old_contents: import paddle.v2 as paddle
  import pcloud.dataset.uci_housing as uci_housing
  
  def main():
      # init
      paddle.init()
  
      # network config
      x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
      y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
      y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
      cost = paddle.layer.mse_cost(input=y_predict, label=y)
  
      # create parameters
      parameters = paddle.parameters.create(cost)
  
      # create optimizer
      optimizer = paddle.optimizer.Momentum(momentum=0)
  
      trainer = paddle.trainer.SGD(
          cost=cost, parameters=parameters, update_equation=optimizer)
  
      feeding = {'x': 0, 'y': 1}
  
      # event_handler to print training and testing info
      def event_handler(event):
          if isinstance(event, paddle.event.EndIteration):
              if event.batch_id % 100 == 0:
                  print "Pass %d, Batch %d, Cost %f" % (
                      event.pass_id, event.batch_id, event.cost)
  
          if isinstance(event, paddle.event.EndPass):
              result = trainer.test(
                  reader=paddle.batch(uci_housing.test(), batch_size=2),
                  feeding=feeding)
              print "Test %d, Cost %f" % (event.pass_id, result.cost)
  
      # training
      trainer.train(
          reader=paddle.batch(
              paddle.reader.shuffle(uci_housing.train(), buf_size=500),
              batch_size=2),
          feeding=feeding,
          event_handler=event_handler,
          num_passes=30)
  
  
  if __name__ == '__main__':
      main()
  
new_contents: import paddle.v2 as paddle
  import pcloud.dataset.uci_housing as uci_housing
  
  def main():
      # init
      paddle.init()
  
      # network config
      x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
      y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
      y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
      cost = paddle.layer.mse_cost(input=y_predict, label=y)
  
      # create parameters
      parameters = paddle.parameters.create(cost)
  
      # create optimizer
      optimizer = paddle.optimizer.Momentum(momentum=0)
  
      trainer = paddle.trainer.SGD(
          cost=cost, parameters=parameters, update_equation=optimizer, is_local=False)
  
      feeding = {'x': 0, 'y': 1}
  
      # event_handler to print training and testing info
      def event_handler(event):
          if isinstance(event, paddle.event.EndIteration):
              if event.batch_id % 100 == 0:
                  print "Pass %d, Batch %d, Cost %f" % (
                      event.pass_id, event.batch_id, event.cost)
  
          if isinstance(event, paddle.event.EndPass):
              result = trainer.test(
                  reader=paddle.batch(uci_housing.test(), batch_size=2),
                  feeding=feeding)
              print "Test %d, Cost %f" % (event.pass_id, result.cost)
  
      # training
      trainer.train(
          reader=paddle.batch(
              paddle.reader.shuffle(uci_housing.train(), buf_size=500),
              batch_size=2),
          feeding=feeding,
          event_handler=event_handler,
          num_passes=30)
  
  
  if __name__ == '__main__':
      main()
  
subject: Make is_local=False