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python/paddle/v2/__init__.py

@@ -21,7 +21,7 @@ import data_type
 import topology
 import data_feeder
 import networks
-import evaluators
+import evaluator
 from . import dataset
 from . import reader
 from . import plot
@@ -36,7 +36,7 @@ import plot
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
     'event', 'data_type', 'attr', 'pooling', 'data_feeder', 'dataset', 'reader',
-    'topology', 'networks', 'infer', 'plot', 'evaluators'
+    'topology', 'networks', 'infer', 'plot', 'evaluator'
 ]
 
 

python/paddle/v2/evaluator.py

@@ -20,21 +20,28 @@ __all__ = []
 
 
 def initialize():
+    def convert_to_new_name(nm):
+        return nm[:-len("_evaluator")]
+
     for __ev_name__ in filter(lambda x: x.endswith('_evaluator'), evs.__all__):
         __ev__ = getattr(evs, __ev_name__)
         if hasattr(__ev__, 'argspec'):
             argspec = __ev__.argspec
         else:
             argspec = inspect.getargspec(__ev__)
-        parent_names = filter(lambda x: x in ['input', 'label'], argspec.args)
+        parent_names = filter(lambda x: x in ['input', 'label', 'weight'],
+                              argspec.args)
         v2_ev = __convert_to_v2__(
             __ev_name__,
             parent_names=parent_names,
             is_default_name='name' in argspec.args,
             attach_parent=True)
-        globals()[__ev_name__] = v2_ev
+
-        globals()[__ev_name__].__name__ = __ev_name__
+        __new_name__ = convert_to_new_name(__ev_name__)
-        __all__.append(__ev_name__)
+
+        globals()[__new_name__] = v2_ev
+        globals()[__new_name__].__name__ = __new_name__
+        __all__.append(__new_name__)
 
 
 initialize()

python/paddle/v2/tests/test_layer.py

@@ -19,7 +19,7 @@ import paddle.v2.data_type as data_type
 import paddle.v2.layer as layer
 import paddle.v2.pooling as pooling
 import paddle.v2.networks as networks
-import paddle.v2.evaluators as evaluators
+import paddle.v2.evaluator as evaluator
 
 pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
 label = layer.data(name='label', type=data_type.integer_value(10))
@@ -273,7 +273,7 @@ class EvaluatorTest(unittest.TestCase):
         lbl = layer.data(name='label', type=data_type.integer_value(10))
         cost = layer.cross_entropy_cost(input=output, label=lbl)
 
-        evaluators.classification_error_evaluator(input=output, label=lbl)
+        evaluator.classification_error(input=output, label=lbl)
         print layer.parse_network(cost)
         print layer.parse_network(output)