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annotate _test_nnet_ops.py @ 421:e01f17be270a

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Kernel regression learning algorithm
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 19 Jul 2008 10:11:22 -0400
parents 43d9aa93934e
children 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_CrossentropySoftmax1Hot(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 y_idx = [0,1,3]
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26 class Dummy(object):
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27 def make_node(self, a,b):
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28 return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1]
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29 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
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30 numpy.random.rand(4)])
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32 def test1(self):
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33 y_idx = [0,1,3]
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34 class Dummy(object):
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35 def make_node(self, a):
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36 return crossentropy_softmax_1hot(a, y_idx)[0:1]
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37 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
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38
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39 class T_prepend(unittest.TestCase):
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40 def test0(self):
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41 """basic functionality"""
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42 x=tensor.matrix('x')
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43 y=Prepend_scalar_constant_to_each_row(4.)(x)
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44 f=theano.function([x],[y])
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45 m=numpy.random.rand(3,5)
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46 my = f(m)
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47 self.failUnless(my.shape == (3, 6), my.shape)
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48 self.failUnless(numpy.all( my[:,0] == 4.0))
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51 class T_prepend(unittest.TestCase):
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52 def test0(self):
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53 """basic functionality"""
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54 x=tensor.matrix('x')
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55 y=Prepend_scalar_to_each_row()(5.,x)
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56 f=theano.function([x],[y])
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57 m=numpy.ones((3,5),dtype="float32")
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58 my = f(m)
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59 self.failUnless(str(my.dtype) == 'float64')
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60 self.failUnless(my.shape == (3, 6))
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61 self.failUnless(numpy.all(my[:,0] == 5.0))
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63 class T_solve(unittest.TestCase):
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64 def setUp(self):
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65 self.rng = numpy.random.RandomState(666)
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67 def test0(self):
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68 A=self.rng.randn(5,5)
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69 b=numpy.array(range(5),dtype=float)
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70 x=numpy.linalg.solve(A,b)
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71 Ax = numpy.dot(A,x)
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72 are = theano.gradient.numeric_grad.abs_rel_err(Ax, b)
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73 self.failUnless(numpy.all(are < 1.0e-5), (are, Ax, b))
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74 #print A,b
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75 #print numpy.dot(A,x)
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78 if __name__ == '__main__':
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79 unittest.main()