New pull request -- much cleaner

tests/auto/keras/test_constraints.py

@@ -0,0 +1,69 @@
+import unittest
+import numpy as np
+from numpy.testing import assert_allclose
+from theano import tensor as T
+
+
+class TestConstraints(unittest.TestCase):
+    def setUp(self):
+        self.some_values = [0.1, 0.5, 3, 8, 1e-7]
+        np.random.seed(3537)
+        self.example_array = np.random.random((100, 100)) * 100. - 50.
+        self.example_array[0, 0] = 0.  # 0 could possibly cause trouble
+
+    def test_maxnorm(self):
+        from keras.constraints import maxnorm
+
+        for m in self.some_values:
+            norm_instance = maxnorm(m)
+            normed = norm_instance(self.example_array)
+            assert (np.all(normed.eval() < m))
+
+        # a more explicit example
+        norm_instance = maxnorm(2.0)
+        x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T
+        x_normed_target = np.array([[0, 0, 0], [1.0, 0, 0], [2.0, 0, 0], [2./np.sqrt(3), 2./np.sqrt(3), 2./np.sqrt(3)]]).T
+        x_normed_actual = norm_instance(x).eval()
+        assert_allclose(x_normed_actual, x_normed_target)
+
+    def test_nonneg(self):
+        from keras.constraints import nonneg
+
+        nonneg_instance = nonneg()
+
+        normed = nonneg_instance(self.example_array)
+        assert (np.all(np.min(normed.eval(), axis=1) == 0.))
+
+    def test_identity(self):
+        from keras.constraints import identity
+
+        identity_instance = identity()
+
+        normed = identity_instance(self.example_array)
+        assert (np.all(normed == self.example_array))
+
+    def test_identity_oddballs(self):
+        """
+        test the identity constraint on some more exotic input.
+        this does not need to pass for the desired real life behaviour,
+        but it should in the current implementation.
+        """
+        from keras.constraints import identity
+        identity_instance = identity()
+
+        oddball_examples = ["Hello", [1], -1, None]
+        assert(oddball_examples == identity_instance(oddball_examples))
+
+    def test_unitnorm(self):
+        from keras.constraints import unitnorm
+        unitnorm_instance = unitnorm()
+
+        normalized = unitnorm_instance(self.example_array)
+
+        norm_of_normalized = np.sqrt(np.sum(normalized.eval()**2, axis=1))
+        difference = norm_of_normalized - 1. #in the unit norm constraint, it should be equal to 1.
+        largest_difference = np.max(np.abs(difference))
+        self.assertAlmostEqual(largest_difference, 0.)
+
+if __name__ == '__main__':
+    unittest.main()