TensorFlow北大公開課學習筆記-8 復現vgg16並實現圖片識別
注:本系列文章主要是復現北京大學TensorFlow筆記中的程式碼,方便以後使用,並沒有詳細講解流程,因為我並不是專門做教程的。何況北大的教程講的已經很好了,有需要了解詳細過程的可以去看北大的教程哈。
一,app.py
#coding:utf-8 import numpy as np # Linux 伺服器沒有 GUI 的情況下使用 matplotlib 繪圖,必須置於 pyplot 之前 #import matplotlib #matplotlib.use('Agg') import tensorflow as tf import matplotlib.pyplot as plt # 下面三個是引用自定義模組 import vgg16 import utils from Nclasses import labels img_path = input('Input the path and image name:') img_ready = utils.load_image(img_path) # 呼叫 load_image()函式,對待測試的影象做一些預處理操作 #定義一個 figure 畫圖視窗,並指定視窗的名稱,也可以設定視窗修的大小 fig=plt.figure(u"Top-5 預測結果") with tf.Session() as sess: # 定義一個維度為[1,224,224,3],型別為 float32 的 tensor 佔位符 x = tf.placeholder(tf.float32, [1, 224, 224, 3]) vgg = vgg16.Vgg16() # 類 Vgg16 例項化出 vgg # 呼叫類的成員方法 forward(),並傳入待測試影象,這也就是網路前向傳播的過程 vgg.forward(x) # 將一個 batch 的資料喂入網路,得到網路的預測輸出 probability = sess.run(vgg.prob, feed_dict={x:img_ready}) # np.argsort 函式返回預測值(probability 的資料結構[[各預測類別的概率值]])由小到大的索引值, # 並取出預測概率最大的五個索引值 top5 = np.argsort(probability[0])[-1:-6:-1] print ("top5:",top5) # 定義兩個 list---對應的概率值和實際標籤(zebra) values = [] bar_label = [] for n, i in enumerate(top5): # 列舉上面取出的五個索引值 print ("n:",n) print ("i:",i) values.append(probability[0][i]) # 將索引值對應的預測概率值取出並放入 values bar_label.append(labels[i]) # 根據索引值取出對應的實際標籤並放入 bar_label print (i, ":", labels[i], "----", utils.percent(probability[0][i])) # 列印屬於某個類別的概率 ax = fig.add_subplot(111) # 將畫布劃分為一行一列,並把下圖放入其中 # bar()函式繪製柱狀圖,引數 range(len(values)是柱子下標, values 表示柱高的列表(也就是五個預測概率值, # tick_label 是每個柱子上顯示的標籤(實際對應的標籤), width 是柱子的寬度, fc 是柱子的顏色) ax.bar(range(len(values)), values, tick_label=bar_label, width=0.5, fc='g') ax.set_ylabel(u'probability') # 設定橫軸標籤 ax.set_title(u'Top-5') # 新增標題 for a,b in zip(range(len(values)), values): # 在每個柱子的頂端新增對應的預測概率值, a, b 表示座標, b+0.0005 表示要把文字資訊放置在高於每個柱子頂端 #0.0005 的位置, # center 是表示文字位於柱子頂端水平方向上的的中間位置, bottom 是將文字水平放置在柱子頂端垂直方向上的底端 #位置, fontsize 是字號 ax.text(a, b+0.0005, utils.percent(b), ha='center', va = 'bottom', fontsize=7) plt.savefig('./result.jpg') # 儲存圖片 plt.show() # 彈窗展示影象(linux 伺服器上將該句註釋掉)
#!/usr/bin/python #coding:utf-8 # 每個影象的真實標籤,以及對應的索引值 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'}
#!/usr/bin/python #coding:utf-8 from skimage import io, transform import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from pylab import mpl mpl.rcParams['font.sans-serif']=['SimHei'] # 正常顯示中文標籤 mpl.rcParams['axes.unicode_minus']=False # 正常顯示正負號 def load_image(path): fig = plt.figure("Centre and Resize") img = io.imread(path) # 根據傳入的路徑讀入圖片 img = img / 255.0 # 將畫素歸一化到[0,1] # 將該畫布分為一行三列 ax0 = fig.add_subplot(131) # 把下面的影象放在該畫布的第一個位置 ax0.set_xlabel(u'Original Picture') # 新增子標籤 ax0.imshow(img) # 新增展示該影象 short_edge = min(img.shape[:2]) # 找到該影象的最短邊 y = (img.shape[0] - short_edge) // 2 x = (img.shape[1] - short_edge) // 2 # 把影象的 w 和 h 分別減去最短邊,並求平均 crop_img = img[y:y+short_edge, x:x+short_edge] # 取出切分出的中心影象 print(crop_img.shape) ax1 = fig.add_subplot(132) # 把下面的影象放在該畫布的第二個位置 ax1.set_xlabel(u"Centre Picture") # 新增子標籤 ax1.imshow(crop_img) re_img = transform.resize(crop_img, (224, 224)) # resize 成固定的 imag_szie ax2 = fig.add_subplot(133) # 把下面的影象放在該畫布的第三個位置 ax2.set_xlabel(u"Resize Picture") # 新增子標籤 ax2.imshow(re_img) img_ready = re_img.reshape((1, 224, 224, 3)) return img_ready # 定義百分比轉換函式 def percent(value): return '%.2f%%' % (value * 100)
#!/usr/bin/python
#coding:utf-8
import inspect
import os
import numpy as np
import tensorflow as tf
import time
import matplotlib.pyplot as plt
VGG_MEAN = [103.939, 116.779, 123.68] # 樣本 RGB 的平均值
