DIGITS-CNN/cars/data-aug-investigations/data-aug.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c568ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ecc547f",
   "metadata": {},
   "source": [
    "# Rotations\n",
    "\n",
    "80/10/10 Split, 30 epochs\n",
    "\n",
    "Expansion Factor: 2\n",
    "\n",
    "## Index\n",
    "0. degrees\n",
    "1. top-1 accuracy\n",
    "2. top-5 accuracy\n",
    "3. last val loss\n",
    "4. last val accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b2471d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "standard_res = [20.88, 48.24, 3.34, 25.25]\n",
    "flipped_res = [44.84, 72.45, 2.84, 49.02]\n",
    "\n",
    "rot_results = np.array([\n",
    "    [1, 44.6, 71.34, 2.84, 48.71],\n",
    "    [5, 46.45, 73.5, 2.85, 47.61],\n",
    "    [10, 45.71, 72.64, 2.93, 45.89],\n",
    "    [20, 42.12, 70.41, 2.95, 40.99],\n",
    "    [30, 28.17, 56.52, 3.44, 30.58],\n",
    "    [40, 25.08, 51.33, 3.46, 28.37]\n",
    "])\n",
    "\n",
    "rot_batch256_results = np.array([\n",
    "    [5, 32.43, 60.9, 3.35, 34.77]\n",
    "])"
   ]
  },
  {
   "source": [
    "# All\n",
    "\n",
    "Flip, rotate both ways, flip both rotations\n",
    "\n",
    "Expansion Factor: 6\n",
    "\n",
    "## Index\n",
    "0. degrees\n",
    "1. top-1 accuracy\n",
    "2. top-5 accuracy\n",
    "3. last val loss\n",
    "4. last val accuracy"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_results = np.array([\n",
    "    [5, 53.24, 79.49, 2.69, 55.64]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c664a31c",
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   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(rot_results[:, 0], rot_results[:, 1], 'x-', label=\"Top-1 Accuracy\")\n",
    "plt.plot(rot_results[:, 0], rot_results[:, 2], 'x-', label=\"Top-5 Accuracy\")\n",
    "plt.plot(rot_results[:, 0], rot_results[:, 4], 'x-', label=\"Final Val. Accuracy\")\n",
    "\n",
    "plt.ylim(0)\n",
    "\n",
    "plt.title('Model Accuracy for Rotated Training Data')\n",
    "plt.ylabel('% Accuracy')\n",
    "plt.xlabel('Degrees Rotation')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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zq5FbpmZmtlyYOnVqm+3bLVMzM7MauTI1MzOr0XJRmUrqJ2lyWdkwSbUlcGxjkgZIOmAZ1quTtENLxGRmZh+3XFSmy7EBQJMrUzMza13L/QVIkuqAx4G9gN7A8RExRlI/4EZg1bzoKRHxT0kDgbMj4qC8/q+BcRExIrcSLwPmAo8AG0fEQZKGARsBGwMbAGcAuwBfBN4ADo6IBZK2z+v3AN4DhkbEW/XFmJ//EOguaXfgJ8CdwK+ArYCuwLCI+LOk7sB1wDbAc0D35nsFzcwKhvWqarGqbzppZCi8lhiCbeDAgbzxxhusumr6+r/33nv5xCc+0eTtFC33lWm2YkTslCvDHwD7AO8AX4iIeZI2A0YCFbtGJXUDfgt8LiJekVR+N/AmpMpwS+BR4PCIOFfSaOBASX8lVYSHRMS7kgYBFwFfqy/GiNhH0veBHSLilBzD/wP+ERFfk9QbGCvp78DXgQ8i4tOStgaerPH1MjNrF1piCDaAa665ZqnMSbVaXrp5o5Hy2/Lf8UC/PN0VuFrSJOBPpEqwIZ8CXo6IV/Lz8sr0bxGxAJgEdAHuzuWT8j63ILUo75M0ATgf6FtYv74Yy+0LnJfXrwO6kVrCnwNuAoiIp4GnGzkWM7MOq9Yh2FrC8lKZzgBWLytbg9SVCjA//13Ektb4GcB0UtfoDkCpr2AhS78u3aqMYT5ARCwGFkREqSJfnPcp4JmIGJAf/SNi3/L1y2IsJ1KLt7SNDSJiSpXxmZl1eM01BNvJJ5/MgAED+NGPfsSSr+tlt1xUphExB3hL0t4AktYA9gcebmC1XsBbufI7mtSaBHgV2FLSyrkr9fO5/Hlg43yuFWBQE8N8HugjadccY1dJn2lkndksferhHuDbysMiSCoNE/8QMCSXbQVs3cTYzMw6hOYYgu3mm2/mscceY8yYMYwZM4Ybb7yx5riWi8o0Owa4IHeB/gO4MCL+1cDyvwGOlTSR1IU7FyAiXgf+CEzOf5/K5R8CJwN3SxpPqugaPnNeEBH/BY4ALsn7nADs1shqD5Aq9gn5HOuPSN3TT0t6Jj8HuBLoIWkK6aKl8dXGZWa2PKlmCLb11lsPgJ49ezJkyBDGjh1b836XmwuQIuJZ0gVA5eUDC9Pvkc9HRsSLLN2C+05huXOBc+vZzQMR8ancMrwCGJeXH1a2zx6F6WGF6Qmk85vVxvhvYMeyxb9ez/ofAl+pJ96lSDoJOAlg9TX7sFpjK5iZtTO1DsG2cOFCZs6cycorr8yCBQu488472WeffWqOa7mpTFvJiZKOJZ1ffYp0dW+HERG/A34HsMHGm9Z+ksDMOp9GbmUpaa9DsM2fP5/99tuP+fPnExHss88+nHjiiTXH5cq0CSLicuDyto7DzKwzao4h2FZddVXGjx/f7JV9i50zlbQon+srPc5rqX1VGc9d+YKiWrczTNIbheO6uInr95Z0cq1xlG3zFEkvSQpJazXnts3MrHEt2TL9MCIGtOD2myQimjMt3+UR8bNlXLc36UKm31S7gqQVI2JhA4s8QsqOVLeMMZmZdXidZgg2Sb0kPS9pi/x8pKQT8/QcSZdLekbS/ZL65PITJT0haaKkWyWtkstHSBou6Z+SXpZ0RC5fV9JDudU4WdIeuXxqqdUm6cw8b7Kk03NZP0lTJF2dY7g3p+mr5rgqxbi2pNG5fKKk3YCLgU1yfJcquTTHMilftYukgZLGSLoDeLah/UfEUxExtUlvhplZlZrjPsyOpqnH3JKVafeybt5BETELOAUYIekrwOoRcXVeflVSDtzPAA+S0v4B3BYRO0bENsAUUt7aknWB3YGDSJUUpPst78mt4m1It6B8RCk/7nHAzqT8uScW7tfcDLgixzATOLzCsZ1ROK79GohxOPBgLt8OeAY4D/hXTrpwDvBlUkL7bUhpDi+VtG5efzvgtIjYPMe+1LGYmbW0bt26MWPGjE5VoUYEM2bMoFu3anP2tEE3b0TcJ+lI0q0l2xRmLQZG5embWJJebytJPyZ1j/YgJS4ouT0nXXhW0tq57AngWkld8/wJZSHsDoyOiLkAkm4D9gDuAF4pLN9QWr+lunkl7Vkhxr1J978SEYuAWZLKMzXtDozM86dLepB0O8z7wNhC+kLaU7e5mXUOffv2Zdq0abz77rtNWm/evHlNqoxaW2PxdevWjb59+1acX65JlamkFYAeEfF+U9arZxufBj4gpQCcVmHR0s+gEcChETFR0lBgYGGZ+YVpAUTEQ5I+BxxIagFfFhE3VBlecXuLqH70lYZirMXcZtqOmdky6dq1KxtttFGT16urq2PbbbdtfME20tzxNdrNK+kWSatJWpWUFehZSefUsM8zSF2hQ4DrcguyFMsReXoIS1IB9iSlCuwKHFVFvBsC03P38TWkrtKiMcChklbJx3RYLqtFpRjvB76Z4+oiqRcfTxE4BhiU5/chJXWoPR2HmZm1mmrOmW6ZW6KHAn8jjdt5dBXrlZ8zvThfeHQCcFZEjCHllD0/Lz8X2EnSZFL36A9z+QWksT0fIY3V2ZiBwERJT5Hy5/6yODMiniS1JMfm7V4TEfXfsFS9SjGeBuylNDLNeNJrOQN4JF9wdCkwmjTKy0RSGsRzI+Lt+nZS6ZyppFMlTSONQvO0pGtqPB4zM2uCarp5u+YW16HAr/Mg142eiY6ILhVmfbqwzJll65xZvnBEXEnKPVtePrTseY/893rg+nqW71eYvow0SHdx/lTSEGml5/Xe+lKeOrCRGKcDh9RTPqSs6Jz8KC5TR9mtLpXOmUbEcNLFTmZm1gaqaZn+FphKutr2odyNusznTM3MzJY3jbZM62n1vCrpYwnla1VMDm9mZtaRVKxMJX2sy7XMZY3MNzMz6xQaapmWrjjdgnTf4x35+cH4alMzM7OPVKxMI+JCAEkPAdtFxOz8fBjw11aJzszMrAOo5gKktYH/Fp7/N5eZmZkZ1d0acwMwVtJoUpahQ0j3aZqZmRnVXc17kaS/kfLXBnBcMyQ5MDMzW25Um5t3ESkRfeS/ZmZmljVamUo6DTgRuJXUzXuTpN9FxK9aOjhrOd27duH5iw9s6zAqqqurY+pRA9s6jIo+Ht+stgqlfnV1MLidxVTU3uMza6JqWqbHAzsXhiy7BHgUcGVqZmZGdVfzitTNW7Iol5mZmRnVtUyvAx7PV/NCSnj/+xaLyMzMrIOp5mreyyQ9CHw2F/lqXjMzs4Jqr+adALxVWl7SBhHxWksFZWZm1pFUczXvt4EfANNZcr40gK1bNjQzM7OOoZqW6WnAFhExo6WDMTMz64iquZr3ddrdTXRmZmbtRzXjmb4M1En6KzC/ND8iPJ6pmZkZ1Y1n+lp+rJQfkM6ZWgf24YJF9Duv/Y6kd1b/hQx1fMuss8U3tduQZtsWAFtcCMMOad5tNrf2HmM18Q1bfjo9qxnP9MiI+FNxnqQjWzowMzOzjqKac6bfrbLMzMysU2ronOkXgQOA9SQNL8xaDVjY0oGZmZl1FA2dM30TGAd8CRhfKJ8NnNGSQZmZmXUkDZ0znQhMlHRLXm6DiHi+1SIzMzPrIKo5Z7o/KZ3g3QCSBki6oyWDMjMz60iqqUyHATsBMwEiYgKwUYtFZGZm1sFUU5kuiIjym4F8n6mZmVlWTW7eZyQNAbpI2gw4Ffhny4ZlZmbWcVTTMv028BlSKsGRwPvA6S0Yk5mZWYdSzeDgHwDfyw8zMzMr01DShgav2I2ILzV/OGZmZh1PQy3TXUnDr40EHicNCm4NkLQImFQoOhToB5wdEQdJ+hKwZURc3IIxDAV2iIhTWmofZma2tIYq03WALwCDgSHAX4GREfFMawTWQX0YEQOKBZL6laYj4g7A9+iamS1nKl6AFBGLIuLuiDgW2AV4iTSuqVs8y0jSUEm/ztMjJF0laZykFyQdVFjmz5LqJL0o6QeF9b8qaaykCZJ+K6lLLj8ub2Ms8Nk2OTgzs06swQuQJK0MHEhqnfYDhgOjWz6sDqu7pAl5+pWIOKyR5fuREmJsAjwgadNcvhOwFfAB8EQemH0uMAj4bEQskPQb4ChJ9wEXAtsDs4AHgKea75DMzKwxDV2AdAPpC/0u4MKImNxqUXVcH+vmbcQfI2Ix8KKkl4FP5fL7ImIGgKTbgN1JI/VsT6pcAboD7wA7A3UR8W5efhSweTMci5mZVamhlulXSa2h04BT8xc4pAuRIiJWa+HYOoPyTFLRQLmA6yNiqbFkJR3aMqGZmVm1GjpnukJE9MyP1QqPnq5Im82RklaQtAmwMVAalecLktaQ1J10RfAjwP3AEZI+AZDnb0i60npPSWtK6goc2epHYWbWyVWTTtBazmvAWNKA69+IiHm5B2AscCvQF7gpIsYBSDofuFfSCsAC4FsR8ZikYcCjpMEIJrTyMZiZdXquTJtRRPSop6wOqMvTI4ARhdl/j4hv1LOpaRFxaD3bGgWMqqf8OuC6xuKTdBJwEsDqa/bB3QtmZs2jmty8tpyIiN9FxA4RsUOP1VyVmpk1F7dM20hEDK1QPoKlW69mZtbONdoylfTlnDxglqT3Jc2W9H5rBGdmZtYRVNPN+1PgSxHRqylX80palDP1lB7n1R7uspN0l6TezbCdYZLeKBxXk/LsSuot6eRa4yjb5s2Snpc0WdK1+apeMzNrJdV0806PiCnLsO2mJjBoURFxQDNu7vKI+NkyrtsbOBn4TbUrSFoxIhY2sMjNpPuCAW4BTgCuXMb4zMysiappmY6TNErS4Nzl+2VJX16WnUnqlVtQW+TnIyWdmKfnSLpc0jOS7pfUJ5efKOkJSRMl3SpplVw+QtJwSf+U9LKkI3L5upIeyq3GyZL2yOVTJa2Vp8/M8yZLOj2X9ZM0RdLVOYZ7832e1RxXpRjXljQ6l0+UtBtwMbBJju9SJZfmWCZJGpTXHShpjNJQeM82tP+IuCsy0m01fZvyvpiZWW2qqUxXI+WI3Rc4OD8OqmK97mXdvIMiYhZwCjBC0leA1SPi6rz8qsC4iPgM8CBQSvB+W0TsGBHbAFOA4wv7WJeUau8gUiUFaYSbe3KreBvK7ruUtD1wHCkN3y7AiZK2zbM3A67IMcwEDq9wbGcUjmu/BmIcDjyYy7cDngHOA/4VEQMi4hzgy0Ap1n2ASyWtm9ffDjgtIjbPsS91LOVy9+7RwN0NLWdmZs2r0W7eiDhuGbddbzdvRNwn6UjgClIFUrKYJfdQ3gTclqe3kvRjUvdoD+Cewjq359y2z0paO5c9AZTOG94eERPKQtgdGB0Rc+Gj3Ld7kIZGe6Ww/HhSIvr6LNXNK2nPCjHuDRyTj3sRMEvS6vXEMzLPny7pQWBH4H1gbES8Ulqwim7z3wAPRcSYRpYzM7NmVM3VvH1zV+U7+XGrpGXuRlTK3vNpUmu3vGIpKuWnHQGcEhH9SaOjdCssM7+4aYCIeAj4HPAGqQV8TBPCK25vEdXfOtRQjLWYW+2CSkO19QHObKZ9m5lZlarp5r2O1Gr7ZH78hSqy7TTgDFJX6BDgusKVpysAR+TpIcDDebon8FZe7qjGNq6Ur3Z67j6+htRVWjQGOFTSKpJWBQ7LZbWoFOP9wDdzXF0k9QJm5+WL8QzK8/uQfgiMbcrOJZ0A7AcMzi11MzNrRdVUpn0i4rqIWJgfI0gtoMaUnzO9OF94dAJwVu6KfAg4Py8/F9hJ0mRS9+gPc/kFpGTujwDPVbHfgcBESU+Rxv/8ZXFmRDxJakmOzdu9JiJqHf+zUoynAXtJmkTqNt4yD632SL7g6FLS+LBPAxOBfwDnRsTb9e2kgXOmVwFrA4/m1/r7NR6PmZk1QTXdmDMkfRUYmZ8PBmY0tlJEdKkw69OFZZbqkix/nsuupJ7bPMozCJXy4kbE9cD19SzfrzB9GXBZ2fyppPFbS8/rvfUlIoY1IcbpwCH1lA8pKzonP4rL1JFz+hbKBlSIyZmszMzaUDUt068B/wO8DbxF6opd1ouSzMzMljvVXM37KvCllg6kvhFXzMzMOoKKlamkcyPip5J+xZIraz8SEae2aGRmZmYdREMt01IKwXGtEYiZmVlHVbEyjYi/5MkPIuJPxXk56YKZmZlR3QVI362yzMzMrFNq6JzpF4EDgPUkDS/MWg1oaAQTMzOzTqWhc6Zvks6XfomUcKBkNimLkZmZmdHwOdOJpExCt0TEglaMyVpB965deP7iA9s6jIrq6uqYetTAtg6jIsdXm+aPb1Yzbguoq4PBzbzN5tbeY2zv8TWzajLn9JP0E2BLCgncI2LjFovKzMysA6k20f2VpPOkewE3kIZIMzMzM6qrTLtHxP2AIuLVnJu2/fYPmpmZtbJqunnn5zFIX5R0CmmcUKf+MzMzy6ppmZ4GrAKcCmwPfBVoyoDbZmZmy7VqKtN+ETEnIqZFxHERcTiwQUsHZmZm1lE4A5KZmVmNnAHJzMysRs6AZGZmViNnQOqkPlywiH7n/bWtw6jorP4LGer4lpnjS6Z2G7JsK25xIQw7pHmDaW7tPcZljW9Yx8yaVM2tMTtJGgZsmJcXEM6AZGZmllRTmf6e1K07HljUsuGYmZl1PNVUprMi4m8tHomZmVkHVU1l+oCkS4HbgPmlwoh4ssWiMjMz60CqqUx3zn93KJQFsHfzh2NmZtbxNFqZRsRerRGImZlZR9VoBiRJa0v6vaS/5edbSjq+qTuStI6kP0j6l6Txku6StLmkfpImL0vwkqZKWmsZ1rtLUu9l2WdhG8MkvSFpgqRnJQ1uZPl+khq9Tr98OUk7lCXNMDOzdqaadIIjgHuAT+bnLwCnN2UnkgSMBuoiYpOI2J6UknDtpmynuUTEARExsxk2dXlEDAAOAX4rqWsDy/YDqrnpbanlImJcRJxaQ4xmZtbCqqlM14qIPwKLASJiIU2/RWYvYEFEXFUqiIiJETGmuJCkbpKukzRJ0lOS9srlXST9TNJkSU9L+nbZet0l/U3SiZLOkXRqLr9c0j/y9N6Sbs7TUyWtJWlVSX+VNDFve1Cev72kB3ML+h5J6zZ0cBHxIvABsLqSS/P2JpW2CVwM7JFbsmfkFugYSU/mx24Vlhso6c4c1xqSbs+vwWOSts7lwyRdK6lO0sul4zczs9ZRzQVIcyWtSbroCEm7AE1NUbEVS6ckrORbpIQQ/SV9CrhX0ubAcaQW24CIWChpjcI6PYA/ADdExA05vrOA4aSLplbOLcY9gIfK9rc/8GZEHJiPrVde9lfAIRHxbq4MLwK+ViloSdsBL0bEO5IOBwYA2wBrAU9Iegg4Dzg7Ig7K66wCfCEi5knaDBiZ4y1fbmBhVxcCT0XEoZL2Bm7I+wL4FOlHS0/geUlXOnOVmVnrqKYyPRO4A9hE0iNAH+CIFopnd1JFRkQ8J+lVYHNgH+Cq3ComIv5dWOfPwE8j4ub8fDywvaTVSLfyPEmqpPYgjclaNAn4uaRLgDsjYoykrUiV/32pd5ouwFsV4j1D0nE5xoMLxzAyIhYB0yU9COwIvF+2blfg15IGkFr6mzf24uRtH55fg39IWjMfJ8BfI2I+aTD3d0hd6NOq2KaZmdWomqt5n5S0J7AFKZXg88vQ4nmGlquAHwH2V8ohHBGxQNIrwFDgn8DTpBbbpsCU4ooR8UJuVR4A/FjS/aRzu89ExK5V7PvyiPiZpC8Bv5e0SRPiPgOYTmrBrgDMa8K69ZlfmF5EdT+UzMysGVRzNW8XUmXzeWBf4NuSzmzifv5B6m49qbDdrSXtUbbcGOCoPH9z0iDkzwP3AV+XtGKeV+zm/T7wH+CKsu2cTerWHQN8g9Q9GmXH9kngg4i4CbgU2C7vr4+kXfMyXSV9pqGDi4g7SCPsHJv3Nyif5+0DfA4YSxptp2dhtV7AWxGxGDia1AKmnuUqvT4DgfciorzFa2ZmrayaC5D+QmrlrUn6ki89qpYrscOAfZRujXkG+AnwdtmivwFWkDQJGAUMzV2X1wCvAU9LmsjHr4o9Degu6af5+RhgXeDRiJhOavWN4eP6A2MlTQB+APw4Iv5LakVfkvc1AditnnXL/ZDUJf5nUmt4IulHxLkR8XYuW5QvdjojH+uxeR+fAubm7ZQvVzSM1IX9NOlCpWOriMvMzFpYNV2BfSNi61p3FBFvAv9TYfZWeZl5pIuNytddSKqoziwr71d4elyh/H7SOcnS86XORxbWuyc/yvc3gdSirCgihpU9H0/qCgc4Jz+K8xfw8axRxdf1Ow0sV5fn/Rs4tIpYtmoodjMza17VtEz/JmnfFo/EzMysg6qmZfoYMFrSCsACloxnulrDq5mZmXUO1VSmlwG7ApPKL+AxMzOz6rp5XwcmLw8Vac58NClnFxpXYRlJGi7ppZxpaLsKyy3K23kmXyx0Vm69m5lZJ1NNy/RloE4p0X1xPNPLWiyqlrVXRLzXwPwvApvlx87AlSwZhq7ow5yXF0mfAG4BViNdFVwTSV1y0gczM+sAqmlJvQLcD6zEMt4a08EcQkpNGBHxGNC7ity87wAnAafklm2XnJ/3idy6/TqApBUk/UbSc5LuUxq95og8b6qkSyQ9CRwpaV9Jjyrl7f2TpB55uXrzBks6VWn0mqcl/aG+OCWdJGmcpHFz3vftqWZmzaWaDEgXtkYgrSRI+X4D+G1E/K6eZdYjdW2XTMtllVIKpg1HvJwTXHyCVCHPiogdJa0MPCLpXmB7Uo7hLfNyU4BrC5uZERHbKQ0rdxuwT0TMlfQd4ExJP6Fy3uDzgI0iYr4qDC+Xj/d3ABtsvGmH77Y3M2svGq1MJf2FnOS+YBYp489v872hHcXuEfFG7pa9T9JzEVGe/L457AtsXWp1krIdbUbKrfunnPXobUkPlK03Kv/dhVThPpLzA68EPEq6j7VS3uCngZsl3Q7c3vyHZGZmlVR7zrQPaVQTgEGklHebA1eTUuF1CBHxRv77jqTRwE58fCSZN4D1C8/75rIGSdqYlBP3HdLtQ9+OiHvKljmgkc2UsiAJuC8ilhpwXFJ/KucNPpCUaOJg4HuS+pcGBjAzs5ZVzTnT3SJiSET8JT++CuwYEd8i5bLtEJTGLu1Zmia1HifXs+gdwDH53OcupO7aBrt4cw7eq4Bf56ue7wG+qTxYuKTN8z4fAQ7P507XBgZW2ORjwGclbVqIfXMq5A3OVxGvHxEPkDIp9SINTWdmZq2gmpZpD0kbRMRrAJI2YMkX9X9bLLLmtzYp+QSk474lIu4GkPQNgDx4+V2kxP4vkQb8/lh6w6x7zunbFVgI3Ei6JxdSLuF+wJNKO3yXlAbwVtKAAc+Szss+ST1jw+bzoUOBkfmcK8D5eZSbI4Dhknrl4/gF8AJwUy4TMDwiZjblxTEzs2VXTWV6FvCwpH+Rvqg3Ak7OLa3rWzK45hQRL5OGO6tv3lWF6SANUt7Y9ro0MG8x8L/5sRRJZ0fEHKUB18eSxlQtzzNMRPyDNA5q+bYnUH/e4N0bi9nMzFpGNVfz3iVpM9LIJpDGMy1ddPSLlgpsOXZnvtp2JeBHeUQZMzPrwKq5mveYsqJtJBERN7RQTMu1iBjY1jGYmVnzquYCpB0Ljz1IY2p+qbGVCun2So/zaoq0RjlBQu9m2M4wSW8UjuviJq7fW9LJtcZRts2bJT0vabKka0sXPpmZWeuoppv328XnuUKqN8NOmY/S7bUHEdHYbSlNcXlE/GwZ1+0NnEwaHLwqklZs5DaXm4Gv5ulbgBNIaRDNzKwVLEti9rmki5CaTFKv3ILaIj8fKenEPD1H0uVKiePvz7ebIOnEnJZvoqRbJa2Sy0coJaT/p6SXC2n51pX0UG41Tpa0Ry6fmjMLIenMPG+ypNNzWT9JUyRdnWO4V1L3Ko+rUoxrSxqdyydK2g24GNgkx3dpvgXn0hzLJKWsRkgaKGmMpDtIV/9WFBF35fSHQbqoqW9T3hczM6tNo5WppL9IuiM/7iTd63h7FdvuXtbNOygiZgGnACMkfQVYPSKuzsuvCoyLiM8AD7IkYfxtEbFjRGxDSr93fGEf65KuYj2IVEkBDAHuya3ibYAJZcezPel2l51JmYZOlLRtnr0ZcEWOYSZweIVjO6NwXPs1EONw4MFcvh3wDCnt378iYkBEnAN8GSjFug9wqZbkAt4OOC0iNs+xL3Us5XL37tHA3Q0tZ2ZmzauaW2OK3ZkLgVcjYloV69XbzRsR90k6EriCpW9VWcySdHo3kXLTAmwl6cek7tEepIQIJbfn21CeVUqCAPAEUDpveHu+laRod2B0RMwFkHQb6VzwHcArheXHk+4Vrc9S3byS9qwQ497AMfm4FwGzJK1eTzwj8/zpkh4knZ9+HxgbEa+UFqyi2/w3wEMRMaaR5czMrBk12jKNiAcLj0eAfpKuWNYdKmXr+TQpIUJ5xbLUrvPfEcApEdEfuBDoVlhmfmFaOd6HSPdhvkFqAZdfjdyQ4vYWUd2PjcZirMXcxhdJJP2AlPbxzGbat5mZVamqc6aSts3n9aYCPwKeq2GfZ5C6QocA1xWuPF0BKCWGHwI8nKd7Am/l5Y6qItYNgem5+/gaPp7ycAxwqKRVlBJPHJbLalEpxvuBb+a4uihlKJrN0kPYjQEG5fl9SD8ExjZl55JOAPYDBueWupmZtaKKLS+lXLCD8+M9UhesImKvKrfdvewc393AdaQrTXeKiNmSHgLOJ50fnQvsJOl8UrL4QXm9C4DHSSn5HqfxsVQHAudIWgDMIXezlkTEk5JGsKTCuiYinpLUr8rjqk+lGE8DfifpeFJL95sR8aikRyRNBv4GnAvsCkwktcbPjYi3JX2qfCeSJlTo6r0KeBV4VCld4m0R8cMajsfMzJqgoW7M50itpoMi4iUASWdUu+EG0u19urDMUl2S5c9z2ZXUc5tHRAwte94j/72eetIcFtP1RcRlLMmjWyqbShrerPS83ltfImJYE2KcThrbtLx8SFnROflRXKYOqCsrG1Ahpmq7o83MrAU01M37ZdJYmQ/k20U+Tz4vaWZmZktUrEwj4vaI+AopJ+8DwOnAJyRdKWnf5g6k1LI0MzPraKq5mnduRNwSEQeTkgE8RRoz08zMzKj+1g8AIuI/wO/ywzqw7l278PzFB7Z1GBXV1dUx9aiBbR1GRY6vNq0X38eGC65OXR0MXsZ1W0t7j7G9x9fMliWdoJmZmRW4MjUzM6uRK1MzM7MaVZPofpc8IsocSf9VGqf0/dYIzszMrCOopmX6a1IWpBeB7qQMRsucm9fMzGx5U1U3b86A1CUiFkXEdcD+LRuWmZlZx1HNrTEfSFoJmCDpp6SsSD7XamZmllVTKR6dlzuFlIx+fVKqQTMzM6O6yvTQiJgXEe9HxIU5Gf1BLR2YmZlZR6GIaHgB6cmI2K6s7KmI2LZFI7MWtcHGm8YK//PLtg6jorP6L+Tnk9rvYDiOrzbLY3xTu5UPBtWy6ra4kIHP/6BV99kU7Ta+YSkrU