try_GraphLab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphlab as gl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cPickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load each batch as dictionary:\n",
    "def unpickle(file):\n",
    "    fo = open(file, 'rb')\n",
    "    dict = cPickle.load(fo)\n",
    "    fo.close()\n",
    "    return dict\n",
    "\n",
    "#Make dictionaries by calling the above function:\n",
    "batch1 = unpickle('cifar-10-batches-py/data_batch_1')\n",
    "batch2 = unpickle('cifar-10-batches-py/data_batch_2')\n",
    "batch3 = unpickle('cifar-10-batches-py/data_batch_3')\n",
    "batch4 = unpickle('cifar-10-batches-py/data_batch_4')\n",
    "batch5 = unpickle('cifar-10-batches-py/data_batch_5')\n",
    "batch_test = unpickle('cifar-10-batches-py/test_batch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'batch_label': 'training batch 1 of 5',\n",
       " 'data': array([[ 59,  43,  50, ..., 140,  84,  72],\n",
       "        [154, 126, 105, ..., 139, 142, 144],\n",
       "        [255, 253, 253, ...,  83,  83,  84],\n",
       "        ..., \n",
       "        [ 71,  60,  74, ...,  68,  69,  68],\n",
       "        [250, 254, 211, ..., 215, 255, 254],\n",
       "        [ 62,  61,  60, ..., 130, 130, 131]], dtype=uint8),\n",
       " 'filenames': ['leptodactylus_pentadactylus_s_000004.png',\n",
       "  'camion_s_000148.png',\n",
       "  'tipper_truck_s_001250.png',\n",
       "  'american_elk_s_001521.png',\n",
       "  'station_wagon_s_000293.png',\n",
       "  'coupe_s_001735.png',\n",
       "  'cassowary_s_001300.png',\n",
       "  'cow_pony_s_001168.png',\n",
       "  'sea_boat_s_001584.png',\n",
       "  'tabby_s_001355.png',\n",
       "  'muntjac_s_001000.png',\n",
       "  'arabian_s_001354.png',\n",
       "  'quarter_horse_s_000672.png',\n",
       "  'passerine_s_000343.png',\n",
       "  'camion_s_001895.png',\n",
       "  'trailer_truck_s_000335.png',\n",
       "  'dumper_s_000821.png',\n",
       "  'alley_cat_s_000200.png',\n",
       "  'accentor_s_000677.png',\n",
       "  'frog_s_001671.png',\n",
       "  'capreolus_capreolus_s_000051.png',\n",
       "  'tomcat_s_000772.png',\n",
       "  'pickerel_frog_s_000446.png',\n",
       "  'bufo_s_001242.png',\n",
       "  'cassowary_s_001246.png',\n",
       "  'toad_s_001748.png',\n",
       "  'cat_s_000081.png',\n",
       "  'chihuahua_s_000825.png',\n",
       "  'alces_alces_s_000959.png',\n",
       "  'stealth_bomber_s_000554.png',\n",
       "  'twinjet_s_000663.png',\n",
       "  'trucking_rig_s_001402.png',\n",
       "  'auto_s_000609.png',\n",
       "  'tabby_cat_s_000983.png',\n",
       "  'wapiti_s_000416.png',\n",
       "  'monoplane_s_000895.png',\n",
       "  'true_cat_s_000247.png',\n",
       "  'tennessee_walker_s_000486.png',\n",
       "  'house_cat_s_000243.png',\n",
       "  'house_cat_s_001196.png',\n",
       "  'pekinese_s_001337.png',\n",
       "  'ostrich_s_001368.png',\n",
       "  'ostrich_s_001150.png',\n",
       "  'stallion_s_000046.png',\n",
       "  'station_waggon_s_000041.png',\n",
       "  'coupe_s_001944.png',\n",
       "  'estate_car_s_000580.png',\n",
       "  'accentor_s_000759.png',\n",
       "  'emu_novaehollandiae_s_000795.png',\n",
       "  'dive_bomber_s_001390.png',\n",
       "  'articulated_lorry_s_000131.png',\n",
       "  'pekinese_s_001093.png',\n",
       "  'broodmare_s_001463.png',\n",
       "  'delivery_truck_s_000834.png',\n",
       "  'songbird_s_001052.png',\n",
       "  'emu_s_000692.png',\n",
       "  'puppy_s_000115.png',\n",
       "  'wagtail_s_001821.png',\n",
       "  'dama_dama_s_000658.png',\n",
       "  'domestic_cat_s_001970.png',\n",
       "  'ambulance_s_003039.png',\n",
       "  'convertible_s_001763.png',\n",
       "  'tank_ship_s_001229.png',\n",
       "  'cassowary_s_001055.png',\n",
       "  'wagon_s_001142.png',\n",
       "  'police_cruiser_s_000620.png',\n",
       "  'moose_s_002308.png',\n",
       "  'aerial_ladder_truck_s_000584.png',\n",
       "  'saddle_horse_s_000717.png',\n",
       "  'tanker_s_001350.png',\n",
       "  'mongrel_s_001571.png',\n",
       "  'truck_s_000835.png',\n",
       "  'pickerel_frog_s_001195.png',\n",
       "  'lipizzan_s_000399.png',\n",
       "  'tabby_s_000074.png',\n",
       "  'automobile_s_001887.png',\n",
       "  'moving_van_s_001665.png',\n",
       "  'attack_aircraft_s_000153.png',\n",
       "  'domestic_cat_s_001596.png',\n",
       "  'compact_car_s_000048.png',\n",
       "  'domestic_cat_s_000009.png',\n",
       "  'pekingese_s_002089.png',\n",
       "  'capreolus_capreolus_s_001095.png',\n",
       "  'blenheim_spaniel_s_001103.png',\n",
       "  'stallion_s_000040.png',\n",
       "  'stud_mare_s_001672.png',\n",
       "  'elk_s_000920.png',\n",
       "  'lipizzan_s_001123.png',\n",
       "  'fire_truck_s_002721.png',\n",
       "  'elk_s_001888.png',\n",
       "  'finch_s_000750.png',\n",
       "  'tabby_s_000880.png',\n",
       "  'banana_boat_s_001324.png',\n",
       "  'twinjet_s_000591.png',\n",
       "  'shooting_brake_s_000029.png',\n",
       "  'rana_pipiens_s_000101.png',\n",
       "  'station_wagon_s_001387.png',\n",
       "  'station_wagon_s_002712.png',\n",
       "  'odocoileus_hemionus_s_000221.png',\n",
       "  'convertible_s_001715.png',\n",
       "  'abandoned_ship_s_000574.png',\n",
       "  'true_cat_s_000114.png',\n",
       "  'dustcart_s_000063.png',\n",
       "  'frog_s_000797.png',\n",
       "  'green_frog_s_001320.png',\n",
       "  'compact_car_s_001038.png',\n",
       "  'freighter_s_001351.png',\n",
       "  'mutt_s_000251.png',\n",
       "  'accentor_s_000303.png',\n",
       "  'lorry_s_001154.png',\n",
       "  'fire_truck_s_000894.png',\n",
       "  'cargo_vessel_s_000731.png',\n",
       "  'cruiser_s_000774.png',\n",
       "  'lippizan_s_000359.png',\n",
       "  'broodmare_s_001741.png',\n",
       "  'fighter_aircraft_s_000876.png',\n",
       "  'amphibious_aircraft_s_000216.png',\n",
       "  'leopard_frog_s_000339.png',\n",
       "  'trucking_rig_s_001315.png',\n",
       "  'shooting_brake_s_000886.png',\n",
       "  'pipit_s_000549.png',\n",
       "  'ostrich_s_002148.png',\n",
       "  'trucking_rig_s_001600.png',\n",
       "  'alauda_arvensis_s_000755.png',\n",
       "  'rana_temporaria_s_001087.png',\n",
       "  'bufo_s_000136.png',\n",
       "  'estate_car_s_000529.png',\n",
       "  'aerial_ladder_truck_s_000997.png',\n",
       "  'toy_spaniel_s_000384.png',\n",
       "  'amphibious_aircraft_s_001195.png',\n",
       "  'fallow_deer_s_001598.png',\n",
       "  'quarter_horse_s_000414.png',\n",
       "  'anuran_s_000712.png',\n",
       "  'arabian_s_001366.png',\n",
       "  'auto_s_000040.png',\n",
       "  'cargo_ship_s_001063.png',\n",
       "  'motorcar_s_000121.png',\n",
       "  'motorcar_s_000305.png',\n",
       "  'anthus_pratensis_s_001071.png',\n",
