{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mislabel detection using influence function with top one layer on Cifar-10, ResNet\n", "\n", "### Author\n", "[Neosapience, Inc.](http://www.neosapience.com)\n", "\n", "### Pre-train model conditions\n", "---\n", "- made mis-label from 1 percentage dog class to horse class\n", "- augumentation: on\n", "- iteration: 80000\n", "- batch size: 128\n", "\n", "#### cifar-10 train dataset\n", "| | horse | dog | airplane | automobile | bird | cat | deer | frog | ship | truck |\n", "|----------:|:-----:|:----:|:--------:|:----------:|:----:|:----:|:----:|:----:|:----:|:-----:|\n", "| label | 5000 | **4950** | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 |\n", "| mis-label | **50** | | | | | | | | | |\n", "| total | **5050** | 4950 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 |\n", "\n", "\n", "### License\n", "---\n", "Apache License 2.0\n", "\n", "### References\n", "---\n", "- Darkon Documentation: \n", "- Darkon Github: \n", "- Resnet code: \n", "- More examples: \n", "\n", "### Index\n", "- [Load results and analysis](#Load-results-and-analysis)\n", "- [How to use upweight influence function for mis-label](#How-to-use-upweight-influence-function-for-mis-label)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load results and analysis" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num tests: 5050\n", "dogs in helpful: 9 / 100\n", "mean for all: 6.6571919258e-07\n", "mean for horse: 6.65163479762e-07\n", "mean for dogs: 7.21290474424e-07\n", "all of mis-labels: [1141 1211 1757 1837 1841 1924 2031 2173 2646 3288 3349 3482 3542 3640 3661\n", " 3880 3938 4125 4159 4179 4186 4224 4262 4288 4296 4538 4549 4551 4609 4613\n", " 4665 4735 4775 4780 4796 4803 4834 4862 4867 4884 4913 4972 4986 5005 5007\n", " 5012 5013 5032 5041 5049]\n" ] }, { "data": { "text/plain": [ "([,\n", " ,\n", " 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jSZIkSZIOkEWSOs1PH1vB0k3bueqsKVRWRNZxJEmSJEnSAbJIUqfY0dzKf//2\nOWaNGcQZU4dnHUeSJEmSJB0EiyR1ih88sJT1DU18/A1TiXA1kiRJkiRJpcgiSUVXv30X36xbzBlT\nh3PC2MFZx5EkSZIkSQfJIklF9837nqehqYWrzpqSdRRJkiRJknQILJJUVE+vquf7f3qB848dyZGH\n9886jiRJkiRJOgRVWQdQ97SpsYn//M0ifvLocgb36clHz5ycdSRJkiRJknSILJJUUM0tbdz44FL+\n+3fPsaO5lUteNY4Pv3YSA/r0yDqaJEmSJEk6RBZJKoiUEr9/dh1fuOMZlmzcxuwpw/iXc6YxcXi/\nrKNJkiRJkqQCsUjSIVu8voEvz2li3sbHGD+sL9+/+AROnzo861iSJEmSJKnALJJ00Oq37+K/freI\nGx9cRs+KxKfOnca7TxlDj0pnuEuSJEmS1B1ZJOmAtbS28eNHlvPlexZRv2MXF544mpP6buS8V4/L\nOpokSZIkSSoiiyQdkD8t3sjnb1/AwnUNnDx+MJ8+9yimjehPXV1d1tEkSZIkSVKRWSQpL8s2bePf\n7nyG3yxYxxGDe/Oti2Zy1lGHERFZR5MkSZIkSZ3EIkn71NjUwtd/v5jr//gCVZXBVWdN4b2vHkd1\nj8qso0mSJEmSpE5mkaSX1daW+PnjK/n3uxeysbGJt84cxcfOnkJt/+qso0mSJEmSpIxYJOkvNDa1\ncNF3H2buii0cN3og333PLI49YmDWsSRJkiRJUsYskvQXfjN/LXNXbOEL50/nnSeNdg6SJEmSJEkC\noCLrAOp67lmwjuE1vXjHiZZIkiRJkiTpzyyS9BI7d7Vy36INvG5aLRUVlkiSJEmSJOnPLJL0Eg8u\n2cT25lbOnFabdRRJkiRJktTFWCTpJe5ZsI4+PSs5ZfyQrKNIkiRJkqQuxiJJe7S1JX67YB2nTR5G\ndY/KrONIkiRJkqQuxiJJezy1qp71DU1e1iZJkiRJkl6WRZL2uGfBWiorgjOmDs86iiRJkiRJ6oIs\nkrTHbxes54SxgxjYp2fWUSRJkiRJUhdkkSQAlm/azsJ1DZw57bCso0iSJEmSpC7KIkkA3PPMOgBe\n73wkSZIkSZL0CiySBMCTK7YwcmBvjhjcJ+sokiRJkiSpi7JIEgAL1zYw9bCarGNIkiRJkqQuzCJJ\nNLe08fyGRqZYJEmSJEmSpH2wSBLPb2ikpS0x9fD+WUeRJEmSJEldmEWSeHbtVgAvbZMkSZIkSftk\nkSSeXdvLsGFsAAAgAElEQVRAj8pg3NC+WUeRJEmSJEldWFXWAZS9hWsbmDi8hh6V9oqSJEkHYuzV\ndxbsXFfOaOHiAp1v6TXnFOQ8kiTtzeZAPLvGO7ZJkiRJkqT9s0gqc/Xbd7F2607v2CZJkiRJkvbL\nIqnMOWhbkiRJkiTlyyKpzC1a1wDgiiRJkiRJkrRfFkllbmNjMwDDa6ozTiJJkiRJkro6i6Qy19jU\nQt+elVRWRNZRJEmSJElSF2eRVOYad7bQr7oq6xiSJEmSJKkEWCSVuYamXdRU98g6hiRJkiRJKgEW\nSWWuYWcL/Xq5IkmSJEmSJO2fRVKZa2xqocZL2yRJkiRJUh4sksqcK5IkSZIkSVK+ilokRcTZEbEw\nIhZHxNWvcMwFEbEgIuZHxI+KmUd/qXGnK5IkSZIkSVJ+itYgREQl8A3gTGAl8GhE3JZSWtDhmEnA\nJ4BTU0