tensorflow 随笔－－－－－－－－－－－ＶGG网络的模型的复现

推荐个课程北京大学的tensorflow 笔记

ＶＧG网络是谷歌千分类用的，实现的是对图像的识别

由于模型过大，需要的可以私聊我（１３０７２５０９３８３微信）

vgg16.py 网络对参数的读取

import os
import numpy as np
import tensorflow as tf
import time
# import matplotlib.pyplot as pltVGG_MEAN = [103.939, 116.779, 123.68] class Vgg16():def __init__(self, vgg16_path=None):if vgg16_path is None:vgg16_path = os.path.join(os.getcwd(), "vgg16.npy") self.data_dict = np.load(vgg16_path, encoding='latin1').item() def forward(self, images):print("build model started")start_time = time.time() rgb_scaled = images * 255.0 red, green, blue = tf.split(rgb_scaled,3,3) bgr = tf.concat([     blue - VGG_MEAN[0],green - VGG_MEAN[1],red - VGG_MEAN[2]], 3)self.conv1_1 = self.conv_layer(bgr, "conv1_1") self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")self.pool1 = self.max_pool_2x2(self.conv1_2, "pool1")self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")self.pool2 = self.max_pool_2x2(self.conv2_2, "pool2")self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")self.pool3 = self.max_pool_2x2(self.conv3_3, "pool3")self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")self.pool4 = self.max_pool_2x2(self.conv4_3, "pool4")self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")self.pool5 = self.max_pool_2x2(self.conv5_3, "pool5")self.fc6 = self.fc_layer(self.pool5, "fc6") self.relu6 = tf.nn.relu(self.fc6) self.fc7 = self.fc_layer(self.relu6, "fc7")self.relu7 = tf.nn.relu(self.fc7)self.fc8 = self.fc_layer(self.relu7, "fc8")self.prob = tf.nn.softmax(self.fc8, name="prob")end_time = time.time() print(("time consuming: %f" % (end_time-start_time)))self.data_dict = None def conv_layer(self, x, name):with tf.variable_scope(name): w = self.get_conv_filter(name) conv = tf.nn.conv2d(x, w, [1, 1, 1, 1], padding='SAME') conv_biases = self.get_bias(name) result = tf.nn.relu(tf.nn.bias_add(conv, conv_biases)) return resultdef get_conv_filter(self, name):return tf.constant(self.data_dict[name][0], name="filter") def get_bias(self, name):return tf.constant(self.data_dict[name][1], name="biases")def max_pool_2x2(self, x, name):return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)def fc_layer(self, x, name):with tf.variable_scope(name): shape = x.get_shape().as_list() dim = 1for i in shape[1:]:dim *= i x = tf.reshape(x, [-1, dim])w = self.get_fc_weight(name) b = self.get_bias(name) result = tf.nn.bias_add(tf.matmul(x, w), b) return resultdef get_fc_weight(self, name):  return tf.constant(self.data_dict[name][0], name="weights")

utils.py 对图片的处理

from skimage import io, transform
# import numpy as np
import matplotlib.pyplot as plt
# import tensorflow as tf
from pylab import mplmpl.rcParams['font.sans-serif']=['SimHei'] # 正常显示中文标签
mpl.rcParams['axes.unicode_minus']=False # 正常显示正负号def load_image(path):fig = plt.figure("Centre and Resize")img = io.imread(path) img = img / 255.0 ax0 = fig.add_subplot(131)  ax0.set_xlabel(u'Original Picture') ax0.imshow(img) short_edge = min(img.shape[:2]) y = (img.shape[0] - short_edge) / 2  x = (img.shape[1] - short_edge) / 2 crop_img = img[y:y+short_edge, x:x+short_edge] ax1 = fig.add_subplot(132) ax1.set_xlabel(u"Centre Picture") ax1.imshow(crop_img)re_img = transform.resize(crop_img, (224, 224)) ax2 = fig.add_subplot(133) ax2.set_xlabel(u"Resize Picture") ax2.imshow(re_img)img_ready = re_img.reshape((1, 224, 224, 3))return img_readydef percent(value):return '%.2f%%' % (value * 100)

app.py 实现对图片的应用

#coding:utf-8
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import vgg16
import utils
from Nclasses import labelsimg_path = raw_input('Input the path and image name:')
# img_ready = utils.load_image(img_path
img_ready = utils.load_image(img_path)
print img_readyfig = plt.figure(u"Top-5 预测结果")with tf.Session() as sess:images = tf.placeholder(tf.float32, [1, 224, 224, 3])vgg = vgg16.Vgg16() vgg.forward(images) probability = sess.run(vgg.prob, feed_dict={images:img_ready})top5 = np.argsort(probability[0])[-1:-6:-1]print "top5:", top5values = []bar_label = []for n, i in enumerate(top5): print "n:",nprint "i:",ivalues.append(probability[0][i]) bar_label.append(labels[i]) print i, ":", labels[i], "----", utils.percent(probability[0][i]) ax = fig.add_subplot(111) ax.bar(range(len(values)), values, tick_label=bar_label, width=0.5, fc='g')ax.set_ylabel(u'probabilityit') ax.set_title(u'Top-5') for a, b in zip(range(len(values)), values):ax.text(a, b+0.0005, utils.percent(b), ha='center', va = 'bottom', fontsize=7)   plt.show()