class Vgg16():
def __init__(self, vgg16_path=None):
if vgg16_path is None:
vgg16_path = os.path.join(os.getcwd(), "vgg16.npy") # os.getcwd() 方法用於返回當前工作目錄。
print(vgg16_path)
self.data_dict = np.load(vgg16_path, encoding='latin1').item() # 遍歷其內鍵值對,匯入模型引數
for x in self.data_dict: #遍歷 data_dict 中的每個鍵
print (x)
def forward(self, images):
# plt.figure("process pictures")
print("build model started")
start_time = time.time() # 獲取前向傳播的開始時間
rgb_scaled = images * 255.0 # 逐畫素乘以 255.0(根據原論文所述的初始化步驟)
# 從 GRB 轉換色彩通道到 BGR,也可使用 cv 中的 GRBtoBGR
red, green, blue = tf.split(rgb_scaled,3,3)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
# 以上 assert 都是加入斷言,用來判斷每個操作後的維度變化是否和預期一致
bgr = tf.concat([
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2]],3)
# 逐樣本減去每個通道的畫素平均值,這種操作可以移除影象的平均亮度值,該方法常用在灰度影象上
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
# 接下來構建 VGG 的 16 層網路(包含 5 段卷積, 3 層全連線),並逐層根據名稱空間讀取網路引數
# 第一段卷積,含有兩個卷積層,後面接最大池化層,用來縮小圖片尺寸
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
# 傳入名稱空間的 name,來獲取該層的卷積核和偏置,並做卷積運算,最後返回經過經過啟用函式後的值
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
# 根據傳入的 pooling 名字對該層做相應的池化操作
self.pool1 = self.max_pool_2x2(self.conv1_2, "pool1")
# 下面的前向傳播過程與第一段同理
# 第二段卷積,同樣包含兩個卷積層,一個最大池化層
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")
# 第三段卷積,包含三個卷積層,一個最大池化層
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")
# 第四段卷積,包含三個卷積層,一個最大池化層
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")
# 第五段卷積,包含三個卷積層,一個最大池化層
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")
# 第六層全連線
self.fc6 = self.fc_layer(self.pool5, "fc6") # 根據名稱空間 name 做加權求和運算
assert self.fc6.get_shape().as_list()[1:] == [4096] # 4096 是該層輸出後的長度
self.relu6 = tf.nn.relu(self.fc6) # 經過 relu 啟用函式
# 第七層全連線,和上一層同理
self.fc7 = self.fc_layer(self.relu6, "fc7")
self.relu7 = tf.nn.relu(self.fc7)
# 第八層全連線
self.fc8 = self.fc_layer(self.relu7, "fc8")
# 經過最後一層的全連線後,再做 softmax 分類,得到屬於各類別的概率
self.prob = tf.nn.softmax(self.fc8, name="prob")
end_time = time.time() # 得到前向傳播的結束時間
print(("time consuming: %f" % (end_time-start_time)))
self.data_dict = None # 清空本次讀取到的模型引數字典
# 定義卷積運算
def conv_layer(self, x, name):
with tf.variable_scope(name): # 根據名稱空間找到對應卷積層的網路引數
w = self.get_conv_filter(name) # 讀到該層的卷積核
conv = tf.nn.conv2d(x, w, [1, 1, 1, 1], padding='SAME') # 卷積計算
conv_biases = self.get_bias(name) # 讀到偏置項
result = tf.nn.relu(tf.nn.bias_add(conv, conv_biases)) # 加上偏置,並做啟用計算
return result
# 定義獲取卷積核的函式
def get_conv_filter(self, name):
# 根據名稱空間 name 從引數字典中取到對應的卷積核
return tf.constant(self.data_dict[name][0], name="filter")
# 定義獲取偏置項的函式
def get_bias(self, name):
# 根據名稱空間 name 從引數字典中取到對應的卷積核
return tf.constant(self.data_dict[name][1], name="biases")
# 定義最大池化操作
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)
# 定義全連線層的前向傳播計算
def fc_layer(self, x, name):
with tf.variable_scope(name): # 根據名稱空間 name 做全連線層的計算
shape = x.get_shape().as_list() # 獲取該層的維度資訊列表
# print ("fc_layer shape ",shape)
dim = 1
for i in shape[1:]:
dim *= i # 將每層的維度相乘
# 改變特徵圖的形狀,也就是將得到的多維特徵做拉伸操作,只在進入第六層全連線層做該操作
x = tf.reshape(x, [-1, dim])
w = self.get_fc_weight(name)# 讀到權重值
b = self.get_bias(name) # 讀到偏置項值
result = tf.nn.bias_add(tf.matmul(x, w), b) # 對該層輸入做加權求和,再加上偏置
return result
# 定義獲取權重的函式
def get_fc_weight(self, name): # 根據名稱空間 name 從引數字典中取到對應的權重
return tf.constant(self.data_dict[name][0], name="weights")
五,相關資料夾裡的檔案
六,測試一張圖,看效果
執行app.py,輸入圖片路徑,回車
歡迎掃碼關注我的微信公眾號“人工智慧與影象處理”,本公眾號專注人工智慧與影象處理技術,並定期分享最前沿的專業訊息。
PS:CSDN部落格適合網頁看,公眾號適合手機看。
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