       "  'cruiser_s_000294.png',\n",
       "  'wagon_s_000763.png',\n",
       "  'alley_cat_s_002579.png',\n",
       "  'tabby_cat_s_000825.png',\n",
       "  'spadefoot_s_000051.png',\n",
       "  'wagtail_s_002075.png',\n",
       "  'fallow_deer_s_001697.png',\n",
       "  'garbage_truck_s_000777.png',\n",
       "  'moving_van_s_000067.png',\n",
       "  'mongrel_s_000696.png',\n",
       "  'red_deer_s_000741.png',\n",
       "  'true_cat_s_001768.png',\n",
       "  'spadefoot_s_000377.png',\n",
       "  'lipizzan_s_001233.png',\n",
       "  'japanese_deer_s_000163.png',\n",
       "  'toad_s_002436.png',\n",
       "  'police_boat_s_001421.png',\n",
       "  'puppy_s_002062.png',\n",
       "  'lapdog_s_001869.png',\n",
       "  'elk_s_001196.png',\n",
       "  'domestic_cat_s_001235.png',\n",
       "  'car_s_000057.png',\n",
       "  'lightship_s_000064.png',\n",
       "  'fallow_deer_s_001997.png',\n",
       "  'quarter_horse_s_000022.png',\n",
       "  'bufo_calamita_s_000566.png',\n",
       "  'biplane_s_000624.png',\n",
       "  'fire_truck_s_001616.png',\n",
       "  'japanese_spaniel_s_000037.png',\n",
       "  'ambulance_s_001394.png',\n",
       "  'domestic_cat_s_001056.png',\n",
       "  'tugboat_s_001384.png',\n",
       "  'emu_s_000072.png',\n",
       "  'gelding_s_000206.png',\n",
       "  'puppy_s_000637.png',\n",
       "  'tabby_cat_s_002607.png',\n",
       "  'elk_s_001198.png',\n",
       "  'estate_car_s_001496.png',\n",
       "  'peke_s_001338.png',\n",
       "  'arabian_s_001018.png',\n",
       "  'fighter_aircraft_s_000538.png',\n",
       "  'fallow_deer_s_001164.png',\n",
       "  'lippizan_s_000390.png',\n",
       "  'toy_dog_s_000932.png',\n",
       "  'maltese_s_002151.png',\n",
       "  'car_s_000002.png',\n",
       "  'jumbo_jet_s_000697.png',\n",
       "  'dump_truck_s_000008.png',\n",
       "  'spadefoot_s_000084.png',\n",
       "  'motortruck_s_000014.png',\n",
       "  'jetliner_s_001502.png',\n",
       "  'tanker_s_000157.png',\n",
       "  'riding_horse_s_001870.png',\n",
       "  'cabin_cruiser_s_000096.png',\n",
       "  'dredger_s_001623.png',\n",
       "  'pipit_s_001102.png',\n",
       "  'mongrel_s_002430.png',\n",
       "  'skylark_s_000150.png',\n",
       "  'cat_s_001604.png',\n",
       "  'puppy_s_000211.png',\n",
       "  'airbus_s_001124.png',\n",
       "  'leopard_frog_s_001021.png',\n",
       "  'wagon_s_001824.png',\n",
       "  'garbage_truck_s_000147.png',\n",
       "  'tabby_s_001354.png',\n",
       "  'rana_temporaria_s_000295.png',\n",
       "  'lorry_s_000364.png',\n",
       "  'estate_car_s_000075.png',\n",
       "  'tabby_s_000970.png',\n",
       "  'fire_engine_s_000985.png',\n",
       "  'toad_s_000022.png',\n",
       "  'texas_toad_s_000215.png',\n",
       "  'broodmare_s_000707.png',\n",
       "  'car_s_000286.png',\n",
       "  'twinjet_s_000565.png',\n",
       "  'truck_s_001385.png',\n",
       "  'toy_dog_s_000752.png',\n",
       "  'police_boat_s_000092.png',\n",
       "  'toy_dog_s_001197.png',\n",
       "  'cassowary_s_002446.png',\n",
       "  'articulated_lorry_s_000050.png',\n",
       "  'monoplane_s_000781.png',\n",
       "  'lightship_s_000343.png',\n",
       "  'powerboat_s_001486.png',\n",
       "  'jumbo_jet_s_000071.png',\n",
       "  'bufo_debilis_s_000024.png',\n",
       "  'transporter_s_000272.png',\n",
       "  'estate_car_s_000743.png',\n",
       "  'motorcar_s_000697.png',\n",
       "  'rana_catesbeiana_s_001655.png',\n",
       "  'tabby_s_000675.png',\n",
       "  'remount_s_000285.png',\n",
       "  'rana_catesbeiana_s_001521.png',\n",
       "  'true_frog_s_000223.png',\n",
       "  'airliner_s_002304.png',\n",
       "  'natterjack_s_000987.png',\n",
       "  'leptodactylid_s_000082.png',\n",
       "  'coupe_s_001892.png',\n",
       "  'quarter_horse_s_000419.png',\n",
       "  'car_s_001323.png',\n",
       "  'maltese_dog_s_001043.png',\n",
       "  'boat_s_000543.png',\n",
       "  'tabby_cat_s_001537.png',\n",
       "  'rana_catesbeiana_s_001096.png',\n",
       "  'bufo_bufo_s_000326.png',\n",
       "  'pilot_boat_s_000646.png',\n",
       "  'toad_frog_s_001672.png',\n",
       "  'boat_s_000988.png',\n",
       "  'deer_s_001383.png',\n",
       "  'bufo_bufo_s_000261.png',\n",
       "  'leopard_frog_s_000763.png',\n",
       "  'coupe_s_002178.png',\n",
       "  'cat_s_000663.png',\n",
       "  'ship_s_000175.png',\n",
       "  'house_cat_s_001471.png',\n",
       "  'stag_s_001458.png',\n",
       "  'station_wagon_s_001337.png',\n",
       "  'quarter_horse_s_002201.png',\n",
       "  'coupe_s_000698.png',\n",
       "  'tomcat_s_002553.png',\n",
       "  'scow_s_001266.png',\n",
       "  'chihuahua_s_000385.png',\n",
       "  'convertible_s_001500.png',\n",
       "  'convertible_s_000408.png',\n",
       "  'odocoileus_hemionus_s_000512.png',\n",
       "  'jumbo_jet_s_000971.png',\n",
       "  'truck_s_000650.png',\n",
       "  'domestic_cat_s_001288.png',\n",
       "  'walking_horse_s_000194.png',\n",
       "  'elk_s_001657.png',\n",
       "  'tipper_truck_s_000287.png',\n",
       "  'dumper_s_000648.png',\n",
       "  'struthio_camelus_s_000225.png',\n",
       "  'woodland_caribou_s_000803.png',\n",
       "  'ladder_truck_s_000795.png',\n",
       "  'pantechnicon_s_000230.png',\n",
       "  'shooting_brake_s_000229.png',\n",
       "  'jetliner_s_001349.png',\n",
       "  'toy_dog_s_001280.png',\n",
       "  'dump_truck_s_002104.png',\n",
       "  'twinjet_s_001360.png',\n",
       "  'tugboat_s_000715.png',\n",
       "  'passerine_s_000360.png',\n",
       "  'automobile_s_002337.png',\n",
       "  'honey_eater_s_001422.png',\n",
       "  'attack_aircraft_s_001304.png',\n",
       "  'toy_dog_s_000114.png',\n",
       "  'anuran_s_000550.png',\n",
       "  'mouser_s_000382.png',\n",
       "  'sparrow_s_000037.png',\n",
       "  'stud_mare_s_001038.png',\n",
       "  'speedboat_s_002588.png',\n",
       "  'passenger_ship_s_002153.png',\n",
       "  'bufo_bufo_s_001876.png',\n",
       "  'biplane_s_000651.png',\n",
       "  'gelding_s_001785.png',\n",
       "  'trucking_rig_s_001598.png',\n",
       "  'deer_s_000309.png',\n",
       "  'pekingese_s_001946.png',\n",
       "  'frog_s_002802.png',\n",
       "  'cervus_unicolor_s_000290.png',\n",
       "  'honey_eater_s_000478.png',\n",
       "  'automobile_s_001152.png',\n",
       "  'coupe_s_000846.png',\n",
       "  'flying_bird_s_000252.png',\n",
       "  'automobile_s_002343.png',\n",
       "  'japanese_spaniel_s_001162.png',\n",
       "  'fire_truck_s_000043.png',\n",
       "  'semi_s_001207.png',\n",
       "  'fighter_aircraft_s_001840.png',\n",
       "  'ship_s_001976.png',\n",
       "  'caribou_s_001222.png',\n",
       "  'police_cruiser_s_001347.png',\n",
       "  'coupe_s_000042.png',\n",
       "  'bufo_bufo_s_000995.png',\n",
       "  'tabby_s_001148.png',\n",
