轉載請注明鏈接：http://www.cnblogs.com/SSSR/p/5630534.html

tflearn中的例子訓練vgg16項目：https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py 尚未測試成功。

下面的項目是使用別人已經訓練好的模型進行預測，測試效果非常好。

github：https://github.com/ry/tensorflow-vgg16 此項目已經測試成功，效果非常好，

如果在Ubuntu中的terminal中運行出現問題，可以參照以下部分解決（解決skimage讀取圖片的問題）。

#coding:utf-8import skimage import skimage.io import skimage.transform a=skimage.io.imread('cat.jpg') import PIL import numpy as np import tensorflow as tf synset = [l.strip() for l in open('/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/synset.txt').readlines()]def load_image(path):# load imageimg = skimage.io.imread(path)#img1=PIL.Image.open("/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/pic/pig.jpg")#img=np.array(PIL.Image.open(path))#imgx=np.array(img) #print type(imgx),imgx.shapeimg = img/ 255.0assert (0 <= img).all() and (img <= 1.0).all()#print "Original Image Shape: ", img.shape# we crop image from centershort_edge = min(img.shape[:2])yy = int((img.shape[0] - short_edge) / 2)xx = int((img.shape[1] - short_edge) / 2)crop_img = img[yy : yy + short_edge, xx : xx + short_edge]# resize to 224, 224resized_img = skimage.transform.resize(crop_img, (224, 224))return resized_img# returns the top1 string def print_prob(prob):#print probprint "prob shape", prob.shapepred = np.argsort(prob)[::-1]# Get top1 labeltop1 = synset[pred[0]]#print "Top1: ", top1# Get top5 labeltop5 = [synset[pred[i]] for i in range(5)]#print "Top5: ", top5return top1print u'加載模型文件' with open("/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/vgg16.tfmodel", mode='rb') as f:fileContent = f.read()print u'創建圖' graph_def = tf.GraphDef() graph_def.ParseFromString(fileContent)images = tf.placeholder("float", [None, 224, 224, 3])tf.import_graph_def(graph_def, input_map={ "images": images }) print "graph loaded from disk"graph = tf.get_default_graph() print u'加載圖片' #img=np.array(PIL.Image.open("/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/pic/pig.jpg")) #cat = load_image(path) print u'進入sess執行'sess=tf.Session() result=[] for i in ['cat.jpg','airplane.jpg','zebra.jpg','pig.jpg','12.jpg','23.jpg']:img=load_image('pic/'+i)init = tf.initialize_all_variables()sess.run(init)print "variables initialized"batch = img.reshape((1, 224, 224, 3))assert batch.shape == (1, 224, 224, 3)feed_dict = { images: batch }print u'開始執行'prob_tensor = graph.get_tensor_by_name("import/prob:0")prob = sess.run(prob_tensor, feed_dict=feed_dict)print u'輸出結果'#print_prob(prob[0])result.append(print_prob(prob[0]))print result sess.close()''' with tf.Session() as sess:init = tf.initialize_all_variables()sess.run(init)print "variables initialized"batch = cat.reshape((1, 224, 224, 3))assert batch.shape == (1, 224, 224, 3)feed_dict = { images: batch }print u'開始執行'prob_tensor = graph.get_tensor_by_name("import/prob:0")prob = sess.run(prob_tensor, feed_dict=feed_dict)print u'輸出結果' print_prob(prob[0])'''

　　

轉載于:https://www.cnblogs.com/SSSR/p/5630534.html

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