Allow user to specify shape of inputs by using .tag.shape. This avoids the

theano/tensor/opt.py

@@ -6,6 +6,7 @@
 import logging
 _logger = logging.getLogger('theano.tensor.opt')
 
+import copy
 import operator
 import itertools
 import sys
@@ -572,6 +573,11 @@ class ShapeFeature(object):
 
         if hasattr(r.type,"broadcastable") and r.type.broadcastable[i]:
             return self.lscalar_one
+        # If user provided size
+        elif ( hasattr(r.tag,'shape') and
+              r.tag.shape is not None and
+              r.tag.shape[i] is not None):
+            return T.constant(copy.copy(r.tag.shape[i]),dtype='int64')
         else:
             return Shape_i(i).make_node(r).outputs[0]
 
@@ -2732,7 +2738,14 @@ register_specialize(local_mul_specialize)
 @gof.local_optimizer([T.add])
 def local_add_specialize(node):
     def fill_chain(v):
-        return _fill_chain(v, node.inputs)
+        # Not sure why this happens .. but I did not had the time to look
+        # into it, it probably has something to do with the dtype I'm
+        # providing the tag.shape of my variable
+        out = _fill_chain(v, node.inputs)
+        if out[0].dtype != node.outputs[0].dtype:
+            return [T.cast(out[0], dtype = node.outputs[0].dtype)]
+        else:
+            return out
 
     #here, we are past the point of canonicalization, so we don't want to put in un-necessary fills.
     if node.op == T.add: