Mercurial > pylearn

comparison _nnet_ops.py @ 117:3ef569b92fba

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
ported nnet_ops to new theano
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 07 May 2008 15:28:17 -0400
parents 76e5c0f37165
children
comparison
equal deleted inserted replaced
116:9330d941fa1f 117:3ef569b92fba
7 7
8 class T_sigmoid(unittest.TestCase): 8 class T_sigmoid(unittest.TestCase):
9 def setUp(self): 9 def setUp(self):
10 numpy.random.seed(9999) 10 numpy.random.seed(9999)
11 def test_elemwise(self): 11 def test_elemwise(self):
12 TT.verify_grad(self, Sigmoid, [numpy.random.rand(3,4)]) 12 TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)])
13 13
14 class T_softplus(unittest.TestCase): 14 class T_softplus(unittest.TestCase):
15 def setUp(self): 15 def setUp(self):
16 numpy.random.seed(9999) 16 numpy.random.seed(9999)
17 def test_elemwise(self): 17 def test_elemwise(self):
18 TT.verify_grad(self, Softplus, [numpy.random.rand(3,4)]) 18 TT.verify_grad(self, softplus, [numpy.random.rand(3,4)])
19 19
20 class T_CrossentropySoftmax1Hot(unittest.TestCase): 20 class T_CrossentropySoftmax1Hot(unittest.TestCase):
21 def setUp(self): 21 def setUp(self):
22 numpy.random.seed(9999) 22 numpy.random.seed(9999)
23 def test0(self): 23 def test0(self):
24 y_idx = [0,1,3] 24 y_idx = [0,1,3]
25 def output1(a,b): 25 class Dummy(object):
26 return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1] 26 def make_node(self, a,b):
27 TT.verify_grad(self, output1, [numpy.random.rand(3,4), 27 return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1]
28 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
28 numpy.random.rand(4)]) 29 numpy.random.rand(4)])
29 30
30 def test1(self): 31 def test1(self):
31 y_idx = [0,1,3] 32 y_idx = [0,1,3]
32 def output1(a): 33 class Dummy(object):
33 return crossentropy_softmax_1hot(a, y_idx)[0:1] 34 def make_node(self, a):
34 TT.verify_grad(self, output1, [numpy.random.rand(3,4)]) 35 return crossentropy_softmax_1hot(a, y_idx)[0:1]
36 TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
35 37
36 38
37 39
38 if __name__ == '__main__': 40 if __name__ == '__main__':
39 unittest.main() 41 unittest.main()