标签对分类的准确标注

#!/usr/bin/python
#coding:utf-8
# 每个图像的真实标签，以及对应的索引值
labels = {0: 'tench\n Tinca tinca',1: 'goldfish\n Carassius auratus',2: 'great white shark\n white shark\n man-eater\n man-eating shark\n Carcharodon carcharias',3: 'tiger shark\n Galeocerdo cuvieri',4: 'hammerhead\n hammerhead shark',5: 'electric ray\n crampfish\n numbfish\n torpedo',6: 'stingray',7: 'cock',8: 'hen',9: 'ostrich\n Struthio camelus',10: 'brambling\n Fringilla montifringilla',11: 'goldfinch\n Carduelis carduelis',12: 'house finch\n linnet\n Carpodacus mexicanus',13: 'junco\n snowbird',14: 'indigo bunting\n indigo finch\n indigo bird\n Passerina cyanea',15: 'robin\n American robin\n Turdus migratorius',16: 'bulbul',17: 'jay',18: 'magpie',19: 'chickadee',20: 'water ouzel\n dipper',21: 'kite',22: 'bald eagle\n American eagle\n Haliaeetus leucocephalus',23: 'vulture',24: 'great grey owl\n great gray owl\n Strix nebulosa',25: 'European fire salamander\n Salamandra salamandra',26: 'common newt\n Triturus vulgaris',27: 'eft',28: 'spotted salamander\n Ambystoma maculatum',29: 'axolotl\n mud puppy\n Ambystoma mexicanum',30: 'bullfrog\n Rana catesbeiana',31: 'tree frog\n tree-frog',32: 'tailed frog\n bell toad\n ribbed toad\n tailed toad\n Ascaphus trui',33: 'loggerhead\n loggerhead turtle\n Caretta caretta',34: 'leatherback turtle\n leatherback\n leathery turtle\n Dermochelys coriacea',35: 'mud turtle',36: 'terrapin',37: 'box turtle\n box tortoise',38: 'banded gecko',39: 'common iguana\n iguana\n Iguana iguana',40: 'American chameleon\n anole\n Anolis carolinensis',41: 'whiptail\n whiptail lizard',42: 'agama',43: 'frilled lizard\n Chlamydosaurus kingi',44: 'alligator lizard',45: 'Gila monster\n Heloderma suspectum',46: 'green lizard\n Lacerta viridis',47: 'African chameleon\n Chamaeleo chamaeleon',48: 'Komodo dragon\n Komodo lizard\n dragon lizard\n giant lizard\n Varanus komodoensis',49: 'African crocodile\n Nile crocodile\n Crocodylus niloticus',50: 'American alligator\n Alligator mississipiensis',51: 'triceratops',52: 'thunder snake\n worm snake\n Carphophis amoenus',53: 'ringneck snake\n ring-necked snake\n ring snake',54: 'hognose snake\n puff adder\n sand viper',55: 'green snake\n grass snake',56: 'king snake\n kingsnake',57: 'garter snake\n grass snake',58: 'water snake',59: 'vine snake',60: 'night snake\n Hypsiglena torquata',61: 'boa constrictor\n Constrictor constrictor',62: 'rock python\n rock snake\n Python sebae',63: 'Indian cobra\n Naja naja',64: 'green mamba',65: 'sea snake',66: 'horned viper\n cerastes\n sand viper\n horned asp\n Cerastes cornutus',67: 'diamondback\n diamondback rattlesnake\n Crotalus adamanteus',68: 'sidewinder\n horned rattlesnake\n Crotalus cerastes',69: 'trilobite',70: 'harvestman\n daddy longlegs\n Phalangium opilio',71: 'scorpion',72: 'black and gold garden spider\n Argiope aurantia',73: 'barn spider\n Araneus cavaticus',74: 'garden spider\n Aranea diademata',75: 'black widow\n Latrodectus mactans',76: 'tarantula',77: 'wolf spider\n hunting spider',78: 'tick',79: 'centipede',80: 'black grouse',81: 'ptarmigan',82: 'ruffed grouse\n partridge\n Bonasa umbellus',83: 'prairie chicken\n prairie grouse\n prairie fowl',84: 'peacock',85: 'quail',86: 'partridge',87: 'African grey\n African gray\n Psittacus erithacus',88: 'macaw',89: 'sulphur-crested cockatoo\n Kakatoe galerita\n Cacatua galerita',90: 'lorikeet',91: 'coucal',92: 'bee eater',93: 'hornbill',94: 'hummingbird',95: 'jacamar',96: 'toucan',97: 'drake',98: 'red-breasted merganser\n Mergus serrator',99: 'goose',100: 'black swan\n Cygnus atratus',101: 'tusker',102: 'echidna\n spiny anteater\n anteater',103: 'platypus\n duckbill\n duckbilled platypus\n duck-billed platypus\n Ornithorhynchus anatinus',104: 'wallaby\n brush kangaroo',105: 'koala\n koala bear\n kangaroo bear\n native bear\n Phascolarctos cinereus',106: 'wombat',107: 'jellyfish',108: 'sea anemone\n anemone',109: 'brain coral',110: 'flatworm\n platyhelminth',111: 'nematode\n nematode worm\n roundworm',112: 'conch',113: 'snail',114: 'slug',115: 'sea slug\n nudibranch',116: 'chiton\n coat-of-mail shell\n sea cradle\n polyplacophore',117: 'chambered nautilus\n pearly nautilus\n nautilus',118: 'Dungeness crab\n Cancer magister',119: 'rock crab\n Cancer irroratus',120: 'fiddler crab',121: 'king crab\n Alaska crab\n Alaskan king crab\n Alaska king crab\n Paralithodes camtschatica',122: 'American lobster\n Northern lobster\n Maine lobster\n Homarus americanus',123: 'spiny lobster\n langouste\n rock lobster\n crawfish\n crayfish\n sea crawfish',124: 'crayfish\n crawfish\n crawdad\n crawdaddy',125: 'hermit crab',126: 'isopod',127: 'white stork\n Ciconia ciconia',128: 'black stork\n Ciconia nigra',129: 'spoonbill',130: 'flamingo',131: 'little blue heron\n Egretta caerulea',132: 'American egret\n great white heron\n Egretta albus',133: 'bittern',134: 'crane',135: 'limpkin\n Aramus pictus',136: 'European gallinule\n Porphyrio