doodleNet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6H3ATAdp_URp"
   },
   "source": [
    "# Get the Class names "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zlx6-LFL_jbi"
   },
   "source": [
    "This file contains a subset of the quick draw classes. I choose around 100 classes from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "XXv-xzU1sd88",
    "outputId": "7aa617d8-9dea-4bc3-e257-4f5f93630d45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-04-21 19:34:59--  https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2791 (2.7K) [text/plain]\n",
      "Saving to: ‘categories.txt’\n",
      "\n",
      "categories.txt      100%[===================>]   2.73K  --.-KB/s    in 0s      \n",
      "\n",
      "2019-04-21 19:35:00 (71.2 MB/s) - ‘categories.txt’ saved [2791/2791]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4GL_TdMffD6-"
   },
   "source": [
    "Read the classes names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eP-OxOx5sy0b"
   },
   "outputs": [],
   "source": [
    "f = open(\"categories.txt\",\"r\")\n",
    "# And for reading use\n",
    "classes = f.readlines()\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "lTE6D3uxtMc5",
    "outputId": "a84b4675-2c55-44de-fce9-7ac13b4df626"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['aircraft_carrier', 'airplane', 'alarm_clock', 'ambulance', 'angel', 'animal_migration', 'ant', 'anvil', 'apple', 'arm', 'asparagus', 'axe', 'backpack', 'banana', 'bandage', 'barn', 'baseball', 'baseball_bat', 'basket', 'basketball', 'bat', 'bathtub', 'beach', 'bear', 'beard', 'bed', 'bee', 'belt', 'bench', 'bicycle', 'binoculars', 'bird', 'birthday_cake', 'blackberry', 'blueberry', 'book', 'boomerang', 'bottlecap', 'bowtie', 'bracelet', 'brain', 'bread', 'bridge', 'broccoli', 'broom', 'bucket', 'bulldozer', 'bus', 'bush', 'butterfly', 'cactus', 'cake', 'calculator', 'calendar', 'camel', 'camera', 'camouflage', 'campfire', 'candle', 'cannon', 'canoe', 'car', 'carrot', 'castle', 'cat', 'ceiling_fan', 'cello', 'cell_phone', 'chair', 'chandelier', 'church', 'circle', 'clarinet', 'clock', 'cloud', 'coffee_cup', 'compass', 'computer', 'cookie', 'cooler', 'couch', 'cow', 'crab', 'crayon', 'crocodile', 'crown', 'cruise_ship', 'cup', 'diamond', 'dishwasher', 'diving_board', 'dog', 'dolphin', 'donut', 'door', 'dragon', 'dresser', 'drill', 'drums', 'duck', 'dumbbell', 'ear', 'elbow', 'elephant', 'envelope', 'eraser', 'eye', 'eyeglasses', 'face', 'fan', 'feather', 'fence', 'finger', 'fire_hydrant', 'fireplace', 'firetruck', 'fish', 'flamingo', 'flashlight', 'flip_flops', 'floor_lamp', 'flower', 'flying_saucer', 'foot', 'fork', 'frog', 'frying_pan', 'garden', 'garden_hose', 'giraffe', 'goatee', 'golf_club', 'grapes', 'grass', 'guitar', 'hamburger', 'hammer', 'hand', 'harp', 'hat', 'headphones', 'hedgehog', 'helicopter', 'helmet', 'hexagon', 'hockey_puck', 'hockey_stick', 'horse', 'hospital', 'hot_air_balloon', 'hot_dog', 'hot_tub', 'hourglass', 'house', 'house_plant', 'hurricane', 'ice_cream', 'jacket', 'jail', 'kangaroo', 'key', 'keyboard', 'knee', 'knife', 'ladder', 'lantern', 'laptop', 'leaf', 'leg', 'light_bulb', 'lighter', 'lighthouse', 'lightning', 'line', 'lion', 'lipstick', 'lobster', 'lollipop', 'mailbox', 'map', 'marker', 'matches', 'megaphone', 'mermaid', 'microphone', 'microwave', 'monkey', 'moon', 'mosquito', 'motorbike', 'mountain', 'mouse', 'moustache', 'mouth', 'mug', 'mushroom', 'nail', 'necklace', 'nose', 'ocean', 'octagon', 'octopus', 'onion', 'oven', 'owl', 'paintbrush', 'paint_can', 'palm_tree', 'panda', 'pants', 'paper_clip', 'parachute', 'parrot', 'passport', 'peanut', 'pear', 'peas', 'pencil', 'penguin', 'piano', 'pickup_truck', 'picture_frame', 'pig', 'pillow', 'pineapple', 'pizza', 'pliers', 'police_car', 'pond', 'pool', 'popsicle', 'postcard', 'potato', 'power_outlet', 'purse', 'rabbit', 'raccoon', 'radio', 'rain', 'rainbow', 'rake', 'remote_control', 'rhinoceros', 'rifle', 'river', 'roller_coaster', 'rollerskates', 'sailboat', 'sandwich', 'saw', 'saxophone', 'school_bus', 'scissors', 'scorpion', 'screwdriver', 'sea_turtle', 'see_saw', 'shark', 'sheep', 'shoe', 'shorts', 'shovel', 'sink', 'skateboard', 'skull', 'skyscraper', 'sleeping_bag', 'smiley_face', 'snail', 'snake', 'snorkel', 'snowflake', 'snowman', 'soccer_ball', 'sock', 'speedboat', 'spider', 'spoon', 'spreadsheet', 'square', 'squiggle', 'squirrel', 'stairs', 'star', 'steak', 'stereo', 'stethoscope', 'stitches', 'stop_sign', 'stove', 'strawberry', 'streetlight', 'string_bean', 'submarine', 'suitcase', 'sun', 'swan', 'sweater', 'swing_set', 'sword', 'syringe', 'table', 'teapot', 'teddy-bear', 'telephone', 'television', 'tennis_racquet', 'tent', 'The_Eiffel_Tower', 'The_Great_Wall_of_China', 'The_Mona_Lisa', 'tiger', 'toaster', 'toe', 'toilet', 'tooth', 'toothbrush', 'toothpaste', 'tornado', 'tractor', 'traffic_light', 'train', 'tree', 'triangle', 'trombone', 'truck', 'trumpet', 't-shirt', 'umbrella', 'underwear', 'van', 'vase', 'violin', 'washing_machine', 'watermelon', 'waterslide', 'whale', 'wheel', 'windmill', 'wine_bottle', 'wine_glass', 'wristwatch', 'yoga', 'zebra', 'zigzag']\n"
     ]
    }
   ],
   "source": [
    "classes = [c.replace('\\n','').replace(' ','_') for c in classes]\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NDfBHVjACAt"
   },
   "source": [
    "# Download the Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7MC_PUS-fKjH"
   },
   "source": [
    "Loop over the classes and download the currospondent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rdSUnpL0u22Q",
    "outputId": "2a91c7a4-453b-4a04-fb7b-b77b6e9f80bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘data’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "22DPhL5FtWcQ"
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "def download():\n",
    "  \n",
    "  base = 'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/'\n",
    "  for c in classes:\n",
    "    cls_url = c.replace('_', '%20')\n",
    "    path = base+cls_url+'.npy'\n",
    "    print(path)\n",
    "    urllib.urlretrieve(path, 'data/'+c+'.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 5882
    },
    "colab_type": "code",
    "id": "O5jF6TXXu-Bu",