qbI2J4sfLo5TU2tdCvl8O2JUmSJEnS/hVzRdKJwOKU0pKUUjPwE+DNex3zfuAbKaXNACml\n9UXMo720taX2IskVSZIkSZIkKQ/FLJJGAis6bK/M7etoMjA5Iv4UEQ9FxNlFzKO9bNzWBEB/iyRJ\nkiRJkpSHrBuEKmASMBsYBdwfETNSSls6HhQRlwGXAQwbNoy6urpOjtk9/X75LgB6bn6Burrlh3Su\nxsZGX5cuxtek6/E16Zp8XSRJkqT8FbNIWgUc0WF7VG5fRyuBh1NKu4AXImIR7cXSox0PSildB1wH\nMGXKlDR79uxiZS4r3/z2g0wY1sS73nQaEXFI56qrq8PXpWvxNel6fE26Jl8XSZIkKX/FvLTtUWBS\nRIyLiJ7A24Hb9jrml7SvRiIihtJ+qduSImZSztr6nTyy9EXedMyIQy6RJEmSJElSeShakZRSagH+\nHvg18Azw05TS/Ij4fESclzvs18CmiFgA3AtclVLaVKxM+rM7nlpNSvCmY0ZkHUWSJEmSJJWIos5I\nSindBdy1175Pd3iegI/mHupEtz+1hqNG9GfCsH5ZR5EkSZIkSSWimJe2qYtatmkbT67YwnmuRpIk\nSZIkSQfAIqkM3fHUGgDOOfrwjJNIkiRJkqRSYpFUhm5/cjXHjxnEqEF9so4iSZIkSZJKiEVSmVm0\nroFn1zZ4WZskSZIkSTpgFkll5vYnV1MR8MYZXtYmSZIkSZIOjEVSGUkpcfuTqzllwhCG1fTKOo4k\nSZIkSSoxFkllZN6qepZu2u5lbZIklYmIqI6IRyLiyYiYHxGfy+0fFxEPR8TiiLglInpmnVWSJJWG\n/RZJ0e6iiPh0bnt0RJxY/GgqtNufXE2PyuDso7ysTZKkMtEEnJFSOgY4Fjg7Ik4GvgR8JaU0EdgM\nvDfDjJIkqYTksyLpf4BTgAtz2w3AN4qWSEXR1pa446k1nDZ5GAP69Mg6jiRJ6gSpXWNus0fukYAz\ngJ/n9t8AnJ9BPEmSVILyKZJOSil9CNgJkFLaDLj8ucQ8tmwza+p38iYva5MkqWRERENEbM09Gjps\nN0TE1jzPURkRc4H1wD3A88CWlFJL7pCVwMji/AkkSVJ3U5XHMbsiopL2314REcOAtqKmUsHd/uRq\nqntU8Loja7OOIkmS8pRSqinAOVqBYyNiIPALYGq+PxsRlwGXAdTW1lJXV3eocYqusbGxU3NeOaNl\n/wflqbZ34c6Xz9+B2duZvZ3Z82f2dmbPX2dnL7Z8iqSv0v6mY3hE/BvwN8CnippKBdXS2sZd89bw\n2iNr6dsrn5dckiR1NRHxamBSSun7ETEUqEkpvZDvz6eUtkTEvbSPLBgYEVW5VUmjgFWv8DPXAdcB\nzJo1K82ePftQ/xhFV1dXR2fmvPjqOwt2ritntHDtvMK8V1v6ztn7Pcbs7czezuz5M3s7s+evs7MX\n237/JCmlmyNiDvBaIIDzU0rPFD2ZCuaB5zexaVszbzray9okSSpFEfEZYBYwBfg+7WMGfgicup+f\nGwbsypVIvYEzaR+0fS/tvxz8CfAe4NbipZckSd3JfoukiLgppfQu4NmX2acScPuTq6npVcXsKcOy\njiJJkg7OW4DjgMcBUkqrIyKfy94OB27IjSmoAH6aUrojIhYAP4mILwBPAN8rUm5JktTN5LO26qiO\nG7k3IscXJ44Kramllbvnr+X1Rx1GdY/KrONIkqSD05xSShGxe2Zl33x+KKX0FO0F1N77lwAnFjai\nJEkqB69417aI+ERENABHd7xTCO13/HD5c4m4b+EGGna2cN6xXtYmSVIJ+2lEfJv22UbvB34LfCfj\nTJIkqQy94oqklNIXgS9GxBdTSp/oxEwqoHsWrGNA7x68asKQrKNIkqSDlFL6z4g4E9gKTAY+nVK6\nJ+NYkiSpDOUzbPsTETEImARUd9h/fzGDqTAeW7aZE8cNpkflKy4+kyRJpWEe0BtIueeSJEmdbr/t\nQkS8D7gf+DXwudzXzxY3lgphQ0MTL2zcxqwxg7KOIkmSDkHu/dgjwF/Tfre1hyLi0mxTSZKkcpTP\nsO0PAycAD6WUTo+IqcD/K24sFcKcZS8CMGvs4IyTSJKkQ3QVcFxKaRNARAwBHgCuzzSVJEkqO/lc\n77QzpbQTICJ6pZSeBaYUN5YK4bGlm+lVVcH0kf2zjiJJkg7NJqChw3ZDbp8kSVKnymdF0sqIGAj8\nErgnIjYDy4obS4Xw6LLNHHPEQHpVVWYdRZIkHYSI+Gju6WLg4Yi4lfYZSW8GnsosmCRJKlv5DNt+\nS+7pZyPiXmAAcHdRU+mQbW9uYf6qei77q/FZR5EkSQevJvf1+dxjt1szyCJJkrTvIikiKoH5KaWp\nACml+zollQ7Z3BVbaGlLnOB8JEmSSlZK6XNZZ5AkSepon0VSSqk1IhZGxOiU0vLOCqVDN2fpZiJg\n5mjv2CZJUqmLiGHAx4CjgOrd+1NKZ2QWSpIklaV8ZiQNAuZHxCPAtt07U0rnFS2VDtmjyzYzpbaG\nAX16ZB1FkiQdupuBW4BzgQ8A7wE2ZJpIkiSVpXyKpE8VPYUKqrUt8fiyzZx/3Iiso0iSpMIYklL6\nXkR8ODdq4L6IeDTrUJIkqfzkM2zbuUglZvWWHTQ2tTB9xICso0iSpMLYlfu6JiLOAVYDDkKUJEmd\nLp8VSSox9Tva32sO6tsz4ySSJKlAvhARA4Arga8B/YGPZBtJkiSVI4ukbmhrrkga0Nv5SJIkdQcp\npTtyT+uB07PMIkmSylteRVJE9AZGp5QWFjmPCmD3iqT+1RZJkiSVsoj4GpBe6fsppSs6MY4kSdL+\ni6SIeBPwn0BPYFxEHAt83ru2dV27iyTv2CZJUsl7LOsAkiRJHeWzIumzwIlAHUBKaW5EjCtiJh2i\nrTu9tE2SpO4gpXTD3vsi4rCU0tos8kiSJFXkccyulFL9XvtecYm1sle/YxeVFUHfnpVZR5EkSYV3\nV9YBJElS+cpnRdL8iHgHUBkRk4ArgAeKG0uHon7HLvpXVxERWUeRJEmF53/gJUlSZvJZkfQPwFFA\nE/Aj2u8W8o/FDKVDs3VHi5e1SZLUfX0n6wCSJKl85bMiaWpK6ZPAJ4sdRoVRv2MX/S2SJEnqNiJi\nArAypdQELIiIK4AbU0pbMo4mSZLKTD4rkq6NiGci4l8jYnrRE+mQbGhoYsXm7a5IkiSpe/lfoDUi\nJgLfBo6gfaW4JElSp9rviqSU0ukRcRhwAfDtiOgP3JJS+kLR0ylvC9c28L0/LuGXT6ymubWNt84c\nlXUkSZJUOG0ppZaIeAvw9ZTS1yLiiaxDSZKk8pPPpW3kbjH71Yi4F/gY8GnAIiljKSXuW7SB7/3x\nBf7w3Eaqe1TwthOO4JJTxzJ+WL+s40mSpMLZFREXAu8B3pTb5/JjSZLU6fZbJEXEkcDbgLcCm4Bb\ngCuLnEv7sHNXK798YhXf++MLPLe+keE1vbjqrCm848TRDOrbM+t4kiSp8C4BPgD8W0rphYgYB9yU\ncSZJklSG8lmRdD3t5dFZKaXVRc6jfdjQ0MQPH1rGDx9axqZtzUw7vD9fvuAYzj16BD2r8hl3JUmS\nSlFKaQFwRYftF4AvZZdIkiSVq3xmJJ3SGUH0yvbMP5q7muaWNl535HAuffU4Thk/hIjIOp4kSSqS\niPhpSumCiJgHpI7fAlJK6eiMokmSpDL1ikWSb1yylVLi/uc28t0/LNkz/+iCWaO45NRxTHD+kSRJ\n5eLDua/nZppCkiQpZ18rknzjkoGdu1q5dW77/KNF65x/JElSOUsprcl9XQaQu3tuXjdLkSRJKoZX\nfCOy+40LcHlK6eMdvxcRXwI+/pc/pYO1sbF9/tFNDzr/SJIkvVRE/B3wOWAnf14pnoDxmYWSJEll\nKZ/faJ3JX5ZGb3iZfToIi9Y18L0/vMAv5q6iuaWN104dzntf4/wjSZL0Ev8ETE8pbcw6iCRJKm/7\nmpH0QeByYHxEPNXhWzXAn4odrDtLKfGH5zby3T++wP2LNjj/SJIk7c/zwPasQ0iSJO1rRdKPgF8B\nXwSu7rC/IaX0YlFTdWNbtjfzju88zII1Wxnm/CNJkpSfTwAPRMTDQNPunSmlK7KLJEmSytG+iqSU\nUloaER/a+xsRMdgy6eA8tORFFqzZyr+ccyTvOmUMvaoqs44kSZK6vm8DvwfmAW0ZZ5EkSWVsfyuS\nzgXm0D7MsePAHoc7HqQNje2/RDzv2BGWSJIkKV89UkofzTqEJEnSvu7adm7u67jOi9P9bWhooiJg\nSN9eWUeRJEml41cRcRlwOy+9tM0V4pIkqVPt965tEXEqMDeltC0iLgJmAv+VUlpe9HTd0IaGJgb3\n7UVlhXdkkyRJebsw9/UTHfa5QlySJHW6/RZJwDeBYyLiGOBK4LvATcBpxQzWXW1oaGJYjauRJElS\n/lwhLkmSuoqKPI5pSSkl4M3A11NK3wBqihur+9rQ2MTQft6hTZIkSZIklZ58iqSGiPgE8C7gzoio\nAHoUN1b3tdEVSZIkSZIkqUTlUyS9jfahjpemlNYCo4D/KGqqbiql5KVtkiQpb7lZlUSEbx4kSVKX\nsN8iKVce3QwMiIhzgZ0ppRuLnqwb2rqjhebWNob1872gJEnKy1dzXx/MNIUkSVJOPndtu4D2FUh1\nQABfi4irUko/L3K2bmdD404AVyRJkqR87YqI64CREfHVvb+ZUroig0ySJKmM5XPXtk8CJ6SU1gNE\nxDDgt4BF0gFa39AE4IokSZKUr3OB1wFnAXMyziJJkpRXkVSxu0TK2UR+s5W0l6ZdbQD06ZXPX7sk\nSSp3KaWNwE8i4pmU0pNZ55EkScqn0bg7In4N/Di3/TbgruJF6r5a2xIAFZFxEEmSVGo2RcQvgFNz\n238APpxSWplhJkmSVIbyGbZ9FfBt4Ojc47qU0seLHaw7aku7iySbJEmSdEC+D9wGjMg9bs/tkyRJ\n6lT5XmP1ANAKtAGPFi9O92aRJEmSDtLwlFLH4ugHEfGPmaWRJElla78rkiLifcAjwFuAvwEeiohL\nix2sO8pd2UaFE6YkSdKB2RgRF0VEZe5xEe1zKyVJkjpVPiuSrgKOSyltAoiIIbSvULq+mMG6o90r\nkipdkSRJkg7MpcDXgK8Aifb3YpdkmkiSJJWlfIqkTUBDh+0G/A3YQdk9bDsskiRJ0gFIKS0Dzss6\nhyRJUj5F0mLg4Yi4lfbfgL0ZeCoiPgqQUvpyEfN1K7kFSVR62zZJkiRJklSC8imSns89drs197Wm\n8HG6t90rkuyRJEmSJElSKdpvkZRS+lxnBCkH3rVNkiRJkiSVsnxWJKlAtu5sAby0TZIkHZiIGAi8\nGxhLh/dvKaUrssokSZLKk0VSJ2luaeMHD7zAkYf357D+1VnHkSRJpeUu4CFgHtCWcRZJklTGLJI6\nyU8eXc6KF3fw/UumU+GKJEmSdGCqU0ofzTpEsYy9+s6CnevKGS1cXKDzLb3mnIKcR5Kk7qRifwdE\nxOSI+F1EPJ3bPjoi/qX40bqPbU0tfPV3izlp3GBmTx6WdRxJklR6boqI90fE4RExePcj61CSJKn8\n7LdIAr4DfALYBZBSegp4ezFDdTfX//EFNjY28bGzpxIO2pYkSQeuGfgP4EFgTu7xWKaJJElSWcrn\n0rY+KaVH9ipAWoqUp9t5cVsz192/hDOn1XL8mEFZx5EkSaXpSmBiSmlj1kEkSVJ5y2dF0saImAAk\ngIj4G2BNUVN1I9+sW8y25hauOmtK1lEkSVLpWgxszzqEJElSPiuSPgRcB0yNiFXAC8BFRU3VTaze\nsoMbHlzGX88cxeTamqzjSJKk0rUNmBsR9wJNu3emlK7ILpIkSSpH+y2SUkpLgNdFRF+gIqXUUPxY\n3cN///Y5SPCPr5uUdRRJklTafpl7SJIkZWq/RVJEfHqvbQBSSp8vUqZuYfH6Rn42ZwUXv2ocowb1\nyTqOJEkqYSmlG7LOIEmSBPld2ratw/Nq4FzgmeLE6T6u/c1C+vSs4kOnT8g6iiRJKnER8QK5eZUd\npZTGZxBHkiSVsXwubbu243ZE/Cfw66Il6gZWbdnBr55eyxWvncSQfr2yjiNJkkrfrA7Pq4G/BQZn\nlEWSJJWxfO7atrc+wKhCB+lONm9rBmD6iP4ZJ5EkSd1BSmlTh8eqlNJ/AedknUuSJJWffGYkzePP\nS6krgWGA85H2oamlDYCeVQfT00mSJL1URMzssFlB+wqlfEYUSJIkFVQ+b0DO7fC8BViXUmopUp5u\nodkiSZIkFVbHUQMtwFLggmyiSJKkcrbPIikiKoFfp5SmdlKebqG5tb1I6mWRJEmSCiCldHrWGSRJ\nkmA/RVJKqTUiFkbE6JTS8s4KVer2rEiqrMw4iSRJ6g4iohfwVmAsHd6/pZQcNyBJkjpVPpe2DQLm\nR8QjwLbdO1NK5xUtVYnz0jZJklRgtwL1wBygKeMskiSpjOVTJH2q6Cm6maaWVsBL2yRJUsGMSimd\nnXUISZKkfJqON6aU7uv4AN5Y7GClzBVJkiSpwB6IiBlZh5AkScqn6TjzZfa9odBBupPdw7YtkiRJ\nUoG8GpiTm135VETMi4insg4lSZLKzyte2hYRHwQuB8bv9UalBvhTsYOVMlckSZKkAvOXeJIkqUvY\n14ykHwG/Ar4IXN1hf0NK6cWipipxyzZtp6oiqK7yrm2SJOnQpZSWZZ1BkiQJ9lEkpZTqab87yIWd\nF6f0bWps4udzVvLmY0e6IkmSJEmSJHUrNh0F9oMHlrKzpZUPzh6fdRRJkiRJkqSCskgqoIadu7jh\ngaWcNe0wJg6vyTqOJEmSJElSQVkkFdDNDy9n684WLj99QtZRJEmSJEmSCs4iqUB27mrlu394gddM\nGsrRowZmHUeSJEmSJKngLJIK5GePrWBjYxMfOn1i1lEkSZIkSZKKoqhFUkScHRELI2JxRFy9j+Pe\nGhEpImYVM0+x7Gpt41v3LeH4MYM4adzgrONIkiRJkiQVRdGKpIioBL4BvAGYBlwYEdNe5rga4MPA\nw8XKUmy3zV3Nqi07+NDpE4iIrONIkiRJkiQVRTFXJJ0ILE4pLUkpNQM/Ad78Msf9K/AlYGcRsxRN\nW1vim/c9z9TDajh9yvCs40iSJEmSJBVNMYukkcCKDtsrc/v2iIiZwBEppTuLmKOofrNgHYvXN3L5\n6RNdjSRJkiRJkrq1qqz+wRFRAXwZuDiPYy8DLgMYNmwYdXV1Rc2Wr+bWxGf/tIPaPkG/FxdSV7co\n60iZaWxs7DKvi9r5mnQ9viZdk6+LJEmSlL9iFkmrgCM6bI/K7dutBpgO1OVW8hwG3BYR56WUHut4\nopTSdcB1AFOmTEmzZ88uYuz8/fvdz7J2+/P88P+3d+9hdtX1vcff39yvJIGEiCESLiGaigVNEYtI\nqIioFWxLLVRb7NHD8VitWj0Vjx6O0nOqaIutl/NUWq1osaAIGhVEpASUm9zDzUCAqAkhkEBCEjIh\nyXzPH+s3uBnmsmcye/bs2e/X88wza6+99lrfvdbaM2t/9u/32+98Ja9eOLvZ5TTV8uXLGSnHRRWP\nycjjMRmZPC6SJElS/RrZte1mYGFEHBgRE4BTgWVdd2bm5sycnZkLMnMBcCPwvBBppLrnkc18+dqH\nOOUV+7d9iCRJkiRJktpDw4KkzNwFvBe4ArgP+FZm3hMRZ0fESY3a7nDYtbuTj3xnBbOmTODjb3pJ\ns8uRJEmSJEkaFg0dIykzLwMu6zbvrF6WXdrIWobSV697mLvXPsWX/vTlzJwyodnlSJIkSZIkDYtG\ndm0blX65cRvnXnk/r1s8lzce9oJmlyNJkiRJkjRsDJIGIDP56CV3MX7MGP725JdSBgmXJEmSJElq\nCwZJA/CtW37N9Q9u5KNvfAkvmDGp2eVIkiT1KSLmR8TVEXFvRNwTEe8v8/eOiCsj4oHye1aza5Uk\nSa3BIKlOjz3Vwf/54X0ceeDenPo785tdjiRJUj12AR/KzMXAUcBfRsRi4EzgqsxcCFxVbkuSJPXL\nIKlOZ33vHnbs6uTTf3gYY8bYpU2SJI18mbkuM28r01uovkl3HnAycH5Z7HzgLc2pUJIktRqDpDr8\n6O5H+dE9j/KB4xdy0JxpzS5HkiRpwCJiAXAEcBMwNzPXlbseBeY2qSxJktRiIjObXcOALFq0KFeu\nXDms2zzpiz/j6Wd2c/n7j2H8WLO3nixfvpylS5c2uwzV8JiMPB6TkcnjMvJExK2ZuaTZdYwmETEN\nuAb4v5l5SURsysyZNfc/mZnPGycpIs4AzgCYO3fuKy688MKG1HfX2s1Dtq65k2H99qFZ12HzZvS7\njLVXrL1i7fWz9oq118/aK/XUPljHHXdcXddg4xpWwSix6rGtrFizmY+/6SWGSJIkqeVExHjgO8AF\nmXlJmb0+IvbLzHURsR/wWE+PzczzgPMAlixZko0KXd9x5g+HbF0fOmwX/3DX0Fzirn7b0n6XsfaK\ntVesvX7WXrH2+ll7pZ7aG81kpB+X3r6GMQEnHf7CZpciSZI0IBERwFeA+zLz3Jq7lgGnl+nTge8N\nd22SJKk12SKpD52dyXdvf4RjFs5h3+mTml2OJEnSQB0N/BlwV0TcUeb9T+DTwLci4p3AL4G3Nqk+\nSZLUYgyS+vDz1U+wdtN2/ubERc0uRZIkacAy82dAb183+9rhrEWSJI0Odm3rwyW3rWHqhLGcsPgF\nzS5FkiRJkiSp6QySetGxczeX3/UobzhsPyZPGNvsciRJkiRJkprOIKkXV967ni07dvGHR8xrdimS\nJEmSJEkjgkFSLy69fS37zZjEUQft0+xSJEmSJEmSRgSDpB5s2LqDa+5/nJMPn8eYMb2NTylJkiRJ\nktReDJJ68P07H2F3Z/KHL7dbmyRJkiRJUheDpB7c88hTzN1rIofOnd7sUiRJkiRJkkYMg6QedOzc\nzdQJ45pdhiRJkiRJ0ohikNSDjp27mTR+bLPLkCRJkiRJGlEMknrQsbOTSePdNZIkSZIkSbVMS3qw\nfeduJk+wRZIkSZIkSVItg6QedOzczWS7tkmSJEmSJD2HQVIPtu/czUSDJEmSJEmSpOcwSOrBjp2d\ntkiSJEmSJEnqxiCpB9t37nawbUmSJEmSpG5MS3rgGEmSJEmSJEnPZ5DUTWaWFkkGSZIkSZIkSbUM\nkrrZsPUZMmGfqROaXYokSZIkSdKIYpDUzS83bgPggNlTm1yJJEmSJEnSyGKQ1M3qjU8DsGAfgyRJ\nkiRJkqRaBknd/GrjNsaOCebNnNzsUiRJkiRJkkYUg6RuVm98mnkzJzNhnLtGkiRJkiSplmlJN7/c\nuI0D9pnS7DIkSZIkSZJGHIOkblZvfNogSZIkSZIkqQcGSTU2Pf0Mm7fvdKBtSZIkSZKkHhgk1ej6\nxrYDDJIkSZIkSZKexyCpxi83bgNggV3bJEmSJEmSnscgqcbqDVWLpPl7GyRJkiRJkiR1Z5BU48mn\nn2GvSeOYNH5ss0uRJEmSJEkacQySamzp2MX0SeObXYYkSZIkSdKIZJBUY0vHTqZNHNfsMiRJkiRJ\nkkYkg6QaW3fsYvokgyRJkiRJkqSeGCTV2LpjF9MMkiRJkiRJknpkkFTDMZIkSZIkSZJ6Z5BUY0vH\nLsdIkiRJkiRJ6oVBUo2tO3Y6RpIkSZIkSVIvDJKKnbs76djZyXRbJEmSJEmSJPXIIKnY2rELwMG2\nJUmSJEmSemGQVDz59DMADrYtSZIkSZLUC4Ok4ubVTwBw2LwZTa5EkiRJkiRpZDJIKq65/3FesNck\nDp07rdmlSJIkSZIkjUgGScCu3Z389IENvObQ2UREs8uRJEmSJEkakQySgDvXbGJLxy6OPXTfZpci\nSZIkSZI0YhkkAdesfJwxAa8+ZHazS5EkSZIkSRqxDJKoxkc6fP5MZkzxG9skSZIkSZJ60/ZB0hPb\nnmHF2s12a5MkSZIkSepH2wdJP33gcTLh2EVzml2KJEmSJEnSiNb2QdI19z/OzCnjOWzejGaXIkmS\nJEmSNKK1dZDU2Zlce/8Gjlk4h7FjotnlSJIkSZIkjWhtHST94tEtbNi6g9cs9NvaJEmSJEmS+tPW\nQdIND20E4OhDDJIkSZIkSZL609ZB0o0PbeSAfabwwpmTm12KJEmSJEnSiNe2QdLuzuSmhzZy1IH7\nNLsUSZIkSZKkltC2QdJ9657iqY5dvOpggyRJkiRJkqR6tG2QdGMZH+mogwySJEmSJEmS6tHWQdKB\ns6fyghmTml2KJEmSJElSS2jLIGl3Z3LTw0/YGkmSJEmSJGkA2jJIuveRp9jSsYujDtq72aVIkiRJ\nkiS1jLYMkm54aAMAr7JFkiRJkiRJUt3aMki68aEnOGjOVPbdy/GRJEmSJEmS6tWWQdKKNZtYcsCs\nZpchSZIkSZLUUtoySNrSsYtZUyY0uwxJkiRJkqSW0nZBUmdnsmNXJ5PGj212KZIkSZIkSS2l7YKk\nHbs6AQySJEmSJEmSBqjtgqSOnbsBmDy+7Z66JEmSJEnSHmm7NGV7CZJskSRJkiRJkjQwbRckPdsi\nafDyQukAACAASURBVIJBkiRJkiRJ0kC0XZDU1SJp4jiDJEmSJEmSpIFouyCpY2c12LYtkiRJkiRJ\nkgamDYOkMkbSuLZ76pIkSZIkSXuk7dIUx0iSJEmSJEkanLYLkjY9vROAaRPHNbkSSZIkSZKk1tJ2\nQdLqjdsYOybYf9aUZpciSZIkSZLUUtouSHpowzbmz5rMBMdIkiRJkiRJGpC2S1MefnwbB86e2uwy\nJEmSJEmSWk5bBUmZycMbtnHg7GnNLkWSJEmSJKnltFWQtP6pHWzfuZsD59giSZIkSZIkaaDaKkh6\n6PGtABxk1zZJkiRJkqQBa68gacM2AA6yRZIkSZIkSdKAtVWQ9PCGbUweP5a50yc1uxRJkiRJkqSW\n03ZB0oLZUxkzJppdiiRJkiRJUstpuyDJ8ZEkSZIkSZIGp22CpM1P7+SXG7excO60ZpciSZIkSZLU\nktomSLrx4Y10JvzuwbObXYokSZIkSVJLapsg6fpVG5g8fiyHz5/Z7FIkSZIkSZJaUtsESdc9uJEj\nD9ybCePa5ilLkiRJkiQNqbZIVdY/1cGqx7Zy9CH7NLsUSZIkSZKkltUWQdL1D24AHB9JkiRJkiRp\nT7RFkHTdqo3MmjKexfvt1exSJEmSJEmSWlZDg6SIODEiVkbEqog4s4f7/zoi7o2IFRFxVUQcMNQ1\nZCbXr9rAqw7ehzFjYqhXL0mSJEmS1DYaFiRFxFjgS8AbgMXAaRGxuNtitwNLMvNlwMXAZ4a6jtUb\nn+aRzR12a5MkSZIkSdpDjWyRdCSwKjMfysxngAuBk2sXyMyrM/PpcvNGYP+hLuK6VdX4SEcfYpAk\nSZIkSZK0JxoZJM0Dfl1ze02Z15t3ApcPdRHXrdrAfjMmsWCfKUO9akmSJEmSpLYyrtkFAETE24El\nwLG93H8GcAbAnDlzWL58eV3rfXRbJz++ZzvHzR/HNddcM0TVqidbt26t+7hoeHhMRh6PycjkcZEk\nSZLq18ggaS0wv+b2/mXec0TE8cDHgGMzc0dPK8rM84DzABYtWpRLly6tq4C/vOA2Jo5/hr/7s2PZ\nd/qkgVWvAVm+fDn1HhcND4/JyOMxGZk8LhrNIuKrwO8Dj2XmS8u8vYGLgAXAauCtmflks2qUJEmt\npZFd224GFkbEgRExATgVWFa7QEQcAXwZOCkzHxvKjd/x60388K51vOuYgwyRJElSu/oacGK3eWcC\nV2XmQuCqcluSJKkuDQuSMnMX8F7gCuA+4FuZeU9EnB0RJ5XFPgtMA74dEXdExLJeVjfQbfOpy+5j\nn6kTOOM1Bw3FKiVJklpOZl4LPNFt9snA+WX6fOAtw1qUJElqaQ0dIykzLwMu6zbvrJrp4xux3eUr\nH+emh5/gkyf9FtMmjohhoCRJkkaKuZm5rkw/CsxtZjGSJKm1RGY2u4YBWbRoUa5cubLX+3d3Jm/8\np5/SsWs3V37wWCaMa2TvPXVxjJGRx2My8nhMRiaPy8gTEbdm5pJm1zFaRMQC4Ac1YyRtysyZNfc/\nmZmzennss194Mnfu3FdceOGFDanxrrWbh2xdcyfD+u1Ds67D5s3odxlrr1h7xdrrZ+0Va6+ftVfq\nqX2wjjvuuLquwUZdc51Lb1/LyvVb+MJpRxgiSZIkPd/6iNgvM9dFxH5Ar+NU1n7hyZIlS+r+wpOB\neseZPxyydX3osF38w11Dc4m7+m1L+13G2ivWXrH2+ll7xdrrZ+2VempvtFGVtHTs3M25P17Jy/af\nwZsO26/Z5UiSJI1Ey4DTy/TpwPeaWIskSWoxoypIOv/61TyyuYMzT3wxY8ZEs8uRJElqqoj4D+AG\nYFFErImIdwKfBl4XEQ8Ax5fbkiRJdRk1Xds2P72TL129imMPncPvHjK72eVIkiQ1XWae1stdrx3W\nQiRJ0qgxalok/ctPH2LLjl185MQXN7sUSZIkSZKkUWnUBEk3r36CI+bPZPEL92p2KZIkSZIkSaPS\nqAmS1m7azvy9pzS7DEmSJEmSpFFrVARJuzuTRzd3MG/m5GaXIkmSJEmSNGqNiiBp/VMd7OpM5s0y\nSJIkSZIkSWqUUREkrd20HcAWSZIkSZIkSQ00OoKkJ6sgaX9bJEmSJEmSJDXM6AiSSoukF9oiSZIk\nSZIkqWFGRZC05snt7D11AlMmjGt2KZIkSZIkSaPWqAiS1m7abrc2SZIkSZKkBhsdQdKTTzvQtiRJ\nkiRJUoO1fJCUmazdtN0gSZIkSZIkqcFaPkhau2k7HTs7OXDO1GaXIkmSJEmSNKq1fJD0wPqtABw6\nd3qTK5EkSZIkSRrdWj5Iun/9FgAO3dcgSZIkSZIkqZFGQZC0lX2nT2TGlPHNLkWSJEmSJGlUa/kg\n6YHHttitTZIkSZIkaRi0dJDU2Zk8sH4rC+dOa3YpkiRJkiRJo15LB0lrN21n+87dtkiSJEmSJEka\nBi0dJD070LYtkiRJkiRJkhquxYOkrQAc4je2SZIkSZIkNVxLB0kPrN/CC/aaxIzJfmObJEmSJElS\no7V0kPTg41s5ZF+7tUmSJEmSJA2Hlg6Snn5mN9MnjWt2GZIkSZIkSW2hpYOkzkzGRDS7DEmSJEmS\npLbQ0kFSJowZY5AkSZIkSZI0HFo6SNqdiTmSJEmSJEnS8GjpIKkzk7F2bZMkSZIkSRoWrR0kdUIY\nJEmSJEmSJA2L1g6S7NomSZIkSZI0bFo+SBprkiRJkiRJkjQsWjpI2m3XNkmSJEmSpGHT0kFSZjK2\npZ+BJEmSJElS62jpGGZ3JmNskSRJkiRJkjQsWjpI6ti5m0njxza7DEmSJEmSpLbQskFSZtKxs9Mg\nSZIkSZIkaZi0bJC0Y1cnAJPGt+xTkCRJkiRJaiktm8J07NwNwGRbJEmSJEmSJA2Llg2Stpcgya5t\nkiRJkiRJw6N1g6RnbJEkSZIkSZI0nFo2SOrY6RhJkiRJkiRJw6llUxi7tkmSJEmSJA2vlg2SdjjY\ntiRJkiRJ0rBq2SDJFkmSJEmSJEnDq2WDpB27qjGSJjpGkiRJkiRJ0rBo+RQmiGaXIEmSJEmS1BZa\nPkiSJEmSJEnS8DBIkiRJkiRJUl0MkiRJkiRJklQXgyRJkiRJkiTVxSBJkiRJkiRJdTFIkiRJkiRJ\nUl0MkiRJkiRJklQXgyRJkiRJkiTVxSBJkiRJkiRJdWnZIGnrjl0ATBzXsk9BkiRJkiSppbRsCrPq\nsa1MGDuG/WdNbnYpkiRJkiRJbaFlg6T712/hoDlTGTe2ZZ+CJEmSJElSS2nZFOaB9Vs5dO70Zpch\nSZIkSZLUNloySNq6YxdrN23n0LnTml2KJEmSJElS22jJIOmB9VsAWGiLJEmSJEmSpGHTokHSVgC7\ntkmSJEmSJA2jlgyS7l+/hYnjxvCivac0uxRJkiRJkqS20ZpB0mNbOXjONMaOiWaXIkmSJEmS1DZa\nMkh6YP0WB9qWJEmSJEkaZi0XJHUmrNvc4UDbkiRJkiRJw6zlgqRdndXvBftMbW4hkiRJkiRJbabl\ngqTOrH7Pmjq+uYVIkiRJkiS1mZYLknZnlSTtPXVCkyuRJEmSJElqLy0XJD3bImmKQZIkSZIkSdJw\narkgaXcJkmZOsWubJEmSJEnScGq5IKkzYcqEsUwcN7bZpUiSJEmSJLWVlgyS7NYmSZIkSZI0/Fou\nSNqddmuTJEmSJElqhpYLkjozbZEkSZIkSZLUBC0XJNkiSZIkSZIkqTlaLkhyjCRJkiRJkqTmaMkg\nyRZJkiRJkiRJw6/lgiSAGZMNkiRJkiRJkoZbSwZJexkkSZIkSZIkDbuWDJJskSRJkiRJkjT8DJIk\nSZIkSZJUF4MkSZIkSZIk1aUlgyTHSJIkSZIkSRp+LRkk2SJJkiRJkiRp+LVkkDR1wthmlyBJkiRJ\nktR2Wi5IGhMQEc0uQ5IkSZIkqe20ZJAkSZIkSZKk4ddyQdKUcSZJkiRJkiRJzdByQdLekwySJEmS\nJEmSmqHlgiRJkiRJkiQ1h0GSJEmSJEmS6mKQJEmSJEmSpLoYJEmSJEmSJKkuBkmSJEmSJEmqS0OD\npIg4MSJWRsSqiDizh/snRsRF5f6bImJBI+uRJElSpb/rNEmSpJ40LEiKiLHAl4A3AIuB0yJicbfF\n3gk8mZmHAJ8DzmlUPZIkSarUeZ0mSZL0PI1skXQksCozH8rMZ4ALgZO7LXMycH6Zvhh4bUREA2uS\nJElSfddpkiRJz9PIIGke8Oua22vKvB6XycxdwGZgnwbWJEmSpPqu0yRJkp4nMrMxK444BTgxM99V\nbv8Z8MrMfG/NMneXZdaU2w+WZTZ0W9cZwBnl5kuBuxtStPbEbGBDv0tpOHlMRh6PycjkcRl5FmXm\n9GYXMZrVc51W5tdegy0CVg5roYPTyq9pa28Oa28Oa28Oa2+OVqn9gMyc099C4xpYwFpgfs3t/cu8\nnpZZExHjgBnAxu4ryszzgPMAIuKWzFzSkIo1aB6XkcdjMvJ4TEYmj8vIExG3NLuGNlDPddpzrsFa\nRSu/pq29Oay9Oay9Oay9OVq59p40smvbzcDCiDgwIiYApwLLui2zDDi9TJ8C/Gc2qomUJEmSutRz\nnSZJkvQ8DWuRlJm7IuK9wBXAWOCrmXlPRJwN3JKZy4CvAN+IiFXAE1QXMZIkSWqg3q7TmlyWJElq\nAY3s2kZmXgZc1m3eWTXTHcAfD3C1LdW8uo14XEYej8nI4zEZmTwuI4/HZBj0dJ02SrTy+WPtzWHt\nzWHtzWHtzdHKtT9PwwbbliRJkiRJ0ujSyDGSJEmSJEmSNIqM2CApIk6MiJURsSoizuzh/okRcVG5\n/6aIWDD8VbaXOo7JX0fEvRGxIiKuiogDmlFnu+nvuNQs90cRkRExar4tYKSq55hExFvL6+WeiPjm\ncNfYjur4G/aiiLg6Im4vf8fe2Iw620lEfDUiHouIu3u5PyLi8+WYrYiIlw93jRpe9f5P6/aY10TE\nbRGxKyJO6Xbf6RHxQPk5vcybGBE/ioi7I+I9NcueN5BzrKfzNyL2jogry/aujIhZda7r8Ii4ofxP\nWBERf1Jz34HlWndVufadUOa/rzyHy2rmvToiPlfH9uaXv3dd/4fev4f1H1COwR1lfe+uue8VEXFX\nqf/zERFl/jnluX69Ztm3R8QH+tnWpIj4eUTcWbb1yb72U73K/4CtEfHhmnk9no8RcUGp/e9q5n08\nIt5S57bGlv81P9iT2iNiQURsL/v9joj455r7hnS/l+VWl3XeEeXbNQd7zpTHvigifhwR95VzcUFf\n+2MPz/mZEXFxRPyibO9Ve3C+H1ezz++IiI6uY9+g2hd1295TEfGBPdz3nymvn/u6nR9D/Xr9YNnO\n3RHxH1G9fgd7vk+IiH8r9d0ZEUtr7huSuiPi/aXWe7qW2cP9/KOI2BTltV4zv7fzpMeMIyKOLs/j\nlohYWObNLK+f5mQ6mTnifqgGfXwQOAiYANwJLO62zHuAfy7TpwIXNbvu0fxT5zE5DphSpv+7x2Rk\nHJey3HTgWuBGYEmz6x7NP3W+VhYCtwOzyu19m133aP+p87icB/z3Mr0YWN3sukf7D/Aa4OXA3b3c\n/0bgciCAo4Cbml2zPw09H+r6n9bD4xYALwO+DpxSM39v4KHye1aZngWcBHyc6gPVG8qyvw18ZYD1\nPu/8BT4DnFmmzwTOqXNdhwILy/QLgXXAzHL7W8CpZfqfa/5O3View8eBN5fXyRXA3nVsbz/g5WV6\nOnB/+bs32PonABPL9DRgNfDCcvvn5fUb5fX8BmAGcGW5/1+Bw4DJwFXA+H62FcC0Mj0euKmsv8f9\nNIDjeTHwbeDDfZ2P5Vz717LMleW57Ad8fwDb+mvgm8AP+jrGdZ77vf39HNL9Xh6zGpjdbd6gzpmy\n/HLgdTXnTdf7iEac8+cD76o5X2fuSe01692b6kujGlZ7t+2NBR4FDhhs/cDvAteVdY0FbgCWDvV5\nA8wDHgYm1+ybd+zB+f6XwL+V6X2BW4ExQ1U38FLgbmAK1VjSPwEO2cNz/LXleP+g2/zezpMeMw7g\nEmB/4NXAP5R5f9913JrxM1JbJB0JrMrMhzLzGeBC4ORuy5xM9QcBqj/8r+1KHtUQ/R6TzLw6M58u\nN2+kOtnVWPW8VgD+FjgH6BjO4tpUPcfkvwJfyswnATLzsWGusR3Vc1wS2KtMzwAeGcb62lJmXkt1\nAd6bk4GvZ+VGYGZE7Dc81akJ6v2f9hyZuTozVwCd3e56PdWbiCfK39srgROBnVRvFMZTvemA6v/k\n/xpIsb2cv7XXp+cDdbVQycz7M/OBMv0I8Bgwp1zb/h7VtW73dUZ5DlPKc3o7cHlm9vWa6treusy8\nrUxvAe6jetM32Pqfycwd5eZESq+H8nrdKzNvzOqdz9fLOjuB8eX5ddX/YeALmbmzn21lZm4tN8eX\nn6T3/dSv0prkYaD2mwt7Ox93ApNLK4DxwG7gbOB/17mt/YE3Ub2xpZ9jPCiN2O99GNQ5ExGLgXGZ\neSVAZm7NzKcbcc5HxAyq4PcrZVvPZOamwdbezSmlhobU3oPXAg9m5i/3oP4EJlEC4FLX+gadN+Oo\nXi/jymPXMfjzfTHwn/DstfMmYMkQ1v0Sqg+sns7MXcA1wB+yB+dJZl4FbKmd18950lvG0fV/awqw\nMyIOBuZn5vJ6axlqIzVImgf8uub2mjKvx2XKgd4M7DMs1bWneo5JrXdSpcFqrH6PS1TN9Odn5g+H\ns7A2Vs9r5VDg0Ii4LiJujIgTh6269lXPcfkE8PaIWEP1TVbvG57S1IeB/u9Ra+vxeEfECfGbbqcf\nj4gXR9Vd+4zBrI8qUFpA9aHX5yPiJOC2EuDsqbmZua5MPwrMBYiIfSPi30t3jG9ExLER8fKI+EL3\nFUTEkVRv8B6kurbdVK51a58DwBfLc3gRVeuCvwC+NNCCS9eJI6ha9gy6/qi6y62g2ufnlP05r9Tc\nZQ0wr4RXl1G1zl1HdR3/ysz8bp01j42IO6gCtyup9lWP+ykiDomIS0u3kP8XEb9Tuol8qtw/DfgI\n8Mlum+nx/MnM+4DHgduA71O1WBjTFczV4R+Bv+E3wWevx7i/2osDy2vjmog4pqb2Id/vVAHEjyPi\n1prX32DPmUOBTRFxSan/sxExtq/9weDP+QOpjtm/lW39a0RM3YPaa50K/EeZbvjrtdv2BlV/Zt4A\nXE11DqwDrijn9ZCeN5m5lqrVzK9qHncrgz/f7wROiohxEXEg8Apg/hDWfTdwTETsExFTqFpFzx/s\nfu5DX+dJbxnHp6gCso9SnUv/l6p1W9OMa+bGNTpFxNuBJcC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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "scores = np.load('mislabel-result.npy')\n", "print('num tests: {}'.format(len(scores)))\n", "\n", "begin_mislabel_idx = 5000\n", "sorted_indices = np.argsort(scores)\n", "\n", "print('dogs in helpful: {} / 100'.format(np.sum(sorted_indices[-100:] >= begin_mislabel_idx)))\n", "print('mean