{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":7,"outputs":[{"output_type":"stream","text":"['test', 'labels.csv', 'sample_submission.csv', 'train']\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"ss = pd.read_csv('../input/sample_submission.csv')\nss.head(4)","execution_count":28,"outputs":[{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"                                 id        ...          yorkshire_terrier\n0  000621fb3cbb32d8935728e48679680e        ...                   0.008333\n1  00102ee9d8eb90812350685311fe5890        ...                   0.008333\n2  0012a730dfa437f5f3613fb75efcd4ce        ...                   0.008333\n3  001510bc8570bbeee98c8d80c8a95ec1        ...                   0.008333\n\n[4 rows x 121 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>...</th>\n      <th>norwegian_elkhound</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000621fb3cbb32d8935728e48679680e</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>...</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>00102ee9d8eb90812350685311fe5890</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>...</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0012a730dfa437f5f3613fb75efcd4ce</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>...</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>001510bc8570bbeee98c8d80c8a95ec1</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>...</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n      <td>0.008333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"df= pd.read_csv('../input/labels.csv')\nnames = df[\"breed\"].unique()\nsort_names = sorted(names)\n","execution_count":65,"outputs":[{"output_type":"execute_result","execution_count":65,"data":{"text/plain":"'affenpinscher'"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"! rm -rf train\n!mkdir train","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from os import listdir\nfrom tqdm import tqdm_notebook\nfrom shutil import copyfile\ntrain1 = []\nfor i in tqdm_notebook(listdir('../input/train/train')):\n#     print(i)\n    j = i.split(\".\")[0]\n#     print(df[df[\"id\"]==j].values[0][1])\n    \n    if not os.path.exists('./train/' + df[df[\"id\"]==j].values[0][1] + '/'):\n        os.mkdir('./train/' + df[df[\"id\"]==j].values[0][1] + '/')\n\n    copyfile('../input/train/train/'+i, './train/' + df[df[\"id\"]==j].values[0][1] + '/' + i)\n\n","execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=10222), 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sa\n09927a82d649607a7704ac6043cdfa9a.jpg\nwest_highland_white_terrier\n96e2ca5c370468190471ddf41514170c.jpg\npomeranian\nad15d290cc87d4c3afbe21061c71e465.jpg\nscottish_deerhound\nb9cdf1e7b56c671e985c6910ee5a4ce0.jpg\ngreat_pyrenees\n7889edf05a5af5ad63b08764ffead960.jpg\nirish_wolfhound\n54fc3e6172a6202d1c66ccc9ac3d4aff.jpg\naffenpinscher\n7f999db69fa9af1b8bd9e15fa686ae2c.jpg\ncocker_spaniel\n23bd48ba6a538419e640ef14f8342e31.jpg\nmalinois\n5de015c8cb8ca98ec78407b1535e2e4d.jpg\nminiature_poodle\n6608429a87b9395181c7f500f2ead9ee.jpg\nshetland_sheepdog\n60666d549888377cc4af1c64d087e5d4.jpg\nbasenji\n2fa7759ee1c0f54ab691f17fde66d56d.jpg\nmexican_hairless\nb7f69b255149ea94edf291e0b6bf1bc8.jpg\nstaffordshire_bullterrier\n71a21699adbabcc9a82ee32f2743ec9b.jpg\nkeeshond\n6e2ba14aa4a753d6f5ad94ccfb2f666b.jpg\nbriard\n18178aae17060733ebce2cff03413a8c.jpg\nsamoyed\n2f946a96b0d3c0f43950cb0792f28548.jpg\ngordon_setter\n79e5eceb21f356f2588ae36ec2c91599.jpg\nkuvasz\n5522867aa22e18f38e627fb9fd9b1847.jpg\nmaltese_dog\n904f13f221653c8389dc5fa88d68541b.jpg\nsaint_bernard\n5b1d8663b493e54b6cb6e393abe58546.jpg\nafghan_hound\nd58b5b82b7d9ee79e4206e2be1c49528.jpg\ndandie_dinmont\n2c2a4f529131c3109a7ab66b133b8461.jpg\nbasenji\nd9dfa1ef019b07ccbf09deeed7093cd3.jpg\nredbone\n3fc2dde0964144cd07747c740469e60b.jpg\ngreat_pyrenees\n1f71ad688af967118708c855ca975594.jpg\nnorfolk_terrier\n34fc5c4709c9d815dbf4b78d4d816d10.jpg\nstandard_poodle\n6e9fa5651852b77caea00941cad23f95.jpg\nbouvier_des_flandres\nf550e44352e4eeb99932441678a8af34.jpg\ncairn\n\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls train","execution_count":66,"outputs":[{"output_type":"stream","text":"affenpinscher\t\t\titalian_greyhound\r\nafghan_hound\t\t\tjapanese_spaniel\r\nafrican_hunting_dog\t\tkeeshond\r\nairedale\t\t\tkelpie\r\namerican_staffordshire_terrier\tkerry_blue_terrier\r\nappenzeller\t\t\tkomondor\r\naustralian_terrier\t\tkuvasz\r\nbasenji\t\t\t\tlabrador_retriever\r\nbasset\t\t\t\tlakeland_terrier\r\nbeagle\t\t\t\tleonberg\r\nbedlington_terrier\t\tlhasa\r\nbernese_mountain_dog\t\tmalamute\r\nblack-and-tan_coonhound\t\tmalinois\r\nblenheim_spaniel\t\tmaltese_dog\r\nbloodhound\t\t\tmexican_hairless\r\nbluetick\t\t\tminiature_pinscher\r\nborder_collie\t\t\tminiature_poodle\r\nborder_terrier\t\t\tminiature_schnauzer\r\nborzoi\t\t\t\tnewfoundland\r\nboston_bull\t\t\tnorfolk_terrier\r\nbouvier_des_flandres\t\tnorwegian_elkhound\r\nboxer\t\t\t\tnorwich_terrier\r\nbrabancon_griffon\t\told_english_sheepdog\r\nbriard\t\t\t\totterhound\r\nbrittany_spaniel\t\tpapillon\r\nbull_mastiff\t\t\tpekinese\r\ncairn\t\t\t\tpembroke\r\ncardigan\t\t\tpomeranian\r\nchesapeake_bay_retriever\tpug\r\nchihuahua\t\t\tredbone\r\nchow\t\t\t\trhodesian_ridgeback\r\nclumber\t\t\t\trottweiler\r\ncocker_spaniel\t\t\tsaint_bernard\r\ncollie\t\t\t\tsaluki\r\ncurly-coated_retriever\t\tsamoyed\r\ndandie_dinmont\t\t\tschipperke\r\ndhole\t\t\t\tscotch_terrier\r\ndingo\t\t\t\tscottish_deerhound\r\ndoberman\t\t\tsealyham_terrier\r\nenglish_foxhound\t\tshetland_sheepdog\r\nenglish_setter\t\t\tshih-tzu\r\nenglish_springer\t\tsiberian_husky\r\nentlebucher\t\t\tsilky_terrier\r\neskimo_dog\t\t\tsoft-coated_wheaten_terrier\r\nflat-coated_retriever\t\tstaffordshire_bullterrier\r\nfrench_bulldog\t\t\tstandard_poodle\r\ngerman_shepherd\t\t\tstandard_schnauzer\r\ngerman_short-haired_pointer\tsussex_spaniel\r\ngiant_schnauzer\t\t\ttibetan_mastiff\r\ngolden_retriever\t\ttibetan_terrier\r\ngordon_setter\t\t\ttoy_poodle\r\ngreat_dane\t\t\ttoy_terrier\r\ngreat_pyrenees\t\t\tvizsla\r\ngreater_swiss_mountain_dog\twalker_hound\r\ngroenendael\t\t\tweimaraner\r\nibizan_hound\t\t\twelsh_springer_spaniel\r\nirish_setter\t\t\twest_highland_white_terrier\r\nirish_terrier\t\t\twhippet\r\nirish_water_spaniel\t\twire-haired_fox_terrier\r\nirish_wolfhound\t\t\tyorkshire_terrier\r\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch\nfrom torch import nn, optim\nfrom torchvision import datasets, transforms, models\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\n\ntrain_transform = transforms.Compose([transforms.RandomRotation(30),\n                               transforms.RandomResizedCrop(224),\n                               transforms.RandomHorizontalFlip(),\n                               transforms.ToTensor(),\n                               transforms.Normalize([0.485, 0.456, 0.406],\n                                                    [0.229, 0.224, 0.225])])\n\ntest_transform = transforms.Compose([transforms.Resize(255),\n                               transforms.CenterCrop(224),\n                               transforms.ToTensor(),\n                               transforms.Normalize([0.485, 0.456, 0.406],\n                                                    [0.229, 0.224, 0.225])])\n\n\ntrain_data = datasets.ImageFolder(\"./train/\", transform = train_transform)\ntest_data = datasets.ImageFolder(\"../input/test/\", transform = test_transform)\n\nvalid_size = 0.2\nl = list(range(len(train_data)))\nnp.random.shuffle(l)\nsplit = int(np.floor(valid_size*len(train_data)))\ntrain_idx, valid_idx = l[split:], l[:split]\n\ntrain_sampler = torch.utils.data.sampler.SubsetRandomSampler(train_idx)\nvalid_sampler = torch.utils.data.sampler.SubsetRandomSampler(valid_idx)\n\ntrain_loader = torch.utils.data.DataLoader(train_data, batch_size = 20, sampler=train_sampler)\nvalid_loader = torch.utils.data.DataLoader(train_data, sampler=valid_sampler, batch_size = 20)\ntest_loader = torch.utils.data.DataLoader(test_data, batch_size=20)","execution_count":68,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nfirst = iter(train_loader)\nimage, label = next(first)\nprint(image.shape)\nimage = image.numpy()\nimage = image[0] / 2 + 0.5  # unnormalize\nplt.imshow(np.transpose(image, (1, 2, 0)))","execution_count":70,"outputs":[{"output_type":"stream","text":"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","name":"stderr"},{"output_type":"stream","text":"torch.Size([20, 3, 224, 224])\n","name":"stdout"},{"output_type":"execute_result","execution_count":70,"data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fc1c4b77eb8>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(device)","execution_count":72,"outputs":[{"output_type":"stream","text":"cuda\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmodel = models.densenet121(pretrained=True)\n\nmodel","execution_count":73,"outputs":[{"output_type":"stream","text":"Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /tmp/.torch/models/densenet121-a639ec97.pth\n32342954it [00:00, 36402805.00it/s]\n","name":"stderr"},{"output_type":"execute_result","execution_count":73,"data":{"text/plain":"DenseNet(\n  (features): Sequential(\n    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    (relu0): ReLU(inplace)\n    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n    (denseblock1): _DenseBlock(\n      (denselayer1): _DenseLayer(\n        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer2): _DenseLayer(\n        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer3): _DenseLayer(\n        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer4): _DenseLayer(\n        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer5): _DenseLayer(\n        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer6): _DenseLayer(\n        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n    )\n    (transition1): _Transition(\n      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace)\n      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n    )\n    (denseblock2): _DenseBlock(\n      (denselayer1): _DenseLayer(\n        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer2): _DenseLayer(\n        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer3): _DenseLayer(\n        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer4): _DenseLayer(\n        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer5): _DenseLayer(\n        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer6): _DenseLayer(\n        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer7): _DenseLayer(\n        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer8): _DenseLayer(\n        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer9): _DenseLayer(\n        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer10): _DenseLayer(\n        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer11): _DenseLayer(\n        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer12): _DenseLayer(\n        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n    )\n    (transition2): _Transition(\n      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace)\n      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n    )\n    (denseblock3): _DenseBlock(\n      (denselayer1): _DenseLayer(\n        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer2): _DenseLayer(\n        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer3): _DenseLayer(\n        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer4): _DenseLayer(\n        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer5): _DenseLayer(\n        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer6): _DenseLayer(\n        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer7): _DenseLayer(\n        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer8): _DenseLayer(\n        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer9): _DenseLayer(\n        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer10): _DenseLayer(\n        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer11): _DenseLayer(\n        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer12): _DenseLayer(\n        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer13): _DenseLayer(\n        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer14): _DenseLayer(\n        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer15): _DenseLayer(\n        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer16): _DenseLayer(\n        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer17): _DenseLayer(\n        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer18): _DenseLayer(\n        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer19): _DenseLayer(\n        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer20): _DenseLayer(\n        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer21): _DenseLayer(\n        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer22): _DenseLayer(\n        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer23): _DenseLayer(\n        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer24): _DenseLayer(\n        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n    )\n    (transition3): _Transition(\n      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace)\n      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n    )\n    (denseblock4): _DenseBlock(\n      (denselayer1): _DenseLayer(\n        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer2): _DenseLayer(\n        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer3): _DenseLayer(\n        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer4): _DenseLayer(\n        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer5): _DenseLayer(\n        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer6): _DenseLayer(\n        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer7): _DenseLayer(\n        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer8): _DenseLayer(\n        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer9): _DenseLayer(\n        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer10): _DenseLayer(\n        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer11): _DenseLayer(\n        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer12): _DenseLayer(\n        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer13): _DenseLayer(\n        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer14): _DenseLayer(\n        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer15): _DenseLayer(\n        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (denselayer16): _DenseLayer(\n        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu1): ReLU(inplace)\n        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (relu2): ReLU(inplace)\n        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n    )\n    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  )\n  (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Freeze parameters so we don't backprop through them\nfor param in model.parameters():\n    param.requires_grad = False\n    \nmodel.classifier = nn.Sequential(nn.Linear(1024, 512),\n                                 nn.ReLU(),\n                                 nn.Dropout(0.2),\n                                 nn.Linear(512, 120),\n                                 nn.LogSoftmax(dim=1))\n\ncriterion = nn.NLLLoss()\n\n# Only train the classifier parameters, feature parameters are frozen\noptimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n\nmodel.to(device);","execution_count":74,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# number of epochs to train the model\nn_epochs = 12\n\nvalid_loss_min = np.Inf # track change in validation loss\n\nfor epoch in range(1, n_epochs+1):\n\n    # keep track of training and validation loss\n    train_loss = 0.0\n    valid_loss = 0.0\n    \n    ###################\n    # train the model #\n    ###################\n    model.train()\n    for data, target in tqdm_notebook(train_loader):\n        # move tensors to GPU if CUDA is available\n        if torch.cuda.is_available():\n            data, target = data.cuda(), target.cuda()\n        # clear the gradients of all optimized variables\n        optimizer.zero_grad()\n        # forward pass: compute predicted outputs by passing inputs to the model\n        output = model.forward(data)\n        # calculate the batch loss\n        loss = criterion(output, target)\n        # backward pass: compute gradient of the loss with respect to model parameters\n        loss.backward()\n        # perform a single optimization step (parameter update)\n        optimizer.step()\n        # update training loss\n        train_loss += loss.item()*data.size(0)\n        \n    ######################    \n    # validate the model #\n    ######################\n    model.eval()\n    for data, target in valid_loader:\n        # move tensors to GPU if CUDA is available\n        if torch.cuda.is_available():\n            data, target = data.cuda(), target.cuda()\n        # forward pass: compute predicted outputs by passing inputs to the model\n        output = model.forward(data)\n        # calculate the batch loss\n        loss = criterion(output, target)\n        # update average validation loss \n        valid_loss += loss.item()*data.size(0)\n    \n    # calculate average losses\n    train_loss = train_loss/len(train_loader.dataset)\n    valid_loss = valid_loss/len(valid_loader.dataset)\n        \n    # print training/validation statistics \n    print('Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}'.format(\n        epoch, train_loss, valid_loss))\n    \n    # save model if validation loss has decreased\n    if valid_loss <= valid_loss_min:\n        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(\n        valid_loss_min,\n        valid_loss))\n        torch.save(model.state_dict(), 'model_cifar.pt')\n        valid_loss_min = valid_loss","execution_count":75,"outputs":[{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5251fbee1f254388b6a179548e9fab41"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 1 \tTraining Loss: 2.674236 \tValidation Loss: 0.401289\nValidation loss decreased (inf --> 0.401289).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c3e139af02f4924964afae493a83539"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 2 \tTraining Loss: 1.892111 \tValidation Loss: 0.375413\nValidation loss decreased (0.401289 --> 0.375413).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"30e3802273404e23848d44287e70e38a"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 3 \tTraining Loss: 1.795424 \tValidation Loss: 0.351803\nValidation loss decreased (0.375413 --> 0.351803).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f57e97c438914653ba230977542a42ed"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 4 \tTraining Loss: 1.749169 \tValidation Loss: 0.320274\nValidation loss decreased (0.351803 --> 0.320274).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6d37903edc964795bbef9478603d9280"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 5 \tTraining Loss: 1.720449 \tValidation Loss: 0.331305\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4fa680985cdb43f29634e72c7188db11"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 6 \tTraining Loss: 1.685867 \tValidation Loss: 0.324710\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3127427d63bf42f597c00807fc8b8e7c"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 7 \tTraining Loss: 1.688728 \tValidation Loss: 0.336214\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3cd605a6063d433f8e240703f38933ea"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 8 \tTraining Loss: 1.675808 \tValidation Loss: 0.322578\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b0ab89e7982d49f390a0666780063f62"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 9 \tTraining Loss: 1.658955 \tValidation Loss: 0.313085\nValidation loss decreased (0.320274 --> 0.313085).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1ad2f7e165f49b99e5593aa9186814b"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 10 \tTraining Loss: 1.665374 \tValidation Loss: 0.342908\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"32756cd2d1164436bfc22670e40e1f76"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 11 \tTraining Loss: 1.653898 \tValidation Loss: 0.322214\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fee3709593904d34800cfb0d5a58360d"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 12 \tTraining Loss: 1.660682 \tValidation Loss: 0.332894\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c40a6e134b5463a8a57d238204b400c"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 13 \tTraining Loss: 1.664510 \tValidation Loss: 0.322003\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dfa8b1e651264b6d9ca4404f6dc39e65"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 14 \tTraining Loss: 1.659383 \tValidation Loss: 0.328998\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd711b0e73c442a7ba672a37177ea64c"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 15 \tTraining Loss: 1.694331 \tValidation Loss: 0.316326\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e2db66d1bd3540d383da4b35e597e8f1"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 16 \tTraining Loss: 1.636401 \tValidation Loss: 0.314383\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"726f757898274bd6ae169182c6028deb"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 17 \tTraining Loss: 1.658140 \tValidation Loss: 0.311417\nValidation loss decreased (0.313085 --> 0.311417).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97d9b0a62c9d428cb728e3a1ba2d39ce"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 18 \tTraining Loss: 1.662716 \tValidation Loss: 0.308376\nValidation loss decreased (0.311417 --> 0.308376).  Saving model ...\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d7b53870c8004579a53757f589a5b52d"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 19 \tTraining Loss: 1.647647 \tValidation Loss: 0.324484\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5bed6c1f1a224a878fcec64c1754a582"}},"metadata":{}},{"output_type":"stream","text":"\nEpoch: 20 \tTraining Loss: 1.632392 \tValidation Loss: 0.314309\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=409), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4916c7155e74714a0174a8d15ed47f7"}},"metadata":{}},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-75-2c66c2b5282e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;31m###################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m         \u001b[0;31m# move tensors to GPU if CUDA is available\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tqdm/_tqdm_notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m                 \"\"\"), fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/torch/utils/data/dataloader.py\u001b[0m in 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loading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m             \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    616\u001b[0m             \u001b[0;32mif\u001b[0m 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\u001b[0m_is_pil_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'img should be PIL Image'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/torchvision/transforms/functional.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(img, size, interpolation)\u001b[0m\n\u001b[1;32m    244\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mresize\u001b[0;34m(self, size, resample, box)\u001b[0m\n\u001b[1;32m   1747\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1749\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1751\u001b[0m     def rotate(self, angle, resample=NEAREST, expand=0, center=None,\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch.utils.data as data\n\nfrom PIL import Image\n\nimport os\nimport os.path\nimport sys\n\n\ndef has_file_allowed_extension(filename, extensions):\n    \"\"\"Checks if a file is an allowed extension.\n    Args:\n        filename (string): path to a file\n        extensions (iterable of strings): extensions to consider (lowercase)\n    Returns:\n        bool: True if the filename ends with one of given extensions\n    \"\"\"\n    filename_lower = filename.lower()\n    return any(filename_lower.endswith(ext) for ext in extensions)\n\n\ndef is_image_file(filename):\n    \"\"\"Checks if a file is an allowed image extension.\n    Args:\n        filename (string): path to a file\n    Returns:\n        bool: True if the filename ends with a known image extension\n    \"\"\"\n    return has_file_allowed_extension(filename, IMG_EXTENSIONS)\n\n\ndef make_dataset(dir, class_to_idx, extensions):\n    images = []\n    dir = os.path.expanduser(dir)\n    for target in sorted(class_to_idx.keys()):\n        d = os.path.join(dir, target)\n        if not os.path.isdir(d):\n            continue\n\n        for root, _, fnames in sorted(os.walk(d)):\n            for fname in sorted(fnames):\n                if has_file_allowed_extension(fname, extensions):\n                    path = os.path.join(root, fname)\n                    item = (path, class_to_idx[target])\n                    images.append(item)\n\n    return images\n\n\nclass DatasetFolder(data.Dataset):\n    \"\"\"A generic data loader where the samples are arranged in this way: ::\n        root/class_x/xxx.ext\n        root/class_x/xxy.ext\n        root/class_x/xxz.ext\n        root/class_y/123.ext\n        root/class_y/nsdf3.ext\n        root/class_y/asd932_.ext\n    Args:\n        root (string): Root directory path.\n        loader (callable): A function to load a sample given its path.\n        extensions (list[string]): A list of allowed extensions.\n        transform (callable, optional): A function/transform that takes in\n            a sample and returns a transformed version.\n            E.g, ``transforms.RandomCrop`` for images.