    "outputId": "44326350-8640-4a7e-d032-2cdb7e17a04e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/aircraft%20carrier.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/airplane.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/alarm%20clock.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ambulance.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/angel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/animal%20migration.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ant.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/anvil.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/apple.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/arm.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/asparagus.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/axe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/backpack.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/banana.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bandage.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/barn.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/baseball.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/baseball%20bat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/basket.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/basketball.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bathtub.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/beach.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bear.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/beard.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bed.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bee.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/belt.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bench.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bicycle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/binoculars.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bird.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/birthday%20cake.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/blackberry.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/blueberry.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/book.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/boomerang.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bottlecap.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bowtie.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bracelet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/brain.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bread.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bridge.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/broccoli.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/broom.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bucket.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bulldozer.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bus.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/bush.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/butterfly.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cactus.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cake.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/calculator.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/calendar.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/camel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/camera.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/camouflage.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/campfire.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/candle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cannon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/canoe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/car.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/carrot.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/castle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ceiling%20fan.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cello.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cell%20phone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/chair.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/chandelier.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/church.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/circle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/clarinet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/clock.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cloud.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/coffee%20cup.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/compass.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/computer.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cookie.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cooler.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/couch.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cow.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/crab.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/crayon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/crocodile.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/crown.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cruise%20ship.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/cup.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/diamond.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dishwasher.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/diving%20board.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dog.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dolphin.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/donut.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/door.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dragon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dresser.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/drill.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/drums.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/duck.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/dumbbell.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ear.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/elbow.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/elephant.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/envelope.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/eraser.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/eye.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/eyeglasses.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/face.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fan.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/feather.