old_contents: #!/usr/bin/env python
  # coding:utf-8
  # vi:tabstop=4:shiftwidth=4:expandtab:sts=4
  
  import deepstacks
  from deepstacks.framework.main import *
  import deepstacks.framework.using_mnist
  from deepstacks.macros import *
  from deepstacks.framework.macros import *
  from deepstacks.lasagne import curr_layer,curr_stacks,curr_flags,curr_model,curr_batchsize
  
  from deepstacks.util.curry import curry
  
  
  def build_cnn(inputs):
      network=inputs['image']
      if 'mean' in inputs:
          network=lasagne.layers.ElemwiseMergeLayer((network,inputs['mean']),T.sub)
      y=inputs['target']
      return deepstacks.lasagne.build_network(network, prettylayers
          (0,32,5,1,0,0)
          (0,0,2,2,0,0,maxpool=True)
          (0,32,5,1,0,0)
          (0,0,2,2,0,0,maxpool=True)
          (0,0,0,0,0,0,layer=(lasagne.layers.DropoutLayer,curr_layer,{'p':0.5}))
          (0,256,0,0,0,0,dense=True)
          (0,0,0,0,0,0,layer=(lasagne.layers.DropoutLayer,curr_layer,{'p':0.5}))
          (0,10,0,0,0,0,
              dense=True,
              nonlinearity=lasagne.nonlinearities.softmax,
              )
          (classify,'y')
          ,{
              'y':y
              })
  
  register_network_builder(build_cnn)
  
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python
  # coding:utf-8
  # vi:tabstop=4:shiftwidth=4:expandtab:sts=4
  
  import deepstacks
  from deepstacks.framework.main import *
  import deepstacks.framework.using_mnist
  from deepstacks.macros import *
  from deepstacks.framework.macros import *
  from deepstacks.lasagne import curr_layer,curr_stacks,curr_flags,curr_model,curr_batchsize
  
  from deepstacks.utils.curry import curry
  
  
  def build_cnn(inputs):
      network=inputs['image']
      if 'mean' in inputs:
          network=lasagne.layers.ElemwiseMergeLayer((network,inputs['mean']),T.sub)
      y=inputs['target']
      return deepstacks.lasagne.build_network(network, prettylayers
          (0,32,5,1,0,0)
          (0,0,2,2,0,0,maxpool=True)
          (0,32,5,1,0,0)
          (0,0,2,2,0,0,maxpool=True)
          (0,0,0,0,0,0,layer=(lasagne.layers.DropoutLayer,curr_layer,{'p':0.5}))
          (0,256,0,0,0,0,dense=True)
          (0,0,0,0,0,0,layer=(lasagne.layers.DropoutLayer,curr_layer,{'p':0.5}))
          (0,10,0,0,0,0,
              dense=True,
              nonlinearity=lasagne.nonlinearities.softmax,
              )
          (classify,'y')
          ,{
              'y':y
              })
  
  register_network_builder(build_cnn)
  
  
  if __name__ == '__main__':
      main()
  
subject: fix bug