Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb

      "Epoch 9/30\n",
      "15/15 [==============================] - 1s 47ms/step - loss: 4.1241 - accuracy: 0.8542 - val_loss: 23.3027 - val_accuracy: 0.5375\n",
      "Epoch 10/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 2.8569 - accuracy: 0.8958 - val_loss: 28.1831 - val_accuracy: 0.5000\n",
      "Epoch 11/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.8484 - accuracy: 0.9312 - val_loss: 27.4439 - val_accuracy: 0.4500\n",
      "Epoch 12/30\n",
      "15/15 [==============================] - 1s 36ms/step - loss: 2.2897 - accuracy: 0.9292 - val_loss: 25.6774 - val_accuracy: 0.5375\n",
      "Epoch 13/30\n",
      "15/15 [==============================] - 1s 36ms/step - loss: 2.3126 - accuracy: 0.9271 - val_loss: 31.6493 - val_accuracy: 0.4750\n",
      "Epoch 14/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.7738 - accuracy: 0.9292 - val_loss: 25.8194 - val_accuracy: 0.5375\n",
      "Epoch 15/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.2570 - accuracy: 0.9458 - val_loss: 25.7935 - val_accuracy: 0.5750\n",
      "Epoch 16/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 2.5836 - accuracy: 0.9167 - val_loss: 29.3070 - val_accuracy: 0.5125\n",
      "Epoch 17/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 1.7006 - accuracy: 0.9563 - val_loss: 35.8275 - val_accuracy: 0.5000\n",
      "Epoch 18/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.7990 - accuracy: 0.9396 - val_loss: 26.9765 - val_accuracy: 0.5500\n",
      "Epoch 19/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.9373 - accuracy: 0.9646 - val_loss: 32.0846 - val_accuracy: 0.5125\n",
      "Epoch 20/30\n",
      "15/15 [==============================] - 0s 34ms/step - loss: 2.0872 - accuracy: 0.9500 - val_loss: 30.0126 - val_accuracy: 0.5875\n",
      "Epoch 21/30\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 1.4677 - accuracy: 0.9604 - val_loss: 32.2186 - val_accuracy: 0.5500\n",
      "Epoch 22/30\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 1.6923 - accuracy: 0.9438 - val_loss: 32.9531 - val_accuracy: 0.5125\n",
      "Epoch 23/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 1.2237 - accuracy: 0.9542 - val_loss: 44.3842 - val_accuracy: 0.4375\n",
      "Epoch 24/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 0.7080 - accuracy: 0.9833 - val_loss: 32.2801 - val_accuracy: 0.5250\n",
      "Epoch 25/30\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 1.4296 - accuracy: 0.9667 - val_loss: 34.0057 - val_accuracy: 0.5125\n",
      "Epoch 26/30\n",
      "15/15 [==============================] - 1s 39ms/step - loss: 1.3958 - accuracy: 0.9542 - val_loss: 39.1364 - val_accuracy: 0.5500\n",
