正文
隨機找一張圖片
讀取圖片進行分類識別
最後輸出
正文谷歌在大型圖像數據庫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圖像分類。
下載鏈接: https://pan.baidu.com/s/1XGfwYer5pIEDkpM3nM6o2A
提取碼: hu66
下載完解壓後，得到幾個文件：
其中
隨機找一張圖片classify_image_graph_def.pb 文件就是訓練好的Inception-v3模型。
imagenet_synset_to_human_label_map.txt是類別文件。
對這張圖片進行識別，看它屬於什麼類？
代碼如下：先創建一個類NodeLookup來將softmax概率值映射到標簽上。
然後創建一個函數create_graph()來讀取模型。
讀取圖片進行分類識別
# -*- coding: utf-8 -*-import tensorflow as tfimport numpy as npimport reimport osmodel_dir='D:/tf/model/'image='d:/cat.jpg'#將類別ID轉換為人類易讀的標簽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模型來創建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='')#讀取圖片image_data = tf.gfile.FastGFile(image, 'rb').read()#創建graphcreate_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')#輸入圖像數據，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)
以上就是python深度學習tensorflow訓練好的模型進行圖像分類的詳細內容，更多關於tensorflow訓練模型圖像分類的資料請關注軟件開發網其它相關文章！
