clean topology.py

python/paddle/v2/tests/topology_test.py

@@ -19,25 +19,6 @@ import paddle.trainer_config_helpers as conf_helps
 
 
 class TestTopology(unittest.TestCase):
-    def test_parse(self):
-        pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
-        label = layer.data(name='label', type=data_type.integer_value(10))
-        hidden = layer.fc(input=pixel,
-                          size=100,
-                          act=conf_helps.SigmoidActivation())
-        inference = layer.fc(input=hidden,
-                             size=10,
-                             act=conf_helps.SoftmaxActivation())
-        maxid = layer.max_id(input=inference)
-        cost1 = layer.classification_cost(input=inference, label=label)
-        cost2 = layer.cross_entropy_cost(input=inference, label=label)
-
-        print topology.Topology(cost2).proto()
-        print topology.Topology([cost1]).proto()
-        print topology.Topology([cost1, cost2]).proto()
-        print topology.Topology(cost2).proto()
-        print topology.Topology([inference, maxid]).proto()
-
     def test_data_type(self):
         pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
         label = layer.data(name='label', type=data_type.integer_value(10))
@@ -74,6 +55,24 @@ class TestTopology(unittest.TestCase):
         self.assertEqual(pixel_layer, pixel)
         self.assertEqual(label_layer, label)
 
+    def test_parse(self):
+        pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
+        label = layer.data(name='label', type=data_type.integer_value(10))
+        hidden = layer.fc(input=pixel,
+                          size=100,
+                          act=conf_helps.SigmoidActivation())
+        inference = layer.fc(input=hidden,
+                             size=10,
+                             act=conf_helps.SoftmaxActivation())
+        maxid = layer.max_id(input=inference)
+        cost1 = layer.classification_cost(input=inference, label=label)
+        cost2 = layer.cross_entropy_cost(input=inference, label=label)
+
+        topology.Topology(cost2).proto()
+        topology.Topology([cost1]).proto()
+        topology.Topology([cost1, cost2]).proto()
+        topology.Topology([inference, maxid]).proto()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/topology.py

@@ -14,10 +14,8 @@
 
 import collections
 
-import paddle.trainer_config_helpers as conf_helps
 from paddle.proto.ModelConfig_pb2 import ModelConfig
 
-import data_type
 import layer as v2_layer
 
 __all__ = ['Topology']
@@ -62,7 +60,7 @@ class Topology(object):
 
         return result_layer[0]
 
-    def get_data_layer(self):
+    def data_layer(self):
         """
         get all data layer
         :return:
@@ -101,23 +99,3 @@ class Topology(object):
 def __check_layer_type__(layer):
     if not isinstance(layer, v2_layer.LayerV2):
         raise ValueError('layer should have type paddle.layer.Layer')
-
-
-if __name__ == '__main__':
-    pixel = v2_layer.data(name='pixel', type=data_type.dense_vector(784))
-    label = v2_layer.data(name='label', type=data_type.integer_value(10))
-    hidden = v2_layer.fc(input=pixel,
-                         size=100,
-                         act=conf_helps.SigmoidActivation())
-    inference = v2_layer.fc(input=hidden,
-                            size=10,
-                            act=conf_helps.SoftmaxActivation())
-    maxid = v2_layer.max_id(input=inference)
-    cost1 = v2_layer.classification_cost(input=inference, label=label)
-    cost2 = v2_layer.cross_entropy_cost(input=inference, label=label)
-
-    print Topology(cost2).proto()
-    print Topology([cost1]).proto()
-    print Topology([cost1, cost2]).proto()
-    print Topology(cost2).proto()
-    print Topology([inference, maxid]).proto()