change topology to layer

demo/mnist/api_train_v2.py

@@ -25,8 +25,7 @@ def main():
                                 act=paddle.activation.Softmax())
     cost = paddle.layer.classification_cost(input=inference, label=label)
 
-    topology = paddle.layer.parse_network(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)
@@ -46,7 +45,7 @@ def main():
     trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
 
     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

 import numpy as np
-
-from paddle.proto.ModelConfig_pb2 import ModelConfig
-from paddle.proto.ParameterConfig_pb2 import ParameterConfig
+from . import layer as v2_layer
 import py_paddle.swig_paddle as api
+from paddle.proto.ParameterConfig_pb2 import ParameterConfig
 
 __all__ = ['Parameters', 'create']
 
 
-def create(*topologies):
+def create(*layers):
     """
-    Create parameter pool by topologies.
+    Create parameter pool by layers. In paddle, layer can be represent a
+    model config.
 
-    :param topologies:
+    :param layers:
     :return:
     """
-    pool = Parameters()
-    for topo in topologies:
-        if not isinstance(topo, ModelConfig):
+    for layer in layers:
+        if not isinstance(layer, v2_layer.Layer):
             raise ValueError(
-                'create must pass a topologies which type is ModelConfig')
-
-        for param in topo.parameters:
-            pool.__append_config__(param)
+                'create must pass a topologies which type is paddle.layer.Layer')
+    model_config = v2_layer.parse_network(*layers)
+    pool = Parameters()
+    for param in model_config.parameters:
+        pool.__append_config__(param)
     return pool
 
 

python/paddle/v2/trainer.py

 import collections
 
 import py_paddle.swig_paddle as api
+from paddle.proto.ModelConfig_pb2 import ModelConfig
 from py_paddle import DataProviderConverter
 
-from paddle.proto.ModelConfig_pb2 import ModelConfig
+from . import event as v2_event
+from . import layer as v2_layer
 from . import optimizer as v2_optimizer
 from . import parameters as v2_parameters
-from . import event as v2_event
 
 __all__ = ['ITrainer', 'SGD']
 
@@ -73,7 +74,7 @@ class SGD(ITrainer):
         Training method. Will train num_passes of input data.
 
         :param train_data_reader:
-        :param topology: Network Topology, a protobuf ModelConfig message.
+        :param topology: Network Topology, 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 +88,8 @@ class SGD(ITrainer):
         if event_handler is None:
             event_handler = default_event_handler
 
+        topology = v2_layer.parse_network(topology)
+
         __check_train_args__(**locals())
 
         gm = api.GradientMachine.createFromConfigProto(