prise en compte output_grad ou inputs de fonction 'mult' complexes

theano/scalar/basic.py

@@ -1170,27 +1170,21 @@ class Mul(ScalarOp):
 
     def grad(self, inputs, (gz, )):
         retval = []
+        input_type = theano.tensor.as_tensor_variable(inputs).type
+        if input_type in discrete_types:
+            retval = None
+        elif input_type in complex_types or gz.type in complex_types:
             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)]
-                else:
-                    retval += [cast(mul(*([gz] + utils.difference(inputs,
-                                                                  [input]))),
-                                    input.type.dtype)]
+                retval += [mul(*([gz] +
+                                 utils.difference(inputs, [input])))]
         else:
-                retval += [None]
+            for input in inputs:
+                retval += [cast(mul(*([gz] +
+                                      utils.difference(inputs, [input]))),
+                                input_type.dtype)]
         return retval
+
+
 mul = Mul(upcast_out, name='mul')