\n        target_transform (callable, optional): A function/transform that takes\n            in the target and transforms it.\n     Attributes:\n        classes (list): List of the class names.\n        class_to_idx (dict): Dict with items (class_name, class_index).\n        samples (list): List of (sample path, class_index) tuples\n        targets (list): The class_index value for each image in the dataset\n    \"\"\"\n\n    def __init__(self, root, loader, extensions, transform=None, target_transform=None):\n        classes, class_to_idx = self._find_classes(root)\n        samples = make_dataset(root, class_to_idx, extensions)\n        if len(samples) == 0:\n            raise(RuntimeError(\"Found 0 files in subfolders of: \" + root + \"\\n\"\n                               \"Supported extensions are: \" + \",\".join(extensions)))\n\n        self.root = root\n        self.loader = loader\n        self.extensions = extensions\n\n        self.classes = classes\n        self.class_to_idx = class_to_idx\n        self.samples = samples\n        self.targets = [s[1] for s in samples]\n\n        self.transform = transform\n        self.target_transform = target_transform\n\n    def _find_classes(self, dir):\n        \"\"\"\n        Finds the class folders in a dataset.\n        Args:\n            dir (string): Root directory path.\n        Returns:\n            tuple: (classes, class_to_idx) where classes are relative to (dir), and class_to_idx is a dictionary.\n        Ensures:\n            No class is a subdirectory of another.\n        \"\"\"\n        if sys.version_info >= (3, 5):\n            # Faster and available in Python 3.5 and above\n            classes = [d.name for d in os.scandir(dir) if d.is_dir()]\n        else:\n            classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]\n        classes.sort()\n        class_to_idx = {classes[i]: i for i in range(len(classes))}\n        return classes, class_to_idx\n\n    def __getitem__(self, index):\n        \"\"\"\n        Args:\n            index (int): Index\n        Returns:\n            tuple: (sample, target) where target is class_index of the target class.\n        \"\"\"\n        path, target = self.samples[index]\n        sample = self.loader(path)\n        if self.transform is not None:\n            sample = self.transform(sample)\n        if self.target_transform is not None:\n            target = self.target_transform(target)\n\n        return sample, target, path\n\n    def __len__(self):\n        return len(self.samples)\n\n    def __repr__(self):\n        fmt_str = 'Dataset ' + self.__class__.__name__ + '\\n'\n        fmt_str += '    Number of datapoints: {}\\n'.format(self.__len__())\n        fmt_str += '    Root Location: {}\\n'.format(self.root)\n        tmp = '    Transforms (if any): '\n        fmt_str += '{0}{1}\\n'.format(tmp, self.transform.__repr__().replace('\\n', '\\n' + ' ' * len(tmp)))\n        tmp = '    Target Transforms (if any): '\n        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\\n', '\\n' + ' ' * len(tmp)))\n        return fmt_str\n\n\nIMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', 'webp']\n\n\ndef pil_loader(path):\n    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\n    with open(path, 'rb') as f:\n        img = Image.open(f)\n        return img.convert('RGB')\n\n\ndef accimage_loader(path):\n    import accimage\n    try:\n        return accimage.Image(path)\n    except IOError:\n        # Potentially a decoding problem, fall back to PIL.Image\n        return pil_loader(path)\n\n\ndef default_loader(path):\n    from torchvision import get_image_backend\n    if get_image_backend() == 'accimage':\n        return accimage_loader(path)\n    else:\n        return pil_loader(path)\n\n\nclass ImageFolder(DatasetFolder):\n    \"\"\"A generic data loader where the images are arranged in this way: ::\n        root/dog/xxx.png\n        root/dog/xxy.png\n        root/dog/xxz.png\n        root/cat/123.png\n        root/cat/nsdf3.png\n        root/cat/asd932_.png\n    Args:\n        root (string): Root directory path.\n        transform (callable, optional): A function/transform that  takes in an PIL image\n            and returns a transformed version. E.g, ``transforms.RandomCrop``\n        target_transform (callable, optional): A function/transform that takes in the\n            target and transforms it.\n        loader (callable, optional): A function to load an image given its path.\n     Attributes:\n        classes (list): List of the class names.\n        class_to_idx (dict): Dict with items (class_name, class_index).\n        imgs (list): List of (image path, class_index) tuples\n    \"\"\"\n    def __init__(self, root, transform=None, target_transform=None,\n                 loader=default_loader):\n        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,\n                                          transform=transform,\n                                          target_transform=target_transform)\n        self.imgs = self.samples","execution_count":91,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.load_state_dict(torch.load('model_dog.pt'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_dir = '../input/test'\na = ImageFolder(data_dir, transform = test_transform)\ntest_loader = torch.utils.data.DataLoader(a,batch_size=20,shuffle=False)","execution_count":94,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"answer = {}\nmodel.eval()\nfor images, target, names in test_loader:\n    l = []\n    for i in range(len(names)):\n        q = names[i].split(\"/\")[4]\n        l.append(q.split(\".\")[0])\n\n    #print(l)\n    if torch.cuda.is_available():\n        images = images.cuda()\n    output = model(images)\n    ps = torch.exp(output)\n    \n\n    for i in range(len(names)):\n        answer[l[i]] = ps[i].cpu().detach().numpy()\n        \n    ","execution_count":97,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.DataFrame.from_dict(answer, orient='index')\ndf.head()\n","execution_count":99,"outputs":[{"output_type":"execute_result","execution_count":99,"data":{"text/plain":"                                           0        ...                119\n000621fb3cbb32d8935728e48679680e  1.363083e-06      ...       2.829852e-07\n00102ee9d8eb90812350685311fe5890  4.057263e-08      ...       6.315826e-13\n0012a730dfa437f5f3613fb75efcd4ce  3.694571e-10      ...       5.122094e-05\n001510bc8570bbeee98c8d80c8a95ec1  1.177955e-01      ...       4.456414e-06\n001a5f3114548acdefa3d4da05474c2e  2.391912e-02      ...       9.417806e-06\n\n[5 rows x 120 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>...</th>\n      <th>80</th>\n      <th>81</th>\n      <th>82</th>\n      <th>83</th>\n      <th>84</th>\n      <th>85</th>\n      <th>86</th>\n      <th>87</th>\n      <th>88</th>\n      <th>89</th>\n      <th>90</th>\n      <th>91</th>\n      <th>92</th>\n      <th>93</th>\n      <th>94</th>\n      <th>95</th>\n      <th>96</th>\n      <th>97</th>\n      <th>98</th>\n      <th>99</th>\n      <th>100</th>\n      <th>101</th>\n      <th>102</th>\n      <th>103</th>\n      <th>104</th>\n      <th>105</th>\n      <th>106</th>\n      <th>107</th>\n      <th>108</th>\n      <th>109</th>\n      <th>110</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>115</th>\n      <th>116</th>\n      <th>117</th>\n      <th>118</th>\n      <th>119</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000621fb3cbb32d8935728e48679680e</th>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>3.194038e-14</td>\n      <td>...</td>\n      <td>1.208456e-13</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>5.210538e-06</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n    </tr>\n    <tr>\n      <th>00102ee9d8eb90812350685311fe5890</th>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>2.534063e-13</td>\n      <td>...</td>\n      <td>7.674328e-06</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>5.875272e-04</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n    </tr>\n    <tr>\n      <th>0012a730dfa437f5f3613fb75efcd4ce</th>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>5.146080e-05</td>\n      <td>...</td>\n      <td>5.035051e-13</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>1.404505e-05</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n    </tr>\n    <tr>\n      <th>001510bc8570bbeee98c8d80c8a95ec1</th>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>2.844901e-08</td>\n      <td>...