       "  'true_cat_s_000476.png',\n",
       "  'garbage_truck_s_001442.png',\n",
       "  'fighter_aircraft_s_000385.png',\n",
       "  'quarter_horse_s_001602.png',\n",
       "  'camion_s_000116.png',\n",
       "  'tennessee_walker_s_000539.png',\n",
       "  'lipizzan_s_000452.png',\n",
       "  'trucking_rig_s_001594.png',\n",
       "  'shooting_brake_s_001611.png',\n",
       "  'puppy_s_002096.png',\n",
       "  'auto_s_000855.png',\n",
       "  'bufo_marinus_s_001364.png',\n",
       "  'natterjack_s_000036.png',\n",
       "  'pontoon_s_000643.png',\n",
       "  'stallion_s_001256.png',\n",
       "  'coupe_s_001863.png',\n",
       "  'house_cat_s_000079.png',\n",
       "  'jetliner_s_000981.png',\n",
       "  'felis_catus_s_000853.png',\n",
       "  'tabby_cat_s_000827.png',\n",
       "  'struthio_camelus_s_001250.png',\n",
       "  'fawn_s_001915.png',\n",
       "  'canis_familiaris_s_001298.png',\n",
       "  'lipizzan_s_002122.png',\n",
       "  'toy_spaniel_s_001133.png',\n",
       "  'dump_truck_s_000029.png',\n",
       "  'fighter_s_001042.png',\n",
       "  'domestic_cat_s_000493.png',\n",
       "  'cervus_elaphus_s_001764.png',\n",
       "  'airbus_s_000627.png',\n",
       "  'fawn_s_001452.png',\n",
       "  'fallow_deer_s_000959.png',\n",
       "  'spadefoot_s_000777.png',\n",
       "  'seaplane_s_001639.png',\n",
       "  'plane_s_001066.png',\n",
       "  'bufo_viridis_s_000348.png',\n",
       "  'crapaud_s_000051.png',\n",
       "  'seaplane_s_001447.png',\n",
       "  'boat_s_000404.png',\n",
       "  'convertible_s_001242.png',\n",
       "  'toad_s_000201.png',\n",
       "  'finch_s_000494.png',\n",
       "  'fire_engine_s_001437.png',\n",
       "  'pipit_s_000226.png',\n",
       "  'toy_dog_s_000960.png',\n",
       "  'delivery_truck_s_001121.png',\n",
       "  'bufo_marinus_s_001506.png',\n",
       "  'studhorse_s_000010.png',\n",
       "  'woodland_caribou_s_001003.png',\n",
       "  'station_wagon_s_000260.png',\n",
       "  'pilot_boat_s_000356.png',\n",
       "  'stud_mare_s_001497.png',\n",
       "  'tabby_cat_s_001966.png',\n",
       "  'american_toad_s_000006.png',\n",
       "  'tipper_s_000010.png',\n",
       "  'tomcat_s_002230.png',\n",
       "  'dive_bomber_s_001120.png',\n",
       "  'wapiti_s_000802.png',\n",
       "  'biplane_s_000862.png',\n",
       "  'blenheim_spaniel_s_000589.png',\n",
       "  'shooting_brake_s_001161.png',\n",
       "  'jetliner_s_000400.png',\n",
       "  'tabby_s_000923.png',\n",
       "  'alces_alces_s_000361.png',\n",
       "  'police_boat_s_000002.png',\n",
       "  'mutt_s_001696.png',\n",
       "  'cervus_elaphus_s_001695.png',\n",
       "  'quarter_horse_s_000752.png',\n",
       "  'accentor_s_000449.png',\n",
       "  'tabby_cat_s_000144.png',\n",
       "  'fire_engine_s_001513.png',\n",
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       "  'jumbojet_s_000789.png',\n",
       "  'delivery_truck_s_000510.png',\n",
       "  'biplane_s_001901.png',\n",
       "  'lorry_s_001185.png',\n",
       "  'crapaud_s_000511.png',\n",
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       "  'stud_mare_s_001477.png',\n",
       "  'arabian_s_000751.png',\n",
       "  'rangifer_caribou_s_000586.png',\n",
       "  'monoplane_s_000036.png',\n",
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       "  'stealth_fighter_s_000240.png',\n",
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       "  'roe_deer_s_000358.png',\n",
       "  'rhea_americana_s_000475.png',\n",
       "  'lippizaner_s_000517.png',\n",
       "  'cassowary_s_000297.png',\n",
       "  'pekingese_s_001675.png',\n",
       "  'accentor_s_001058.png',\n",
       "  'lapdog_s_000227.png',\n",
       "  'auto_s_001153.png',\n",
       "  'fire_engine_s_000443.png',\n",
       "  'muntjac_s_000815.png',\n",
       "  'lightship_s_000090.png',\n",
       "  'dog_s_002441.png',\n",
       "  'auto_s_001951.png',\n",
       "  'arabian_s_000328.png',\n",
       "  'odocoileus_hemionus_s_000188.png',\n",
       "  'barking_deer_s_000019.png',\n",
       "  'dive_bomber_s_001486.png',\n",
       "  'bufo_viridis_s_000503.png',\n",
       "  'wrecker_s_002294.png',\n",
       "  'monoplane_s_000206.png',\n",
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       "  'speedboat_s_002160.png',\n",
       "  'cargo_vessel_s_002136.png',\n",
       "  'garbage_truck_s_000726.png',\n",
       "  'delivery_truck_s_001468.png',\n",
       "  'house_cat_s_002240.png',\n",
       "  'house_cat_s_000045.png',\n",
       "  'caribou_s_002007.png',\n",
       "  'propeller_plane_s_000451.png',\n",
       "  'fallow_deer_s_001082.png',\n",
       "  'dog_s_001897.png',\n",
       "  'bufo_bufo_s_001631.png',\n",
       "  'spadefoot_s_000191.png',\n",
       "  'multiengine_airplane_s_000186.png',\n",
       "  'auto_s_002359.png',\n",
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       "  'passenger_ship_s_000582.png',\n",
       "  'airliner_s_001297.png',\n",
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       "  'speedboat_s_000178.png',\n",
       "  'boat_s_001444.png',\n",
       "  'estate_car_s_000712.png',\n",
       "  'english_toy_spaniel_s_000888.png',\n",
       "  'passerine_s_000699.png',\n",
       "  'rana_pipiens_s_000286.png',\n",
       "  'tanker_s_000025.png',\n",
       "  'car_s_001956.png',\n",
       "  'stealth_bomber_s_000424.png',\n",
       "  'airliner_s_001070.png',\n",
       "  'stud_mare_s_001348.png',\n",
       "  'quarter_horse_s_000282.png',\n",
       "  'maltese_s_001818.png',\n",
       "  'lorry_s_001786.png',\n",
       "  'true_frog_s_000064.png',\n",
       "  'cassowary_s_001372.png',\n",
       "  'houseboat_s_000626.png',\n",
       "  'domestic_cat_s_000035.png',\n",
       "  'deer_s_001596.png',\n",
       "  'cow_pony_s_000803.png',\n",
       "  'tabby_cat_s_001466.png',\n",
       "  'tipper_truck_s_001525.png',\n",
       "  'stealth_fighter_s_000533.png',\n",
       "  'station_wagon_s_000750.png',\n",
       "  'wagtail_s_001549.png',\n",
       "  'rangifer_caribou_s_000985.png',\n",
       "  'cargo_ship_s_000408.png',\n",
       "  'cab_s_000788.png',\n",
       "  'flatboat_s_000044.png',\n",
       "  'bufo_viridis_s_000457.png',\n",
       "  'fallow_deer_s_000040.png',\n",
       "  'barren_ground_caribou_s_000077.png',\n",
       "  'pekinese_s_000453.png',\n",
       "  'female_horse_s_000358.png',\n",
       "  'compact_car_s_000869.png',\n",
       "  'tabby_cat_s_002454.png',\n",
       "  'delivery_truck_s_000864.png',\n",
       "  'cargo_ship_s_000802.png',\n",
       "  'attack_aircraft_s_000037.png',\n",
       "  'convertible_s_000037.png',\n",
       "  'broodmare_s_001448.png',\n",
       "  'toy_spaniel_s_001286.png',\n",
       "  'packet_boat_s_001206.png',\n",
       "  'ostrich_s_001564.png',\n",
       "  'hospital_ship_s_001658.png',\n",
       "  'stealth_bomber_s_001035.png',\n",
