谷歌在大型影象資料庫ImageNet上訓練好了一個Inception-v3模型，這個模型我們可以直接用來進來影象分類。

下載地址：github：https://github.com/taey16/tf/tree/master/imagenet

下載完解壓後，得到幾個檔案：

其中的classify_image_graph_def.pb 檔案就是訓練好的Inception-v3模型。

imagenet_synset_to_human_label_map.txt是類別檔案。

隨機找一張圖片：如

對這張圖片進行識別，看它屬於什麼類？

程式碼如下：先建立一個類NodeLookup來將softmax概率值對映到標籤上。

然後建立一個函式create_graph()來讀取模型。

最後讀取圖片進行分類識別：

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np
import re
import os

model_dir='D:/tf/model/'
image='d:/cat.jpg'


#將類別ID轉換為人類易讀的標籤
class NodeLookup(object):
  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path 
 
= os.path.join(
          model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):
    
 
if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]

#讀取訓練好的Inception-v3模型來建立graph
def create_graph():
  with tf.gfile.FastGFile(os.path.join(
      model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')


#讀取圖片
image_data = tf.gfile.FastGFile(image, 'rb').read()

#建立graph
create_graph()

sess=tf.Session()
#Inception-v3模型的最後一層softmax的輸出
softmax_tensor= sess.graph.get_tensor_by_name('softmax:0')
#輸入影象資料，得到softmax概率值（一個shape=(1,1008)的向量）
predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})
#(1,1008)->(1008,)
predictions = np.squeeze(predictions)

# ID --> English string label.
node_lookup = NodeLookup()
#取出前5個概率最大的值（top-5)
top_5 = predictions.argsort()[-5:][::-1]
for node_id in top_5:
  human_string = node_lookup.id_to_string(node_id)
  score = predictions[node_id]
  print('%s (score = %.5f)' % (human_string, score))
  
sess.close()

最後輸出：

tiger cat (score = 0.40316)
Egyptian cat (score = 0.21686)
tabby, tabby cat (score = 0.21348)
lynx, catamount (score = 0.01403)

Persian cat (score = 0.00394)

轉自徐其華的學習專欄

http://www.cnblogs.com/denny402/p/6942580.html