Mercurial > pylearn
annotate _test_nnet_ops.py @ 433:200a5b0e24ea
Example showing parameter updates.
author | Pascal Lamblin <lamblinp@iro.umontreal.ca> |
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date | Thu, 31 Jul 2008 17:25:35 -0400 |
parents | 43d9aa93934e |
children | 9cfc2fc0f4d1 |
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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 |
21 class T_CrossentropySoftmax1Hot(unittest.TestCase): | |
22 def setUp(self): | |
23 numpy.random.seed(9999) | |
24 def test0(self): | |
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), |
30
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30 numpy.random.rand(4)]) |
24 | 31 |
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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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49 |
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50 |
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parents:
286
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51 class T_prepend(unittest.TestCase): |
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286
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52 def test0(self): |
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parents:
286
diff
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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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62 |
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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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66 |
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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) |
24 | 76 |
77 | |
78 if __name__ == '__main__': | |
79 unittest.main() |