       "  'stag_s_002583.png',\n",
       "  'police_cruiser_s_000629.png',\n",
       "  'merchant_ship_s_001242.png',\n",
       "  'dump_truck_s_001584.png',\n",
       "  'cargo_ship_s_001830.png',\n",
       "  'nandu_s_000721.png',\n",
       "  'garbage_truck_s_001119.png',\n",
       "  'lorry_s_002317.png',\n",
       "  'wagtail_s_001445.png',\n",
       "  'lipizzan_s_002049.png',\n",
       "  'maltese_s_000948.png',\n",
       "  'quarter_horse_s_000798.png',\n",
       "  'house_cat_s_001472.png',\n",
       "  'tank_ship_s_001155.png',\n",
       "  'hospital_ship_s_001838.png',\n",
       "  'mule_deer_s_000581.png',\n",
       "  'deer_s_001329.png',\n",
       "  'rhea_americana_s_000268.png',\n",
       "  'lippizan_s_000440.png',\n",
       "  'convertible_s_002117.png',\n",
       "  'bufo_marinus_s_001567.png',\n",
       "  'roe_deer_s_000690.png',\n",
       "  'twinjet_s_000481.png',\n",
       "  'american_elk_s_001743.png',\n",
       "  'bufo_viridis_s_000254.png',\n",
       "  'dumper_s_000179.png',\n",
       "  'lippizan_s_000323.png',\n",
       "  'leopard_frog_s_000058.png',\n",
       "  'prunella_modularis_s_000578.png',\n",
       "  'maltese_s_001867.png',\n",
       "  'toy_dog_s_001358.png',\n",
       "  'police_cruiser_s_000500.png',\n",
       "  'broodmare_s_001589.png',\n",
       "  'passerine_s_000381.png',\n",
       "  'accentor_s_000424.png',\n",
       "  'finch_s_000617.png',\n",
       "  'trucking_rig_s_001111.png',\n",
       "  'canis_familiaris_s_001059.png',\n",
       "  'cervus_elaphus_s_000813.png',\n",
       "  'cassowary_s_000339.png',\n",
       "  'buckskin_s_000325.png',\n",
       "  'lightship_s_001019.png',\n",
       "  'shooting_brake_s_001330.png',\n",
       "  'tomcat_s_002649.png',\n",
       "  'barren_ground_caribou_s_000174.png',\n",
       "  'cat_s_000572.png',\n",
       "  'walking_horse_s_002079.png',\n",
       "  'rana_temporaria_s_001139.png',\n",
       "  'tipper_s_000007.png',\n",
       "  'freighter_s_000661.png',\n",
       "  'stealth_fighter_s_000020.png',\n",
       "  'goliath_frog_s_000205.png',\n",
       "  'stealth_bomber_s_000703.png',\n",
       "  'prunella_modularis_s_000795.png',\n",
       "  'struthio_camelus_s_000331.png',\n",
       "  'dunnock_s_000956.png',\n",
       "  'auto_s_001284.png',\n",
       "  'scow_s_000831.png',\n",
       "  'fallow_deer_s_001256.png',\n",
       "  'stealth_fighter_s_001297.png',\n",
       "  'estate_car_s_000845.png',\n",
       "  'ship_s_001691.png',\n",
       "  'abandoned_ship_s_000977.png',\n",
       "  'wagon_s_001298.png',\n",
       "  'feist_s_000354.png',\n",
       "  'lipizzan_s_001600.png',\n",
       "  'leopard_frog_s_001700.png',\n",
       "  'cervus_elaphus_s_000066.png',\n",
       "  'toy_s_000517.png',\n",
       "  'cargo_ship_s_000556.png',\n",
       "  'stallion_s_001717.png',\n",
       "  'coupe_s_001004.png',\n",
       "  'camion_s_001068.png',\n",
       "  'coupe_s_000001.png',\n",
       "  'garbage_truck_s_001253.png',\n",
       "  'sea_boat_s_001960.png',\n",
       "  'capreolus_capreolus_s_001169.png',\n",
       "  'lippizan_s_000681.png',\n",
       "  'tabby_s_000512.png',\n",
       "  'pontoon_s_000271.png',\n",
       "  'sea_boat_s_001502.png',\n",
       "  'lark_s_000700.png',\n",
       "  'leopard_frog_s_001074.png',\n",
       "  'rana_palustris_s_000022.png',\n",
       "  'lippizaner_s_001181.png',\n",
       "  'wagon_s_000155.png',\n",
       "  'american_toad_s_001410.png',\n",
       "  'cargo_vessel_s_000659.png',\n",
       "  'station_wagon_s_001705.png',\n",
       "  'transporter_s_000076.png',\n",
       "  'broodmare_s_001098.png',\n",
       "  'passenger_ship_s_000050.png',\n",
       "  'tabby_cat_s_000484.png',\n",
       "  'fighter_s_000276.png',\n",
       "  'car_s_000506.png',\n",
       "  'stealth_fighter_s_000139.png',\n",
       "  'pleasure_craft_s_000076.png',\n",
       "  'cruiser_s_000744.png',\n",
       "  'tabby_cat_s_002478.png',\n",
       "  'attack_aircraft_s_000423.png',\n",
       "  'stealth_fighter_s_000460.png',\n",
       "  'police_cruiser_s_000787.png',\n",
       "  'mongrel_s_000381.png',\n",
       "  'attack_aircraft_s_001166.png',\n",
       "  'supertanker_s_000557.png',\n",
       "  'tugboat_s_000807.png',\n",
       "  'stud_mare_s_000413.png',\n",
       "  'lorry_s_001039.png',\n",
       "  'trailer_truck_s_001496.png',\n",
       "  'airliner_s_001477.png',\n",
       "  'camion_s_001521.png',\n",
       "  'alces_alces_s_000404.png',\n",
       "  'automobile_s_000315.png',\n",
       "  'tabby_cat_s_001542.png',\n",
       "  'pickerel_frog_s_000003.png',\n",
       "  'spring_frog_s_000318.png',\n",
       "  'roebuck_s_000153.png',\n",
       "  'reindeer_s_000121.png',\n",
       "  'male_horse_s_000008.png',\n",
       "  'dog_s_000052.png',\n",
       "  'bufo_bufo_s_001959.png',\n",
       "  'stealth_bomber_s_000492.png',\n",
       "  'pilot_boat_s_001819.png',\n",
       "  'attack_aircraft_s_001187.png',\n",
       "  'house_cat_s_000812.png',\n",
       "  'cassowary_s_000138.png',\n",
       "  'boat_s_000490.png',\n",
       "  'wapiti_s_000884.png',\n",
       "  'bufo_bufo_s_001492.png',\n",
       "  'delivery_truck_s_000157.png',\n",
       "  'truck_s_000060.png',\n",
       "  'stud_mare_s_000506.png',\n",
       "  'fighter_aircraft_s_000655.png',\n",
       "  'true_cat_s_000127.png',\n",
       "  'tabby_s_000228.png',\n",
       "  'american_green_toad_s_000007.png',\n",
       "  'quarter_horse_s_001833.png',\n",
       "  'odocoileus_hemionus_s_001090.png',\n",
       "  'tipper_lorry_s_000527.png',\n",
       "  'compact_car_s_001200.png',\n",
       "  'pickerel_frog_s_000242.png',\n",
       "  'finch_s_000308.png',\n",
       "  'arabian_s_002456.png',\n",
       "  'dunnock_s_001476.png',\n",
       "  'emu_s_002196.png',\n",
       "  'plane_s_000065.png',\n",
       "  'european_toad_s_000360.png',\n",
       "  'arabian_s_002383.png',\n",
       "  'dog_s_002098.png',\n",
       "  'quarter_horse_s_001757.png',\n",
       "  'rana_temporaria_s_001922.png',\n",
       "  'cargo_ship_s_001318.png',\n",
       "  'tip_truck_s_001017.png',\n",
       "  'biplane_s_001055.png',\n",
       "  'camion_s_001255.png',\n",
       "  'red_deer_s_000756.png',\n",
       "  'capreolus_capreolus_s_000822.png',\n",
       "  'lippizaner_s_000840.png',\n",
       "  'propeller_plane_s_001438.png',\n",
       "  'dumper_s_000646.png',\n",
       "  'capreolus_capreolus_s_000284.png',\n",
       "  'truck_s_000526.png',\n",
       "  'bufo_s_001473.png',\n",
       "  'fire_engine_s_001449.png',\n",
       "  'elk_s_001164.png',\n",
       "  'canis_familiaris_s_001319.png',\n",
       "  'tennessee_walking_horse_s_000005.png',\n",
       "  'truck_s_000838.png',\n",
       "  'rhea_s_001649.png',\n",
       "  'mule_deer_s_002514.png',\n",
       "  'canis_familiaris_s_000143.png',\n",
