old_contents: #!/usr/bin/env python
  
  from paddle.trainer_config_helpers import *
  
  height = 227
  width = 227
  num_class = 1000
  batch_size = get_config_arg('batch_size', int, 128)
  gp = get_config_arg('layer_num', int, 1)
  is_infer = get_config_arg("is_infer", bool, False)
  num_samples = get_config_arg('num_samples', int, 2560)
  
  args = {
      'height': height,
      'width': width,
      'color': True,
      'num_class': num_class,
      'is_infer': is_infer,
      'num_samples': num_samples
  }
  define_py_data_sources2(
      "train.list", None, module="provider", obj="process", args=args)
  
  settings(
      batch_size=batch_size,
      learning_rate=0.01 / batch_size,
      learning_method=MomentumOptimizer(0.9),
      regularization=L2Regularization(0.0005 * batch_size))
  
  # conv1
  net = data_layer('data', size=height * width * 3)
  net = img_conv_layer(
      input=net,
      filter_size=11,
      num_channels=3,
      num_filters=96,
      stride=4,
      padding=1)
  net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  # conv2
  net = img_conv_layer(
      input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
  net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  # conv3
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=384, stride=1, padding=1)
  # conv4
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
  
  # conv5
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  net = fc_layer(
      input=net,
      size=4096,
      act=ReluActivation(),
      layer_attr=ExtraAttr(drop_rate=0.5))
  net = fc_layer(
      input=net,
      size=4096,
      act=ReluActivation(),
      layer_attr=ExtraAttr(drop_rate=0.5))
  net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
  
  if is_infer:
      outputs(net)
  else:
      lab = data_layer('label', num_class)
      loss = cross_entropy(input=net, label=lab)
      outputs(loss)
  
new_contents: #!/usr/bin/env python
  
  from paddle.trainer_config_helpers import *
  
  height = 227
  width = 227
  num_class = 1000
  batch_size = get_config_arg('batch_size', int, 128)
  gp = get_config_arg('layer_num', int, 1)
  is_infer = get_config_arg("is_infer", bool, False)
  num_samples = get_config_arg('num_samples', int, 2560)
  
  args = {
      'height': height,
      'width': width,
      'color': True,
      'num_class': num_class,
      'is_infer': is_infer,
      'num_samples': num_samples
  }
  define_py_data_sources2(
      "train.list" if not is_infer else None,
      "test.list" if is_infer else None,
      module="provider",
      obj="process",
      args=args)
  
  settings(
      batch_size=batch_size,
      learning_rate=0.01 / batch_size,
      learning_method=MomentumOptimizer(0.9),
      regularization=L2Regularization(0.0005 * batch_size))
  
  # conv1
  net = data_layer('data', size=height * width * 3)
  net = img_conv_layer(
      input=net,
      filter_size=11,
      num_channels=3,
      num_filters=96,
      stride=4,
      padding=1)
  net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  # conv2
  net = img_conv_layer(
      input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
  net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  # conv3
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=384, stride=1, padding=1)
  # conv4
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
  
  # conv5
  net = img_conv_layer(
      input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
  net = img_pool_layer(input=net, pool_size=3, stride=2)
  
  net = fc_layer(
      input=net,
      size=4096,
      act=ReluActivation(),
      layer_attr=ExtraAttr(drop_rate=0.5))
  net = fc_layer(
      input=net,
      size=4096,
      act=ReluActivation(),
      layer_attr=ExtraAttr(drop_rate=0.5))
  net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
  
  if is_infer:
      outputs(net)
  else:
      lab = data_layer('label', num_class)
      loss = cross_entropy(input=net, label=lab)
      outputs(loss)
  
subject: fix inference crash of alexnet benchmark