      "Epoch 27/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 1.0060 - accuracy: 0.9604 - val_loss: 33.8739 - val_accuracy: 0.4875\n",
      "Epoch 28/30\n",
      "15/15 [==============================] - 0s 34ms/step - loss: 0.8546 - accuracy: 0.9563 - val_loss: 33.2485 - val_accuracy: 0.4750\n",
      "Epoch 29/30\n",
      "15/15 [==============================] - 0s 33ms/step - loss: 0.9944 - accuracy: 0.9563 - val_loss: 35.6355 - val_accuracy: 0.5125\n",
      "Epoch 30/30\n",
      "15/15 [==============================] - 1s 35ms/step - loss: 0.8821 - accuracy: 0.9604 - val_loss: 36.2544 - val_accuracy: 0.5375\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])\n",
    "\n",
    "\n",
    "logdir = os.path.join(\"logs_feature_extraction\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "train_features, train_labels,\n",
    "epochs=30,\n",
    "validation_data=(val_features, val_labels),\n",
    "callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we’ll also use a `ModelCheckpoint` callback to save the model after each\n",
    "epoch. We’ll configure it with the path specifying where to save the file, as well as the\n",
    "arguments `save_best_only=True` and `monitor=\"val_loss\"`: they tell the callback to\n",
    "only save a new file (overwriting any previous one) when the current value of the\n",
    "`val_loss` metric is lower than at any previous time during training. This guarantees\n",
    "that your saved file will always contain the state of the model corresponding to its bestperforming\n",
    "training epoch, in terms of its performance on the validation data. As a\n",
    "result, we won’t have to retrain a new model for a lower number of epochs if we start\n",
    "overfitting: we can just reload our saved file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s look at the loss and accuracy curves during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FcZoIVJ2jyUB5XX5RMb/4aB1rDqTx7LU9aN005IyDdOkDd1ZeIQeO55CVZx8/mV/xRB8RCPATCosrvgIO9BeahgZyUz+bBKpSxyY4wJ//jO1Lu2ZhvLp4D4cy8s6pqfPD7lT++Pk2hndtzh+u6e7yvpWqLZoMlFcVFTuYNHMj3+9K5Z8392bswHZVfv7J/KKfz/jLOPsvKHKcvpoId149NAkJpOnp+wIJCfSrUXOUiPDkFV2JjQzld/O2MmbKitPVNnenZPPI9PV0btGYV8f1I6Ca9XaU8iRNBsprHA7Dr+du5n/bjvLHa3tUOREABPj7EREWVK3yDZ4wdmA7WjcN5ZHp67nh9R95Ycz5/G7eFoID/Hln/MAqVeBUqjbpKYryipJ6PJ9uOMSTl3fhvos7eDsktxnapTlzHx6Cnwh3vbOaY1n5vH1PfIUlIpTyNk0GqtYZY/jHNzuYviqRh4Z15NFLz/N2SG7XrVUTPpt4EaN6tuL12/vrGr2qztNmIlXrXl28h7eW7eOuC9rzzKhuDXZGassmIUy5a4C3w1DKJR69MhCRUSKyU0T2iMgzZTzeXkS+E5HNIrJURGI9GY/yvneW7+elhbu4uX8sz13fs8EmAqXqG48lAxHxB14HrgJ6AONEpMdZm70AfGiM6QM8D/zdU/