small PEP-8 changes
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@ -2,11 +2,12 @@ import unittest
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import numpy as np
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from theano import tensor as T
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class TestConstraints(unittest.TestCase):
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def setUp(self):
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self.some_values = [0.1,0.5,3,8,1e-7]
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self.example_array = np.random.random((100,100))*100. - 50.
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self.example_array[0,0] = 0. # 0 could possibly cause trouble
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self.some_values = [0.1, 0.5, 3, 8, 1e-7]
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self.example_array = np.random.random((100, 100)) * 100. - 50.
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self.example_array[0, 0] = 0. # 0 could possibly cause trouble
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def test_maxnorm(self):
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from keras.constraints import maxnorm
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@ -14,27 +15,28 @@ class TestConstraints(unittest.TestCase):
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for m in self.some_values:
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norm_instance = maxnorm(m)
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normed = norm_instance(self.example_array)
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assert(np.all(normed.eval() < m))
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assert (np.all(normed.eval() < m))
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def test_nonneg(self):
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from keras.constraints import nonneg
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normed = nonneg(self.example_array)
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assert(np.all(np.min(normed.eval(),axis=1) == 0.))
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assert (np.all(np.min(normed.eval(), axis=1) == 0.))
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def test_identity(self):
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from keras.constraints import identity
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normed = identity(self.example_array)
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assert(np.all(normed == self.example_array))
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assert (np.all(normed == self.example_array))
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def test_unitnorm(self):
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from keras.constraints import unitnorm
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normed = unitnorm(self.example_array)
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self.assertAlmostEqual(
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np.max(np.abs(np.sqrt(np.sum(normed.eval()**2,axis=1))-1.))
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,0.)
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np.max(np.abs(np.sqrt(np.sum(normed.eval() ** 2, axis=1)) - 1.))
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, 0.)
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if __name__ == '__main__':
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unittest.main()
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