TensorFlow学习笔记(七) 使用训练好的inception_v3模型预测分类图片

下载需要练习的inception模型并看起流程

import tensorflow as tf
import os
import tarfile
import requests

#inception_v3模型下载
inception_pretrain_model_url = 'https://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'

# 模型存放地址
inception_pretrain_model_dir = "inception_model"
if not os.path.exists(inception_pretrain_model_dir):
    os.makedirs(inception_pretrain_model_dir)

#获取文件名，以及文件路径
filename = inception_pretrain_model_url.split('/')[-1]
filepath = os.path.join(inception_pretrain_model_dir, filename)

#下载模型
if not os.path.exists(filepath):
    print('download: ', filename)
    r = requests.get(inception_pretrain_model_url, stream=True)
    with open(filepath,'wb') as f:
        for chunk in r.iter_content(chunk_size=1024):
            if chunk:
                f.write(chunk)
print("finishn: ", filename)

#解压文件
tarfile.open(filepath, 'r:gz').extractall(inception_pretrain_model_dir)

#模型结构存放文件
log_dir = 'inception_log'
if not os.path.exists(log_dir):
    os.makedirs(log_dir)

#classify_image_graph_def.pb为google训练好的模型
inception_graph_def_file = os.path.join(inception_pretrain_model_dir, 'classify_image_graph_def.pb')
with tf.Session() as sess:
    #创建一个图来存放google训练好的模型，load graph 具体实现方法看下面的链接
    with tf.gfile.FastGFile(inception_graph_def_file, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        tf.import_graph_def(graph_def, name='')

    #保存图的结构
    writer = tf.summary.FileWriter(log_dir, sess.graph)
    writer.close()import tensorflow as