Make the assert compare with equivalent tensorconstant and bypass a few op.

theano/tensor/opt.py

@@ -821,7 +821,9 @@ class ShapeFeature(object):
                 else:
                     shape_vars.append(self.unpack(s[i]))
             assert all([not r.type.broadcastable[i] or
-                        shape_vars[i] == self.lscalar_one
+                        self.lscalar_one.equals(shape_vars[i]) or
+                        self.lscalar_one.equals(
+                            T.extract_constant(shape_vars[i]))
                         for i in range(r.ndim)])
             self.shape_of[r] = tuple(shape_vars)
             for sv in shape_vars:
@@ -866,7 +868,9 @@ class ShapeFeature(object):
                 merged_shape.append(other_shape[i])
         assert all([(not r.type.broadcastable[i] and
                      not other_r.type.broadcastable[i]) or
-                    merged_shape[i] == self.lscalar_one
+                    self.lscalar_one.equals(merged_shape[i]) or
+                    self.lscalar_one.equals(
+                        T.extract_constant(merged_shape[i]))
                     for i in range(r.ndim)])
         self.shape_of[r] = tuple(merged_shape)
         for sv in self.shape_of[r]:
@@ -885,7 +889,8 @@ class ShapeFeature(object):
             else:
                 new_shape.append(s_j)
         assert all([not r.type.broadcastable[i] or
-                    new_shape[i] == self.lscalar_one
+                    self.lscalar_one.equals(new_shape[i]) or
+                    self.lscalar_one.equals(T.extract_constant(new_shape[i]))
                     for i in range(r.ndim)])
         self.shape_of[r] = tuple(new_shape)
         for sv in self.shape_of[r]: