Fix tests due to change to gradient check dtype.

theano/sparse/tests/test_basic.py

@@ -2322,7 +2322,7 @@ class CastTester(utt.InferShapeTester):
 
                     eps = None
                     if o_dtype == 'float32':
-                        eps = 7e-4
+                        eps = 1e-2
 
                     verify_grad_sparse(Cast(o_dtype), data, eps=eps)
 
@@ -2386,7 +2386,7 @@ class _HVStackTester(utt.InferShapeTester):
                         self.op_class(format=out_f, dtype=dtype),
                         self.mat[format],
                         structured=False,
-                        eps=7e-4)
+                        eps=1e-1)
 
 
 def _hv_switch(op, expected_function):

theano/tensor/nnet/tests/test_conv3d2d.py

@@ -117,4 +117,4 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
 
     signals = numpy.random.rand(Ns, Ts, C, Hs, Ws).astype('float32')
     filters = numpy.random.rand(Nf, Tf, C, Hf, Wf).astype('float32')
-    utt.verify_grad(conv3d, [signals, filters])
+    utt.verify_grad(conv3d, [signals, filters], eps=1e-1)

theano/tensor/tests/test_basic.py

@@ -3277,18 +3277,18 @@ class T_Join_and_Split(unittest.TestCase):
         self.assertTrue((out == want).all())
 
     def test_join_matrix1(self):
-        av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
-        bv = numpy.array([[7], [8]], dtype='float32')
+        av = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype='float32')
+        bv = numpy.array([[.7], [.8]], dtype='float32')
         a = self.shared(av)
         b = as_tensor_variable(bv)
         s = join(1, a, b)
-        want = numpy.array([[1, 2, 3, 7], [4, 5, 6, 8]], dtype='float32')
+        want = numpy.array([[.1, .2, .3, .7], [.4, .5, .6, .8]],
+                           dtype='float32')
         out = self.eval_outputs_and_check_join([s])
         self.assertTrue((out == want).all())
 
-#        assert tensor.grad(join(1,a,b), a
         utt.verify_grad(lambda a, b: join(1, a, b), [av, bv],
-                        eps=1.0e-4, rel_tol=1.0e-3, mode=self.mode)
+                        mode=self.mode)
 
     def test_join_matrix_dtypes(self):
         if "float32" in self.shared.__name__:
@@ -3308,7 +3308,7 @@ class T_Join_and_Split(unittest.TestCase):
         grad(s.sum(), b)
         grad(s.sum(), a)
         utt.verify_grad(lambda b: join(1, a, b), [bv],
-                        eps=1.0e-4, rel_tol=1.0e-3, mode=self.mode)
+                        eps=1.0e-2, mode=self.mode)
 
     def test_join_matrix_ints(self):
         if "float32" in self.shared.__name__:
@@ -3339,20 +3339,21 @@ class T_Join_and_Split(unittest.TestCase):
         self.assertTrue((out == want).all())
 
     def test_join_matrix1_using_horizontal_stack(self):
-        av = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='float32')
-        bv = numpy.array([[7], [8]], dtype='float32')
-        cv = numpy.array([[3, 2, 1], [6, 5, 4]], dtype='float32')
+        av = numpy.array([[.1, .2, .3], [.4, .5, .6]], dtype='float32')
+        bv = numpy.array([[.7], [.8]], dtype='float32')
+        cv = numpy.array([[.3, .2, .1], [.6, .5, .4]], dtype='float32')
         a = self.shared(av)
         b = as_tensor_variable(bv)
         c = as_tensor_variable(cv)
         s = horizontal_stack(a, b, c)
-        want = numpy.array([[1, 2, 3, 7, 3, 2, 1], [4, 5, 6, 8, 6, 5, 4]],
+        want = numpy.array([[.1, .2, .3, .7, .3, .2, .1],
+                            [.4, .5, .6, .8, .6, .5, .4]],
                            dtype='float32')
         out = self.eval_outputs_and_check_join([s])
         self.assertTrue((out == want).all())
 
         utt.verify_grad(lambda a, b: join(1, a, b), [av, bv],
-                        eps=1.0e-4, rel_tol=1.0e-3, mode=self.mode)
+                        mode=self.mode)
 
     def test_join_matrixV(self):
         """variable join axis"""

theano/tensor/tests/test_elemwise.py

@@ -571,7 +571,7 @@ class test_Prod(unittest.TestCase):
 
         # including zeros, as the case with zeros is important
         # (and special cases: 1 zero in the row, more than 1 zero in the row)
-        x_val = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+        x_val = numpy.asarray([[.1, .2, .3], [.4, .5, .6], [.7, .8, .9]],
                               dtype='float32')
         # now with verify_grad
         unittest_tools.verify_grad(Prod(axis=1), [x_val], mode=self.mode)