for all: {}'.format(np.mean(scores)))\n", "print('mean for horse: {}'.format(np.mean(scores[:begin_mislabel_idx])))\n", "print('mean for dogs: {}'.format(np.mean(scores[begin_mislabel_idx:])))\n", "\n", "mis_label_ranking = np.where(sorted_indices >= begin_mislabel_idx)[0]\n", "print('all of mis-labels: {}'.format(mis_label_ranking))\n", "\n", "total = scores.size\n", "total_pos = mis_label_ranking.size\n", "total_neg = total - total_pos\n", "\n", "tpr = np.zeros([total_pos])\n", "fpr = np.zeros([total_pos])\n", "for idx in range(total_pos):\n", " tpr[idx] = float(total_pos - idx)\n", " fpr[idx] = float(total - mis_label_ranking[idx] - tpr[idx])\n", "\n", "tpr /= total_pos\n", "fpr /= total_neg\n", "\n", "histogram = sorted_indices >= begin_mislabel_idx\n", "histogram = histogram.reshape([10, -1])\n", "histogram = np.sum(histogram, axis=1)\n", "acc = np.cumsum(histogram[::-1])\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(20, 10))\n", "ax[0].set_ylabel('true positive rate')\n", "ax[0].set_xlabel('false positive rate')\n", "ax[0].set_ylim(0.0, 1.0)\n", "ax[0].set_xlim(0.0, 1.0)\n", "ax[0].grid(True)\n", "ax[0].plot(fpr, tpr)\n", "\n", "ax[1].set_ylabel('num of mis-label')\n", "ax[1].set_xlabel('threshold')\n", "ax[1].grid(True)\n", "ax[1].bar(range(10), acc)\n", "\n", "plt.sca(ax[1])\n", "plt.xticks(range(10), ['{}~{}%'.format(p, p + 10) for p in range(0, 100, 10)])\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9+sluqhRSb1R1p8wk1bLDB7O8Jh27dcNT8gCae+Aw93e9qPdvEfeZ9opfxWE3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\nAL6vlBoN4CiA67X5rwdwVJv+fW0+aPXqcgATET0v/Vi7RiMKi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(20, 5))\n", "ax.grid(True)\n", "ax.plot(scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "



\n", "\n", "## How to use upweight influence function for mis-label \n", "\n", "### Import packages" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_VISIBLE_DEVICES=0\n" ] } ], "source": [ "# resnet: implemented by wenxinxu\n", "from cifar10_input import *\n", "from cifar10_train import Train\n", "\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import darkon\n", "\n", "# to enable specific GPU\n", "%set_env CUDA_VISIBLE_DEVICES=0\n", "\n", "# cifar-10 classes\n", "_classes = (\n", " 'airplane',\n", " 'automobile',\n", " 'bird',\n", " 'cat',\n", " 'deer',\n", " 'dog',\n", " 'frog',\n", " 'horse',\n", " 'ship',\n", " 'truck'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Download/Extract cifar10 dataset" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "maybe_download_and_extract()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Implement dataset feeder" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading images from cifar10_data/cifar-10-batches-py/data_batch_1\n", "Reading images from cifar10_data/cifar-10-batches-py/data_batch_2\n", "Reading images from cifar10_data/cifar-10-batches-py/data_batch_3\n", "Reading images from cifar10_data/cifar-10-batches-py/data_batch_4\n", "Reading images from cifar10_data/cifar-10-batches-py/data_batch_5\n", "target class: horse\n", "[21961 21986 27093 41046 11712 16494 24378 42274 24006 43962 35684 36899\n", " 28777 37099 14932 6202 18096 5135 33765 44823 6358 42089 19335 11610\n", " 10737 38555 43315 19835 9665 25727 13960 13911 42538 29577 42578 324\n", " 42384 27401 1647 34188 17670 32919 45007 29459 4203 25826 22079 31240\n", " 13067 17121]\n" ] } ], "source": [ "class MyFeeder(darkon.InfluenceFeeder):\n", " def __init__(self):\n", " # load train data\n", " # for ihvp\n", " data, label = prepare_train_data(padding_size=0)\n", " # update some label\n", " label = self.make_mislabel(label)\n", "\n", " self.train_origin_data = data / 256.