use a more sensible default argument in favor of apache#24

src/model.jl

@@ -158,9 +158,9 @@ end
    :param Bool overwrite: an :class:`Executor` is initialized the first time predict is called. The memory
                           allocation of the :class:`Executor` depends on the mini-batch size of the test
                           data provider. If you call predict twice with data provider of the same batch-size,
-                          then the executor can be re-used. Otherwise, if ``overwrite`` is false (default),
-                          an error will be raised; if ``overwrite`` is set to true, a new :class:`Executor`
-                          will be created to replace the old one.
+                          then the executor can be potentially be re-used. So, if ``overwrite`` is false,
+                          we will try to re-use, and raise an error if batch-size changed. If ``overwrite``
+                          is true (the default), a new :class:`Executor` will be created to replace the old one.
 
    .. note::
 
@@ -172,12 +172,19 @@ end
       For the same reason, currently prediction will only use the first device even if multiple devices are
       provided to construct the model.
 
+   .. note::
+
+      If you perform further after prediction. The weights are not automatically synchronized if ``overwrite``
+      is set to false and the old predictor is re-used. In this case
+      setting ``overwrite`` to true (the default) will re-initialize the predictor the next time you call
+      predict and synchronize the weights again.
+
    :seealso: :func:`train`, :func:`fit`, :func:`init_model`, :func:`load_checkpoint`
 =#
-function predict(callback :: Function, self :: FeedForward, data :: AbstractDataProvider; overwrite :: Bool = false)
+function predict(callback :: Function, self :: FeedForward, data :: AbstractDataProvider; overwrite :: Bool = true)
   predict(self, data; overwrite = overwrite, callback=callback)
 end
-function predict(self :: FeedForward, data :: AbstractDataProvider; overwrite::Bool=false, callback::Union{Function,Void}=nothing)
+function predict(self :: FeedForward, data :: AbstractDataProvider; overwrite::Bool=true, callback::Union{Function,Void}=nothing)
   data_shapes = provide_data(data)
   data_names  = [x[1] for x in data_shapes]
   _setup_predictor(self, overwrite; data_shapes...)