porphyrio',137: 'American coot\n marsh hen\n mud hen\n water hen\n Fulica americana',138: 'bustard',139: 'ruddy turnstone\n Arenaria interpres',140: 'red-backed sandpiper\n dunlin\n Erolia alpina',141: 'redshank\n Tringa totanus',142: 'dowitcher',143: 'oystercatcher\n oyster catcher',144: 'pelican',145: 'king penguin\n Aptenodytes patagonica',146: 'albatross\n mollymawk',147: 'grey whale\n gray whale\n devilfish\n Eschrichtius gibbosus\n Eschrichtius robustus',148: 'killer whale\n killer\n orca\n grampus\n sea wolf\n Orcinus orca',149: 'dugong\n Dugong dugon',150: 'sea lion',151: 'Chihuahua',152: 'Japanese spaniel',153: 'Maltese dog\n Maltese terrier\n Maltese',154: 'Pekinese\n Pekingese\n Peke',155: 'Shih-Tzu',156: 'Blenheim spaniel',157: 'papillon',158: 'toy terrier',159: 'Rhodesian ridgeback',160: 'Afghan hound\n Afghan',161: 'basset\n basset hound',162: 'beagle',163: 'bloodhound\n sleuthhound',164: 'bluetick',165: 'black-and-tan coonhound',166: 'Walker hound\n Walker foxhound',167: 'English foxhound',168: 'redbone',169: 'borzoi\n Russian wolfhound',170: 'Irish wolfhound',171: 'Italian greyhound',172: 'whippet',173: 'Ibizan hound\n Ibizan Podenco',174: 'Norwegian elkhound\n elkhound',175: 'otterhound\n otter hound',176: 'Saluki\n gazelle hound',177: 'Scottish deerhound\n deerhound',178: 'Weimaraner',179: 'Staffordshire bullterrier\n Staffordshire bull terrier',180: 'American Staffordshire terrier\n Staffordshire terrier\n American pit bull terrier\n pit bull terrier',181: 'Bedlington terrier',182: 'Border terrier',183: 'Kerry blue terrier',184: 'Irish terrier',185: 'Norfolk terrier',186: 'Norwich terrier',187: 'Yorkshire terrier',188: 'wire-haired fox terrier',189: 'Lakeland terrier',190: 'Sealyham terrier\n Sealyham',191: 'Airedale\n Airedale terrier',192: 'cairn\n cairn terrier',193: 'Australian terrier',194: 'Dandie Dinmont\n Dandie Dinmont terrier',195: 'Boston bull\n Boston terrier',196: 'miniature schnauzer',197: 'giant schnauzer',198: 'standard schnauzer',199: 'Scotch terrier\n Scottish terrier\n Scottie',200: 'Tibetan terrier\n chrysanthemum dog',201: 'silky terrier\n Sydney silky',202: 'soft-coated wheaten terrier',203: 'West Highland white terrier',204: 'Lhasa\n Lhasa apso',205: 'flat-coated retriever',206: 'curly-coated retriever',207: 'golden retriever',208: 'Labrador retriever',209: 'Chesapeake Bay retriever',210: 'German short-haired pointer',211: 'vizsla\n Hungarian pointer',212: 'English setter',213: 'Irish setter\n red setter',214: 'Gordon setter',215: 'Brittany spaniel',216: 'clumber\n clumber spaniel',217: 'English springer\n English springer spaniel',218: 'Welsh springer spaniel',219: 'cocker spaniel\n English cocker spaniel\n cocker',220: 'Sussex spaniel',221: 'Irish water spaniel',222: 'kuvasz',223: 'schipperke',224: 'groenendael',225: 'malinois',226: 'briard',227: 'kelpie',228: 'komondor',229: 'Old English sheepdog\n bobtail',230: 'Shetland sheepdog\n Shetland sheep dog\n Shetland',231: 'collie',232: 'Border collie',233: 'Bouvier des Flandres\n Bouviers des Flandres',234: 'Rottweiler',235: 'German shepherd\n German shepherd dog\n German police dog\n alsatian',236: 'Doberman\n Doberman pinscher',237: 'miniature pinscher',238: 'Greater Swiss Mountain dog',239: 'Bernese mountain dog',240: 'Appenzeller',241: 'EntleBucher',242: 'boxer',243: 'bull mastiff',244: 'Tibetan mastiff',245: 'French bulldog',246: 'Great Dane',247: 'Saint Bernard\n St Bernard',248: 'Eskimo dog\n husky',249: 'malamute\n malemute\n Alaskan malamute',250: 'Siberian husky',251: 'dalmatian\n coach dog\n carriage dog',252: 'affenpinscher\n monkey pinscher\n monkey dog',253: 'basenji',254: 'pug\n pug-dog',255: 'Leonberg',256: 'Newfoundland\n Newfoundland dog',257: 'Great Pyrenees',258: 'Samoyed\n Samoyede',259: 'Pomeranian',260: 'chow\n chow chow',261: 'keeshond',262: 'Brabancon griffon',263: 'Pembroke\n Pembroke Welsh corgi',264: 'Cardigan\n Cardigan Welsh corgi',265: 'toy poodle',266: 'miniature poodle',267: 'standard poodle',268: 'Mexican hairless',269: 'timber wolf\n grey wolf\n gray wolf\n Canis lupus',270: 'white wolf\n Arctic wolf\n Canis lupus tundrarum',271: 'red wolf\n maned wolf\n Canis rufus\n Canis niger',272: 'coyote\n prairie wolf\n brush wolf\n Canis latrans',273: 'dingo\n warrigal\n warragal\n Canis dingo',274: 'dhole\n Cuon alpinus',275: 'African hunting dog\n hyena dog\n Cape hunting dog\n Lycaon pictus',276: 'hyena\n hyaena',277: 'red fox\n Vulpes vulpes',278: 'kit fox\n Vulpes macrotis',279: 'Arctic fox\n white fox\n Alopex lagopus',280: 'grey fox\n gray fox\n Urocyon cinereoargenteus',281: 'tabby\n tabby cat',282: 'tiger cat',283: 'Persian cat',284: 'Siamese cat\n Siamese',285: 'Egyptian cat',286: 'cougar\n puma\n catamount\n mountain lion\n painter\n panther\n Felis concolor',287: 'lynx\n catamount',288: 'leopard\n Panthera pardus',289: 'snow leopard\n ounce\n Panthera uncia',290: 'jaguar\n panther\n Panthera