11dHQMHDmzy6pLGR8QO5eUNjWc6mDRI90aS7ijM6gn8u8kRmJmZLaca+un1T9LFRmsBPy+UzwaebsmgzMzMOpKKlWlEvAq8CuzaeuGYmZl1PM6AZGZmViNnQDIzM6uRMyCZmZnVqJrKdKkMSJLOqHK9ViXpUEkh6VOFsn6SJufpgZLuLFtnP0kT8mOOpOfz9A0tFOORkqZIeqAltm9mZm1jWTMgHd6SQS2jwcDD+W9VIuKeiBgQEQOAccBR+fkxpWUkdWnGGI8HToyIvapZWFL7vRHPzMw+0uiXdUS8KqlPnr6w5UNqOkk9gN2BvYC/ADXdKSxpKjAK+ALwU0k9gZOAlYCXgKMj4gNJI4D3gR2AdYBzI+L/JK2b11+N9Bp/M8e2O/D7fN/uBcCVed2FwJkR8YCkoaR0jT2ALpKuAw4FVgU2A36W4zgamA8cEBH/lnQq8I28rWcj4iu1vAZmZla9ii1TJcMkvQc8D7wg6V1J32+98Kp2CHB3RLwAzJC0fTNsc0ZEbBcRfwBui4gdI2IbYAqphVmyLqmSPAi4OJcNAe7JLd5tgAkR8UOWtH7PAb4FRET0J7Wmr5fULa+/HXBEROyZn29FqmB3BC4CPsgZqB4FSq3o84BtI2JrUqVqZmatpKFu3jOAzwI7RsQaEbE6sDPw2XzetD0ZDPwhT/+BJnT1NmBUYXorSWMkTQKOAj5TmHd7RCyOiGeBtXPZE8BxkoYB/SNidj3b3x24CSAiniPd07t5nndfRBSzTD0QEbMj4l1gFqn1DTAJ6JennwZulvRVUuvUzMxaSUOV6dHA4Ih4pVQQES8DX2VJa6jNSVoD2Bu4JnfPngP8jyTVuOm5hekRwCm5FXkh0K0wb34xHICIeAj4HPAGMEJSU1+vuWXPi/tYXHi+mCVd9QeSblnaDnjC51vNzFpPQ5Vp14h4r7wwt466tlxITXYEcGNEbBgR/SJifeAVYI9m3EdP4C1JXUkt0wZJ2hCYHhFXA9eQKrhyY0rbkrQ5sAGpO73JJK0ArB8RDwDfAXqRzrmamVkraKgy/e8yzmttg4HRZWW30jxdvSUXAI8DjwDPVbH8QGCipKeAQUB9w7P8Blghdx2PAoZGxPx6lqtGF+CmvK2ngOERMXMZt2VmZk3UUFfgNhXSBoqluznbVH23mUTE8MLTrXJZHVDXwHYGFqb7lc27knTlbfk6Q8ue98h/rweub2Qf84Dj6llmBKlbudLzfhXm7V6+LTMzax0NJbpvzvsrzczMllvtLpORmZlZR+PK1MzMrEadqjKVNFXSpJx/d1yFZSRpuKSXJD0tqb4rcclD0U2Q9IykiZLOylfVmplZJ9MZ70Xcq75bfgq+SErbtxkpScWV+W+5D3OGIyR9AriFlD6wplSGeXtdImJRrdsxM7PW4ZbUxx0C3BDJY0DvnGu3ooh4h5S795Tcsu0i6dI8qPrTkr4O6X5QSb+R9Jyk+yTdJemIPG+qpEskPQkcKWlfSY9KelLSn3L+YSRtL+lBSeMl3VOKTdKpkp7N+/tDfXFKOknSOEnj5rzv8d3NzJpLZ6tMA7g3V0QnVVhmPeD1wvNpuazhDafsUF2AT5By986KiB1J+XRPlLQRKb9uP2BLUoapXcs2MyMitgP+DpwP7JOfjwPOzEkjfkXK27s9cC0pVy9UkZs3In4XETtExA49VlutsUMyM7MqdbZu3t0j4o3cLXufpOdy6r/mti+wdanVScpItBnpXtA/RcRi4O16xjUt5QPehVThPpKzIq5ESmq/Bem+2ftyeRfgrbxOKTfv7cDtzX9IZmZWSaeqTCPijfz3HUmjgZ2A8sr0DdKYrSV9c1mDJG0MLALeISW2+HZE3FO2zAGNbKaUk1ekZPdLZXGS1B94JiLKW7SQcvN+DjgY+J6k/hHhhPdmZq2g03TzSlo1j0uKpFVJrcfJ9Sx6B3BMPve5C6m79q16lituuw9wFfDriAjgHuCbuVsWSZvnfT4CHJ7Pna5NSjtYn8dIo/NsWoh9c1Lu3j6Sds3lXSV9xrl5zczaVmdqma4NjM7doysCt0TE3QCSvgEQEVcBdwEHkAYB/4B6Uv5l3SVNICX9XwjcCFyW511DOjf6ZB695l3SAN+3Ap8HniWdl32SNKTaUiLiXaVBwkdKWjkXnx8RL+Su4+GSeuXj+AXwAik3by9Sq9a5ec3MWlGnqUzzBULbVJh3VWE6SAN3N7a9iukW8znR/82PpUg6OyLmSFoTGEsak7S+fMD/IF28VL7tCaTu3HLOzWtm1kY6TWXajtwpqTfpoqIfRcTbbRyPmZnVyJVpKyuOHGNmZsuHFrsAqZBur/Q4r6X2VWU8d+UWYa3bGSbpjcJxXdzE9XtLOrnWOMq2KUkXSXpB0hRJpzbn9s3MrGEt2TL9KN1eexARjd2W0hSXR8TPlnHd3sDJpMHBqyJpxUZucxlKup3nUxGxON9Ha2ZmraRVb42R1EvS85K2yM9HSjoxT8+RdHlOHH9/vt0ESSfmtHwTJd0qaZVcPkIpIf0/Jb1cSMu3rqSHcqtxsqQ9cvlUSWvl6TPzvMmSTs9l/XKr7uocw72Suld5XJViXFvS6Fw+UdJuwMXAJjm+S3Or8tIcyyRJg/K6AyWNkXQH6erfhnwT+GG+8KmU3tDMzFpJS1am3cu6eQdFxCzgFGCEpK8Aq0fE1Xn5VYFxEfEZ4EGWJIy/LSJ2jIhtgCmkVH0l65KuYj2IVEkBDAHuya3ibYAJxaAkbU+63WVnUqahEyVtm2dvBlyRY5gJHF7h2M4oHNd+DcQ4HHgwl28HPENK+/eviBgQEeeQUgyWYt0HuFRLcgFvB5wWEZvn2Jc6loJNgEFKeXf/JmmzCsuZmVkLaPVu3oi4T9KRwBUsfavKYpak07sJuC1PbyXpx6Tu0R6khAglt+fW2LM5CQLAE8C1SgkTbs+3khTtDoyOiLkAkm4D9iAla3ilsPx40r2i9Vmqm1fSnhVi3Bs4Jh/3ImCWpNXriWdknj9d0oOkW2LeB8ZGxCulBRvoNl8ZmBcRO0j6Miln7x4VljUzs2bW6hmQlLL1fJqUEKG8YimK/HcEcEpE9AcuBLoVlplf3DRAzrX7OVIKwBGSjmlCeMXtLaL6HxsNxViLuY0vAqRk/KUfH6OBrZtp/2ZmVoW2SCd4BqkrdAhwXW5BlmIpJYYfAjycp3sCb+Xljmps45I2BKbn7uNrSF2lRWOAQyWtopTi77BcVotKMd5POp+J0rBsvYDZefliPIPy/D6kHwJjm7j/24G98vSepIxIZmbWSlqym7d72Tm+u4HrgBOAnSJitqSHSEON/YDUCttJ0vmkZPGD8noXAI+TUvI9ztIVUX0GAudIWgDMIXezlkTEk5JGsKTCuiYinpLUbxmOsaRSjKcBv5N0PKml+82IeFTSI5ImA38DziUNxTaR1Bo/NyLelvSp8p1ImlChq/di0ogxZ+RjPqGGYzEzsyZqscq0gXR7ny4sc2bZOmeWLxwRVwJX1lM+tOx5j/z3euD6epbvV5i+jCV5dEtlU0nDm5We13vrS0QMa0KM00mDjZeXDykrOic/isvUAXVlZQMqxDSTNGqMmZm1gU4zaoyZmVlLaTeVaallaWZm1tG0m8rUzMyso3Ki+06qe9cuPH9x+z3NWldXx9SjBrZ1GBU5vtosn/F9bGjillVXB4NbeZ9N0d7ja2ZumZqZmdXIlamZmVmNXJmamZnVyJWpmZlZjVyZmpmZ1ciVqZmZWY1cmZqZmdXIlamZmVmNXJmamZnVSBHR+FK23JE0G3i+reNowFrAe20dRAMcX20cX+3ae4zLa3wbRkSf8kKnE+y8no+IHdo6iEokjXN8y87x1aa9xwftP8bOFp+7ec3MzGrkytTMzKxGrkw7r9+1dQCNcHy1cXy1ae/xQfuPsVPF5wuQzMzMauSWqZmZWY1cmZqZmdXIlWknI2l/Sc9LeknSeW0dD4CkayW9I2lyoWwNSfdJejH/Xb2NYltf0gOSnpX0jKTT2lN8OZZuksZKmphjvDCXbyTp8fxej5K0UhvG2EXSU5LubG+x5XimSpokaYKkcbmsPb3HvSX9n6TnJE2RtGt7iU/SFvl1Kz3el3R6e4kvx3hG/t+YLGlk/p9p1s+gK9NORFIX4Argi8CWwGBJW7ZtVACMAPYvKzsPuD8iNgPuz8/bwkLgrIjYEtgF+FZ+zdpLfADzgb0jYhtgALC/pF2AS4DLI2JT4D/A8W0XIqcBUwrP21NsJXtFxIDCvYft6T3+JXB3RHwK2Ib0WraL+CLi+fy6DQC2Bz4ARreX+CStB5wK7BARWwFdgK/Q3J/BiPCjkzyAXYF7Cs+/C3y3rePKsfQDJheePw+sm6fXJSWZaA9x/hn4QjuObxXgSWBnUnaXFet771s5pr6kL9O9gTsBtZfYCjFOBdYqK2sX7zHQC3iFfMFoe4uvLKZ9gUfaU3zAesDrwBqkREV3Avs192fQLdPOpfShKpmWy9qjtSPirTz9NrB2WwYDIKkfsC3wOO0svtyNOgF4B7gP+BcwMyIW5kXa8r3+BXAusDg/X5P2E1tJAPdKGi/ppFzWXt7jjYB3getyV/k1klZtR/EVfQUYmafbRXwR8QbwM+A14C1gFjCeZv4MujK1di/ST8c2vYdLUg/gVuD0iHi/OK89xBcRiyJ1s/UFdgI+1ZbxlEg6CHgnIsa3dSyN2D0itiOdAvmWpM8VZ7bxe7wisB1wZURsC8ylrMu0PXwG8znHLwF/Kp/XlvHlc7WHkH6UfBJYlY+fVqqZK9PO5Q1g/cLzvrmsPZouaV2A/PedtgpEUldSRXpzRNzW3uIrioiZwAOkbqvekkr5t9vqvf4s8CVJU4E/kLp6f9lOYvtIbr0QEe+QzvftRPt5j6cB0yLi8fz8/0iVa3uJr+SLwJMRMT0/by/x7QO8EhHvRsQC4DbS57JZP4OuTDuXJ4DN8lVsK5G6ZO5o45gquQM4Nk8fSzpX2eokCfg9MCUiLivMahfxAUjqI6l3nu5OOqc7hVSpHpEXa5MYI+K7EdE3IvqRPm//iIij2kNsJZJWldSzNE067zeZdvIeR8TbwOuStshFnweepZ3EVzCYJV280H7iew3YRdIq+f+59Po172ewrU9Y+9G6D+AA4AXSObXvtXU8OaaRpHMZC0i/wo8nnVe7H3gR+DuwRhvFtjupe+ppYEJ+HNBe4ssxbg08lWOcDHw/l28MjAVeInW9rdzG7/NA4M72FluOZWJ+PFP6v2hn7/EAYFx+j28HVm9n8a0KzAB6FcraU3wXAs/l/48bgZWb+zPodIJmZmY1cjevmZlZjVyZmpmZ1ciVqZmZWY1cmZqZmdXIlamZmVmNXJmadTL5vtSH8wgahxbK/yzpk/Us/73CiCCLCtOnNmGf/9vI/AGSQlKzZ6Yxaw2+Ncask8mV4L9JmWDuioiBkg4Gto+IYY2sOycieizDPhtcT9IlwG7AyxFxbKXlaiWpS0QsaqntW+fllqlZ57OANLrMysCinFLtdOCn1W4gJ9a/VNITkp6W9PVcvq6kh3LLdbKkPSRdDHTPZTfXsy0BRwJDgS9I6laY9x2lcUYn5u0gaVNJf89lT0raRNJA5bFS8zK/ljQ0T0+VdImkJ4EjJZ2Y454o6VZJq+Tl1pY0OpdPlLSbpB9KOr2w3YuUx7Q1K1qx8UXMbDlzS36cBHwHOBm4MSI+aMI2jgdmRcSOklYGHpF0L/Bl0lBWF+Xxc1eJiDGSTomUiL8+u5Fyp/5LUh1wIHCrpC+SEpTvHBEfSFojL38zcHFEjM4V7wosnXO6PjMiJbJH0poRcXWe/nE+ll8Bw4EHI+KwHHsP4E1SC/4XklYgpUTcqQmvk3USrkzNOpmImEWqsEojapwHHCbpalKaup9HxKONbGZfYGtJpdymvYDNSPmfr82DA9weEROqCGkwKQk++e8xpIEF9gGuK1XyEfHvnEN3vYgYncvm5eNobB+jCtNb5Uq0N6nCvCeX7533Te4KngXMkjRD0rakIcSeiogZVRyTdTKuTM06twuAi0gV2sOkEUluIw2e3BAB346Iez42Iw1fdiAwQtJlEXFDxY2kFuDhwCGSvpe3u2Yp8XwTLGTp01bdyubPLUyPAA6NiIm5K3hgI9u+htQFvQ5wbRPjsk7C50zNOilJmwF9I6KOdA51MSmpf/cqVr8H+GZugSJp8zz6yobA9NyNeg1pqDCABaVly3weeDoi1o+IfhGxIalVehhpkPPjCuc014iI2cC00lXIklbO818FtszPe+ftVtITeCvHc1Sh/H7gm3m7XST1yuWjSeNf7siSVqzZUlyZmnVeFwHfy9MjSRXJE6TxRhtzDWkYqyclTQZ+S+rpGghMlPQUMKiwrd8BT9dzAdJgUmVVdCswOCLuJg3jNU7SBODsPP9o4FRJTwP/BNaJiNeBP5JGBfkjaRSdSi4AHgceIY0kUnIasJekScB4YEuAiPgvabiuP/pKYKvEt8aYmTUgX3j0JHBkRLzY1vFY++SWqZlZBZK2JI13eb8rUmuIW6ZmZmY1csvUzMysRq5MzczMauTK1MzMrEauTM3MzGrkytTMzKxG/x9HTx8NsiinkwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "best_results = [standard_res, flipped_res]\n",
    "best_labels = ['Unaugmented\\nExpansion Factor: 1', 'Flipped\\nExpansion Factor: 2']\n",
    "\n",
    "# Clockwise Rotation\n",
    "b_clock = rot_results[np.argmax(rot_results[:, 1])]\n",
    "best_results.append(b_clock[1:])\n",
    "best_labels.append(f'Clockwise Rotation\\n{b_clock[0]} Degrees\\nExpansion Factor: 2')\n",
    "\n",
    "best_results.append(all_results[0, 1:])\n",
    "best_labels.append(f'All Transforms\\n{all_results[0, 0]} Degrees\\nExpansion Factor: 6')\n",
    "\n",
    "best_results = best_results[::-1]\n",
    "best_labels = best_labels[::-1]\n",
    "\n",
    "plt.barh(range(len(best_labels)), [i[0] for i in best_results], tick_label=best_labels, label='Top-1')\n",
    "plt.barh(range(len(best_labels)), [i[1] - i[0] for i in best_results], tick_label=best_labels, label='Top-5', left=[i[0] for i in best_results])\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(axis='x')\n",
    "plt.title('Best Test Accuracy for Data Augmentations')\n",
    "plt.xlabel('% Test Accuracy')\n",
    "plt.ylabel('Data Augmentation Methods')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "pythonjvsc74a57bd0333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b",
   "display_name": "Python 3.9.3  ('.venv': venv)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.3"
  },
  "metadata": {
   "interpreter": {
    "hash": "333605e348ea7c6bf4ca805dbc845da062650cb5bf1d8f33f5f4a9d3bca7d68b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}