Fix last few comments about the tests.

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -195,7 +195,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
         assert (numpy.asarray(gpuval) == numpy.asarray(gpuval2)).all()
     gpuval = numpy.asarray(gpuval)
     assert gpuval.shape == cpuval.shape, ("shape mismatch", gpuval.shape, cpuval.shape)
-    assert_allclose(cpuval, gpuval, rtol=rtol)
+    assert_allclose(cpuval, gpuval, rtol=rtol, atol=atol)
     assert numpy.all(numpy.isfinite(gpuval)), gpuval
 
     if (t2 is not None):
@@ -852,10 +852,9 @@ def dnn_op(mode, subsample):
     return f
 
 
-def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy, op):
+def conv_grad(mode, bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsample, op):
     ishape = (bs, ch, rImg1, rImg2)
     kshape = (nf, ch, rFlt1, rFlt2)
-    subsample = (subsx, subsy)
 
     npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
     npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
@@ -913,16 +912,11 @@ def test_conv_grads():
                         for rImg2 in [2, 8]:
                             for rFlt1 in [1, 2]:
                                 for rFlt2 in [1, 2]:
-                                    for op in [gemm_op, dnn_op]:
-                                        yield (conv_grad, mode, bs, ch, nf,
-                                               rImg1, rImg2, rFlt1, rFlt2,
-                                               1, 1, op)
-                                        yield (conv_grad, mode, bs, ch, nf,
-                                               rImg1, rImg2, rFlt1, rFlt2,
-                                               1, 2, op)
-                                        yield (conv_grad, mode, bs, ch, nf,
-                                               rImg1, rImg2, rFlt1, rFlt2,
-                                               2, 2, op)
+                                    for subsample in (1, 1), (1, 2), (2, 2):
+                                        for op in [gemm_op, dnn_op]:
+                                            yield (conv_grad, mode, bs, ch, nf,
+                                                   rImg1, rImg2, rFlt1, rFlt2,
+                                                   subsample, op)
 
 
 def benchmark():