Tensorflow学习（7）用别人训练好的模型进行图像分类

谷歌在大型图像数据库ImageNet上训练好了一个Inception-v3模型，这个模型我们可以直接用来进来图像分类。
下载地址：https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip
下载完解压后，得到几个文件：

其中的classify_image_graph_def.pb 文件就是训练好的Inception-v3模型。

imagenet_synset_to_human_label_map.txt是类别文件。

随机找一张图片：如

对这张图片进行识别，看它属于什么类？

代码如下：先创建一个类NodeLookup来将softmax概率值映射到标签上。

然后创建一个函数create_graph()来读取模型。

最后读取图片进行分类识别：

# -*- coding: utf-8 -*-import tensorflow as tfimport numpy as npimport reimport osmodel_dir='D:/tf/model/'image='d:/cat.jpg'#将类别ID转换为人类易读的标签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]#读取训练好的Inception-v3模型来创建graphdef 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='')#读取图片image_data = tf.gfile.FastGFile(image, 'rb').read()#创建graphcreate_graph()sess=tf.Session()#Inception-v3模型的最后一层softmax的输出softmax_tensor= sess.graph.get_tensor_by_name('softmax:0')#输入图像数据，得到softmax概率值（一个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个概率最大的值（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()

最后输出：

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)