</td>\n      <td>7.513454e-05</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>5.323157e-06</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n    </tr>\n    <tr>\n      <th>001a5f3114548acdefa3d4da05474c2e</th>\n      <td>2.391912e-02</td>\n      <td>2.589329e-04</td>\n      <td>1.140744e-13</td>\n      <td>1.757637e-12</td>\n      <td>4.581536e-10</td>\n      <td>1.074078e-12</td>\n      <td>3.716196e-05</td>\n      <td>1.611588e-12</td>\n      <td>2.730181e-15</td>\n      <td>1.513523e-12</td>\n      <td>1.584887e-10</td>\n      <td>7.783239e-09</td>\n      <td>8.061837e-14</td>\n      <td>4.789176e-06</td>\n      <td>4.934861e-12</td>\n      <td>3.132611e-11</td>\n      <td>4.306790e-09</td>\n      <td>3.275666e-08</td>\n      <td>5.251771e-08</td>\n      <td>1.459595e-07</td>\n      <td>8.513721e-05</td>\n      <td>3.846369e-08</td>\n      <td>4.144123e-05</td>\n      <td>2.935631e-03</td>\n      <td>2.496586e-12</td>\n      <td>3.752498e-08</td>\n      <td>7.814172e-05</td>\n      <td>5.390945e-08</td>\n      <td>2.229151e-14</td>\n      <td>1.203200e-07</td>\n      <td>2.134287e-08</td>\n      <td>2.392615e-08</td>\n      <td>3.406871e-07</td>\n      <td>1.062237e-08</td>\n      <td>3.581167e-14</td>\n      <td>6.821908e-04</td>\n      <td>1.168328e-18</td>\n      <td>1.026392e-12</td>\n      <td>2.098002e-16</td>\n      <td>1.508469e-14</td>\n      <td>...</td>\n      <td>1.002744e-08</td>\n      <td>4.767723e-11</td>\n      <td>3.681407e-06</td>\n      <td>3.749377e-09</td>\n      <td>2.407462e-06</td>\n      <td>0.040213</td>\n      <td>2.205395e-12</td>\n      <td>0.000002</td>\n      <td>3.165792e-05</td>\n      <td>3.508511e-15</td>\n      <td>8.586031e-12</td>\n      <td>9.628360e-12</td>\n      <td>7.907194e-09</td>\n      <td>3.174600e-08</td>\n      <td>1.007555e-10</td>\n      <td>7.818901e-10</td>\n      <td>2.313526e-06</td>\n      <td>4.462649e-08</td>\n      <td>1.578711e-06</td>\n      <td>3.818781e-08</td>\n      <td>5.326075e-01</td>\n      <td>2.010046e-09</td>\n      <td>1.724368e-04</td>\n      <td>2.089988e-05</td>\n      <td>4.098118e-08</td>\n      <td>1.516044e-10</td>\n      <td>1.282794e-03</td>\n      <td>1.897519e-07</td>\n      <td>3.914657e-06</td>\n      <td>2.367584e-02</td>\n      <td>5.757055e-06</td>\n      <td>3.447404e-07</td>\n      <td>2.134458e-15</td>\n      <td>6.130927e-13</td>\n      <td>4.288892e-14</td>\n      <td>4.010298e-13</td>\n      <td>1.817115e-07</td>\n      <td>1.575460e-08</td>\n      <td>5.874419e-07</td>\n      <td>9.417806e-06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.columns =  ['affenpinscher', 'afghan_hound', 'african_hunting_dog', 'airedale', 'american_staffordshire_terrier', 'appenzeller', 'australian_terrier', 'basenji', 'basset', 'beagle', 'bedlington_terrier', 'bernese_mountain_dog', 'black-and-tan_coonhound', 'blenheim_spaniel', 'bloodhound', 'bluetick', 'border_collie', 'border_terrier', 'borzoi', 'boston_bull', 'bouvier_des_flandres', 'boxer', 'brabancon_griffon', 'briard', 'brittany_spaniel', 'bull_mastiff', 'cairn', 'cardigan', 'chesapeake_bay_retriever', 'chihuahua', 'chow', 'clumber', 'cocker_spaniel', 'collie', 'curly-coated_retriever', 'dandie_dinmont', 'dhole', 'dingo', 'doberman', 'english_foxhound', 'english_setter', 'english_springer', 'entlebucher', 'eskimo_dog', 'flat-coated_retriever', 'french_bulldog', 'german_shepherd', 'german_short-haired_pointer', 'giant_schnauzer', 'golden_retriever', 'gordon_setter', 'great_dane', 'great_pyrenees', 'greater_swiss_mountain_dog', 'groenendael', 'ibizan_hound', 'irish_setter', 'irish_terrier', 'irish_water_spaniel', 'irish_wolfhound', 'italian_greyhound', 'japanese_spaniel', 'keeshond', 'kelpie', 'kerry_blue_terrier', 'komondor', 'kuvasz', 'labrador_retriever', 'lakeland_terrier', 'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese_dog', 'mexican_hairless', 'miniature_pinscher', 'miniature_poodle', 'miniature_schnauzer', 'newfoundland', 'norfolk_terrier', 'norwegian_elkhound', 'norwich_terrier', 'old_english_sheepdog', 'otterhound', 'papillon', 'pekinese', 'pembroke', 'pomeranian', 'pug', 'redbone', 'rhodesian_ridgeback', 'rottweiler', 'saint_bernard', 'saluki', 'samoyed', 'schipperke', 'scotch_terrier', 'scottish_deerhound', 'sealyham_terrier', 'shetland_sheepdog', 'shih-tzu', 'siberian_husky', 'silky_terrier', 'soft-coated_wheaten_terrier', 'staffordshire_bullterrier', 'standard_poodle', 'standard_schnauzer', 'sussex_spaniel', 'tibetan_mastiff', 'tibetan_terrier', 'toy_poodle', 'toy_terrier', 'vizsla', 'walker_hound', 'weimaraner', 'welsh_springer_spaniel', 'west_highland_white_terrier', 'whippet', 'wire-haired_fox_terrier', 'yorkshire_terrier']","execution_count":100,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head(4)","execution_count":101,"outputs":[{"output_type":"execute_result","execution_count":101,"data":{"text/plain":"                                  affenpinscher        ...          yorkshire_terrier\n000621fb3cbb32d8935728e48679680e   1.363083e-06        ...               2.829852e-07\n00102ee9d8eb90812350685311fe5890   4.057263e-08        ...               6.315826e-13\n0012a730dfa437f5f3613fb75efcd4ce   3.694571e-10        ...               5.122094e-05\n001510bc8570bbeee98c8d80c8a95ec1   1.177955e-01        ...               4.456414e-06\n\n[4 rows x 120 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>english_foxhound</th>\n      <th>...</th>\n      <th>norwegian_elkhound</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000621fb3cbb32d8935728e48679680e</th>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>3.194038e-14</td>\n      <td>...</td>\n      <td>1.208456e-13</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>0.000005</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n    </tr>\n    <tr>\n      <th>00102ee9d8eb90812350685311fe5890</th>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>2.534063e-13</td>\n      <td>...</td>\n      <td>7.674328e-06</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>0.000588</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n    </tr>\n    <tr>\n      <th>0012a730dfa437f5f3613fb75efcd4ce</th>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>5.146080e-05</td>\n      <td>...</td>\n      <td>5.035051e-13</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>0.000014</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n    </tr>\n    <tr>\n      <th>001510bc8570bbeee98c8d80c8a95ec1</th>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>2.844901e-08</td>\n      <td>...</td>\n      <td>7.513454e-05</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>0.000005</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['id'] = df.index\ndf.head()","execution_count":102,"outputs":[{"output_type":"execute_result","execution_count":102,"data":{"text/plain":"                                  affenpinscher                ...                                               id\n000621fb3cbb32d8935728e48679680e   1.363083e-06                ...                 000621fb3cbb32d8935728e48679680e\n00102ee9d8eb90812350685311fe5890   4.057263e-08                ...                 00102ee9d8eb90812350685311fe5890\n0012a730dfa437f5f3613fb75efcd4ce   3.694571e-10                ...                 0012a730dfa437f5f3613fb75efcd4ce\n001510bc8570bbeee98c8d80c8a95ec1   1.177955e-01                ...                 001510bc8570bbeee98c8d80c8a95ec1\n001a5f3114548acdefa3d4da05474c2e   2.391912e-02                ...                 001a5f3114548acdefa3d4da05474c2e\n\n[5 rows x 121 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>english_foxhound</th>\n      <th>...</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n      <th>id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>000621fb3cbb32d8935728e48679680e</th>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>3.194038e-14</td>\n      <td>...</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>5.210538e-06</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n      <td>000621fb3cbb32d8935728e48679680e</td>\n    </tr>\n    <tr>\n      <th>00102ee9d8eb90812350685311fe5890</th>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>2.534063e-13</td>\n      <td>...</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>5.875272e-04</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n      <td>00102ee9d8eb90812350685311fe5890</td>\n    </tr>\n    <tr>\n      <th>0012a730dfa437f5f3613fb75efcd4ce</th>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>5.146080e-05</td>\n      <td>...</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>1.404505e-05</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n      <td>0012a730dfa437f5f3613fb75efcd4ce</td>\n    </tr>\n    <tr>\n      <th>001510bc8570bbeee98c8d80c8a95ec1</th>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>2.844901e-08</td>\n      <td>...