       "  'police_cruiser_s_001073.png',\n",
       "  'cervus_sika_s_000273.png',\n",
       "  'cat_s_001584.png',\n",
       "  'tipper_lorry_s_000313.png',\n",
       "  'bufo_americanus_s_001202.png',\n",
       "  'english_toy_spaniel_s_000735.png',\n",
       "  'bufo_calamita_s_000043.png',\n",
       "  'tipper_s_000180.png',\n",
       "  'tabby_cat_s_000881.png',\n",
       "  'tabby_cat_s_001644.png',\n",
       "  'blenheim_spaniel_s_000330.png',\n",
       "  'attack_aircraft_s_001138.png',\n",
       "  'arabian_s_002270.png',\n",
       "  'cassowary_s_000845.png',\n",
       "  'wagon_s_000692.png',\n",
       "  'tabby_cat_s_000319.png',\n",
       "  'american_toad_s_000077.png',\n",
       "  'moose_s_001500.png',\n",
       "  'airliner_s_001406.png',\n",
       "  'fighter_aircraft_s_000966.png',\n",
       "  'dunnock_s_000055.png',\n",
       "  'pekingese_s_002399.png',\n",
       "  'twinjet_s_000739.png',\n",
       "  'automobile_s_002249.png',\n",
       "  'airliner_s_000669.png',\n",
       "  'wagtail_s_001656.png',\n",
       "  'tabby_s_001949.png',\n",
       "  'dump_truck_s_001009.png',\n",
       "  'freighter_s_000436.png',\n",
       "  'fallow_deer_s_001171.png',\n",
       "  'trucking_rig_s_000684.png',\n",
       "  'ship_s_000249.png',\n",
       "  'monoplane_s_000416.png',\n",
       "  'songbird_s_002066.png',\n",
       "  'rana_catesbeiana_s_000602.png',\n",
       "  'red_deer_s_000536.png',\n",
       "  'elk_s_001583.png',\n",
       "  'stealth_fighter_s_001313.png',\n",
       "  'station_wagon_s_002498.png',\n",
       "  'pleasure_boat_s_001949.png',\n",
       "  'cargo_ship_s_000060.png',\n",
       "  'tabby_cat_s_001767.png',\n",
       "  'crapaud_s_000055.png',\n",
       "  'dump_truck_s_002008.png',\n",
       "  'bufo_viridis_s_000506.png',\n",
       "  'bullfrog_s_001295.png',\n",
       "  'riding_horse_s_001884.png',\n",
       "  'boat_s_002344.png',\n",
       "  'ostrich_s_000950.png',\n",
       "  'muntjac_s_000681.png',\n",
       "  'pekingese_s_000279.png',\n",
       "  'lipizzan_s_002052.png',\n",
       "  'rana_pipiens_s_000570.png',\n",
       "  'toy_s_002250.png',\n",
       "  'tabby_s_001940.png',\n",
       "  'dive_bomber_s_001294.png',\n",
       "  'mutt_s_000086.png',\n",
       "  'biplane_s_000701.png',\n",
       "  'toy_s_000964.png',\n",
       "  'jetliner_s_000807.png',\n",
       "  'barge_s_000426.png',\n",
       "  'cassowary_s_000255.png',\n",
       "  'rana_clamitans_s_000059.png',\n",
       "  'quarter_horse_s_000222.png',\n",
       "  'tomcat_s_000377.png',\n",
       "  'scow_s_000687.png',\n",
       "  'emu_s_001571.png',\n",
       "  'coupe_s_001625.png',\n",
       "  'roan_s_000199.png',\n",
       "  'bufo_bufo_s_000626.png',\n",
       "  'tennessee_walking_horse_s_001470.png',\n",
       "  'station_wagon_s_001497.png',\n",
       "  'jumbo_jet_s_001056.png',\n",
       "  'trailer_truck_s_000099.png',\n",
       "  'puppy_s_000502.png',\n",
       "  'pekingese_s_002101.png',\n",
       "  'propeller_plane_s_001193.png',\n",
       "  'car_s_001009.png',\n",
       "  'lipizzan_s_002011.png',\n",
       "  'green_frog_s_001375.png',\n",
       "  'tipper_lorry_s_000586.png',\n",
       "  'jetliner_s_000509.png',\n",
       "  'musk_deer_s_000211.png',\n",
       "  'appaloosa_s_002251.png',\n",
       "  'arabian_s_001642.png',\n",
       "  'compact_car_s_000435.png',\n",
       "  'chihuahua_s_000063.png',\n",
       "  'semi_s_000462.png',\n",
       "  'wapiti_s_000455.png',\n",
       "  'twinjet_s_001120.png',\n",
       "  'cargo_vessel_s_001235.png',\n",
       "  'peke_s_000274.png',\n",
       "  'dump_truck_s_001422.png',\n",
       "  'dump_truck_s_001863.png',\n",
       "  'tailed_frog_s_000638.png',\n",
       "  'gelding_s_000965.png',\n",
       "  'coupe_s_000056.png',\n",
       "  'pleasure_craft_s_000625.png',\n",
       "  'house_cat_s_001328.png',\n",
       "  'struthio_camelus_s_000081.png',\n",
       "  'domestic_cat_s_000051.png',\n",
       "  'passenger_ship_s_000948.png',\n",
       "  'sparrow_s_002697.png',\n",
       "  'prunella_modularis_s_000302.png',\n",
       "  'fallow_deer_s_000192.png',\n",
       "  'rana_palustris_s_000107.png',\n",
       "  'attack_aircraft_s_001075.png',\n",
       "  'twinjet_s_000046.png',\n",
       "  'chihuahua_s_001038.png',\n",
       "  'tabby_cat_s_001004.png',\n",
       "  'cargo_vessel_s_001812.png',\n",
       "  'dunnock_s_000037.png',\n",
       "  'tabby_s_001740.png',\n",
       "  'gelding_s_001638.png',\n",
       "  'anthus_pratensis_s_001166.png',\n",
       "  'camion_s_001988.png',\n",
       "  'alley_cat_s_001645.png',\n",
       "  'oil_tanker_s_001626.png',\n",
       "  'tennessee_walking_horse_s_000355.png',\n",
       "  'containership_s_000812.png',\n",
       "  'passerine_s_000801.png',\n",
       "  'lipizzan_s_000635.png',\n",
       "  'truck_s_000635.png',\n",
       "  'twinjet_s_000450.png',\n",
       "  'honey_eater_s_000702.png',\n",
       "  'tabby_s_000039.png',\n",
       "  'pipit_s_000827.png',\n",
       "  'elephant_bird_s_001004.png',\n",
       "  'night_bird_s_000241.png',\n",
       "  'alley_cat_s_001301.png',\n",
       "  'house_cat_s_000332.png',\n",
       "  'bufo_americanus_s_000492.png',\n",
       "  'wagtail_s_001443.png',\n",
       "  'tabby_cat_s_000587.png',\n",
       "  'gamecock_s_000098.png',\n",
       "  'cargo_ship_s_001867.png',\n",
       "  'seaplane_s_000416.png',\n",
       "  'english_toy_spaniel_s_001058.png',\n",
       "  'japanese_spaniel_s_001324.png',\n",
       "  'coupe_s_000179.png',\n",
       "  'american_elk_s_000201.png',\n",
       "  'chihuahua_s_000666.png',\n",
       "  'spadefoot_s_000219.png',\n",
       "  'bufo_s_001733.png',\n",
       "  'struthio_camelus_s_001368.png',\n",
       "  'arab_s_000456.png',\n",
       "  'monoplane_s_001154.png',\n",
       "  'coupe_s_001690.png',\n",
       "  'dawn_horse_s_000752.png',\n",
       "  'tennessee_walking_horse_s_000013.png',\n",
       "  'cabin_cruiser_s_001500.png',\n",
       "  'sparrow_s_001750.png',\n",
       "  'aerial_ladder_truck_s_001306.png',\n",
       "  'dromaius_novaehollandiae_s_000433.png',\n",
       "  'passerine_s_000376.png',\n",
       "  'caribou_s_001447.png',\n",
       "  'bird_s_000356.png',\n",
       "  'motorcar_s_001389.png',\n",
       "  'auto_s_000675.png',\n",
       "  'automobile_s_001833.png',\n",
       "  'rana_temporaria_s_000008.png',\n",
       "  'spring_frog_s_001263.png',\n",
       "  'barking_frog_s_000397.png',\n",
       "  'puppy_s_000785.png',\n",
       "  'convertible_s_001861.png',\n",
       "  'convertible_s_000859.png',\n",
       "  'american_saddle_horse_s_000039.png',\n",
       "  'biplane_s_000244.png',\n",
       "  'red_deer_s_000704.png',\n",
       "  'tabby_s_001071.png',\n",
       "  'tabby_cat_s_002548.png',\n",
       "  'tennessee_walker_s_001791.png',\n",
       "  'auto_s_002048.png',\n",