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fence.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/finger.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fire%20hydrant.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fireplace.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/firetruck.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fish.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/flamingo.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/flashlight.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/flip%20flops.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/floor%20lamp.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/flower.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/flying%20saucer.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/foot.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/fork.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/frog.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/frying%20pan.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/garden.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/garden%20hose.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/giraffe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/goatee.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/golf%20club.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/grapes.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/grass.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/guitar.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hamburger.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hammer.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hand.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/harp.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/headphones.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hedgehog.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/helicopter.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/helmet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hexagon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hockey%20puck.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hockey%20stick.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/horse.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hospital.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hot%20air%20balloon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hot%20dog.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hot%20tub.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hourglass.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/house.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/house%20plant.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/hurricane.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ice%20cream.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/jacket.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/jail.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/kangaroo.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/key.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/keyboard.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/knee.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/knife.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ladder.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lantern.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/laptop.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/leaf.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/leg.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/light%20bulb.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lighter.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lighthouse.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lightning.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/line.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lion.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lipstick.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lobster.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/lollipop.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mailbox.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/map.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/marker.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/matches.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/megaphone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mermaid.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/microphone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/microwave.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/monkey.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/moon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mosquito.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/motorbike.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mountain.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mouse.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/moustache.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mouth.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mug.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/mushroom.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/nail.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/necklace.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/nose.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/ocean.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/octagon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/octopus.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/onion.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/oven.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/owl.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/paintbrush.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/paint%20can.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/palm%20tree.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/panda.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pants.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/paper%20clip.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/parachute.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/parrot.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/passport.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/peanut.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pear.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/peas.