Rearrange event.

demo/mnist/api_train_v2.py

@@ -36,7 +36,7 @@ def main():
         learning_rate=0.01, learning_method=AdamOptimizer())
 
     def event_handler(event):
-        if isinstance(event, paddle.trainer.EndIteration):
+        if isinstance(event, paddle.event.EndIteration):
             para = parameters['___fc_layer_2__.w0']
             print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
                 event.pass_id, event.batch_id, event.cost, para.mean())
@@ -44,7 +44,7 @@ def main():
         else:
             pass
 
-    trainer = paddle.trainer.SGDTrainer(update_equation=adam_optimizer)
+    trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
 
     trainer.train(train_data_reader=train_reader,
                   topology=model_config,

python/paddle/v2/__init__.py

@@ -15,8 +15,9 @@ import optimizer
 import parameters
 import py_paddle.swig_paddle as api
 import trainer
+import event
 
-__all__ = ['optimizer', 'parameters', 'init', 'trainer']
+__all__ = ['optimizer', 'parameters', 'init', 'trainer', 'event']
 
 
 def init(**kwargs):

python/paddle/v2/event.py

+__all__ = ['EndIteration']
+
+
+class EndIteration(object):
+    """
+    Event On One Batch Training Complete.
+    """
+
+    def __init__(self, pass_id, batch_id, cost):
+        self.pass_id = pass_id
+        self.batch_id = batch_id
+        self.cost = cost

python/paddle/v2/trainer.py

@@ -6,19 +6,9 @@ from py_paddle import DataProviderConverter
 from paddle.proto.ModelConfig_pb2 import ModelConfig
 from . import optimizer as v2_optimizer
 from . import parameters as v2_parameters
+from . import event as v2_event
 
-__all__ = ['ITrainer', 'SGDTrainer', 'EndIteration']
-
-
-class EndIteration(object):
-    """
-    Event On One Batch Training Complete.
-    """
-
-    def __init__(self, pass_id, batch_id, cost):
-        self.pass_id = pass_id
-        self.batch_id = batch_id
-        self.cost = cost
+__all__ = ['ITrainer', 'SGD']
 
 
 def default_event_handler(event):
@@ -35,7 +25,7 @@ class ITrainer(object):
         raise NotImplementedError()
 
 
-class SGDTrainer(ITrainer):
+class SGD(ITrainer):
     def __init__(self, update_equation):
         """
         Simple SGD Trainer.
@@ -110,7 +100,7 @@ class SGDTrainer(ITrainer):
                 cost = cost_vec.sum() / len(data_batch)
                 updater.finishBatch(cost)
                 event_handler(
-                    EndIteration(
+                    v2_event.EndIteration(
                         pass_id=pass_id, batch_id=batch_id, cost=cost))
 
             updater.finishPass()