onca\n Felis onca',291: 'lion\n king of beasts\n Panthera leo',292: 'tiger\n Panthera tigris',293: 'cheetah\n chetah\n Acinonyx jubatus',294: 'brown bear\n bruin\n Ursus arctos',295: 'American black bear\n black bear\n Ursus americanus\n Euarctos americanus',296: 'ice bear\n polar bear\n Ursus Maritimus\n Thalarctos maritimus',297: 'sloth bear\n Melursus ursinus\n Ursus ursinus',298: 'mongoose',299: 'meerkat\n mierkat',300: 'tiger beetle',301: 'ladybug\n ladybeetle\n lady beetle\n ladybird\n ladybird beetle',302: 'ground beetle\n carabid beetle',303: 'long-horned beetle\n longicorn\n longicorn beetle',304: 'leaf beetle\n chrysomelid',305: 'dung beetle',306: 'rhinoceros beetle',307: 'weevil',308: 'fly',309: 'bee',310: 'ant\n emmet\n pismire',311: 'grasshopper\n hopper',312: 'cricket',313: 'walking stick\n walkingstick\n stick insect',314: 'cockroach\n roach',315: 'mantis\n mantid',316: 'cicada\n cicala',317: 'leafhopper',318: 'lacewing\n lacewing fly',319: "dragonfly\n darning needle\n devil's darning needle\n sewing needle\n snake feeder\n snake doctor\n mosquito hawk\n skeeter hawk",320: 'damselfly',321: 'admiral',322: 'ringlet\n ringlet butterfly',323: 'monarch\n monarch butterfly\n milkweed butterfly\n Danaus plexippus',324: 'cabbage butterfly',325: 'sulphur butterfly\n sulfur butterfly',326: 'lycaenid\n lycaenid butterfly',327: 'starfish\n sea star',328: 'sea urchin',329: 'sea cucumber\n holothurian',330: 'wood rabbit\n cottontail\n cottontail rabbit',331: 'hare',332: 'Angora\n Angora rabbit',333: 'hamster',334: 'porcupine\n hedgehog',335: 'fox squirrel\n eastern fox squirrel\n Sciurus niger',336: 'marmot',337: 'beaver',338: 'guinea pig\n Cavia cobaya',339: 'sorrel',340: 'zebra',341: 'hog\n pig\n grunter\n squealer\n Sus scrofa',342: 'wild boar\n boar\n Sus scrofa',343: 'warthog',344: 'hippopotamus\n hippo\n river horse\n Hippopotamus amphibius',345: 'ox',346: 'water buffalo\n water ox\n Asiatic buffalo\n Bubalus bubalis',347: 'bison',348: 'ram\n tup',349: 'bighorn\n bighorn sheep\n cimarron\n Rocky Mountain bighorn\n Rocky Mountain sheep\n Ovis canadensis',350: 'ibex\n Capra ibex',351: 'hartebeest',352: 'impala\n Aepyceros melampus',353: 'gazelle',354: 'Arabian camel\n dromedary\n Camelus dromedarius',355: 'llama',356: 'weasel',357: 'mink',358: 'polecat\n fitch\n foulmart\n foumart\n Mustela putorius',359: 'black-footed ferret\n ferret\n Mustela nigripes',360: 'otter',361: 'skunk\n polecat\n wood pussy',362: 'badger',363: 'armadillo',364: 'three-toed sloth\n ai\n Bradypus tridactylus',365: 'orangutan\n orang\n orangutang\n Pongo pygmaeus',366: 'gorilla\n Gorilla gorilla',367: 'chimpanzee\n chimp\n Pan troglodytes',368: 'gibbon\n Hylobates lar',369: 'siamang\n Hylobates syndactylus\n Symphalangus syndactylus',370: 'guenon\n guenon monkey',371: 'patas\n hussar monkey\n Erythrocebus patas',372: 'baboon',373: 'macaque',374: 'langur',375: 'colobus\n colobus monkey',376: 'proboscis monkey\n Nasalis larvatus',377: 'marmoset',378: 'capuchin\n ringtail\n Cebus capucinus',379: 'howler monkey\n howler',380: 'titi\n titi monkey',381: 'spider monkey\n Ateles geoffroyi',382: 'squirrel monkey\n Saimiri sciureus',383: 'Madagascar cat\n ring-tailed lemur\n Lemur catta',384: 'indri\n indris\n Indri indri\n Indri brevicaudatus',385: 'Indian elephant\n Elephas maximus',386: 'African elephant\n Loxodonta africana',387: 'lesser panda\n red panda\n panda\n bear cat\n cat bear\n Ailurus fulgens',388: 'giant panda\n panda\n panda bear\n coon bear\n Ailuropoda melanoleuca',389: 'barracouta\n snoek',390: 'eel',391: 'coho\n cohoe\n coho salmon\n blue jack\n silver salmon\n Oncorhynchus kisutch',392: 'rock beauty\n Holocanthus tricolor',393: 'anemone fish',394: 'sturgeon',395: 'gar\n garfish\n garpike\n billfish\n Lepisosteus osseus',396: 'lionfish',397: 'puffer\n pufferfish\n blowfish\n globefish',398: 'abacus',399: 'abaya',400: "academic gown\n academic robe\n judge's robe",401: 'accordion\n piano accordion\n squeeze box',402: 'acoustic guitar',403: 'aircraft carrier\n carrier\n flattop\n attack aircraft carrier',404: 'airliner',405: 'airship\n dirigible',406: 'altar',407: 'ambulance',408: 'amphibian\n amphibious vehicle',409: 'analog clock',410: 'apiary\n bee house',411: 'apron',412: 'ashcan\n trash can\n garbage can\n wastebin\n ash bin\n ash-bin\n ashbin\n dustbin\n trash barrel\n trash bin',413: 'assault rifle\n assault gun',414: 'backpack\n back pack\n knapsack\n packsack\n rucksack\n haversack',415: 'bakery\n bakeshop\n bakehouse',416: 'balance beam\n beam',417: 'balloon',418: 'ballpoint\n ballpoint pen\n ballpen\n Biro',419: 'Band Aid',420: 'banjo',421: 'bannister\n banister\n balustrade\n balusters\n handrail',422: 'barbell',423: 'barber chair',424: 'barbershop',425: 'barn',426: 'barometer',427: 'barrel\n cask',428: 'barrow\n garden cart\n lawn cart\n wheelbarrow',429: 'baseball',430: 'basketball',431: 'bassinet',432: 'bassoon',433: 'bathing