old_contents: import numpy
  import paddle.v2 as paddle
  
  
  def main():
      paddle.init(use_gpu=False, trainer_count=1)
  
      # define network topology
      images = paddle.layer.data(
          name='pixel', type=paddle.data_type.dense_vector(784))
      label = paddle.layer.data(
          name='label', type=paddle.data_type.integer_value(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)
  
      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)
          parameters.set(parameter_name=param_name, value=array)
  
      adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
  
      def event_handler(event):
          if isinstance(event, paddle.event.EndIteration):
              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())
  
          else:
              pass
  
      trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
  
      reader = paddle.reader.batched(
          paddle.reader.shuffle(
              paddle.dataset.mnist.train_creator(), buf_size=8192),
          batch_size=32)
  
      trainer.train(
          train_reader=paddle.reader.batched(
              paddle.reader.shuffle(paddle.dataset.mnist.train_creator(),
                                    buf_size=8192), batch_size=32),
          topology=cost,
          parameters=parameters,
          event_handler=event_handler,
          data_types=[  # data_types will be removed, It should be in
              # network topology
              ('pixel', images.type),
              ('label', label.type)],
          reader_dict={'pixel': 0, 'label': 1}
      )
  
  
  if __name__ == '__main__':
      main()
  
new_contents: import numpy
  import paddle.v2 as paddle
  
  
  def main():
      paddle.init(use_gpu=False, trainer_count=1)
  
      # define network topology
      images = paddle.layer.data(
          name='pixel', type=paddle.data_type.dense_vector(784))
      label = paddle.layer.data(
          name='label', type=paddle.data_type.integer_value(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)
  
      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)
          parameters.set(parameter_name=param_name, value=array)
  
      adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
  
      def event_handler(event):
          if isinstance(event, paddle.event.EndIteration):
              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())
  
          else:
              pass
  
      trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
  
      trainer.train(
          train_reader=paddle.reader.batched(
              paddle.reader.shuffle(paddle.dataset.mnist.train_creator(),
                                    buf_size=8192), batch_size=32),
          topology=cost,
          parameters=parameters,
          event_handler=event_handler,
          data_types=[  # data_types will be removed, It should be in
              # network topology
              ('pixel', images.type),
              ('label', label.type)],
          reader_dict={'pixel': 0, 'label': 1}
      )
  
  
  if __name__ == '__main__':
      main()
  
subject: Remove test code