Mercurial > pylearn

annotate _test_nnet_ops.py @ 672:27b1344a57b1

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Added preprocessing back in
author Joseph Turian <turian@gmail.com>
date Thu, 20 Nov 2008 06:38:06 -0500
parents 9cfc2fc0f4d1
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1
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2 import unittest
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3 import theano
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4 import theano._test_tensor as TT
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5 import numpy
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6
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7 from nnet_ops import *
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8
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9 class T_sigmoid(unittest.TestCase):
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10 def setUp(self):
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11 numpy.random.seed(9999)
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12 def test_elemwise(self):
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13 TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)])
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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)])
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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
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74 class T_CrossentropySoftmax1Hot(unittest.TestCase):
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75 def setUp(self):
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76 numpy.random.seed(9999)
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77 def test0(self):
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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)])
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84
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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)
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131 if __name__ == '__main__':
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132 unittest.main()