cap\n swimming cap',434: 'bath towel',435: 'bathtub\n bathing tub\n bath\n tub',436: 'beach wagon\n station wagon\n wagon\n estate car\n beach waggon\n station waggon\n waggon',437: 'beacon\n lighthouse\n beacon light\n pharos',438: 'beaker',439: 'bearskin\n busby\n shako',440: 'beer bottle',441: 'beer glass',442: 'bell cote\n bell cot',443: 'bib',444: 'bicycle-built-for-two\n tandem bicycle\n tandem',445: 'bikini\n two-piece',446: 'binder\n ring-binder',447: 'binoculars\n field glasses\n opera glasses',448: 'birdhouse',449: 'boathouse',450: 'bobsled\n bobsleigh\n bob',451: 'bolo tie\n bolo\n bola tie\n bola',452: 'bonnet\n poke bonnet',453: 'bookcase',454: 'bookshop\n bookstore\n bookstall',455: 'bottlecap',456: 'bow',457: 'bow tie\n bow-tie\n bowtie',458: 'brass\n memorial tablet\n plaque',459: 'brassiere\n bra\n bandeau',460: 'breakwater\n groin\n groyne\n mole\n bulwark\n seawall\n jetty',461: 'breastplate\n aegis\n egis',462: 'broom',463: 'bucket\n pail',464: 'buckle',465: 'bulletproof vest',466: 'bullet train\n bullet',467: 'butcher shop\n meat market',468: 'cab\n hack\n taxi\n taxicab',469: 'caldron\n cauldron',470: 'candle\n taper\n wax light',471: 'cannon',472: 'canoe',473: 'can opener\n tin opener',474: 'cardigan',475: 'car mirror',476: 'carousel\n carrousel\n merry-go-round\n roundabout\n whirligig',477: "carpenter's kit\n tool kit",478: 'carton',479: 'car wheel',480: 'cash machine\n cash dispenser\n automated teller machine\n automatic teller machine\n automated teller\n automatic teller\n ATM',481: 'cassette',482: 'cassette player',483: 'castle',484: 'catamaran',485: 'CD player',486: 'cello\n violoncello',487: 'cellular telephone\n cellular phone\n cellphone\n cell\n mobile phone',488: 'chain',489: 'chainlink fence',490: 'chain mail\n ring mail\n mail\n chain armor\n chain armour\n ring armor\n ring armour',491: 'chain saw\n chainsaw',492: 'chest',493: 'chiffonier\n commode',494: 'chime\n bell\n gong',495: 'china cabinet\n china closet',496: 'Christmas stocking',497: 'church\n church building',498: 'cinema\n movie theater\n movie theatre\n movie house\n picture palace',499: 'cleaver\n meat cleaver\n chopper',500: 'cliff dwelling',501: 'cloak',502: 'clog\n geta\n patten\n sabot',503: 'cocktail shaker',504: 'coffee mug',505: 'coffeepot',506: 'coil\n spiral\n volute\n whorl\n helix',507: 'combination lock',508: 'computer keyboard\n keypad',509: 'confectionery\n confectionary\n candy store',510: 'container ship\n containership\n container vessel',511: 'convertible',512: 'corkscrew\n bottle screw',513: 'cornet\n horn\n trumpet\n trump',514: 'cowboy boot',515: 'cowboy hat\n ten-gallon hat',516: 'cradle',517: 'crane',518: 'crash helmet',519: 'crate',520: 'crib\n cot',521: 'Crock Pot',522: 'croquet ball',523: 'crutch',524: 'cuirass',525: 'dam\n dike\n dyke',526: 'desk',527: 'desktop computer',528: 'dial telephone\n dial phone',529: 'diaper\n nappy\n napkin',530: 'digital clock',531: 'digital watch',532: 'dining table\n board',533: 'dishrag\n dishcloth',534: 'dishwasher\n dish washer\n dishwashing machine',535: 'disk brake\n disc brake',536: 'dock\n dockage\n docking facility',537: 'dogsled\n dog sled\n dog sleigh',538: 'dome',539: 'doormat\n welcome mat',540: 'drilling platform\n offshore rig',541: 'drum\n membranophone\n tympan',542: 'drumstick',543: 'dumbbell',544: 'Dutch oven',545: 'electric fan\n blower',546: 'electric guitar',547: 'electric locomotive',548: 'entertainment center',549: 'envelope',550: 'espresso maker',551: 'face powder',552: 'feather boa\n boa',553: 'file\n file cabinet\n filing cabinet',554: 'fireboat',555: 'fire engine\n fire truck',556: 'fire screen\n fireguard',557: 'flagpole\n flagstaff',558: 'flute\n transverse flute',559: 'folding chair',560: 'football helmet',561: 'forklift',562: 'fountain',563: 'fountain pen',564: 'four-poster',565: 'freight car',566: 'French horn\n horn',567: 'frying pan\n frypan\n skillet',568: 'fur coat',569: 'garbage truck\n dustcart',570: 'gasmask\n respirator\n gas helmet',571: 'gas pump\n gasoline pump\n petrol pump\n island dispenser',572: 'goblet',573: 'go-kart',574: 'golf ball',575: 'golfcart\n golf cart',576: 'gondola',577: 'gong\n tam-tam',578: 'gown',579: 'grand piano\n grand',580: 'greenhouse\n nursery\n glasshouse',581: 'grille\n radiator grille',582: 'grocery store\n grocery\n food market\n market',583: 'guillotine',584: 'hair slide',585: 'hair spray',586: 'half track',587: 'hammer',588: 'hamper',589: 'hand blower\n blow dryer\n blow drier\n hair dryer\n hair drier',590: 'hand-held computer\n hand-held microcomputer',591: 'handkerchief\n hankie\n hanky\n hankey',592: 'hard disc\n hard disk\n fixed disk',593: 'harmonica\n mouth organ\n harp\n mouth harp',594: 'harp',595: 'harvester\n reaper',596: 'hatchet',597: 'holster',598: 'home theater\n home theatre',599: 'honeycomb',600: 'hook\n claw',601: 'hoopskirt\n crinoline',602: 'horizontal bar\n high bar',603: 