</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>5.323157e-06</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n      <td>001510bc8570bbeee98c8d80c8a95ec1</td>\n    </tr>\n    <tr>\n      <th>001a5f3114548acdefa3d4da05474c2e</th>\n      <td>2.391912e-02</td>\n      <td>2.589329e-04</td>\n      <td>1.140744e-13</td>\n      <td>1.757637e-12</td>\n      <td>4.581536e-10</td>\n      <td>1.074078e-12</td>\n      <td>3.716196e-05</td>\n      <td>1.611588e-12</td>\n      <td>2.730181e-15</td>\n      <td>1.513523e-12</td>\n      <td>1.584887e-10</td>\n      <td>7.783239e-09</td>\n      <td>8.061837e-14</td>\n      <td>4.789176e-06</td>\n      <td>4.934861e-12</td>\n      <td>3.132611e-11</td>\n      <td>4.306790e-09</td>\n      <td>3.275666e-08</td>\n      <td>5.251771e-08</td>\n      <td>1.459595e-07</td>\n      <td>8.513721e-05</td>\n      <td>3.846369e-08</td>\n      <td>4.144123e-05</td>\n      <td>2.935631e-03</td>\n      <td>2.496586e-12</td>\n      <td>3.752498e-08</td>\n      <td>7.814172e-05</td>\n      <td>5.390945e-08</td>\n      <td>2.229151e-14</td>\n      <td>1.203200e-07</td>\n      <td>2.134287e-08</td>\n      <td>2.392615e-08</td>\n      <td>3.406871e-07</td>\n      <td>1.062237e-08</td>\n      <td>3.581167e-14</td>\n      <td>6.821908e-04</td>\n      <td>1.168328e-18</td>\n      <td>1.026392e-12</td>\n      <td>2.098002e-16</td>\n      <td>1.508469e-14</td>\n      <td>...</td>\n      <td>4.767723e-11</td>\n      <td>3.681407e-06</td>\n      <td>3.749377e-09</td>\n      <td>2.407462e-06</td>\n      <td>0.040213</td>\n      <td>2.205395e-12</td>\n      <td>0.000002</td>\n      <td>3.165792e-05</td>\n      <td>3.508511e-15</td>\n      <td>8.586031e-12</td>\n      <td>9.628360e-12</td>\n      <td>7.907194e-09</td>\n      <td>3.174600e-08</td>\n      <td>1.007555e-10</td>\n      <td>7.818901e-10</td>\n      <td>2.313526e-06</td>\n      <td>4.462649e-08</td>\n      <td>1.578711e-06</td>\n      <td>3.818781e-08</td>\n      <td>5.326075e-01</td>\n      <td>2.010046e-09</td>\n      <td>1.724368e-04</td>\n      <td>2.089988e-05</td>\n      <td>4.098118e-08</td>\n      <td>1.516044e-10</td>\n      <td>1.282794e-03</td>\n      <td>1.897519e-07</td>\n      <td>3.914657e-06</td>\n      <td>2.367584e-02</td>\n      <td>5.757055e-06</td>\n      <td>3.447404e-07</td>\n      <td>2.134458e-15</td>\n      <td>6.130927e-13</td>\n      <td>4.288892e-14</td>\n      <td>4.010298e-13</td>\n      <td>1.817115e-07</td>\n      <td>1.575460e-08</td>\n      <td>5.874419e-07</td>\n      <td>9.417806e-06</td>\n      <td>001a5f3114548acdefa3d4da05474c2e</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = df.reset_index(drop=True)\ndf.index += 1 \ndf.head()\n","execution_count":103,"outputs":[{"output_type":"execute_result","execution_count":103,"data":{"text/plain":"   affenpinscher                ...                                               id\n1   1.363083e-06                ...                 000621fb3cbb32d8935728e48679680e\n2   4.057263e-08                ...                 00102ee9d8eb90812350685311fe5890\n3   3.694571e-10                ...                 0012a730dfa437f5f3613fb75efcd4ce\n4   1.177955e-01                ...                 001510bc8570bbeee98c8d80c8a95ec1\n5   2.391912e-02                ...                 001a5f3114548acdefa3d4da05474c2e\n\n[5 rows x 121 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>english_foxhound</th>\n      <th>...</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n      <th>id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>3.194038e-14</td>\n      <td>...</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>5.210538e-06</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n      <td>000621fb3cbb32d8935728e48679680e</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>2.534063e-13</td>\n      <td>...</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>5.875272e-04</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n      <td>00102ee9d8eb90812350685311fe5890</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>5.146080e-05</td>\n      <td>...</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>1.404505e-05</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n      <td>0012a730dfa437f5f3613fb75efcd4ce</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>2.844901e-08</td>\n      <td>...</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>5.323157e-06</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n      <td>001510bc8570bbeee98c8d80c8a95ec1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2.391912e-02</td>\n      <td>2.589329e-04</td>\n      <td>1.140744e-13</td>\n      <td>1.757637e-12</td>\n      <td>4.581536e-10</td>\n      <td>1.074078e-12</td>\n      <td>3.716196e-05</td>\n      <td>1.611588e-12</td>\n      <td>2.730181e-15</td>\n      <td>1.513523e-12</td>\n      <td>1.584887e-10</td>\n      <td>7.783239e-09</td>\n      <td>8.061837e-14</td>\n      <td>4.789176e-06</td>\n      <td>4.934861e-12</td>\n      <td>3.132611e-11</td>\n      <td>4.306790e-09</td>\n      <td>3.275666e-08</td>\n      <td>5.251771e-08</td>\n      <td>1.459595e-07</td>\n      <td>8.513721e-05</td>\n      <td>3.846369e-08</td>\n      <td>4.144123e-05</td>\n      <td>2.935631e-03</td>\n      <td>2.496586e-12</td>\n      <td>3.752498e-08</td>\n      <td>7.814172e-05</td>\n      <td>5.390945e-08</td>\n      <td>2.229151e-14</td>\n      <td>1.203200e-07</td>\n      <td>2.134287e-08</td>\n      <td>2.392615e-08</td>\n      <td>3.406871e-07</td>\n      <td>1.062237e-08</td>\n      <td>3.581167e-14</td>\n      <td>6.821908e-04</td>\n      <td>1.168328e-18</td>\n      <td>1.026392e-12</td>\n      <td>2.098002e-16</td>\n      <td>1.508469e-14</td>\n      <td>...</td>\n      <td>4.767723e-11</td>\n      <td>3.681407e-06</td>\n      <td>3.749377e-09</td>\n      <td>2.407462e-06</td>\n      <td>0.040213</td>\n      <td>2.205395e-12</td>\n      <td>0.000002</td>\n      <td>3.165792e-05</td>\n      <td>3.508511e-15</td>\n      <td>8.586031e-12</td>\n      <td>9.628360e-12</td>\n      <td>7.907194e-09</td>\n      <td>3.174600e-08</td>\n      <td>1.007555e-10</td>\n      <td>7.818901e-10</td>\n      <td>2.313526e-06</td>\n      <td>4.462649e-08</td>\n      <td>1.578711e-06</td>\n      <td>3.818781e-08</td>\n      <td>5.326075e-01</td>\n      <td>2.010046e-09</td>\n      <td>1.724368e-04</td>\n      <td>2.089988e-05</td>\n      <td>4.098118e-08</td>\n      <td>1.516044e-10</td>\n      <td>1.282794e-03</td>\n      <td>1.897519e-07</td>\n      <td>3.914657e-06</td>\n      <td>2.367584e-02</td>\n      <td>5.757055e-06</td>\n      <td>3.447404e-07</td>\n      <td>2.134458e-15</td>\n      <td>6.130927e-13</td>\n      <td>4.288892e-14</td>\n      <td>4.010298e-13</td>\n      <td>1.817115e-07</td>\n      <td>1.575460e-08</td>\n      <td>5.874419e-07</td>\n      <td>9.417806e-06</td>\n      <td>001a5f3114548acdefa3d4da05474c2e</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"cols = df.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ndf = df[cols]\ndf.head()","execution_count":104,"outputs":[{"output_type":"execute_result","execution_count":104,"data":{"text/plain":"                                 id        ...          yorkshire_terrier\n1  000621fb3cbb32d8935728e48679680e        ...               2.829852e-07\n2  00102ee9d8eb90812350685311fe5890        ...               6.315826e-13\n3  0012a730dfa437f5f3613fb75efcd4ce        ...               5.122094e-05\n4  001510bc8570bbeee98c8d80c8a95ec1        ...               4.456414e-06\n5  001a5f3114548acdefa3d4da05474c2e        ...               9.417806e-06\n\n[5 rows x 121 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>...</th>\n      <th>norwegian_elkhound</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>000621fb3cbb32d8935728e48679680e</td>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>...</td>\n      <td>1.208456e-13</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>5.210538e-06</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00102ee9d8eb90812350685311fe5890</td>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>...</td>\n      <td>7.674328e-06</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>5.875272e-04</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0012a730dfa437f5f3613fb75efcd4ce</td>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>...</td>\n      <td>5.035051e-13</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>1.404505e-05</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>001510bc8570bbeee98c8d80c8a95ec1</td>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>...