* fix for 3D input tensors in ConvOp's grad function

theano/sandbox/conv.py

@@ -119,8 +119,8 @@ class ConvOp(Op):
         ####### Determine gradient on kernels ########
         mode = self.out_mode
         if inputs.ndim == 3:
-            # TODO 
-            img = tensor.shape_padleft(inputs,1)
+            inputs = tensor.shape_padleft(inputs,1)
+        print 'self.imshp = ', self.imshp
 
         img = tensor.DimShuffle(inputs.broadcastable, (1,0,2,3))(inputs)
         imshp = N.hstack((self.bsize, self.imshp[1:]))
@@ -170,7 +170,7 @@ class ConvOp(Op):
 using namespace std;
 """
     def c_code(self, node, name, (img2d, filtersflipped), (z, ), sub):
-        if node.inputs[0].type != node.inputs[1].type:
+        if node.inputs[0].type.dtype != node.inputs[1].type.dtype:
             raise NotImplementedError()
         code="""
 int mode=-1,typenum;

theano/sandbox/test_conv.py

@@ -309,8 +309,9 @@ class TestConvOp(unittest.TestCase):
         kerns = T.dmatrix('kerns')
      
         for mode in 'valid', 'full':
-            for imshp in (2,5,5),(2,10,10): # (12,10), (3,12,11):
+            for imshp in  (5,5),(2,5,5),(2,10,10): # (12,10), (3,12,11):
                 visdim = 1 if len(imshp)!=3 else imshp[0]
+                print 'visdim = ', visdim
                 for kshp in (3,3),:# (6,7):
                     imgvals = N.random.random(N.hstack((bsize,imshp)))
                     print 'imgvals.shape = ', imgvals.shape