Better error message to help user debug error like gh-5347

theano/tensor/opt.py

@@ -1041,11 +1041,14 @@ class ShapeFeature(object):
                 rval.append(None)
         return rval
 
-    def unpack(self, s_i):
+    def unpack(self, s_i, var):
         """Return a symbolic integer scalar for the shape element s_i.
 
         The s_i argument was produced by the infer_shape() of an Op subclass.
 
+        var: the variable that correspond to s_i. This is just for
+        error reporting.
+
         """
         # unpack the s_i that the Op returned
         assert s_i is not None
@@ -1059,7 +1062,10 @@ class ShapeFeature(object):
                 isinstance(s_i, numpy.integer) or
                 (isinstance(s_i, numpy.ndarray) and s_i.ndim == 0)):
             # this shape is a constant
-            assert s_i >= 0
+            if s_i < 0:
+                msg = "There is a negative shape in the graph!"
+                msg += gof.utils.get_variable_trace_string(var)
+                raise ValueError(msg)
             return T.constant(s_i, dtype='int64')
         if type(s_i) in (tuple, list):
             # this dimension is the same as many of the inputs
@@ -1137,7 +1143,7 @@ class ShapeFeature(object):
                         r.type.broadcastable[i]):
                     shape_vars.append(self.lscalar_one)
                 else:
-                    shape_vars.append(self.unpack(s[i]))
+                    shape_vars.append(self.unpack(s[i], r))
             assert all([not hasattr(r.type, "broadcastable") or
                         not r.type.broadcastable[i] or
                         # The two following comparison are a speed optimization
@@ -1238,7 +1244,7 @@ class ShapeFeature(object):
         new_shape = []
         for j, s_j in enumerate(prev_shape):
             if j == i:
-                new_shape.append(self.unpack(s_i))
+                new_shape.append(self.unpack(s_i, r))
             else:
                 new_shape.append(s_j)
         assert all([not hasattr(r.type, "broadcastable") or