TensorFlow on the VGG16 project

Reprint Please specify link: http://www.cnblogs.com/SSSR/p/5630534.html

Examples in Tflearn training VGG16 project: https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py has not been tested successfully.

The next project is to use a model that has been trained by others to make predictions, and the test works very well.

Github:https://github.com/ry/tensorflow-vgg16 This project has been tested successfully, the effect is very good,

If there is a problem running in terminal in Ubuntu, you can resolve it by following the section below (to solve the problem of skimage reading pictures).

#coding: Utf-8Import skimageimport skimage.ioimport skimage.transforma=skimage.io.imread (' cat.jpg ')Import Pilimport NumPy as Npimport tensorflow as Tfsynset = [L.strip () for L in open ('/home/ubuntu/pythonproject/tensorflo W/tensorflow-vgg16/synset.txt '). ReadLines ()]def load_image (path): # Load image img = 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.shape img = img/255.0 assert (0 <= img). All () and ( IMG <= 1.0). All () #print "Original Image Shape:", Img.shape # We crop Image from center Short_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, 224 resized_img = Skimage.transform.resize (crop_img, (224, 224)) return R ESIZED_IMG # returns the Top1 stringdef Print_prob (prob): #print prob print "prob shape", prob.shape pred = Np.argsort (prob) [:: -1] # Get top1 label TOP1 = synset[Pred[0]] #print "TOP1:", Top1 # Get top5 Label top5 = [Synset[pred[i]] for I in range (5)] #print "TOP5:", TOP5 Retu RN top1print u ' load model file ' with open ("/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/vgg16.tfmodel", mode= ' RB ') As F:filecontent = F.read () print u ' Create diagram ' 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 ' load picture ' #img =np.array (PIL. Image.open ("/home/ubuntu/pythonproject/tensorflow/tensorflow-vgg16/pic/pig.jpg")) #cat = load_image (path) print U ' Enter Sess to execute ' 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, 2 (3)) assert batch.shape = = (1, 224, 224, 3) feed_dict = {Images:batch} print U ' start execution ' prob_tensor = Graph.get_tensor_by_name ("import/prob:0") prob = Sess.run (Prob_tensor, feed_dict=feed_dict) prin T u ' output result ' #print_prob (prob[0]) result.append (Print_prob (prob[0])) print resultsess.close () "with TF. Session () as Sess:init = Tf.initialize_all_variables () sess.run (init) print "variables initialized" batch = Cat.resha PE ((1, 224, 224, 3)) assert batch.shape = = (1, 224, 224, 3) feed_dict = {Images:batch} print U ' start execution ' prob_tensor = Graph.get_tensor_by_name ("import/prob:0") prob = Sess.run (prob_tensor, feed_dict=feed_dict) print u ' output result ' print_prob ( Prob[0]) ""

　　

TensorFlow on the VGG16 project