Speed up a test

theano/tensor/tests/test_slinalg.py

@@ -296,30 +296,30 @@ class TestKron(utt.InferShapeTester):
             raise SkipTest('kron tests need the scipy package to be installed')
 
         for shp0 in [(2,), (2, 3), (2, 3, 4), (2, 3, 4, 5)]:
+            x = tensor.tensor(dtype='floatX',
+                              broadcastable=(False,) * len(shp0))
+            a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
             for shp1 in [(6,), (6, 7), (6, 7, 8), (6, 7, 8, 9)]:
                 if len(shp0) + len(shp1) == 2:
                     continue
-                x = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp0))
                 y = tensor.tensor(dtype='floatX',
                                   broadcastable=(False,) * len(shp1))
                 f = function([x, y], kron(x, y))
-                a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
                 b = self.rng.rand(*shp1).astype(config.floatX)
                 out = f(a, b)
                 assert numpy.allclose(out, scipy.linalg.kron(a, b))
 
     def test_numpy_2d(self):
         for shp0 in [(2, 3)]:
+            x = tensor.tensor(dtype='floatX',
+                              broadcastable=(False,) * len(shp0))
+            a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
             for shp1 in [(6, 7)]:
                 if len(shp0) + len(shp1) == 2:
                     continue
-                x = tensor.tensor(dtype='floatX',
-                                  broadcastable=(False,) * len(shp0))
                 y = tensor.tensor(dtype='floatX',
                                   broadcastable=(False,) * len(shp1))
                 f = function([x, y], kron(x, y))
-                a = numpy.asarray(self.rng.rand(*shp0)).astype(config.floatX)
                 b = self.rng.rand(*shp1).astype(config.floatX)
                 out = f(a, b)
                 assert numpy.allclose(out, numpy.kron(a, b))