深度学习tensorflow实战笔记 用预训练好的VGG-16模型提取图像特征

 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‘)