       "  'dromaius_novaehollandiae_s_000296.png',\n",
       "  'felis_catus_s_001460.png',\n",
       "  'peke_s_000440.png',\n",
       "  'chihuahua_s_000349.png',\n",
       "  'lapdog_s_002195.png',\n",
       "  'bufo_marinus_s_001243.png',\n",
       "  'convertible_s_001433.png',\n",
       "  'mule_deer_s_000628.png',\n",
       "  'tabby_s_000410.png',\n",
       "  'stud_s_000099.png',\n",
       "  'passenger_ship_s_001530.png',\n",
       "  'scow_s_000131.png',\n",
       "  'tabby_s_001868.png',\n",
       "  'bufo_bufo_s_002334.png',\n",
       "  'bufo_viridis_s_000152.png',\n",
       "  'passerine_s_001306.png',\n",
       "  'house_cat_s_002116.png',\n",
       "  'jetliner_s_001188.png',\n",
       "  'moving_van_s_001359.png',\n",
       "  'wapiti_s_000663.png',\n",
       "  'felis_catus_s_000998.png',\n",
       "  'sea_boat_s_001979.png',\n",
       "  'fighter_aircraft_s_001776.png',\n",
       "  'fighter_aircraft_s_001096.png',\n",
       "  'shooting_brake_s_000774.png',\n",
       "  'automobile_s_001310.png',\n",
       "  'pekingese_s_001023.png',\n",
       "  'american_elk_s_000455.png',\n",
       "  'trucking_rig_s_001243.png',\n",
       "  'house_cat_s_000877.png',\n",
       "  'taxi_s_000040.png',\n",
       "  'pilot_boat_s_001100.png',\n",
       "  'articulated_lorry_s_000223.png',\n",
       "  'felis_domesticus_s_000164.png',\n",
       "  'trucking_rig_s_001300.png',\n",
       "  'tractor_trailer_s_000254.png',\n",
       "  'flightless_bird_s_000142.png',\n",
       "  'dump_truck_s_000939.png',\n",
       "  'deer_s_000967.png',\n",
       "  'ship_s_001904.png',\n",
       "  'wagtail_s_000309.png',\n",
       "  'trailer_truck_s_000815.png',\n",
       "  'container_ship_s_002477.png',\n",
       "  'guard_boat_s_000285.png',\n",
       "  'estate_car_s_000446.png',\n",
       "  'puppy_s_001621.png',\n",
       "  'tabby_cat_s_000689.png',\n",
       "  'american_toad_s_000650.png',\n",
       "  'boat_s_002381.png',\n",
       "  'female_horse_s_000089.png',\n",
       "  'bufo_viridis_s_001370.png',\n",
       "  'ladder_truck_s_000044.png',\n",
       "  'cargo_ship_s_001321.png',\n",
       "  'twinjet_s_000547.png',\n",
       "  'cascades_frog_s_000061.png',\n",
       "  'odocoileus_hemionus_s_001017.png',\n",
       "  'attack_aircraft_s_001254.png',\n",
       "  'airplane_s_000006.png',\n",
       "  'songbird_s_001232.png',\n",
       "  'peke_s_000388.png',\n",
       "  'boat_s_002143.png',\n",
       "  'accentor_s_001182.png',\n",
       "  'dive_bomber_s_001066.png',\n",
       "  'dunnock_s_001650.png',\n",
       "  'dawn_horse_s_001127.png',\n",
       "  'european_toad_s_000359.png',\n",
       "  'wrecker_s_002325.png',\n",
       "  'lipizzan_s_000118.png',\n",
       "  'automobile_s_000327.png',\n",
       "  'pekinese_s_002027.png',\n",
       "  'maltese_s_001913.png',\n",
       "  'toad_frog_s_000836.png',\n",
       "  'leopard_frog_s_001231.png',\n",
       "  'domestic_cat_s_001066.png',\n",
       "  'barking_frog_s_000840.png',\n",
       "  'finch_s_000038.png',\n",
       "  'capreolus_capreolus_s_000257.png',\n",
       "  'lippizan_s_000126.png',\n",
       "  'attack_aircraft_s_000089.png',\n",
       "  'mutt_s_002278.png',\n",
       "  'true_toad_s_000021.png',\n",
       "  'capreolus_capreolus_s_000178.png',\n",
       "  'bufo_viridis_s_000600.png',\n",
       "  'chihuahua_s_000097.png',\n",
       "  'meadow_pipit_s_001282.png',\n",
       "  'red_deer_s_000008.png',\n",
       "  'leopard_frog_s_001528.png',\n",
       "  'estate_car_s_001220.png',\n",
       "  'bullfrog_s_000408.png',\n",
       "  'stealth_fighter_s_001637.png',\n",
       "  'capreolus_capreolus_s_001669.png',\n",
       "  'airbus_s_000736.png',\n",
       "  'tabby_s_000292.png',\n",
       "  'station_wagon_s_002793.png',\n",
       "  'boat_s_000765.png',\n",
       "  'english_toy_spaniel_s_000814.png',\n",
       "  'european_elk_s_000265.png',\n",
       "  'roe_deer_s_000371.png',\n",
       "  'convertible_s_000414.png',\n",
       "  'cow_pony_s_000145.png',\n",
       "  'tabby_s_000126.png',\n",
       "  'trucking_rig_s_001628.png',\n",
       "  'muntjac_s_001738.png',\n",
       "  'broodmare_s_000582.png',\n",
       "  'fire_engine_s_001540.png',\n",
       "  'gelding_s_000640.png',\n",
       "  'tabby_cat_s_002533.png',\n",
       "  'appaloosa_s_000342.png',\n",
       "  'pipit_s_001066.png',\n",
       "  'speedboat_s_000410.png',\n",
       "  'roe_deer_s_001014.png',\n",
       "  'american_toad_s_001815.png',\n",
       "  'bufo_calamita_s_000773.png',\n",
       "  'estate_car_s_000044.png',\n",
       "  'cassowary_s_000021.png',\n",
       "  'lorry_s_001709.png',\n",
       "  'amphibious_aircraft_s_000141.png',\n",
       "  'capreolus_capreolus_s_000073.png',\n",
       "  'motorboat_s_000773.png',\n",
       "  'lipizzan_s_001658.png',\n",
       "  'tabby_cat_s_000531.png',\n",
       "  'truck_s_000289.png',\n",
       "  'speedboat_s_001711.png',\n",
       "  'quarter_horse_s_000557.png',\n",
       "  'quarter_horse_s_000942.png',\n",
       "  'jumbo_jet_s_001476.png',\n",
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       "  'fallow_deer_s_001623.png',\n",
       "  'compact_car_s_000850.png',\n",
       "  'coupe_s_001912.png',\n",
       "  'roe_deer_s_000678.png',\n",
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       "  'red_deer_s_002698.png',\n",
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     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "batch1"
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  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>[255, 253, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
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       "</table>\n",
       "<p>5 rows × 3073 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9  \\\n",
       "0   59   43   50   68   98  119  139  145  149  149   \n",
       "1  154  126  105  102  125  155  172  180  142  111   \n",
       "2  255  253  253  253  253  253  253  253  253  253   \n",
       "3   28   37   38   42   44   40   40   24   32   43   \n",
       "4  170  168  177  183  181  177  181  184  189  189   \n",
       "\n",
       "                         ...                          3063  3064  3065  3066  \\\n",
       "0                        ...                            58    65    59    46   \n",
       "1                        ...                            42    67   101   122   \n",
       "2                        ...                            83    80    69    66   \n",
       "3                        ...                            39    59    42    44   \n",
       "4                        ...                            88    85    82    83   \n",
       "\n",
       "   3067  3068  3069  3070  3071  \\\n",
       "0    57   104   140    84    72   \n",
       "1   133   136   139   142   144   \n",