'horse cart\n horse-cart',604: 'hourglass',605: 'iPod',606: 'iron\n smoothing iron',607: "jack-o'-lantern",608: 'jean\n blue jean\n denim',609: 'jeep\n landrover',610: 'jersey\n T-shirt\n tee shirt',611: 'jigsaw puzzle',612: 'jinrikisha\n ricksha\n rickshaw',613: 'joystick',614: 'kimono',615: 'knee pad',616: 'knot',617: 'lab coat\n laboratory coat',618: 'ladle',619: 'lampshade\n lamp shade',620: 'laptop\n laptop computer',621: 'lawn mower\n mower',622: 'lens cap\n lens cover',623: 'letter opener\n paper knife\n paperknife',624: 'library',625: 'lifeboat',626: 'lighter\n light\n igniter\n ignitor',627: 'limousine\n limo',628: 'liner\n ocean liner',629: 'lipstick\n lip rouge',630: 'Loafer',631: 'lotion',632: 'loudspeaker\n speaker\n speaker unit\n loudspeaker system\n speaker system',633: "loupe\n jeweler's loupe",634: 'lumbermill\n sawmill',635: 'magnetic compass',636: 'mailbag\n postbag',637: 'mailbox\n letter box',638: 'maillot',639: 'maillot\n tank suit',640: 'manhole cover',641: 'maraca',642: 'marimba\n xylophone',643: 'mask',644: 'matchstick',645: 'maypole',646: 'maze\n labyrinth',647: 'measuring cup',648: 'medicine chest\n medicine cabinet',649: 'megalith\n megalithic structure',650: 'microphone\n mike',651: 'microwave\n microwave oven',652: 'military uniform',653: 'milk can',654: 'minibus',655: 'miniskirt\n mini',656: 'minivan',657: 'missile',658: 'mitten',659: 'mixing bowl',660: 'mobile home\n manufactured home',661: 'Model T',662: 'modem',663: 'monastery',664: 'monitor',665: 'moped',666: 'mortar',667: 'mortarboard',668: 'mosque',669: 'mosquito net',670: 'motor scooter\n scooter',671: 'mountain bike\n all-terrain bike\n off-roader',672: 'mountain tent',673: 'mouse\n computer mouse',674: 'mousetrap',675: 'moving van',676: 'muzzle',677: 'nail',678: 'neck brace',679: 'necklace',680: 'nipple',681: 'notebook\n notebook computer',682: 'obelisk',683: 'oboe\n hautboy\n hautbois',684: 'ocarina\n sweet potato',685: 'odometer\n hodometer\n mileometer\n milometer',686: 'oil filter',687: 'organ\n pipe organ',688: 'oscilloscope\n scope\n cathode-ray oscilloscope\n CRO',689: 'overskirt',690: 'oxcart',691: 'oxygen mask',692: 'packet',693: 'paddle\n boat paddle',694: 'paddlewheel\n paddle wheel',695: 'padlock',696: 'paintbrush',697: "pajama\n pyjama\n pj's\n jammies",698: 'palace',699: 'panpipe\n pandean pipe\n syrinx',700: 'paper towel',701: 'parachute\n chute',702: 'parallel bars\n bars',703: 'park bench',704: 'parking meter',705: 'passenger car\n coach\n carriage',706: 'patio\n terrace',707: 'pay-phone\n pay-station',708: 'pedestal\n plinth\n footstall',709: 'pencil box\n pencil case',710: 'pencil sharpener',711: 'perfume\n essence',712: 'Petri dish',713: 'photocopier',714: 'pick\n plectrum\n plectron',715: 'pickelhaube',716: 'picket fence\n paling',717: 'pickup\n pickup truck',718: 'pier',719: 'piggy bank\n penny bank',720: 'pill bottle',721: 'pillow',722: 'ping-pong ball',723: 'pinwheel',724: 'pirate\n pirate ship',725: 'pitcher\n ewer',726: "plane\n carpenter's plane\n woodworking plane",727: 'planetarium',728: 'plastic bag',729: 'plate rack',730: 'plow\n plough',731: "plunger\n plumber's helper",732: 'Polaroid camera\n Polaroid Land camera',733: 'pole',734: 'police van\n police wagon\n paddy wagon\n patrol wagon\n wagon\n black Maria',735: 'poncho',736: 'pool table\n billiard table\n snooker table',737: 'pop bottle\n soda bottle',738: 'pot\n flowerpot',739: "potter's wheel",740: 'power drill',741: 'prayer rug\n prayer mat',742: 'printer',743: 'prison\n prison house',744: 'projectile\n missile',745: 'projector',746: 'puck\n hockey puck',747: 'punching bag\n punch bag\n punching ball\n punchball',748: 'purse',749: 'quill\n quill pen',750: 'quilt\n comforter\n comfort\n puff',751: 'racer\n race car\n racing car',752: 'racket\n racquet',753: 'radiator',754: 'radio\n wireless',755: 'radio telescope\n radio reflector',756: 'rain barrel',757: 'recreational vehicle\n RV\n R.V.',758: 'reel',759: 'reflex camera',760: 'refrigerator\n icebox',761: 'remote control\n remote',762: 'restaurant\n eating house\n eating place\n eatery',763: 'revolver\n six-gun\n six-shooter',764: 'rifle',765: 'rocking chair\n rocker',766: 'rotisserie',767: 'rubber eraser\n rubber\n pencil eraser',768: 'rugby ball',769: 'rule\n ruler',770: 'running shoe',771: 'safe',772: 'safety pin',773: 'saltshaker\n salt shaker',774: 'sandal',775: 'sarong',776: 'sax\n saxophone',777: 'scabbard',778: 'scale\n weighing machine',779: 'school bus',780: 'schooner',781: 'scoreboard',782: 'screen\n CRT screen',783: 'screw',784: 'screwdriver',785: 'seat belt\n seatbelt',786: 'sewing machine',787: 'shield\n buckler',788: 'shoe shop\n shoe-shop\n shoe store',789: 'shoji',790: 'shopping basket',791: 'shopping cart',792: 'shovel',793: 'shower cap',794: 'shower curtain',795: 'ski',796: 'ski mask',797: 'sleeping bag',798: 'slide rule\n slipstick',799: 'sliding door',800: 'slot\n one-armed