hide Topology

demo/mnist/api_train_v2.py

@@ -26,9 +26,7 @@ def main():
                                 act=paddle.activation.Softmax())
     cost = paddle.layer.classification_cost(input=inference, label=label)
 
-    topology = paddle.topology.Topology(cost)
-
-    parameters = paddle.parameters.create(topology)
+    parameters = paddle.parameters.create([cost])
     for param_name in parameters.keys():
         array = parameters.get(param_name)
         array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
@@ -49,7 +47,7 @@ def main():
 
     trainer.train(
         train_data_reader=train_reader,
-        topology=topology,
+        topology=[cost],
         parameters=parameters,
         event_handler=event_handler,
         batch_size=32)  # batch size should be refactor in Data reader

python/paddle/v2/parameters.py

@@ -7,15 +7,13 @@ import topology as v2_topology
 __all__ = ['Parameters', 'create']
 
 
-def create(topology):
+def create(layers):
     """
     Create parameter pool by topology.
-    :param topology:
+    :param layers:
     :return:
     """
-    if not isinstance(topology, v2_topology.Topology):
-        raise ValueError(
-            'create must pass a topology which type is topology.Topology')
+    topology = v2_topology.Topology(layers)
     pool = Parameters()
     for param in topology.proto().parameters:
         pool.__append_config__(param)

python/paddle/v2/topology.py

@@ -12,10 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from paddle.proto.ModelConfig_pb2 import ModelConfig
+import collections
+
 import paddle.trainer_config_helpers as conf_helps
-import layer as v2_layer
+from paddle.proto.ModelConfig_pb2 import ModelConfig
+
 import data_type
+import layer as v2_layer
 
 __all__ = ['Topology']
 
@@ -26,11 +29,12 @@ class Topology(object):
     and network configs.
     """
 
-    def __init__(self, *layers):
+    def __init__(self, layers):
+        if not isinstance(layers, collections.Sequence):
+            raise ValueError("input of Topology should be a list of Layer")
         for layer in layers:
             if not isinstance(layer, v2_layer.LayerV2):
-                raise ValueError('create must pass a topologies '
-                                 'which type is paddle.layer.Layer')
+                raise ValueError('layer should have type paddle.layer.Layer')
         self.layers = layers
         self.__model_config__ = v2_layer.parse_network(*layers)
         assert isinstance(self.__model_config__, ModelConfig)

python/paddle/v2/trainer.py

@@ -73,7 +73,7 @@ class SGD(ITrainer):
         Training method. Will train num_passes of input data.
 
         :param train_data_reader:
-        :param topology: Network Topology, use one or more Layers to represent it.
+        :param topology: cost layers, use one or more Layers to represent it.
         :param parameters: The parameter pools.
         :param num_passes: The total train passes.
         :param test_data_reader:
@@ -87,6 +87,8 @@ class SGD(ITrainer):
         if event_handler is None:
             event_handler = default_event_handler
 
+        topology = v2_topology.Topology(topology)
+
         __check_train_args__(**locals())
 
         gm = api.GradientMachine.createFromConfigProto(