       "2    72    79    83    83    84   \n",
       "3    48    38    28    37    46   \n",
       "4    79    78    82    78    80   \n",
       "\n",
       "                                               image  \n",
       "0  [59, 43, 50, 68, 98, 119, 139, 145, 149, 149, ...  \n",
       "1  [154, 126, 105, 102, 125, 155, 172, 180, 142, ...  \n",
       "2  [255, 253, 253, 253, 253, 253, 253, 253, 253, ...  \n",
       "3  [28, 37, 38, 42, 44, 40, 40, 24, 32, 43, 30, 3...  \n",
       "4  [170, 168, 177, 183, 181, 177, 181, 184, 189, ...  \n",
       "\n",
       "[5 rows x 3073 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(batch1['data'])\n",
    "df['image'] = df.as_matrix().tolist()\n",
    "df.head()   # drop from col0 to col3071"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[59, 43, 50, 68, 98, 119, 139, 145, 149, 149, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[154, 126, 105, 102, 125, 155, 172, 180, 142, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[255, 253, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[28, 37, 38, 42, 44, 40, 40, 24, 32, 43, 30, 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[170, 168, 177, 183, 181, 177, 181, 184, 189, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image\n",
       "0  [59, 43, 50, 68, 98, 119, 139, 145, 149, 149, ...\n",
       "1  [154, 126, 105, 102, 125, 155, 172, 180, 142, ...\n",
       "2  [255, 253, 253, 253, 253, 253, 253, 253, 253, ...\n",
       "3  [28, 37, 38, 42, 44, 40, 40, 24, 32, 43, 30, 3...\n",
       "4  [170, 168, 177, 183, 181, 177, 181, 184, 189, ..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(range(3072),axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get the dataframe from above dictionary\n",
    "def get_dataframe(batch):\n",
    "    df = pd.DataFrame(batch['data'])\n",
    "    df['image'] = df.as_matrix().tolist()\n",
    "    df.drop(range(3072),axis=1,inplace=True)    # remove the first 3072 cols\n",
    "    df['label'] = batch['labels']\n",
    "    return df\n",
    "\n",
    "\n",
    "train = pd.concat([get_dataframe(batch1),get_dataframe(batch2),get_dataframe(batch3),\n",
    "                   get_dataframe(batch4),get_dataframe(batch5)],ignore_index=True)\n",
    "test = get_dataframe(batch_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[59, 43, 50, 68, 98, 119, 139, 145, 149, 149, ...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[154, 126, 105, 102, 125, 155, 172, 180, 142, ...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[255, 253, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[28, 37, 38, 42, 44, 40, 40, 24, 32, 43, 30, 3...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[170, 168, 177, 183, 181, 177, 181, 184, 189, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  label\n",
       "0  [59, 43, 50, 68, 98, 119, 139, 145, 149, 149, ...      6\n",
       "1  [154, 126, 105, 102, 125, 155, 172, 180, 142, ...      9\n",
       "2  [255, 253, 253, 253, 253, 253, 253, 253, 253, ...      9\n",
       "3  [28, 37, 38, 42, 44, 40, 40, 24, 32, 43, 30, 3...      4\n",
       "4  [170, 168, 177, 183, 181, 177, 181, 184, 189, ...      1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[158, 159, 165, 166, 160, 156, 162, 159, 158, ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[235, 231, 232, 232, 232, 232, 232, 232, 232, ...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[158, 158, 139, 132, 166, 182, 187, 193, 199, ...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[155, 167, 176, 190, 177, 166, 168, 166, 170, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[65, 70, 48, 30, 23, 40, 44, 45, 45, 40, 10, 1...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  label\n",
       "0  [158, 159, 165, 166, 160, 156, 162, 159, 158, ...      3\n",
       "1  [235, 231, 232, 232, 232, 232, 232, 232, 232, ...      8\n",
       "2  [158, 158, 139, 132, 166, 182, 187, 193, 199, ...      8\n",
       "3  [155, 167, 176, 190, 177, 166, 168, 166, 170, ...      0\n",
       "4  [65, 70, 48, 30, 23, 40, 44, 45, 45, 40, 10, 1...      6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 2) (10000, 2)\n"
     ]
    }
   ],
   "source": [
    "print train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gltrain = gl.SFrame(train)\n",
    "gltest = gl.SFrame(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using network:\n",
      "\n",
      "### network layers ###\n",
      "layer[0]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 10\n",
      "layer[1]: SigmoidLayer\n",
      "layer[2]: FullConnectionLayer\n",
      "  init_sigma = 0.01\n",
      "  init_random = gaussian\n",
      "  init_bias = 0\n",
      "  num_hidden_units = 10\n",
      "layer[3]: SoftmaxLayer\n",
      "### end network layers ###\n",
      "\n",
      "### network parameters ###\n",
      "learning_rate = 0.001\n",
      "momentum = 0.9\n",
      "### end network parameters ###\n",
      "\n",
      "PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
      "          You can set ``validation_set=None`` to disable validation tracking.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Creating neuralnet using cpu</pre>"
      ],
      "text/plain": [
       "Creating neuralnet using cpu"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Training with batch size = 100</pre>"
      ],
      "text/plain": [
       "Training with batch size = 100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Examples | Elapsed Time | Training-accuracy | Validation-accuracy | Examples/second |</pre>"
      ],
      "text/plain": [
       "| Iteration | Examples | Elapsed Time | Training-accuracy | Validation-accuracy | Examples/second |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 47600    | 4.722913     | 0.107479          | 0.149235            | 10078.525391    |</pre>"
      ],
      "text/plain": [
       "| 1         | 47600    | 4.722913     | 0.107479          | 0.149235            | 10078.525391    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 47600    | 9.429859     | 0.170840          | 0.169492            | 10129.245117    |</pre>"
      ],
      "text/plain": [
       "| 2         | 47600    | 9.429859     | 0.170840          | 0.169492            | 10129.245117    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 47600    | 14.170055    | 0.169958          | 0.182307            | 10041.901367    |</pre>"
      ],
      "text/plain": [
       "| 3         | 47600    | 14.170055    | 0.169958          | 0.182307            | 10041.901367    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 47600    | 19.630333    | 0.172164          | 0.196362            | 8717.598633     |</pre>"
      ],
      "text/plain": [
       "| 4         | 47600    | 19.630333    | 0.172164          | 0.196362            | 8717.598633     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 47600    | 24.317955    | 0.177941          | 0.195122            | 10154.524414    |</pre>"
      ],
