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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.CompleteTrainOneBatch):
+        if isinstance(event, paddle.trainer.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())

python/paddle/v2/parameters.py

@@ -49,6 +49,9 @@ class Parameters(object):
     def has_key(self, key):
         return key in self.__param_conf__.keys()
 
+    def __iter__(self):
+        return iter(self.__param_conf__)
+
     def __getitem__(self, key):
         shape = self.get_shape(key)
 

python/paddle/v2/trainer.py

@@ -7,17 +7,10 @@ from paddle.proto.ModelConfig_pb2 import ModelConfig
 from . import optimizer as v2_optimizer
 from . import parameters as v2_parameters
 
-__all__ = ['ITrainer', 'SGDTrainer', 'CompleteTrainOneBatch', 'BaseEvent']
+__all__ = ['ITrainer', 'SGDTrainer', 'EndIteration']
 
 
-class BaseEvent(object):
-    """
-    Just a marker class
-    """
-    pass
-
-
-class CompleteTrainOneBatch(BaseEvent):
+class EndIteration(object):
     """
     Event On One Batch Training Complete.
     """
@@ -117,7 +110,7 @@ class SGDTrainer(ITrainer):
                 cost = cost_vec.sum() / len(data_batch)
                 updater.finishBatch(cost)
                 event_handler(
-                    CompleteTrainOneBatch(
+                    EndIteration(
                         pass_id=pass_id, batch_id=batch_id, cost=cost))
 
             updater.finishPass()