Mercurial > pylearn
view _test_nnet_ops.py @ 339:aa8aff6abbf7
n_minibatches in ArrayDataSet automatically computed
author | Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca> |
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date | Mon, 16 Jun 2008 17:26:51 -0400 |
parents | 2ee53bae9ee0 |
children | 43d9aa93934e |
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import unittest import theano._test_tensor as TT import numpy from nnet_ops import * class T_sigmoid(unittest.TestCase): def setUp(self): numpy.random.seed(9999) def test_elemwise(self): TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)]) class T_softplus(unittest.TestCase): def setUp(self): numpy.random.seed(9999) def test_elemwise(self): TT.verify_grad(self, softplus, [numpy.random.rand(3,4)]) class T_CrossentropySoftmax1Hot(unittest.TestCase): def setUp(self): numpy.random.seed(9999) def test0(self): y_idx = [0,1,3] class Dummy(object): def make_node(self, a,b): return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1] TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4), numpy.random.rand(4)]) def test1(self): y_idx = [0,1,3] class Dummy(object): def make_node(self, a): return crossentropy_softmax_1hot(a, y_idx)[0:1] TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)]) if __name__ == '__main__': unittest.main()