named a bunch of variables to help with debugging

theano/tensor/nnet/Conv3D.py

@@ -98,26 +98,26 @@ class Conv3D(theano.Op):
         if 'name' in dir(dCdH) and dCdH.name is not None:
             dCdH_name = dCdH.name
         else:
-            dCdH_name = 'anon'
+            dCdH_name = 'anon_dCdH'
 
         if 'name' in dir(V) and V.name is not None:
             V_name = V.name
         else:
-            V_name = 'anon'
+            V_name = 'anon_V'
 
         if 'name' in dir(W) and W.name is not None:
             W_name = W.name
         else:
-            W_name = 'anon'
+            W_name = 'anon_W'
 
         if 'name' in dir(b) and b.name is not None:
             b_name = b.name
         else:
-            b_name = 'anon'
+            b_name = 'anon_b'
 
-        dCdV.name = 'Conv3D_dCdV.dCdH='+dCdH_name+',V='+V_name
-        dCdW.name = 'Conv3D_dCdW.dCdH='+dCdH_name+',V='+V_name+',W='+W_name
-        dCdb.name = 'Conv3D_dCdb.dCdH='+dCdH_name+',V='+V_name+',W='+W_name+',b='+b_name
+        dCdV.name = 'Conv3D_dCdV(dCdH='+dCdH_name+',V='+V_name+')'
+        dCdW.name = 'Conv3D_dCdW(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+')'
+        dCdb.name = 'Conv3D_dCdb(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+',b='+b_name+')'
 
 
 

theano/tensor/nnet/ConvTransp3D.py

@@ -56,22 +56,22 @@ class ConvTransp3D(theano.Op):
         if 'name' in dir(dCdR) and dCdR.name is not None:
             dCdR_name = dCdR.name
         else:
-            dCdR_name = 'anon'
+            dCdR_name = 'anon_dCdR'
 
         if 'name' in dir(H) and H.name is not None:
             H_name = H.name
         else:
-            H_name = 'anon'
+            H_name = 'anon_H'
 
         if 'name' in dir(W) and W.name is not None:
             W_name = W.name
         else:
-            W_name = 'anon'
+            W_name = 'anon_W'
 
         if 'name' in dir(b) and b.name is not None:
             b_name = b.name
         else:
-            b_name = 'anon'
+            b_name = 'anon_b'
 
 
         dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name

theano/tensor/nnet/conv.py

@@ -780,9 +780,19 @@ class ConvOp(OpenMPOp):
 
             # build a "node", that should be equivalent to the one given by
             # self.make_node, but using conv3D instead of self.
+            shuffled_inputs = inputs.dimshuffle(0, 2, 3, 'x', 1)
+            if inputs.name is not None:
+                shuffled_inputs.name = 'shuffle_for_conv3D(%s)' % inputs.name
+            flipped_kerns = kerns[:, :, ::-1, ::-1]
+            if kerns.name is not None:
+                flipped_kerns.name = 'flipped(%s)' % kerns.name
+            shuffled_kerns = flipped_kerns.dimshuffle(0, 2, 3, 'x', 1)
+            if flipped_kerns.name is not None:
+                shuffled_kerns.name = 'shuffled_for_conv3D(%s)' % flipped_kerns.name
+
             tmp_node = theano.tensor.nnet.conv3D(
-                V=inputs.dimshuffle(0, 2, 3, 'x', 1),
-                W=kerns[:, :, ::-1, ::-1].dimshuffle(0, 2, 3, 'x', 1),
+                V = shuffled_inputs,
+                W= shuffled_kerns,
                 b=theano.tensor.alloc(numpy.asarray(0, dtype=kerns.dtype),
                                       kerns.shape[0]),
                 d=(self.dx, self.dy, 1))

theano/tensor/nnet/tests/test_conv.py

@@ -18,7 +18,9 @@ class TestConv2D(utt.InferShapeTester):
     def setUp(self):
         super (TestConv2D, self).setUp()
         self.input = T.dtensor4('input')
+        self.input.name = 'default_V'
         self.filters = T.dtensor4('filters')
+        self.filters.name = 'default_filters'
 
     def validate(self, image_shape, filter_shape,
                  border_mode='valid', subsample=(1, 1),
@@ -34,7 +36,7 @@ class TestConv2D(utt.InferShapeTester):
             N_filter_shape = [T.get_constant_value(T.
                 as_tensor_variable(x)) for x in filter_shape]
 
-        if not input:
+        if input is None:
             input = self.input
         if not filters:
             filters = self.filters
@@ -44,11 +46,16 @@ class TestConv2D(utt.InferShapeTester):
         # we create a symbolic function so that verify_grad can work
         def sym_conv2d(input, filters):
             # define theano graph and function
-            return conv.conv2d(input, filters, image_shape, filter_shape,
+            input.name = 'input'
+            filters.name = 'filters'
+            rval =  conv.conv2d(input, filters, image_shape, filter_shape,
                           border_mode, subsample, unroll_batch=unroll_batch,
                           unroll_kern=unroll_kern, unroll_patch=unroll_patch)
+            rval.name = 'conv_output'
+            return rval
 
         output = sym_conv2d(input, filters)
+        output.name = 'conv2d(%s,%s)' % (input.name, filters.name)
         theano_conv = theano.function([input, filters], output)
 
         # initialize input and compute result