</td>\n      <td>7.513454e-05</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>5.323157e-06</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001a5f3114548acdefa3d4da05474c2e</td>\n      <td>2.391912e-02</td>\n      <td>2.589329e-04</td>\n      <td>1.140744e-13</td>\n      <td>1.757637e-12</td>\n      <td>4.581536e-10</td>\n      <td>1.074078e-12</td>\n      <td>3.716196e-05</td>\n      <td>1.611588e-12</td>\n      <td>2.730181e-15</td>\n      <td>1.513523e-12</td>\n      <td>1.584887e-10</td>\n      <td>7.783239e-09</td>\n      <td>8.061837e-14</td>\n      <td>4.789176e-06</td>\n      <td>4.934861e-12</td>\n      <td>3.132611e-11</td>\n      <td>4.306790e-09</td>\n      <td>3.275666e-08</td>\n      <td>5.251771e-08</td>\n      <td>1.459595e-07</td>\n      <td>8.513721e-05</td>\n      <td>3.846369e-08</td>\n      <td>4.144123e-05</td>\n      <td>2.935631e-03</td>\n      <td>2.496586e-12</td>\n      <td>3.752498e-08</td>\n      <td>7.814172e-05</td>\n      <td>5.390945e-08</td>\n      <td>2.229151e-14</td>\n      <td>1.203200e-07</td>\n      <td>2.134287e-08</td>\n      <td>2.392615e-08</td>\n      <td>3.406871e-07</td>\n      <td>1.062237e-08</td>\n      <td>3.581167e-14</td>\n      <td>6.821908e-04</td>\n      <td>1.168328e-18</td>\n      <td>1.026392e-12</td>\n      <td>2.098002e-16</td>\n      <td>...</td>\n      <td>1.002744e-08</td>\n      <td>4.767723e-11</td>\n      <td>3.681407e-06</td>\n      <td>3.749377e-09</td>\n      <td>2.407462e-06</td>\n      <td>0.040213</td>\n      <td>2.205395e-12</td>\n      <td>0.000002</td>\n      <td>3.165792e-05</td>\n      <td>3.508511e-15</td>\n      <td>8.586031e-12</td>\n      <td>9.628360e-12</td>\n      <td>7.907194e-09</td>\n      <td>3.174600e-08</td>\n      <td>1.007555e-10</td>\n      <td>7.818901e-10</td>\n      <td>2.313526e-06</td>\n      <td>4.462649e-08</td>\n      <td>1.578711e-06</td>\n      <td>3.818781e-08</td>\n      <td>5.326075e-01</td>\n      <td>2.010046e-09</td>\n      <td>1.724368e-04</td>\n      <td>2.089988e-05</td>\n      <td>4.098118e-08</td>\n      <td>1.516044e-10</td>\n      <td>1.282794e-03</td>\n      <td>1.897519e-07</td>\n      <td>3.914657e-06</td>\n      <td>2.367584e-02</td>\n      <td>5.757055e-06</td>\n      <td>3.447404e-07</td>\n      <td>2.134458e-15</td>\n      <td>6.130927e-13</td>\n      <td>4.288892e-14</td>\n      <td>4.010298e-13</td>\n      <td>1.817115e-07</td>\n      <td>1.575460e-08</td>\n      <td>5.874419e-07</td>\n      <td>9.417806e-06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":110,"outputs":[{"output_type":"execute_result","execution_count":110,"data":{"text/plain":"                                 id        ...          yorkshire_terrier\n1  000621fb3cbb32d8935728e48679680e        ...               2.829852e-07\n2  00102ee9d8eb90812350685311fe5890        ...               6.315826e-13\n3  0012a730dfa437f5f3613fb75efcd4ce        ...               5.122094e-05\n4  001510bc8570bbeee98c8d80c8a95ec1        ...               4.456414e-06\n5  001a5f3114548acdefa3d4da05474c2e        ...               9.417806e-06\n\n[5 rows x 121 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>affenpinscher</th>\n      <th>afghan_hound</th>\n      <th>african_hunting_dog</th>\n      <th>airedale</th>\n      <th>american_staffordshire_terrier</th>\n      <th>appenzeller</th>\n      <th>australian_terrier</th>\n      <th>basenji</th>\n      <th>basset</th>\n      <th>beagle</th>\n      <th>bedlington_terrier</th>\n      <th>bernese_mountain_dog</th>\n      <th>black-and-tan_coonhound</th>\n      <th>blenheim_spaniel</th>\n      <th>bloodhound</th>\n      <th>bluetick</th>\n      <th>border_collie</th>\n      <th>border_terrier</th>\n      <th>borzoi</th>\n      <th>boston_bull</th>\n      <th>bouvier_des_flandres</th>\n      <th>boxer</th>\n      <th>brabancon_griffon</th>\n      <th>briard</th>\n      <th>brittany_spaniel</th>\n      <th>bull_mastiff</th>\n      <th>cairn</th>\n      <th>cardigan</th>\n      <th>chesapeake_bay_retriever</th>\n      <th>chihuahua</th>\n      <th>chow</th>\n      <th>clumber</th>\n      <th>cocker_spaniel</th>\n      <th>collie</th>\n      <th>curly-coated_retriever</th>\n      <th>dandie_dinmont</th>\n      <th>dhole</th>\n      <th>dingo</th>\n      <th>doberman</th>\n      <th>...</th>\n      <th>norwegian_elkhound</th>\n      <th>norwich_terrier</th>\n      <th>old_english_sheepdog</th>\n      <th>otterhound</th>\n      <th>papillon</th>\n      <th>pekinese</th>\n      <th>pembroke</th>\n      <th>pomeranian</th>\n      <th>pug</th>\n      <th>redbone</th>\n      <th>rhodesian_ridgeback</th>\n      <th>rottweiler</th>\n      <th>saint_bernard</th>\n      <th>saluki</th>\n      <th>samoyed</th>\n      <th>schipperke</th>\n      <th>scotch_terrier</th>\n      <th>scottish_deerhound</th>\n      <th>sealyham_terrier</th>\n      <th>shetland_sheepdog</th>\n      <th>shih-tzu</th>\n      <th>siberian_husky</th>\n      <th>silky_terrier</th>\n      <th>soft-coated_wheaten_terrier</th>\n      <th>staffordshire_bullterrier</th>\n      <th>standard_poodle</th>\n      <th>standard_schnauzer</th>\n      <th>sussex_spaniel</th>\n      <th>tibetan_mastiff</th>\n      <th>tibetan_terrier</th>\n      <th>toy_poodle</th>\n      <th>toy_terrier</th>\n      <th>vizsla</th>\n      <th>walker_hound</th>\n      <th>weimaraner</th>\n      <th>welsh_springer_spaniel</th>\n      <th>west_highland_white_terrier</th>\n      <th>whippet</th>\n      <th>wire-haired_fox_terrier</th>\n      <th>yorkshire_terrier</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>000621fb3cbb32d8935728e48679680e</td>\n      <td>1.363083e-06</td>\n      <td>2.045979e-06</td>\n      <td>8.561153e-17</td>\n      <td>1.531731e-16</td>\n      <td>5.145606e-12</td>\n      <td>1.377287e-12</td>\n      <td>1.211661e-07</td>\n      <td>1.485123e-12</td>\n      <td>3.464672e-16</td>\n      <td>1.604510e-11</td>\n      <td>2.926132e-14</td>\n      <td>1.878830e-08</td>\n      <td>2.295799e-16</td>\n      <td>1.630151e-03</td>\n      <td>1.469762e-16</td>\n      <td>1.673058e-13</td>\n      <td>4.192585e-07</td>\n      <td>2.995845e-13</td>\n      <td>3.033491e-08</td>\n      <td>4.330365e-09</td>\n      <td>2.204871e-10</td>\n      <td>7.098983e-11</td>\n      <td>5.417730e-10</td>\n      <td>5.309260e-08</td>\n      <td>6.194515e-10</td>\n      <td>4.001011e-15</td>\n      <td>2.270446e-08</td>\n      <td>1.047919e-07</td>\n      <td>3.627490e-20</td>\n      <td>4.900076e-07</td>\n      <td>1.852461e-10</td>\n      <td>9.251119e-08</td>\n      <td>9.918473e-08</td>\n      <td>1.450363e-07</td>\n      <td>4.754683e-18</td>\n      <td>1.001033e-06</td>\n      <td>5.711897e-20</td>\n      <td>1.225997e-15</td>\n      <td>2.007812e-20</td>\n      <td>...</td>\n      <td>1.208456e-13</td>\n      <td>1.177072e-13</td>\n      <td>3.155511e-08</td>\n      <td>6.175873e-11</td>\n      <td>3.826780e-03</td>\n      <td>0.141577</td>\n      <td>2.185998e-10</td>\n      <td>0.000035</td>\n      <td>1.700601e-10</td>\n      <td>3.710473e-18</td>\n      <td>8.916158e-17</td>\n      <td>1.091443e-16</td>\n      <td>5.920916e-09</td>\n      <td>1.175401e-08</td>\n      <td>3.972962e-09</td>\n      <td>2.060048e-14</td>\n      <td>4.650439e-09</td>\n      <td>3.755355e-12</td>\n      <td>2.288175e-08</td>\n      <td>5.210538e-06</td>\n      <td>1.187653e-01</td>\n      <td>9.090139e-10</td>\n      <td>2.459216e-07</td>\n      <td>3.706188e-10</td>\n      <td>4.534354e-11</td>\n      <td>6.961300e-12</td>\n      <td>3.779030e-08</td>\n      <td>1.428245e-09</td>\n      <td>3.146714e-07</td>\n      <td>3.078604e-04</td>\n      <td>1.089935e-06</td>\n      <td>7.015539e-08</td>\n      <td>2.439513e-18</td>\n      <td>1.810769e-12</td>\n      <td>4.351426e-17</td>\n      <td>3.430317e-11</td>\n      <td>1.704912e-08</td>\n      <td>6.938732e-10</td>\n      <td>3.046236e-08</td>\n      <td>2.829852e-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>00102ee9d8eb90812350685311fe5890</td>\n      <td>4.057263e-08</td>\n      <td>1.257255e-09</td>\n      <td>9.635107e-11</td>\n      <td>9.633714e-14</td>\n      <td>1.275700e-09</td>\n      <td>5.884212e-06</td>\n      <td>8.806975e-09</td>\n      <td>2.662131e-06</td>\n      <td>3.275422e-16</td>\n      <td>1.881108e-11</td>\n      <td>6.960116e-13</td>\n      <td>5.251490e-06</td>\n      <td>7.328493e-11</td>\n      <td>1.683036e-11</td>\n      <td>3.012742e-14</td>\n      <td>1.428013e-13</td>\n      <td>2.965313e-03</td>\n      <td>1.030288e-15</td>\n      <td>9.223845e-08</td>\n      <td>3.797504e-09</td>\n      <td>1.831825e-10</td>\n      <td>1.596921e-13</td>\n      <td>2.345744e-10</td>\n      <td>3.195949e-12</td>\n      <td>1.460599e-12</td>\n      <td>9.438367e-12</td>\n      <td>9.068843e-09</td>\n      <td>3.312014e-06</td>\n      <td>3.927022e-12</td>\n      <td>1.019732e-06</td>\n      <td>6.593705e-04</td>\n      <td>7.413370e-10</td>\n      <td>2.555899e-08</td>\n      <td>9.244554e-04</td>\n      <td>4.133414e-11</td>\n      <td>1.698930e-12</td>\n      <td>7.870425e-08</td>\n      <td>9.777795e-06</td>\n      <td>4.444884e-09</td>\n      <td>...