Follow qq's comments

demo/mnist/api_train_v2.py

@@ -37,7 +37,7 @@ def main():
     trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
 
     trainer.train(
-        train_reader=paddle.reader.batched(
+        reader=paddle.reader.batched(
             paddle.reader.shuffle(paddle.dataset.mnist.train_creator(),
                                   buf_size=8192), batch_size=32),
         topology=cost,

python/paddle/v2/trainer.py

@@ -28,19 +28,13 @@ class ITrainer(object):
     The interface of Trainer. The only exposed method is `train`.
     """
 
-    def train(self,
-              train_reader_creator,
-              topology,
-              parameters,
-              test_data_reader=None,
-              event_handler=None):
+    def train(self, reader, topology, parameters, event_handler=None):
         """
         train method.
 
-        :param train_reader_creator:
+        :param reader:
         :param topology:
         :param parameters:
-        :param test_data_reader:
         :param event_handler:
         :return:
         """
@@ -62,7 +56,7 @@ class SGD(ITrainer):
         self.__optimizer__ = update_equation
 
     def train(self,
-              train_reader,
+              reader,
               topology,
               parameters,
               num_passes=1,
@@ -72,7 +66,7 @@ class SGD(ITrainer):
         """
         Training method. Will train num_passes of input data.
 
-        :param train_reader:
+        :param reader:
         :param topology: Network Topology, use one or more Layers to represent it.
         :param parameters: The parameter pools.
         :param num_passes: The total train passes.
@@ -104,7 +98,7 @@ class SGD(ITrainer):
 
         for pass_id in xrange(num_passes):
             updater.startPass()
-            for batch_id, data_batch in enumerate(train_reader()):
+            for batch_id, data_batch in enumerate(reader()):
                 pass_type = updater.startBatch(len(data_batch))
                 gm.forwardBackward(feeder(data_batch), out_args, pass_type)
                 for each_param in gm.getParameters():
@@ -122,13 +116,11 @@ class SGD(ITrainer):
         gm.finish()
 
 
-def __check_train_args__(train_reader, topology, parameters, event_handler,
-                         **kwargs):
+def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
     """
     Check train function's argument types
     """
-    if not callable(train_reader) or not isinstance(train_reader(),
-                                                    collections.Iterator):
+    if not callable(reader) or not isinstance(reader(), collections.Iterator):
         raise TypeError('train_data_reader should be a function, '
                         'which can return a iterator')