use v2.layer in api_train_v2

demo/mnist/api_train_v2.py

-from paddle.trainer_config_helpers import *
-from paddle.trainer.PyDataProvider2 import dense_vector, integer_value
-import paddle.v2 as paddle
 import numpy
+import paddle.v2 as paddle
+from paddle.trainer.PyDataProvider2 import dense_vector, integer_value
+
 import mnist_util
 
 
@@ -12,32 +12,32 @@ def train_reader():
         yield item
 
 
-def network_config():
-    imgs = data_layer(name='pixel', size=784)
-    hidden1 = fc_layer(input=imgs, size=200)
-    hidden2 = fc_layer(input=hidden1, size=200)
-    inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation())
-    cost = classification_cost(
-        input=inference, label=data_layer(
-            name='label', size=10))
-    outputs(cost)
-
-
 def main():
     paddle.init(use_gpu=False, trainer_count=1)
-    topology = parse_network_config(network_config)
+
+    # define network topology
+    images = paddle.layer.data(name='pixel', size=784)
+    label = paddle.layer.data(name='label', size=10)
+    hidden1 = paddle.layer.fc(input=images, size=200)
+    hidden2 = paddle.layer.fc(input=hidden1, size=200)
+    inference = paddle.layer.fc(input=hidden2,
+                                size=10,
+                                act=paddle.activation.Softmax())
+    cost = paddle.layer.classification_cost(input=inference, label=label)
+
+    topology = paddle.layer.parse_network(cost)
+    print topology
     parameters = paddle.parameters.create(topology)
     for param_name in parameters.keys():
         array = parameters.get(param_name)
         array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
         parameters.set(parameter_name=param_name, value=array)
 
-    adam_optimizer = paddle.optimizer.Optimizer(
-        learning_rate=0.01, learning_method=AdamOptimizer())
+    adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            para = parameters.get('___fc_layer_2__.w0')
+            para = parameters.get('___fc_2__.w0')
             print "Pass %d, Batch %d, Cost %f, Weight Mean Of Fc 2 is %f" % (
                 event.pass_id, event.batch_id, event.cost, para.mean())
 

python/paddle/v2/__init__.py

@@ -19,8 +19,9 @@ import trainer
 import event
 import py_paddle.swig_paddle as api
 
-
-__all__ = ['optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event']
+__all__ = [
+    'optimizer', 'layer', 'activation', 'parameters', 'init', 'trainer', 'event'
+]
 
 
 def init(**kwargs):

python/paddle/v2/layer.py

@@ -72,6 +72,11 @@ from paddle.trainer_config_helpers.config_parser_utils import \
 from paddle.trainer_config_helpers.default_decorators import wrap_name_default
 import collections
 
+__all__ = [
+    'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
+    'cross_entropy_cost'
+]
+
 
 def parse_network(*outputs):
     """
@@ -165,11 +170,6 @@ cross_entropy_cost = __convert_to_v2__(
     name_prefix='cross_entropy',
     parent_names=['input', 'label'])
 
-__all__ = [
-    'parse_network', 'data', 'fc', 'max_id', 'classification_cost',
-    'cross_entropy_cost'
-]
-
 if __name__ == '__main__':
     pixel = data(name='pixel', size=784)
     label = data(name='label', size=10)