Better checks for dimensions or shape mismatch (used in tests).

theano/tensor/raw_random.py

@@ -179,7 +179,9 @@ class RandomFunction(gof.Op):
         r, shape, args = inputs[0], inputs[1], inputs[2:]
         assert type(r) == numpy.random.RandomState
         r_orig = r
-        assert self.outtype.ndim == len(shape) + self.ndim_added
+        if self.outtype.ndim != len(shape) + self.ndim_added:
+            raise ValueError('Shape mismatch: self.outtype.ndim (%i) != len(shape) (%i) + self.ndim_added (%i)'\
+                    %(self.outtype.ndim, len(shape), self.ndim_added))
         if not self.inplace:
             r = copy(r)
         rout[0] = r
@@ -189,6 +191,9 @@ class RandomFunction(gof.Op):
             out[0] = numpy.asarray(rval, dtype = node.outputs[1].type.dtype)
         else:
             out[0] = rval
+        if len(rval.shape) != self.outtype.ndim:
+            raise ValueError('Shape mismatch: "out" should have dimension %i, but the value produced by "perform" has dimension %i'\
+                    % (self.outtype.ndim, len(rval.shape)))
 
     def grad(self, inputs, outputs):
         return [None for i in inputs]