add ParamAttr in V2

python/paddle/v2/__init__.py

@@ -18,11 +18,12 @@ import parameters
 import trainer
 import event
 import data_type
+import attr
 import py_paddle.swig_paddle as api
 
 __all__ = [
     'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer',
-    'event', 'data_type'
+    'event', 'data_type', 'attr'
 ]
 
 

python/paddle/v2/attr.py

@@ -0,0 +1,23 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer_config_helpers.attrs import *
+
+__all__ = [
+    "Param",
+    "Extra",
+]
+
+Param = ParameterAttribute
+Extra = ExtraLayerAttribute

python/paddle/v2/layer.py

@@ -74,6 +74,8 @@ from paddle.trainer_config_helpers.config_parser_utils import \
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 
 import data_type
+import activation
+import attr
 
 __all__ = [
     'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
@@ -230,8 +232,11 @@ if __name__ == '__main__':
     weight = data(name='weight', type=data_type.dense_vector(10))
     score = data(name='score', type=data_type.dense_vector(1))
 
-    hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation())
-    inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation())
+    hidden = fc(input=pixel,
+                size=100,
+                act=activation.Sigmoid(),
+                param_attr=attr.Param(name='hidden'))
+    inference = fc(input=hidden, size=10, act=activation.Softmax())
     maxid = max_id(input=inference)
     cost1 = classification_cost(input=inference, label=label)
     cost2 = classification_cost(input=inference, label=label, weight=weight)