引自：https://www.cnblogs.com/pypypy/p/11830464.html
文件test_vgg16.py可以用于提取特征。其中vgg16.npy是需要单独下载的文件。
2、使用预训练的模型提取特征
打开test_vgg16.py，做如下修改：
1 import numpy as np
2 import tensorflow as tf
3
4 import vgg16
5 import utils
6
7 img1 = utils.load_image("./test_data/tiger.jpeg")
8 img2 = utils.load_image("./test_data/puzzle.jpeg")
9
10 batch1 = img1.reshape((1, 224, 224, 3))
11 batch2 = img2.reshape((1, 224, 224, 3))
12
13 batch = np.concatenate((batch1, batch2), 0)
14
15 # with tf.Session(config=tf.ConfigProto(gpu_options=(tf.GPUOptions(per_process_gpu_memory_fraction=0.7)))) as sess:
16 with tf.device('/cpu:0'):
17 with tf.Session() as sess:
18 images = tf.placeholder("float", [2, 224, 224, 3])
19 feed_dict = {images: batch}
20
21 vgg = vgg16.Vgg16()
22 with tf.name_scope("content_vgg"):
23 vgg.build(images)
24
25 prob = sess.run(vgg.fc7, feed_dict=feed_dict) #需要提取哪一层特征，就在这里做修改，比如fc6，只需要把vgg.fc7修改为vgg.fc6
26 print(prob)
27 # utils.print_prob(prob[0], './synset.txt')
28 # utils.print_prob(prob[1], './synset.txt')