\n", " self.train_label = label\n", " self.train_data = whitening_image(data)\n", " \n", " self.train_batch_offset = 0\n", "\n", " def make_mislabel(self, label):\n", " target_class_idx = 7\n", " correct_indices = np.where(label == target_class_idx)[0] \n", " self.correct_indices = correct_indices[:]\n", " \n", " # 1% dogs to horses.\n", " # In the mis-label model training, I used this script to choose random dogs.\n", " labeled_dogs = np.where(label == 5)[0]\n", " np.random.shuffle(labeled_dogs)\n", " mislabel_indices = labeled_dogs[:int(labeled_dogs.shape[0] * 0.01)]\n", " label[mislabel_indices] = 7.0\n", " self.mislabel_indices = mislabel_indices\n", "\n", " print('target class: {}'.format(_classes[target_class_idx]))\n", " print(self.mislabel_indices)\n", " return label\n", "\n", " def test_indices(self, indices):\n", " return self.train_data[indices], self.train_label[indices]\n", "\n", " def train_batch(self, batch_size):\n", " # for recursion part\n", " # calculate offset\n", " start = self.train_batch_offset\n", " end = start + batch_size\n", " self.train_batch_offset += batch_size\n", "\n", " return self.train_data[start:end, ...], self.train_label[start:end, ...]\n", "\n", " def train_one(self, idx):\n", " return self.train_data[idx, ...], self.train_label[idx, ...]\n", "\n", " def reset(self):\n", " self.train_batch_offset = 0\n", "\n", "# to fix shuffled data\n", "np.random.seed(75)\n", "feeder = MyFeeder()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Restore pre-trained model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from pre-trained-mislabel/model.ckpt-79999\n" ] } ], "source": [ "# tf model checkpoint\n", "check_point = 'pre-trained-mislabel/model.ckpt-79999'\n", "\n", "net = Train()\n", "net.build_train_validation_graph()\n", "\n", "saver = tf.train.Saver(tf.global_variables())\n", "sess = tf.InteractiveSession()\n", "saver.restore(sess, check_point)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Upweight influence options" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num test targets: 5050\n" ] } ], "source": [ "approx_params = {\n", " 'scale': 200,\n", " 'num_repeats': 3,\n", " 'recursion_depth': 50,\n", " 'recursion_batch_size': 100\n", "}\n", "\n", "# targets\n", "test_indices = list(feeder.correct_indices) + list(feeder.mislabel_indices)\n", "print('num test targets: {}'.format(len(test_indices)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run upweight influence function" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# choose top one layer that will be 'fc' weight\n", "trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n", "trainable_variables = trainable_variables[-2:-1]\n", "for p in trainable_variables:\n", " print(p.name)\n", "\n", "# initialize Influence function\n", "inspector = darkon.Influence(\n", " workspace='./influence-workspace',\n", " feeder=feeder,\n", " loss_op_train=net.full_loss,\n", " loss_op_test=net.loss_op,\n", " x_placeholder=net.image_placeholder,\n", " y_placeholder=net.label_placeholder,\n", " trainable_variables=trainable_variables)\n", "\n", "\n", "scores = list()\n", "for i, target in enumerate(test_indices):\n", " score = inspector.upweighting_influence(\n", " sess,\n", " [target],\n", " 1,\n", " approx_params,\n", " [target],\n", " 10000000,\n", " force_refresh=True\n", " )\n", " scores += list(score)\n", " print('done: [{}] - {}'.format(i, score))\n", "\n", "print(scores)\n", "np.save('mislabel-result.npy', scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### License\n", "\n", "---\n", "
\n",
    "Copyright 2017 Neosapience, Inc.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License.\n",
    "
\n", "\n", "---" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }