new_contents: # -*- coding: utf-8 -*-
  
  """
  学习照片的命令如下 可以单张识别
  python label_image.py /home/clg/tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg
  
  """
  import tensorflow as tf,sys
  
  image_path = sys.argv[1]
  
  # Read in the image_data
  image_data = tf.gfile.FastGFile(image_path, 'rb').read()
  
  # Loads label file, strips off carriage return
  label_lines = [line.rstrip() for line 
                     in tf.gfile.GFile("/home/clg/tf_files/retrained_labels.txt")]
  
  # Unpersists graph from file
  with tf.gfile.FastGFile("/home/clg/tf_files/retrained_graph.pb", 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      _ = tf.import_graph_def(graph_def, name='')
  
  with tf.Session() as sess:
      # Feed the image_data as input to the graph and get first prediction
      softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
      
      predictions = sess.run(softmax_tensor,
               {'DecodeJpeg/contents:0': image_data})
      
      # Sort to show labels of first prediction in order of confidence
      top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
      
      for node_id in top_k:
          human_string = label_lines[node_id]
          score = predictions[0][node_id]
          print('%s (score = %.5f)' % (human_string, score))
  
subject: Create label_image.py