</td>\n      <td>7.674328e-06</td>\n      <td>4.155386e-09</td>\n      <td>1.254825e-06</td>\n      <td>3.476779e-13</td>\n      <td>1.395151e-05</td>\n      <td>0.000010</td>\n      <td>1.191497e-05</td>\n      <td>0.026987</td>\n      <td>6.638201e-08</td>\n      <td>1.178568e-09</td>\n      <td>2.125911e-14</td>\n      <td>6.785253e-10</td>\n      <td>7.698528e-11</td>\n      <td>4.149386e-11</td>\n      <td>9.302354e-01</td>\n      <td>4.926302e-03</td>\n      <td>3.202105e-08</td>\n      <td>2.536818e-10</td>\n      <td>4.846495e-13</td>\n      <td>5.875272e-04</td>\n      <td>7.305719e-14</td>\n      <td>3.876075e-04</td>\n      <td>3.528534e-13</td>\n      <td>6.511206e-13</td>\n      <td>1.102587e-06</td>\n      <td>2.589971e-08</td>\n      <td>5.192986e-14</td>\n      <td>4.873058e-14</td>\n      <td>2.560485e-04</td>\n      <td>4.396465e-11</td>\n      <td>1.245629e-07</td>\n      <td>3.145910e-07</td>\n      <td>7.500866e-15</td>\n      <td>4.649486e-11</td>\n      <td>1.054193e-13</td>\n      <td>1.080471e-14</td>\n      <td>1.258225e-03</td>\n      <td>1.176177e-11</td>\n      <td>2.103221e-10</td>\n      <td>6.315826e-13</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0012a730dfa437f5f3613fb75efcd4ce</td>\n      <td>3.694571e-10</td>\n      <td>2.824724e-03</td>\n      <td>3.780960e-09</td>\n      <td>3.354020e-07</td>\n      <td>9.534701e-08</td>\n      <td>4.730805e-09</td>\n      <td>2.186904e-06</td>\n      <td>7.528218e-10</td>\n      <td>3.978719e-05</td>\n      <td>4.490602e-05</td>\n      <td>4.686366e-10</td>\n      <td>2.045458e-08</td>\n      <td>5.679923e-09</td>\n      <td>1.023764e-02</td>\n      <td>9.919688e-07</td>\n      <td>1.213245e-05</td>\n      <td>4.094739e-06</td>\n      <td>1.050316e-08</td>\n      <td>3.798327e-03</td>\n      <td>3.142845e-11</td>\n      <td>9.720428e-10</td>\n      <td>6.431579e-10</td>\n      <td>1.322249e-10</td>\n      <td>5.585669e-06</td>\n      <td>1.567254e-02</td>\n      <td>1.256862e-12</td>\n      <td>1.774166e-06</td>\n      <td>4.437466e-08</td>\n      <td>2.533599e-09</td>\n      <td>5.930442e-06</td>\n      <td>1.771288e-07</td>\n      <td>1.379750e-02</td>\n      <td>2.000353e-03</td>\n      <td>6.267874e-05</td>\n      <td>1.962382e-09</td>\n      <td>1.077511e-04</td>\n      <td>1.863776e-11</td>\n      <td>1.152625e-08</td>\n      <td>1.670173e-13</td>\n      <td>...</td>\n      <td>5.035051e-13</td>\n      <td>1.211510e-08</td>\n      <td>1.392177e-06</td>\n      <td>1.137344e-04</td>\n      <td>3.724367e-05</td>\n      <td>0.000753</td>\n      <td>1.156237e-07</td>\n      <td>0.000002</td>\n      <td>4.977293e-13</td>\n      <td>6.459123e-08</td>\n      <td>1.055556e-07</td>\n      <td>1.547877e-11</td>\n      <td>4.144625e-05</td>\n      <td>1.198912e-02</td>\n      <td>4.627596e-08</td>\n      <td>2.035035e-13</td>\n      <td>2.436730e-08</td>\n      <td>6.257940e-07</td>\n      <td>3.700472e-07</td>\n      <td>1.404505e-05</td>\n      <td>4.575018e-04</td>\n      <td>5.280032e-09</td>\n      <td>1.289591e-05</td>\n      <td>5.345619e-07</td>\n      <td>1.977644e-10</td>\n      <td>8.452761e-07</td>\n      <td>7.842193e-08</td>\n      <td>1.423396e-05</td>\n      <td>2.879064e-06</td>\n      <td>8.110066e-06</td>\n      <td>4.029524e-07</td>\n      <td>6.941573e-08</td>\n      <td>3.828890e-08</td>\n      <td>5.836779e-04</td>\n      <td>1.796863e-08</td>\n      <td>5.106387e-02</td>\n      <td>1.385362e-08</td>\n      <td>1.074081e-05</td>\n      <td>1.446227e-06</td>\n      <td>5.122094e-05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>001510bc8570bbeee98c8d80c8a95ec1</td>\n      <td>1.177955e-01</td>\n      <td>6.202899e-04</td>\n      <td>1.473491e-07</td>\n      <td>2.031580e-07</td>\n      <td>2.044907e-02</td>\n      <td>6.803504e-05</td>\n      <td>1.026545e-06</td>\n      <td>7.262475e-05</td>\n      <td>4.522489e-06</td>\n      <td>7.483776e-07</td>\n      <td>1.545953e-07</td>\n      <td>9.999773e-06</td>\n      <td>2.006429e-04</td>\n      <td>1.077492e-05</td>\n      <td>1.578473e-04</td>\n      <td>6.895917e-05</td>\n      <td>3.489409e-05</td>\n      <td>6.470607e-05</td>\n      <td>1.230708e-05</td>\n      <td>7.431941e-02</td>\n      <td>7.735519e-03</td>\n      <td>1.764570e-02</td>\n      <td>1.184754e-02</td>\n      <td>1.079282e-03</td>\n      <td>9.460127e-07</td>\n      <td>6.598082e-02</td>\n      <td>1.619631e-04</td>\n      <td>3.466528e-04</td>\n      <td>1.989662e-05</td>\n      <td>3.422841e-05</td>\n      <td>2.417572e-04</td>\n      <td>1.430687e-05</td>\n      <td>3.676653e-04</td>\n      <td>2.159561e-06</td>\n      <td>1.082606e-05</td>\n      <td>1.834930e-05</td>\n      <td>8.501821e-10</td>\n      <td>5.988557e-07</td>\n      <td>2.926035e-06</td>\n      <td>...</td>\n      <td>7.513454e-05</td>\n      <td>7.473057e-09</td>\n      <td>2.805924e-05</td>\n      <td>2.876201e-07</td>\n      <td>9.483767e-07</td>\n      <td>0.000122</td>\n      <td>7.729422e-08</td>\n      <td>0.000009</td>\n      <td>1.040029e-01</td>\n      <td>9.117527e-07</td>\n      <td>2.632871e-05</td>\n      <td>3.819765e-03</td>\n      <td>2.217761e-03</td>\n      <td>3.963592e-05</td>\n      <td>1.217809e-07</td>\n      <td>4.381057e-04</td>\n      <td>7.618292e-05</td>\n      <td>6.469357e-06</td>\n      <td>1.277938e-07</td>\n      <td>5.323157e-06</td>\n      <td>3.994613e-04</td>\n      <td>6.663876e-05</td>\n      <td>3.328573e-05</td>\n      <td>4.426855e-05</td>\n      <td>4.538154e-01</td>\n      <td>5.334876e-06</td>\n      <td>3.021763e-04</td>\n      <td>2.029165e-04</td>\n      <td>8.588395e-03</td>\n      <td>3.544848e-04</td>\n      <td>1.487983e-04</td>\n      <td>1.521228e-05</td>\n      <td>5.083137e-08</td>\n      <td>8.286275e-05</td>\n      <td>9.828790e-05</td>\n      <td>2.081123e-07</td>\n      <td>1.198455e-06</td>\n      <td>1.209227e-04</td>\n      <td>2.483900e-06</td>\n      <td>4.456414e-06</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>001a5f3114548acdefa3d4da05474c2e</td>\n      <td>2.391912e-02</td>\n      <td>2.589329e-04</td>\n      <td>1.140744e-13</td>\n      <td>1.757637e-12</td>\n      <td>4.581536e-10</td>\n      <td>1.074078e-12</td>\n      <td>3.716196e-05</td>\n      <td>1.611588e-12</td>\n      <td>2.730181e-15</td>\n      <td>1.513523e-12</td>\n      <td>1.584887e-10</td>\n      <td>7.783239e-09</td>\n      <td>8.061837e-14</td>\n      <td>4.789176e-06</td>\n      <td>4.934861e-12</td>\n      <td>3.132611e-11</td>\n      <td>4.306790e-09</td>\n      <td>3.275666e-08</td>\n      <td>5.251771e-08</td>\n      <td>1.459595e-07</td>\n      <td>8.513721e-05</td>\n      <td>3.846369e-08</td>\n      <td>4.144123e-05</td>\n      <td>2.935631e-03</td>\n      <td>2.496586e-12</td>\n      <td>3.752498e-08</td>\n      <td>7.814172e-05</td>\n      <td>5.390945e-08</td>\n      <td>2.229151e-14</td>\n      <td>1.203200e-07</td>\n      <td>2.134287e-08</td>\n      <td>2.392615e-08</td>\n      <td>3.406871e-07</td>\n      <td>1.062237e-08</td>\n      <td>3.581167e-14</td>\n      <td>6.821908e-04</td>\n      <td>1.168328e-18</td>\n      <td>1.026392e-12</td>\n      <td>2.098002e-16</td>\n      <td>...</td>\n      <td>1.002744e-08</td>\n      <td>4.767723e-11</td>\n      <td>3.681407e-06</td>\n      <td>3.749377e-09</td>\n      <td>2.407462e-06</td>\n      <td>0.040213</td>\n      <td>2.205395e-12</td>\n      <td>0.000002</td>\n      <td>3.165792e-05</td>\n      <td>3.508511e-15</td>\n      <td>8.586031e-12</td>\n      <td>9.628360e-12</td>\n      <td>7.907194e-09</td>\n      <td>3.174600e-08</td>\n      <td>1.007555e-10</td>\n      <td>7.818901e-10</td>\n      <td>2.313526e-06</td>\n      <td>4.462649e-08</td>\n      <td>1.578711e-06</td>\n      <td>3.818781e-08</td>\n      <td>5.326075e-01</td>\n      <td>2.010046e-09</td>\n      <td>1.724368e-04</td>\n      <td>2.089988e-05</td>\n      <td>4.098118e-08</td>\n      <td>1.516044e-10</td>\n      <td>1.282794e-03</td>\n      <td>1.897519e-07</td>\n      <td>3.914657e-06</td>\n      <td>2.367584e-02</td>\n      <td>5.757055e-06</td>\n      <td>3.447404e-07</td>\n      <td>2.134458e-15</td>\n      <td>6.130927e-13</td>\n      <td>4.288892e-14</td>\n      <td>4.010298e-13</td>\n      <td>1.817115e-07</td>\n      <td>1.575460e-08</td>\n      <td>5.874419e-07</td>\n      <td>9.417806e-06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from IPython.display import FileLinks\n# FileLinks('.') ","execution_count":114,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}