cleaned up casting and dtype handling in mean and dot

theano/tensor/basic.py

@@ -1417,15 +1417,19 @@ def mean(input, axis = None):
     if str(input.dtype).startswith('int'):
         # we need to cast eventually anyway, and this helps
         # to prevents overflow
-        input = convert_to_float64(input)
+        input = cast(input, 'float64')
     s = sum(input, axis)
     shp = shape(input)
+    if input.dtype == 'float32':
+        shp = cast(shp, 'float32')
     if axis is None:
         axis = range(input.type.ndim)
     elif isinstance(axis, int):
         axis = [axis]
     for i in axis:
         s = s / shp[i]
+    if input.dtype.startswith('float'):
+        assert input.dtype == s.dtype
     return s
 
 @constructor
@@ -2543,12 +2547,15 @@ class Dot(Op):
 
     def grad(self, (x, y), (gz,)):
         if gz.type.ndim == 0:
-            return gz * y, gz * x
-        if x.type.ndim == 1 and y.type.ndim > 1:
-            return dot(gz, y.T), outer(x.T, gz)
-        if x.type.ndim > 1 and y.type.ndim == 1:
-            return outer(gz, y.T), dot(x.T, gz) 
-        return dot(gz, y.T), dot(x.T, gz)
+            rval = gz * y, gz * x
+        elif x.type.ndim == 1 and y.type.ndim > 1:
+            rval = dot(gz, y.T), outer(x.T, gz)
+        elif x.type.ndim > 1 and y.type.ndim == 1:
+            rval = outer(gz, y.T), dot(x.T, gz) 
+        else:
+            rval = dot(gz, y.T), dot(x.T, gz)
+        return cast(rval[0], x.dtype), cast(rval[1], y.dtype)
+
     def __str__(self):
         return "dot"
 dot = Dot()