      "text/plain": [
       "| 5         | 47600    | 24.317955    | 0.177941          | 0.195122            | 10154.524414    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 47600    | 29.009386    | 0.180756          | 0.181067            | 10146.278320    |</pre>"
      ],
      "text/plain": [
       "| 6         | 47600    | 29.009386    | 0.180756          | 0.181067            | 10146.278320    |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 7         | 47600    | 33.776242    | 0.165483          | 0.177759            | 9985.735352     |</pre>"
      ],
      "text/plain": [
       "| 7         | 47600    | 33.776242    | 0.165483          | 0.177759            | 9985.735352     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 8         | 47600    | 38.537223    | 0.173824          | 0.182720            | 9998.060547     |</pre>"
      ],
      "text/plain": [
       "| 8         | 47600    | 38.537223    | 0.173824          | 0.182720            | 9998.060547     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9         | 47600    | 43.449501    | 0.180630          | 0.205043            | 9690.117188     |</pre>"
      ],
      "text/plain": [
       "| 9         | 47600    | 43.449501    | 0.180630          | 0.205043            | 9690.117188     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 10        | 47600    | 48.420854    | 0.189139          | 0.211244            | 9574.968750     |</pre>"
      ],
      "text/plain": [
       "| 10        | 47600    | 48.420854    | 0.189139          | 0.211244            | 9574.968750     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+-----------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GraphLab can create a neural network on its own, based on the data\n",
    "model = gl.neuralnet_classifier.create(gltrain, target='label', validation_set='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.20749999582767487, 'confusion_matrix': Columns:\n",
       " \ttarget_label\tint\n",
       " \tpredicted_label\tint\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 65\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |      0       |        0        |  247  |\n",
       " |      1       |        0        |  225  |\n",
       " |      2       |        0        |  173  |\n",
       " |      3       |        0        |  138  |\n",
       " |      4       |        0        |  111  |\n",
       " |      5       |        0        |  149  |\n",
       " |      6       |        0        |   91  |\n",
       " |      7       |        0        |  149  |\n",
       " |      8       |        0        |  188  |\n",
       " |      9       |        0        |  148  |\n",
       " +--------------+-----------------+-------+\n",
       " [65 rows x 3 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(gltest)   # 'accuracy': 0.20749999582767487, better to try pre-trained model if you can find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Before using pre-trained model, you may need to convert your image size to the model required size\n",
    "## Here, for example, to convert 32*32*3 images to 256*256*3 size\n",
    "\n",
    "#Convert pixels to graphlab image format\n",
    "gltrain['glimage'] = gl.SArray(gltrain['image']).pixel_array_to_image(32, 32, 3, allow_rounding = True)\n",
    "gltest['glimage'] = gl.SArray(gltest['image']).pixel_array_to_image(32, 32, 3, allow_rounding = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">glimage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 32 Width: 32</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 2 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tlabel\tint\n",
       "\tglimage\tImage\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------+----------------------+\n",
       "| label |       glimage        |\n",
       "+-------+----------------------+\n",
       "|   6   | Height: 32 Width: 32 |\n",
       "|   9   | Height: 32 Width: 32 |\n",
       "|   9   | Height: 32 Width: 32 |\n",
       "|   4   | Height: 32 Width: 32 |\n",
       "|   1   | Height: 32 Width: 32 |\n",
       "|   1   | Height: 32 Width: 32 |\n",
       "|   2   | Height: 32 Width: 32 |\n",
       "|   7   | Height: 32 Width: 32 |\n",
       "|   8   | Height: 32 Width: 32 |\n",
       "|   3   | Height: 32 Width: 32 |\n",
       "+-------+----------------------+\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove the original column\n",
    "gltrain.remove_column('image')\n",
    "gltest.remove_column('image')\n",
    "gltrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">label</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">image</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Height: 256 Width: 256</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 2 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tlabel\tint\n",
       "\timage\tImage\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------+------------------------+\n",
       "| label |         image          |\n",
       "+-------+------------------------+\n",
       "|   6   | Height: 256 Width: 256 |\n",
       "|   9   | Height: 256 Width: 256 |\n",
       "|   9   | Height: 256 Width: 256 |\n",
       "|   4   | Height: 256 Width: 256 |\n",
       "|   1   | Height: 256 Width: 256 |\n",
       "|   1   | Height: 256 Width: 256 |\n",
       "|   2   | Height: 256 Width: 256 |\n",
       "|   7   | Height: 256 Width: 256 |\n",
       "|   8   | Height: 256 Width: 256 |\n",
       "|   3   | Height: 256 Width: 256 |\n",
       "+-------+------------------------+\n",
       "[10 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert into 256x256 size\n",
    "gltrain['image'] = gl.image_analysis.resize(gltrain['glimage'], 256, 256, 3)\n",
    "gltest['image'] = gl.image_analysis.resize(gltest['glimage'], 256, 256, 3)\n",
    "\n",
    "#Remove old column:\n",
    "gltrain.remove_column('glimage')\n",
    "gltest.remove_column('glimage')\n",
    "\n",
    "gltrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# below code has not be ran, since I don't have pre-trained model, and it can take very long time to run\n",
    "\n",
    "#Load the pre-trained model: (change to your pre-trianed model location)\n",
    "pretrained_model = gl.load_model('http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45')\n",
    "\n",
    "gltrain['features'] = pretrained_model.extract_features(gltrain)\n",
    "gltest['features'] = pretrained_model.extract_features(gltest)\n",
    "\n",
    "gltrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The advantage with GraphLab “classifier” function is that \n",
    "## it will automatically create various classifiers and chose the best model.\n",
    "\n",
    "simple_classifier = graphlab.classifier.create(gltrain, features = ['features'], target = 'label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simple_classifier.evaluate(gltest)"
   ]
  }
 ],
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