old_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_fcn import fcn8_vgg
  
  import tensorflow as tf
  
  
  def inference(hypes, images, train=True):
      """Build the MNIST model up to where it may be used for inference.
  
      Args:
        images: Images placeholder, from inputs().
        train: whether the network is used for train of inference
  
      Returns:
        softmax_linear: Output tensor with the computed logits.
      """
      vgg_fcn = fcn8_vgg.FCN8VGG()
  
      num_classes = hypes["fc_size"]
      with tf.name_scope("VGG"):
          vgg_fcn.build(images, train=train, num_classes=num_classes,
                        random_init_fc8=True)
  
      vgg_dict = {'deep_feat': vgg_fcn.pool5,
                  'deep_feat_channels': 512,
                  'early_feat': vgg_fcn.conv4_3,
                  'scored_feat': vgg_fcn.score_fr}
  
      return vgg_dict
  
new_contents: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_fcn import fcn8_vgg
  
  import tensorflow as tf
  
  
  def inference(hypes, images, train=True):
      """Build the MNIST model up to where it may be used for inference.
  
      Args:
        images: Images placeholder, from inputs().
        train: whether the network is used for train of inference
  
      Returns:
        softmax_linear: Output tensor with the computed logits.
      """
      vgg_fcn = fcn8_vgg.FCN8VGG()
  
      num_classes = hypes["fc_size"]
  
      vgg_fcn.build(images, train=train, num_classes=num_classes,
                    random_init_fc8=True)
  
      vgg_dict = {'deep_feat': vgg_fcn.pool5,
                  'deep_feat_channels': 512,
                  'early_feat': vgg_fcn.conv4_3,
                  'scored_feat': vgg_fcn.score_fr}
  
      return vgg_dict
  
subject: Remove VGG