bandit',801: 'snorkel',802: 'snowmobile',803: 'snowplow\n snowplough',804: 'soap dispenser',805: 'soccer ball',806: 'sock',807: 'solar dish\n solar collector\n solar furnace',808: 'sombrero',809: 'soup bowl',810: 'space bar',811: 'space heater',812: 'space shuttle',813: 'spatula',814: 'speedboat',815: "spider web\n spider's web",816: 'spindle',817: 'sports car\n sport car',818: 'spotlight\n spot',819: 'stage',820: 'steam locomotive',821: 'steel arch bridge',822: 'steel drum',823: 'stethoscope',824: 'stole',825: 'stone wall',826: 'stopwatch\n stop watch',827: 'stove',828: 'strainer',829: 'streetcar\n tram\n tramcar\n trolley\n trolley car',830: 'stretcher',831: 'studio couch\n day bed',832: 'stupa\n tope',833: 'submarine\n pigboat\n sub\n U-boat',834: 'suit\n suit of clothes',835: 'sundial',836: 'sunglass',837: 'sunglasses\n dark glasses\n shades',838: 'sunscreen\n sunblock\n sun blocker',839: 'suspension bridge',840: 'swab\n swob\n mop',841: 'sweatshirt',842: 'swimming trunks\n bathing trunks',843: 'swing',844: 'switch\n electric switch\n electrical switch',845: 'syringe',846: 'table lamp',847: 'tank\n army tank\n armored combat vehicle\n armoured combat vehicle',848: 'tape player',849: 'teapot',850: 'teddy\n teddy bear',851: 'television\n television system',852: 'tennis ball',853: 'thatch\n thatched roof',854: 'theater curtain\n theatre curtain',855: 'thimble',856: 'thresher\n thrasher\n threshing machine',857: 'throne',858: 'tile roof',859: 'toaster',860: 'tobacco shop\n tobacconist shop\n tobacconist',861: 'toilet seat',862: 'torch',863: 'totem pole',864: 'tow truck\n tow car\n wrecker',865: 'toyshop',866: 'tractor',867: 'trailer truck\n tractor trailer\n trucking rig\n rig\n articulated lorry\n semi',868: 'tray',869: 'trench coat',870: 'tricycle\n trike\n velocipede',871: 'trimaran',872: 'tripod',873: 'triumphal arch',874: 'trolleybus\n trolley coach\n trackless trolley',875: 'trombone',876: 'tub\n vat',877: 'turnstile',878: 'typewriter keyboard',879: 'umbrella',880: 'unicycle\n monocycle',881: 'upright\n upright piano',882: 'vacuum\n vacuum cleaner',883: 'vase',884: 'vault',885: 'velvet',886: 'vending machine',887: 'vestment',888: 'viaduct',889: 'violin\n fiddle',890: 'volleyball',891: 'waffle iron',892: 'wall clock',893: 'wallet\n billfold\n notecase\n pocketbook',894: 'wardrobe\n closet\n press',895: 'warplane\n military plane',896: 'washbasin\n handbasin\n washbowl\n lavabo\n wash-hand basin',897: 'washer\n automatic washer\n washing machine',898: 'water bottle',899: 'water jug',900: 'water tower',901: 'whiskey jug',902: 'whistle',903: 'wig',904: 'window screen',905: 'window shade',906: 'Windsor tie',907: 'wine bottle',908: 'wing',909: 'wok',910: 'wooden spoon',911: 'wool\n woolen\n woollen',912: 'worm fence\n snake fence\n snake-rail fence\n Virginia fence',913: 'wreck',914: 'yawl',915: 'yurt',916: 'web site\n website\n internet site\n site',917: 'comic book',918: 'crossword puzzle\n crossword',919: 'street sign',920: 'traffic light\n traffic signal\n stoplight',921: 'book jacket\n dust cover\n dust jacket\n dust wrapper',922: 'menu',923: 'plate',924: 'guacamole',925: 'consomme',926: 'hot pot\n hotpot',927: 'trifle',928: 'ice cream\n icecream',929: 'ice lolly\n lolly\n lollipop\n popsicle',930: 'French loaf',931: 'bagel\n beigel',932: 'pretzel',933: 'cheeseburger',934: 'hotdog\n hot dog\n red hot',935: 'mashed potato',936: 'head cabbage',937: 'broccoli',938: 'cauliflower',939: 'zucchini\n courgette',940: 'spaghetti squash',941: 'acorn squash',942: 'butternut squash',943: 'cucumber\n cuke',944: 'artichoke\n globe artichoke',945: 'bell pepper',946: 'cardoon',947: 'mushroom',948: 'Granny Smith',949: 'strawberry',950: 'orange',951: 'lemon',952: 'fig',953: 'pineapple\n ananas',954: 'banana',955: 'jackfruit\n jak\n jack',956: 'custard apple',957: 'pomegranate',958: 'hay',959: 'carbonara',960: 'chocolate sauce\n chocolate syrup',961: 'dough',962: 'meat loaf\n meatloaf',963: 'pizza\n pizza pie',964: 'potpie',965: 'burrito',966: 'red wine',967: 'espresso',968: 'cup',969: 'eggnog',970: 'alp',971: 'bubble',972: 'cliff\n drop\n drop-off',973: 'coral reef',974: 'geyser',975: 'lakeside\n lakeshore',976: 'promontory\n headland\n head\n foreland',977: 'sandbar\n sand bar',978: 'seashore\n coast\n seacoast\n sea-coast',979: 'valley\n vale',980: 'volcano',981: 'ballplayer\n baseball player',982: 'groom\n bridegroom',983: 'scuba diver',984: 'rapeseed',985: 'daisy',986: "yellow lady's slipper\n yellow lady-slipper\n Cypripedium calceolus\n Cypripedium parviflorum",987: 'corn',988: 'acorn',989: 'hip\n rose hip\n rosehip',990: 'buckeye\n horse chestnut\n conker',991: 'coral fungus',992: 'agaric',993: 'gyromitra',994: 'stinkhorn\n carrion fungus',995: 'earthstar',996: 'hen-of-the-woods\n hen of the woods\n Polyporus frondosus\n Grifola frondosa',997: 'bolete',998: 'ear\n spike\n capitulum',999: 'toilet tissue\n toilet paper\n bathroom tissue'}

