grad(self, softplus, [numpy.random.rand(3,4)]) |
24 | 20 |
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21 class T_Softmax(unittest.TestCase): |
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22 def setUp(self): |
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23 numpy.random.seed(9999) |
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24 def test0(self): |
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25 class Dummy(object): |
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26 def make_node(self, a): |
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27 return [softmax(a)[:,0]] |
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28 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) |
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29 def test1(self): |
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30 class Dummy(object): |
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31 def make_node(self, a): |
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32 return [softmax(a)[:,1]] |
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33 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) |
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34 def test2(self): |
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35 class Dummy(object): |
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36 def make_node(self, a): |
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37 return [softmax(a)[:,2]] |
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38 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) |
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39 def test3(self): |
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40 class Dummy(object): |
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41 def make_node(self, a): |
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42 return [softmax(a)[:,3]] |
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43 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) |
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44 |
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45 |
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46 class T_SoftmaxWithBias(unittest.TestCase): |
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47 def setUp(self): |
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48 numpy.random.seed(9999) |
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49 def test0(self): |
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50 class Dummy(object): |
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51 def make_node(self, a, b): |
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52 return [softmax_with_bias(a, b)[:,0]] |
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53 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), |
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54 numpy.random.rand(4)]) |
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55 def test1(self): |
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56 class Dummy(object): |
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57 def make_node(self, a, b): |
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58 return [softmax_with_bias(a, b)[:,1]] |
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59 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), |
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60 numpy.random.rand(4)]) |
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61 def test2(self): |
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62 class Dummy(object): |
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63 def make_node(self, a, b): |
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64 return [softmax_with_bias(a, b)[:,2]] |
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65 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), |
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66 numpy.random.rand(4)]) |
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67 def test3(self): |
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68 class Dummy(object): |
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69 def make_node(self, a, b): |
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70 return [softmax_with_bias(a, b)[:,3]] |
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71 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), |
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72 numpy.random.rand(4)]) |
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73 |
24 | 74 class T_CrossentropySoftmax1Hot(unittest.TestCase): |
75 def setUp(self): | |
76 numpy.random.seed(9999) | |
77 def test0(self): | |
78 y_idx = [0,1,3] | |
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79 class Dummy(object): |
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80 def make_node(self, a,b): |
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81 return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1] |
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82 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), |
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83 numpy.random.rand(4)]) |
24 | 84 |
70
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85 def test1(self): |
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86 y_idx = [0,1,3] |
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87 class Dummy(object): |
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88 def make_node(self, a): |
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89 return crossentropy_softmax_1hot(a, y_idx)[0:1] |
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90 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) |
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91 |
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92 class T_prepend(unittest.TestCase): |
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93 def test0(self): |
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94 """basic functionality""" |
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95 x=tensor.matrix('x') |
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96 y=Prepend_scalar_constant_to_each_row(4.)(x) |
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97 f=theano.function([x],[y]) |
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98 m=numpy.random.rand(3,5) |
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99 my = f(m) |
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100 self.failUnless(my.shape == (3, 6), my.shape) |
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101 self.failUnless(numpy.all( my[:,0] == 4.0)) |
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102 |
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103 |
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104 class T_prepend(unittest.TestCase): |
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105 def test0(self): |
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106 """basic functionality""" |
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107 x=tensor.matrix('x') |
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108 y=Prepend_scalar_to_each_row()(5.,x) |
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109 f=theano.function([x],[y]) |
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110 m=numpy.ones((3,5),dtype="float32") |
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111 my = f(m) |
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112 self.failUnless(str(my.dtype) == 'float64') |
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113 self.failUnless(my.shape == (3, 6)) |
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114 self.failUnless(numpy.all(my[:,0] == 5.0)) |
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115 |
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116 class T_solve(unittest.TestCase): |
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117 def setUp(self): |
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118 self.rng = numpy.random.RandomState(666) |
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119 |
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120 def test0(self): |
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121 A=self.rng.randn(5,5) |
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122 b=numpy.array(range(5),dtype=float) |
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123 x=numpy.linalg.solve(A,b) |
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124 Ax = numpy.dot(A,x) |
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125 are = theano.gradient.numeric_grad.abs_rel_err(Ax, b) |
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126 self.failUnless(numpy.all(are < 1.0e-5), (are, Ax, b)) |
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127 #print A,b |
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128 #print numpy.dot(A,x) |
24 | 129 |
130 | |
131 if __name__ == '__main__': | |
132 unittest.main() |