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pencil.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/penguin.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/piano.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pickup%20truck.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/picture%20frame.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pig.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pillow.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pineapple.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pizza.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pliers.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/police%20car.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pond.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pool.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/popsicle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/postcard.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/potato.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/power%20outlet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/purse.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rabbit.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/raccoon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/radio.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rain.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rainbow.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rake.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/remote%20control.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rhinoceros.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rifle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/river.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/roller%20coaster.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/rollerskates.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sailboat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sandwich.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/saw.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/saxophone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/school%20bus.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/scissors.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/scorpion.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/screwdriver.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sea%20turtle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/see%20saw.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/shark.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sheep.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/shoe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/shorts.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/shovel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sink.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/skateboard.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/skull.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/skyscraper.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sleeping%20bag.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/smiley%20face.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/snail.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/snake.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/snorkel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/snowflake.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/snowman.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/soccer%20ball.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sock.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/speedboat.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/spider.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/spoon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/spreadsheet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/square.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/squiggle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/squirrel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stairs.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/star.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/steak.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stereo.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stethoscope.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stitches.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stop%20sign.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/stove.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/strawberry.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/streetlight.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/string%20bean.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/submarine.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/suitcase.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sun.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/swan.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sweater.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/swing%20set.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/sword.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/syringe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/table.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/teapot.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/teddy-bear.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/telephone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/television.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tennis%20racquet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tent.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/The%20Eiffel%20Tower.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/The%20Great%20Wall%20of%20China.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/The%20Mona%20Lisa.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tiger.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/toaster.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/toe.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/toilet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tooth.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/toothbrush.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/toothpaste.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tornado.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tractor.