TensorFlow.js - Deep Learning with JavaScript

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TensorFlow.js - Building the UI for neural network web app

June 25, 2018 by

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Building the UI

In this post, we’re going to start building the UI for our first client-side neural network application using TensorFlow.js, so let’s get to it.

logo-tensorflow-js.png

Now that we have Express set up to host a web app for us, let’s start building one! The first app we’ll build is going to be similar in nature to the predict app we built in the Flask series with Keras.

Recall this was the app we built in that previous series:

keras-flask-vgg16-predict-app-dog.PNG

We had a fine-tuned VGG16 model running in the back-end as a web service, and as a user, we would select an image of a cat or dog, submit the image to the model, and receive a prediction.

Now, the idea of the app we’ll develop with TensorFlow.js will be similar, but let’s discuss the differences.

Well, first, there won’t be any back-end web service hosting our model. Our model will be running entirely in the browser. Our app will therefore consist only of a front-end application developed with HTML and JavaScript.

Application description

Here’s what the new app will do.

The general layout will be similar to the one we just went over where a user will select an image, submit it to the model, and get a prediction.

We won’t be restricted to choosing only cat and dog images though because we won’t be using fine-tuned models this time. We’ll be using original pre-trained models that were trained on ImageNet, so we’ll have a much wider variety of images we can choose from.

Once we submit our selected image to the model, the app will give us back the top five predictions for that image from the ImageNet classes.

tfjs-mobilenet-predict-app-lizard.PNG

The model

So, which model will we be using?

Well, remember how we discussed in a previous post that models best suited for running in the browser are smaller models? And how TensorFlow recommends using models that are 30 MB or less in size? Well, we’re first going to go against this recommendation, and use VGG16 as our model, which is over 500 MB in size.

We’ll see how that works out for us, but you can imagine that it may be problematic. No worries, though, we’ll have MobileNet to the rescue, coming in at only about 16 MB, so we’ll get to see how these two models compare to each other performance-wise in the browser. It will be interesting.

Project structure

Alright, let’s get set up.

./static
    imagenet_classes.js
    predict-with-tfjs.html
    predict.js
    tfjs-models/
    MobileNet/
        model.json
        group1-shard1of1
        group2-shard1of1
        ...
    VGG16/
        model.json
        group1-shard1of1
        group2-shard1of1
        ...

From within the static directory we created last time, we need to create a few new resources. We first create a file called predict-with-tfjs.html, which will be our web app.

Then, we also need to create a file called predict.js, which will hold all the JavaScript logic for our app.

Then, we need a directory to hold our TensorFlow.js models, so we have this one, which we’re calling tfjs-models. Navigating inside, we have two sub-directories, one for MobileNet and one for VGG16, since these are the two models we’ll be using. These will each contain the model.json and the corresponding weight files for each model.

Navigating inside of VGG16, we can see that. To get these files here, I simply went through the conversion process in Python of loading VGG16 and MobileNet with Keras, and then converting the models with the TensorFlow.js converter we previously discussed. So follow that earlier video to get this same output to place in your model directories.

Alright, navigating back to the static directory, the last resource is this imagenet_class.js file. This is simply a file that contains all the ImageNet classes, which we’ll be making use of later.

imagenet_classes.js:

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

So, we just have this JavaScript object, IMAGENET_CLASSES, that contains the key-value pairs of the ImageNet classes with associated IDs.

The code: predict-with-tfjs.html

Now let’s open the predict-with-tfjs.html file, and jump into the code.

<head>
    <title>deeplizard predict image app</title>
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css"
        integrity="sha384-WskhaSGFgHYWDcbwN70/dfYBj47jz9qbsMId/iRN3ewGhXQFZCSftd1LZCfmhktB" crossorigin="anonymous">
</head>

We’re starting off in the <head> by specifying the title of our web page and importing the styling from this CSS file.

For all the styling on the page, we’ll be using Bootstrap, which is an open source library for developing HTML, CSS, and JavsScript that uses design templates to format elements on the page. Bootstrap is really powerful, but we’ll simply be using it just to make our app look a little nicer.

Now, Bootstrap uses a grid layout, where you can think of the web page having containers that can be thought of as grids, and then UI elements on the page are organized into the rows and columns that make up the grid. By setting the elements’ class attributes, that’s how Bootstrap knows what type of styling to apply to them.

So, given this, that’s how we’re organizing our UI elements. Embedded within the <body>, we’re putting all the UI elements within this <main> tag.

<main>
    <div class="container mt-5">
        <div class="row">
            <div class="col-6">
                <input id="image-selector" class="form-control border-0" type="file">
            </div>
            <div class="col-6">
                <button id="predict-button" class="btn btn-dark float-right">Predict</button>
            </div>
        </div>
        <hr>
        <div class="row">
            <div class="col">
                <h2 class="ml-3">Predictions</h2>
                <ol id="prediction-list"></ol>
            </div>
        </div>
        <hr>
        <div class="row">
            <div class="col-12">
                <h2 class="ml-3">Image</h2>
                <img id="selected-image" class="ml-3" src="" />
            </div>
        </div>
    </div>
</main>

You can see that our first <div> is what’s considered to be a container on the page, and then within the container, we have three rows, and each row has columns.