tensorflow 随笔－－－－－－－－－－－ＶGG网络的模型的复现相关推荐

tensorflow随笔——VGG网络
这次用slim搭个稍微大一点的网络VGG16,VGG16和VGG19实际上差不多,所以本例程的代码以VGG16来做5类花的分类任务. VGG网络相比之前的LeNet,AlexNet引入如下几个特点: ...
tensorflow随笔——LeNet网络
最近总想写点东西,把以前的这些网络都翻出来自己实现一遍.计划上从经典的分类网络到目标检测再到目标分割的都过一下.这篇从最简单的LeNet写起. 先上一张经典的LeNet模型结果图: 该网络结构包含2个 ...
CNN经典之VGG网络+PyTorch复现
一.前情说明: 写在前面的话本系列博客在于汇总CSDN的精华帖,类似自用笔记,方便以后的复习回顾,博文中的引用都注明出处,并点赞收藏原博主. 博客大致分为两部分,第一部是转载于<雪饼>大 ...
首次运行 tensorflow 项目之 vgg 网络
首次运行 tensorflow 项目之 vgg 网络文章目录 1. 下载所需文件 2. 在 pycharm 中打开项目 3. 为项目设置 python 编译器 4. 调试代码 1. train.py ...
slim php dd model,第二十四节，TensorFlow下slim库函数的使用以及使用VGG网络进行预训练、迁移学习(附代码)...
在介绍这一节之前,需要你对slim模型库有一些基本了解,具体可以参考第二十二节,TensorFlow中的图片分类模型库slim的使用.数据集处理,这一节我们会详细介绍slim模型库下面的一些函数的使用 ...
【转载】计算机视觉～VGG网络
[转载:一文读懂VGG网络 - Amusi的文章 - 知乎 https://zhuanlan.zhihu.com/p/41423739] 目录前言 VGG原理 VGG网络结构 VGG优缺点 VGG优 ...
使用PaddleFluid和TensorFlow实现图像分类网络SE_ResNeXt | 文末超大福利
专栏介绍:Paddle Fluid 是用来让用户像 PyTorch 和 Tensorflow Eager Execution 一样执行程序.在这些系统中,不再有模型这个概念,应用也不再包含一个用于描述 ...
TensorFlow载入VGG并可视化每层
一.简介 VGG网络在2014年的 ILSVRC localization and classification 两个问题上分别取得了第一名和第二名.VGG网络非常深,通常有16-19层,如果自己训练 ...
tensorflow随笔——图像分类、检测，语义分割综述
看到一篇关于目标分类/检测/风格的综述型文章,觉得写的挺好,收藏下. 本文旨在介绍深度学习在计算机视觉领域四大基本任务中的应用,包括分类(图a).定位.检测(图b).语义分割(图c).和实例分割(图d ...

tensorflow 随笔－－－－－－－－－－－ＶGG网络的模型的复现

tensorflow 随笔－－－－－－－－－－－ＶGG网络的模型的复现相关推荐

最新文章

热门文章