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/traffic%20light.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/train.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/tree.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/triangle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/trombone.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/truck.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/trumpet.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/t-shirt.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/umbrella.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/underwear.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/van.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/vase.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/violin.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/washing%20machine.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/watermelon.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/waterslide.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/whale.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/wheel.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/windmill.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/wine%20bottle.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/wine%20glass.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/wristwatch.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/yoga.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/zebra.npy\n",
      "https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/zigzag.npy\n"
     ]
    }
   ],
   "source": [
    "download() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uEdnbBVXAI-X"
   },
   "source": [
    "# Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J2FYrPgOKh6t"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow import keras \n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6o30ipBPAQ5Y"
   },
   "source": [
    "# Load the Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UBq3GXEKAYuO"
   },
   "source": [
    "Each class contains different number samples of arrays stored as .npy format. We will load 50000 images per class.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6HEIgQNHYQnl"
   },
   "outputs": [],
   "source": [
    "def load_data(root, vfold_ratio=0.1, max_items_per_class=50000):\n",
    "    all_files = glob.glob(os.path.join(root, '*.npy'))\n",
    "\n",
    "    #initialize variables \n",
    "    x = np.empty([0, 784])\n",
    "    y = np.empty([0])\n",
    "    class_names = []\n",
    "\n",
    "    #load each data file \n",
    "    for idx, file in enumerate(all_files):\n",
    "        print(idx)\n",
    "        print(file)\n",
    "        data = np.load(file)\n",
    "        data = data[0: max_items_per_class, :]\n",
    "        labels = np.full(data.shape[0], idx)\n",
    "\n",
    "        x = np.concatenate((x, data), axis=0)\n",
    "        y = np.append(y, labels)\n",
    "\n",
    "        class_name, ext = os.path.splitext(os.path.basename(file))\n",
    "        class_names.append(class_name)\n",
    "\n",
    "    data = None\n",
    "    labels = None\n",
    "    \n",
    "    #randomize the dataset \n",
    "    permutation = np.random.permutation(y.shape[0])\n",
    "    x = x[permutation, :]\n",
    "    y = y[permutation]\n",
    "\n",
    "    #separate into training and testing \n",
    "    vfold_size = int(x.shape[0]/100*(vfold_ratio*100))\n",
    "\n",
    "    x_test = x[0:vfold_size, :]\n",
    "    y_test = y[0:vfold_size]\n",
    "\n",
    "    x_train = x[vfold_size:x.shape[0], :]\n",
    "    y_train = y[vfold_size:y.shape[0]]\n",
    "    return x_train, y_train, x_test, y_test, class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2754
    },
    "colab_type": "code",
    "id": "K6uUjN-WL2Y9",
    "outputId": "0eee863f-2830-4078-d05f-a1bd629e0551"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "x_train, y_train, x_test, y_test, class_names = load_data('data')\n",
    "num_classes = len(class_names)\n",
    "image_size = 28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "colab_type": "code",
    "id": "VhGEDS0SMgLK",
    "outputId": "9af86ae9-5ae6-470a-eca5-6b09d20a5d27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15525000\n",
      "15525000\n",
      "1725000\n",
      "1725000\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(y_train))\n",
    "print(len(x_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rNZmQvBWBBHE"
   },
   "source": [
    "Show some random data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "KfpDaHRkyMQC",
    "outputId": "0f070893-98d5-42fa-b77d-f0a1d57210f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   4.\n",
      "   67.  77.  67.  15.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  27. 142. 232.\n",
      "  255. 255. 255. 233.  97.   1.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  66. 233. 246. 165.\n",
      "   71.  44.  63. 188. 255. 139.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   1. 119. 252. 207.  34.   0.\n",
      "    0.   0.   0.   0. 140. 254.  79.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.  14. 174. 255. 160.   9.   0.   0.\n",
      "    0.   0.   0.   0.   4. 202. 217.   2.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.  39. 213. 248. 111.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.  78. 255.  80.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.  73. 243. 226.  54.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   2. 215. 197.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   5.  34.  71. 248. 218.  42.  17.  17.   9.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0. 153. 255. 145. 135.  89.  15.   0.   0.]\n",
      " [  0.   0. 146. 255. 255. 255. 255. 255. 255. 255. 255. 244. 223. 201.\n",
      "  181. 161. 153. 153. 153. 153. 248. 255. 241. 242. 255. 249.  55.   0.]\n",
      " [  0.   0. 218. 191.  85.  91. 214. 255. 232. 149. 113. 133. 156. 176.\n",
      "  196. 216. 221. 221. 229. 248. 255. 153.   6.   0.  70. 255.  91.   0.]\n",
      " [  0.  26. 255. 105.   0.   0.   0.  64. 206. 255. 190.  48.   0.   0.\n",
      "    0.   0.  26. 167. 254. 223.  86.   0.   0.   0.  27. 255.  95.   0.]\n",
      " [  0.  82. 255.  45.   0.   0.   0.   0.   2.  98. 230. 253. 157.  50.\n",
      "    8.  54. 230. 242. 120.   8.   0.   0.   0.   0.  23. 255.  99.   0.]\n",
      " [  0.  97. 255.  24.   0.   0.   0.   0.   0.   0.  12. 131. 245. 255.\n",