The columns are where our actual UI elements reside. Our UI elements are the image selector, the predict button, the prediction list, and the selected image.

We’ll explore this grid layout interactively in just a moment, but first let’s finish checking out the remainder of the HTML.

<script src="https://code.jquery.com/jquery-3.3.1.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.11.7"></script>
<script src="imagenet_classes.js"></script>
<script src="predict.js"></script>

All that we have left is to import the required libraries and resources that our app needs. First, we import jQuery. Then, we import TensorFlow.js with this line.

This single line is all it takes to get TensorFlow.js into our app. Then, we import the imagenet_class.js file we checked out earlier. Lastly, we import our predict.js file, which, as mentioned earlier, contains all the logic for what our app does when a user supplies an image to it.

Alright, so that’s it for the HTML.

Bootstrap's grid layout

Let’s check out the page and explore the grid layout.

First, we start up our Express server, which we learned how to do in the last video.

PS C:\deeplizard-code\projects\TensorFlowJS> node server.js
Serving static on 81

Then, in our browser, we’ll navigate to the IP address where our Express server is running, port 81, predict-with-tfjs.html.

And here’s our page!

tfjs-predict-web-app.png

It’s pretty empty right now because we haven’t selected an image, but once we write the JavaScript logic to handle what to do when we select an image, then the name of the selected image file will be displayed next to the Choose File button, the image will be displayed in the Image section, and upon clicking the predict button, the predictions for the image from the model will be displayed in the Predictions section.

If we open the developer tools by right clicking on the page and clicking Inspect, then from the Elements tab, we can explore the grid layout.

Let’s expand the <body>, then <main>, then the first <div> that acts as the container.

Hovering over this <div>, we can see that the blue on the page is what is considered to be the container. Now that we've expanded this <div>, we have access to each of the rows.

tfjs-mobilenet predict-app-page elements.PNG

Hovering over the first row, we can see what that maps to in the UI, and we can do the same for the second and third rows as well. Then, if we expand the rows, we have access to the columns that house the individual UI elements.

Hovering over this first column in the first row, we can see that the image selector is here, and the predict button is within the second column in the first row. The same idea applies for the remaining elements on the page as well.

So hopefully that sheds a bit of light on the grid layout that Bootstrap is making use of.

Wrapping up

Alright, in the next post, we’ll explore all of the JavaScript that handles the predictions and actually makes use of TensorFlow.js.

Let me know if you’ve got all of your HTML set up and you’re ready to jump into the JavaScript, and I’ll see ya in the next one!

Description

Now that we have Express set up to host a web app for us, let’s start building one! In this video, we’re going to begin building our first client-side neural network application using TensorFlow.js. With this app, a user will select an image, submit it to the model, and get a prediction. We won’t be restricted to choosing only cat and dog images this time though because we won’t be using fine-tuned models this time. We’ll be using original pre-trained models (VGG16 and MobileNet) that were trained on ImageNet, so we’ll have a much wider variety of images we can choose from. Once we submit our selected image to the model, the app will give us back the top five predictions for that image from the ImageNet classes. imagenet_classes.js: https://github.com/tensorflow/tfjs-examples/blob/master/mobilenet/imagenet_classes.js 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👀 OUR VLOG: 🔗 https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og 👉 Check out the blog post and other resources for this video: 🔗 https://deeplizard.com/learn/video/vCPLK8I3-I0 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 https://www.patreon.com/posts/27743395 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 🤜 Support collective intelligence, create a quiz question for this video: 🔗 https://deeplizard.com/create-quiz-question 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: yasser Prash 👀 Follow deeplizard: Our vlog: https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og Twitter: https://twitter.com/deeplizard Facebook: https://www.facebook.com/Deeplizard-145413762948316 Patreon: https://www.patreon.com/deeplizard YouTube: https://www.youtube.com/deeplizard Instagram: https://www.instagram.com/deeplizard/ 🎓 Other deeplizard courses: Reinforcement Learning - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv NN Programming - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG DL Fundamentals - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Keras - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL TensorFlow.js - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr83l8w44N_g3pygvajLrJ- Data Science - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrth-Cqs_R9- Trading - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr17PqeytCKiCD-TJj89rII 🛒 Check out products deeplizard recommends on Amazon: 🔗 https://www.amazon.com/shop/deeplizard 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard’s link: 🔗 https://amzn.to/2yoqWRn 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://www.youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ 🔗 http://incompetech.com/ ❤️ Please use the knowledge gained from deeplizard content for good, not evil.