Use the new built-in ftensor5 in the DNN tests.

theano/gpuarray/tests/test_dnn.py

@@ -479,10 +479,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
 
     @parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
     def test_conv3d_none(self, border_mode, conv_mode):
-        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
-        self._test_conv(ftensor5('img'),
-                        ftensor5('kerns'),
-                        ftensor5('out'),
+        self._test_conv(T.ftensor5('img'),
+                        T.ftensor5('kerns'),
+                        T.ftensor5('out'),
                         numpy.random.rand(10, 2, 6, 4, 11),
                         numpy.random.rand(8, 2, 4, 3, 1),
                         border_mode,

theano/sandbox/cuda/tests/test_dnn.py

@@ -938,10 +938,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
     def test_conv3d(self):
         if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
             raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
-        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
-        img = ftensor5('img')
-        kerns = ftensor5('kerns')
-        out = ftensor5('out')
+        img = T.ftensor5('img')
+        kerns = T.ftensor5('kerns')
+        out = T.ftensor5('out')
         img_val = numpy.asarray(
             numpy.random.rand(10, 2, 6, 4, 11),
             dtype='float32'
@@ -1026,10 +1025,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
     def test_conv3d_gradw(self):
         if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
             raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
-        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
-        img = ftensor5('img')
-        kerns = ftensor5('kerns')
-        out = ftensor5('out')
+        img = T.ftensor5('img')
+        kerns = T.ftensor5('kerns')
+        out = T.ftensor5('out')
         img_val = numpy.asarray(
             numpy.random.rand(9, 2, 4, 8, 13),
             dtype='float32'
@@ -1116,10 +1114,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
     def test_conv3d_gradi(self):
         if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
             raise SkipTest('"cuDNN 3D convolution requires cuDNN v2')
-        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
-        img = ftensor5('img')
-        kerns = ftensor5('kerns')
-        out = ftensor5('out')
+        img = T.ftensor5('img')
+        kerns = T.ftensor5('kerns')
+        out = T.ftensor5('out')
         img_val = numpy.asarray(
             numpy.random.rand(8, 4, 6, 7, 11),
             dtype='float32'