fixed grad of Mul,Sgn,Ceil, and Floor

theano/scalar/basic.py

@@ -1184,26 +1184,25 @@ class Mul(ScalarOp):
                         ' expected gz type to be complex, got gz with type '+\
                         str(gz.type))
 
+        if output_type in discrete_types:
+            return [ipt.zeros_like.astype(theano.config.floatX)
+                    for ipt in inputs]
+
         for input in inputs:
-            if input.type in continuous_types:
-                if gz.type in complex_types:
-                    # zr+zi = (xr + xi)(yr + yi)
-                    # zr+zi = (xr*yr - xi*yi) + (xr yi + xi yr )
-                    otherprod = mul(*(utils.difference(inputs, [input])))
-                    yr = real(otherprod)
-                    yi = imag(otherprod)
-                    if input.type in complex_types:
-                        retval += [complex(yr * real(gz) + yi * imag(gz),
-                                           yr * imag(gz) - yi * real(gz))]
-                    else:
-                        retval += [cast(yr * real(gz) + yi * imag(gz),
-                                        input.type.dtype)]
+            if gz.type in complex_types:
+                # zr+zi = (xr + xi)(yr + yi)
+                # zr+zi = (xr*yr - xi*yi) + (xr yi + xi yr )
+                otherprod = mul(*(utils.difference(inputs, [input])))
+                yr = real(otherprod)
+                yi = imag(otherprod)
+                if input.type in complex_types:
+                    retval += [complex(yr * real(gz) + yi * imag(gz),
+                                       yr * imag(gz) - yi * real(gz))]
                 else:
-                    retval += [cast(mul(*([gz] + utils.difference(inputs,
-                                                                  [input]))),
-                                    input.type.dtype)]
+                    retval += [yr * real(gz) + yi * imag(gz)]
             else:
-                retval += [None]
+                retval += [mul(*([gz] + utils.difference(inputs,
+                                                              [input])))]
         return retval
 
 
@@ -1697,7 +1696,13 @@ class Sgn(UnaryScalarOp):
         return numpy.sign(x)
 
     def grad(self, (x, ), (gz, )):
-        return None,
+
+        rval = x.zeros_like()
+
+        if rval.type.dtype in discrete_types:
+            rval = rval.astype(theano.config.floatX)
+
+        return [rval]
 
     def c_code(self, node, name, (x, ), (z, ), sub):
         #casting is done by compiler
@@ -1723,7 +1728,12 @@ class Ceil(UnaryScalarOp):
         return numpy.ceil(x)
 
     def grad(self, (x,), (gz,)):
-        return None,
+        rval = x.zeros_like()
+
+        if rval.type.dtype in discrete_types:
+            rval = rval.astype(theano.config.floatX)
+
+        return [rval]
 
     def c_code(self, node, name, (x,), (z,), sub):
         return "%(z)s = ceil(%(x)s);" % locals()
@@ -1735,7 +1745,12 @@ class Floor(UnaryScalarOp):
         return numpy.floor(x)
 
     def grad(self, (x,), (gz,)):
-        return None,
+        rval = x.zeros_like()
+
+        if rval.type.dtype in discrete_types:
+            rval = rval.astype(theano.config.floatX)
+
+        return [rval]
 
     def c_code(self, node, name, (x,), (z,), sub):
         return "%(z)s = floor(%(x)s);" % locals()