      "  252. 251. 216.  37.   0.   0.   0.   0.   0.   0.  19. 255. 103.   0.]\n",
      " [  0. 106. 255.  29.   0.   0.   0.   0.   0.   0.   0.   0.  21.  77.\n",
      "  120. 121.  17.   0.   0.   0.   0.   0.   0.   0.  15. 255. 107.   0.]\n",
      " [  0.  63. 248. 242. 202. 163. 123. 102.  89.  82.  68.  58.  50.  34.\n",
      "   26.  17.   2.   0.   0.   0.   0.   0.   0.   0.  16. 255. 108.   0.]\n",
      " [  0.   0.  44. 138. 178. 217. 251. 255. 255. 255. 255. 255. 255. 255.\n",
      "  255. 255. 255. 249. 184. 114.  85.  68.  63.  51.  95. 255.  65.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   5.  17.  32.  39.  51.  63.  71.  85.\n",
      "   95. 102. 119. 133. 213. 255. 255. 255. 255. 255. 255. 247.  10.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.  11.  37.  51.  58.  68.  79.  54.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]]\n",
      "purse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "%matplotlib inline  \n",
    "idx = randint(0, len(x_train))\n",
    "print(x_train[idx].reshape(28,28))\n",
    "plt.imshow(x_train[idx].reshape(28,28)) \n",
    "print(class_names[int(y_train[idx].item())])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n8InHz5NBFrV"
   },
   "source": [
    "# Preprocess the Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p2GHUq7D2r9e"
   },
   "outputs": [],
   "source": [
    "# Reshape and normalize\n",
    "x_train = x_train.reshape(x_train.shape[0], image_size, image_size, 1).astype('float32')\n",
    "x_test = x_test.reshape(x_test.shape[0], image_size, image_size, 1).astype('float32')\n",
    "\n",
    "x_train /= 255.0\n",
    "x_test /= 255.0\n",
    "\n",
    "# Convert class vectors to class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rL6XAb4hBMSc"
   },
   "source": [
    "# The Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "colab_type": "code",
    "id": "uYUVV2wf2z8H",
    "outputId": "521cf96e-3039-411e-d14e-5aaca559b518"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15525000\n",
      "15525000\n",
      "1725000\n",
      "1725000\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 14, 14, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 7, 7, 64)          18496     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 7, 7, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 3, 3, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               295424    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 345)               176985    \n",
      "=================================================================\n",
      "Total params: 544,201\n",
      "Trainable params: 544,201\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(y_train))\n",
    "print(len(x_test))\n",
    "print(len(y_test))\n",
    "\n",
    "# Define model\n",
    "model = keras.Sequential()\n",
    "model.add(layers.Convolution2D(16, (3, 3),padding='same',input_shape=x_train.shape[1:], activation='relu'))\n",
    "model.add(layers.Convolution2D(16, (3, 3),padding='same',input_shape=x_train.shape[1:], activation='relu'))\n",
    "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "\n",
    "model.add(layers.Convolution2D(32, (3, 3), padding='same', activation= 'relu'))\n",
    "model.add(layers.Convolution2D(32, (3, 3), padding='same', activation= 'relu'))\n",
    "model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(layers.Convolution2D(64, (3, 3), padding='same', activation= 'relu'))\n",
    "model.add(layers.Convolution2D(64, (3, 3), padding='same', activation= 'relu'))\n",
    "model.add(layers.MaxPooling2D(pool_size =(2,2)))\n",
    "\n",
    "model.add(layers.Dropout(0.1))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation='tanh'))\n",
    "model.add(layers.Dense(345, activation='softmax')) \n",
    "# Train model\n",
    "adam = tf.train.AdamOptimizer()\n",
    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['top_k_categorical_accuracy'])\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_YRSRkOyBP1P"
   },
   "source": [
    "# Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "7OMEJ7kF3lsP",
    "outputId": "4d9a46ad-2ed1-4b4f-c1ec-687997025034"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13972500 samples, validate on 1552500 samples\n",
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/5\n",
      " - 366s - loss: 1.5536 - top_k_categorical_accuracy: 0.8542 - val_loss: 1.3215 - val_top_k_categorical_accuracy: 0.8851\n",
      "Epoch 2/5\n",
      " - 364s - loss: 1.3474 - top_k_categorical_accuracy: 0.8828 - val_loss: 1.2708 - val_top_k_categorical_accuracy: 0.8915\n",
      "Epoch 3/5\n",
      " - 363s - loss: 1.3127 - top_k_categorical_accuracy: 0.8871 - val_loss: 1.2680 - val_top_k_categorical_accuracy: 0.8919\n",
      "Epoch 4/5\n",
      " - 361s - loss: 1.2945 - top_k_categorical_accuracy: 0.8893 - val_loss: 1.2411 - val_top_k_categorical_accuracy: 0.8960\n",
      "Epoch 5/5\n",
      " - 361s - loss: 1.2831 - top_k_categorical_accuracy: 0.8907 - val_loss: 1.2276 - val_top_k_categorical_accuracy: 0.8968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7efe3c393250>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x = x_train, y = y_train, validation_split=0.1, batch_size = 256, verbose=2, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d2KztY7qEn9_"
   },
   "source": [
    "# Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ssaZczS7DxeA",
    "outputId": "63f7726d-205e-4cf7-9540-0c3d379e54e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15525000\n",
      "15525000\n",
      "1725000\n",
      "1725000\n",
      "Test accuarcy: 89.67%\n"
     ]
    }
   ],
   "source": [
    "print(len(x_train))\n",
    "print(len(y_train))\n",
    "print(len(x_test))\n",
    "print(len(y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test accuarcy: {:0.2f}%'.format(score[1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9xBM_w0VBbNr"
   },
   "source": [
    "# Inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "nH3JfoiYHdpk",
    "outputId": "ca2ed216-7069-4caf-b72a-784a11da8553"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['lighter', 'candle', 'lipstick', 'marker', 'toothpaste']\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "%matplotlib inline  \n",
    "idx = randint(0, len(x_test))\n",
    "img = x_test[idx]\n",
    "plt.imshow(img.squeeze()) \n",
    "pred = model.predict(np.expand_dims(img, axis=0))[0]\n",
    "ind = (-pred).argsort()[:5]\n",
    "latex = [class_names[x] for x in ind]\n",
    "print(latex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YPp5D82YBhM-"
   },
   "source": [
    "# Store the classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NoFI1msFYpCN"
   },
   "outputs": [],
   "source": [
    "with open('class_names.txt', 'w') as file_handler:\n",
    "    for item in class_names:\n",
    "        file_handler.write(\"{}\\n\".format(item))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mfJ6dpaDBpRx"
   },
   "source": [
    "# Install TensorFlowJS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 877
    },
    "colab_type": "code",
    "id": "hJJDfp9mY9Xh",
    "outputId": "5f1b0500-99ca-4935-89da-d2359bb5c29b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflowjs\n",
      "  Downloading https://files.pythonhosted.org/packages/79/29/35e1aa467436ff46b98df65a08c49faaedb3429e1c512d1d90fe308040a0/tensorflowjs-1.0.1-py3-none-any.whl\n",
      "Collecting tensorflow-hub==0.3.0 (from tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/f0/3a3ced04c8359e562f1b91918d9bde797c8a916fcfeddc8dc5d673d1be20/tensorflow_hub-0.3.0-py2.py3-none-any.whl (73kB)\n",
      "\u001b[K    100% |████████████████████████████████| 81kB 9.8MB/s \n",
      "\u001b[?25hCollecting numpy==1.15.1 (from tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fe/94/7049fed8373c52839c8cde619acaf2c9b83082b935e5aa8c0fa27a4a8bcc/numpy-1.15.1-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)\n",
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      "\u001b[?25hRequirement already satisfied: keras==2.2.4 in /usr/local/lib/python3.6/dist-packages (from tensorflowjs) (2.2.4)\n",
      "Collecting tf-nightly-2.0-preview>=2.0.0.dev20190304 (from tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0d/79/71e12891d2b8ad3f9b786a877a7c71d484a72280709b20cb2697e9283f8c/tf_nightly_2.0_preview-2.0.0.dev20190421-cp36-cp36m-manylinux1_x86_64.whl (86.8MB)\n",
      "\u001b[K    100% |████████████████████████████████| 86.8MB 405kB/s \n",
      "\u001b[?25hCollecting six==1.11.0 (from tensorflowjs)\n",
      "  Downloading https://files.pythonhosted.org/packages/67/4b/141a581104b1f6397bfa78ac9d43d8ad29a7ca43ea90a2d863fe3056e86a/six-1.11.0-py2.py3-none-any.whl\n",
      "Collecting h5py==2.8.0 (from tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/cb/726134109e7bd71d98d1fcc717ffe051767aac42ede0e7326fd1787e5d64/h5py-2.8.0-cp36-cp36m-manylinux1_x86_64.whl (2.8MB)\n",
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      "\u001b[?25hRequirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-hub==0.3.0->tensorflowjs) (3.7.1)\n",
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      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs) (0.7.1)\n",
      "Collecting wrapt>=1.11.1 (from tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs)\n",
      "  Downloading https://files.pythonhosted.org/packages/67/b2/0f71ca90b0ade7fad27e3d20327c996c6252a2ffe88f50a95bba7434eda9/wrapt-1.11.1.tar.gz\n",
      "Collecting tb-nightly<1.15.0a0,>=1.14.0a0 (from tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/92/ab/429cbd9be08abc716585019feaf46b694e6ec126e362e68ccb1a02f71c42/tb_nightly-1.14.0a20190419-py3-none-any.whl (3.1MB)\n",
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      "Collecting tensorflow-estimator-2.0-preview (from tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e9/3c/230819e56dc1bac6d05eecf1513b40e2831e9846a24c4ef1f97de577f96f/tensorflow_estimator_2.0_preview-1.14.0.dev2019042100-py2.py3-none-any.whl (421kB)\n",
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      "\u001b[?25hCollecting google-pasta>=0.1.2 (from tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/bb/f1bbc131d6294baa6085a222d29abadd012696b73dcbf8cf1bf56b9f082a/google_pasta-0.1.5-py3-none-any.whl (51kB)\n",
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      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.15.0a0,>=1.14.0a0->tf-nightly-2.0-preview>=2.0.0.dev20190304->tensorflowjs) (0.15.1)\n",
      "Building wheels for collected packages: wrapt\n",
      "  Building wheel for wrapt (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/89/67/41/63cbf0f6ac0a6156588b9587be4db5565f8c6d8ccef98202fc\n",
      "Successfully built wrapt\n",
      "Installing collected packages: six, numpy, tensorflow-hub, wrapt, tb-nightly, tensorflow-estimator-2.0-preview, google-pasta, tf-nightly-2.0-preview, h5py, tensorflowjs\n",
      "  Found existing installation: six 1.12.0\n",
      "    Uninstalling six-1.12.0:\n",
      "      Successfully uninstalled six-1.12.0\n",
      "  Found existing installation: numpy 1.16.2\n",
      "    Uninstalling numpy-1.16.2:\n",
      "      Successfully uninstalled numpy-1.16.2\n",
      "  Found existing installation: h5py 2.9.0\n",
      "    Uninstalling h5py-2.9.0:\n",
      "      Successfully uninstalled h5py-2.9.0\n",
      "Successfully installed google-pasta-0.1.5 h5py-2.8.0 numpy-1.15.1 six-1.11.0 tb-nightly-1.14.0a20190419 tensorflow-estimator-2.0-preview-1.14.0.dev2019042100 tensorflow-hub-0.3.0 tensorflowjs-1.0.1 tf-nightly-2.0-preview-2.0.0.dev20190421 wrapt-1.11.1\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflowjs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-oBl0ZKVB00d"
   },
   "source": [
    "# Save and Convert "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XVICB3TbZGb2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:TensorFlow optimizers do not make it possible to access optimizer attributes or optimizer state after instantiation. As a result, we cannot save the optimizer as part of the model save file.You will have to compile your model again after loading it. Prefer using a Keras optimizer instead (see keras.io/optimizers).\n"
     ]
    }
   ],
   "source": [
    "model.save('keras.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "colab_type": "code",
    "id": "bTWWlGdWZOvs",
    "outputId": "98724960-128d-4ecc-cdb0-460f7c06536e"
   },
   "outputs": [],
   "source": [
    "!mkdir model\n",
    "!tensorflowjs_converter --input_format keras keras.h5 model/\n",
    "!cp class_names.txt model/class_names.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JKYxE2MEB6LV"
   },
   "source": [
    "# Download\n",
    "Download all the files in the model folder on the left sidebar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "name": "345 Sketcher.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}