Eo75u1OpE/f5nAVb1a8c+bezf4KpFK1SeevDIYBOwxxuwzxhQAs4DRZ23TA1js/HlJGY+rBuLzjYf47bwtDO/anJdv0+GVStU1nvyPjAGSSt1Odt5X2ibgJufPNwLhIhJ19o5EZIKIrBWRtampqR4JVlnppwpwONxbomRhQgq/mrOJQXHNmHLnAC1brFQd5O3/yqeAYSKyARgGHAKKz97IGDPVGBNvjIlv3rx5bcfoM/alnuSify7mkenr3ZYQNiVlMHHGenrFNOWd8QPPqNmvlKo7PJkMDgFtS92Odd53mjHmsDHmJmNMP+D3zvsyPBiTKkexw/DUx5vsWrnbjvLakj013uex7DwemraO5o2DeW/8wDOWTVRK1S2eTAZrgM4i0kFEgoDbgPmlNxCRaBEpieG3wLsejEdV4J3l+1ifmMH/3dKHG/vF8NLCXSxMSKn2/gqKHDzy0XoycguYeveAcpdtVErVDR5LBsaYIuBR4FtgOzDHGLNNRJ4Xkeudmw0HdorILqAl8FdPxaPKtzslmxcW7OKKHi25oW8Mf7+pN31im/LE7I3sOZZdrX1O/mIbaw+m869bzqdnm6Zujlgp5W66noGPKyp2cPObP5GYlsOCJ4bRPNyusXs4I5frX1tOeEggn028iKahrtfUmb7qIL+ft5VfDOvEM1d181ToSqkqqGw9A293ICsve2vZPjYlZ/LnG3qdTgQAbSJCeeOOASSl5fD4rA0Uu9ihvOZAGpPn21LNT1/Z1VNhK6XcTJOBD9txNIv/LNrFNb1bc22fc5dsHNShGZOv78mSnam8uGBnpfs7kpnLwx+tJzYyjJdv61cn1+ZVSpVNh3f4qMJiB099vIkmIYE8P7pnudvdMbgd2w5n8sbSvfRo06TMpAGQV1jMQ9PWkVtQxMwHB1epWUkp5X16ZeCj3liyl62Hsvjrjb2Iahxc7nYiwuTrezKgfSRPf7yZhMNZ52xjjOF387awOTmTf4/tS+eW4Z4MXSnlAZoMfNC2w5m8ung3o/u2YVSv1pVuHxzgz5t39KdJaAATpq0l/VTBGY+/++MBPl1/iMdHduaKnq08FbZSyoM0GfiYgiIHT87ZRGSjICZfV37z0NlaNAnhrbviOZaVz8QZ6ykqdgDw457j/O3r7VzRoyWPXdrZU2ErpTxMk4GPeW3xbnYczeZvN/YmsooTwfq2jeCvN/bip70n+Ps3O0hKy2HijPV0jG7ES2P7ahVSpeox7UD2IVuSM3l96V5u6h/D5T1aVmsfY+Lbsu1wFu8s38+Xmw/jcBj+e3e8lppQqp7TKwMfkV9UzJMfbyS6cRB/utb15qGy/P6a7gzpGEVqdj6vjOtHXHQjN0WplPIWPZ3zEf9ZtJtdKSd5796BNA2r2bDPQH8/3h0/kOT0HB05pFQDoVcGPuCnPcd56/u9jI1vy4iuLdyyz9Agf00ESjUgemXQgDkchinL9vLigl3ERTXi99d293ZISqk6SpNBA3X8ZD6/mrOJZbtSuaZPa/5+U2+ahOisYKVU2TQZNEAr9p5g0qwNZOQW8tcbe3H7oHaI6LBPpVT5NBk0IMUOw6uLd/PKd7uJi27E+/cOokebJt4OSylVD2gyaCCOZeUxadZGVuw7wU39YvjzDb1opGP/lVIu0qNFHZRTUERooL/LTTvLdqXyxOyNnCoo4v9u6cOYAbHaLKSUqhJNBnVI+qkCnp67iUXbj9E4OIDYyFBiI0OJiQglNjLM/hxpf44MC6TYYfj3ol28sXQvnVs0ZtbtF+hwT6VUtWgyqCPWHkjjlzM3cOJkAQ9e0oHCYkNyei7J6Tms2pdGdn7RGduHBfnTODiAY9n53DawLX+6riehQf5eil4pVd9pMvAyh8Pw5vd7eWnhLmIjQ/nk4QvpHXvuAvKZuYUkp+c4E0Quh9JzScnK48perbj+/LIXnFFKKVdpMvCi4yfzeWL2Rn7YfZxrnXMBwsuZC9A0NJCmoU3p2ebcRKGUUjWlycBLSuYCZOYW8rcbezNuUFvt9FVKeY1HaxOJyCgR2Skie0TkmTIebyciS0Rkg4hsFpGrPRlPXVDsMPxn0S7ueHsljUMC+GziRdw+WCeFKaW8y2NXBiLiD7wOXA4kA2tEZL4xJqHUZn8A5hhj3hSRHsDXQJynYvI2nQuglKqrPHkkGgTsMcbsAxCRWcBooHQyMEDJFNmmwGEPxuNVP+xO5fFZG8kpKOZft/RhTHxbb4eklFKneTIZxABJpW4nA4PP2mYysEBEfgk0AkaWtSMRmQBMAGjXrp3bA/W0tFMFPPDBWtpHhTHr9v46F0ApVed4ez2DccD7xphY4GpgmoicE5MxZqoxJt4YE9+8efNaD7Km5q5LIr/IwWuaCJRSdZQnk8EhoHRbSKzzvtLuB+YAGGNWACFAtAdjqnXGGGauTiK+fSRdNBEopeooTyaDNUBnEekgIkHAbcD8s7ZJBC4DEJHu2GSQ6sGYat2KvSfYf/wUtw+uf81bSinf4bFkYIwpAh4FvgW2Y0cNbROR50XkeudmTwIPisgmYCYw3hhjPBWTN0xflUhEWCBX927t7VCUUqpcHh3XaIz5GjtctPR9fyz1cwJwkSdj8KbU7Hy+3XaUey6MIyRQ6wYppeoub3cgN2gfr0uiyGEYN0ibiJRSdZsmAw9xOAwzVycyuEMzzmvR2NvhKKVUhTQZeMgPe46TlJbLHRe093YoSilVKU0GHjJj1UGaNQriyp4tvR2KUkpVSpOBB6Rk5bFo+zHGDIglOEA7jpVSdZ8mAw+YsyaJYu04VkrVI5oM3KzY2XF88XnRxEU38nY4SinlEk0Gbvb9rmMczszTGcdKqXpFk4GbzViVSHTjYC7voR3HSqn6Q5OBGx3OyGXxjmOMHRhLoL/+apVS9Ycesdxo1pokDHDbQG0iUkrVL5oM3KSo2MHsNYkM7dycts3CvB2OUkpViSYDN1m84xgpWfncoR3HSql6SJOBm0xflUirJiFc2q2Ft0NRSqkq02TgBklpOSzbncqtA9sSoB3HSql6SI9cbjBrTSIC3DawbaXbKqVUXeRSMhCRT0XkmrIWq/d1hcUOZq9J5tJuLWgTEertcJRSqlpcPbi/AdwO7BaRf4hIVw/GVK8sTEjh+Ml8nXGslKrXXEoGxphFxpg7gP7AAWCRiPwkIveKSKAnA6zrZqxKJCYilGFdtONYKVV/udzsIyJRwHjgAWAD8DI2OSz0SGT1wIHjp1i+5zhjB7bF30+8HY5SSlVbgCsbicg8oCswDbjOGHPE+dBsEVnrqeDquk/XJ+MnMFY7jpVS9ZxLyQB4xRizpKwHjDHxboynXlmQkMLAuGa0bBLi7VCUUqpGXG0m6iEiESU3RCRSRB7xTEj1Q+KJHHYczdbqpEqpBsHVZPCgMSaj5IYxJh14sLInicgoEdkpIntE5JkyHv+3iGx0fu0SkYwydlMnLUg4CsAVPVp5ORKllKo5V5uJ/EVEjDEGQET8gaCKnuDc5nXgciAZWCMi840xCSXbGGOeKLX9L4F+VYzfaxYkpNCtVTjtorQonVKq/nP1yuB/2M7iy0TkMmCm876KDAL2GGP2GWMKgFnA6Aq2H+fcb52XdqqAtQfSuEKbiJRSDYSrVwa/AR4CHnbeXgi8XclzYoCkUreTgcFlbSgi7YEOwOJyHp8ATABo1877k7u+256Cw8Dl2kSklGogXEoGxhgH8KbzyxNuA+YaY4rLef2pwFSA+Ph446EYXLYwIYXWTUPoFdPE26EopZRbuFqbqLOIzBWRBBHZV/JVydMOAaUH4Mc67yvLbdSTJqLcgmKW7U7l8h4tEdGJZkqphsHVPoP3sFcFRcAI4EPgo0qeswboLCIdRCQIe8Cff/ZGItINiARWuBq0Ny3fc5y8QocOKVVKNSiuJoNQY8x3gBhjDhpjJgPXVPQEY0wR8CjwLbAdmGOM2SYiz4vI9aU2vQ2YVTJSqa5bsO0o4SEBDO4Q5e1QlFLKbVztQM53lq/eLSKPYpt7Glf2JGPM18DXZ933x7NuT3YxBq8rdhi+23GMEV1bEBSg1byVUg2Hq0e0SUAY8BgwALgTuMdTQdVV6w6mk3aqgCt6ahORUqphqfTKwDl5bKwx5ingJHCvx6OqoxYmHCXQXxjWpbm3Q1FKKbeq9MrAOdzz4lqIpU4zxrAgIYULO0UTHuLTSzgopRogV/sMNojIfOBj4FTJncaYTz0SVR20+9hJDp7I4cFLOno7FKWUcjtXk0EIcAK4tNR9BvCZZLBgmy1Mp0NKlVINkaszkH22n6DEwoQUzm8boWsXKKUaJFdXOnsPeyVwBmPMfW6PqA46mpnHpuRMnr6yq7dDUUopj3C1mejLUj+HADcCh90fTt20cHsKgFYpVUo1WK42E31S+raIzASWeySiOmjBtqN0iG7EeS0qnWenlFL1UnWn0XYGWrgzkLoqK6+QlftOaGE6pVSD5mqfQTZn9hkcxa5x0OAt3ZlKYbHRJiKlVIPmajNRuKcDqasWJqQQ1SiIfu0ivR2KUkp5jKvrGdwoIk1L3Y4QkRs8FlUdUVDkYOmOY4zs3hJ/P20iUko1XK72GfzJGJNZcsMYkwH8ySMR1SEr950gO79IJ5oppRo8V5NBWdu5Oiy13lqQcJTQQH8u7hzt7VCUUsqjXE0Ga0XkJRHp5Px6CVjnycC8zeEwLEo4xtAu0YQE+ns7HKWU8ihXk8EvgQJgNjALyAMmeiqoumDLoUyOZuVxeY9W3g5FKaU8ztXRRKeAZzwcS52yMCEFP4HLuvnEdAqllI9zdTTRQhGJKHU7UkS+9VhUdcCChKMMjGtGZKMgb4eilFIe52ozUbRzBBEAxph0GvAM5APHT7Er5SRX9NQmIqWUb3A1GThEpF3JDRGJo4wqpg3FwgQtTKeU8i2uDg/9PbBcRL4HBLgEmOCxqLxsYUIK3VqF07ZZmLdDUUqpWuHSlYEx5n9APLATmAk8CeRW9jwRGSUiO0Vkj4iU2QEtIreKSIKIbBORGVWI3SOKih2sS0xnWFdd9F4p5TtcLVT3ADAJiAU2AhcAKzhzGcyzn+MPvA5cDiQDa0RkvjEmodQ2nYHfAhcZY9JFxOv9EEcy8yh2GDpGN/J2KEopVWtc7TOYBAwEDhpjRgD9gIxKnjMI2GOM2WeMKcDOTxh91jYPAq87O6QxxhxzNXBPSUzLAaBtpDYRKaV8h6vJIM8YkwcgIsHGmB1AZWtAxgBJpW4nO+8rrQvQRUR+FJGVIjKqrB2JyAQRWSsia1NTU10MuXqSSpKB9hcopXyIqx3Iyc55Bp8BC0UkHTjoptfvDAzHNkEtE5HepYexAhhjpgJTAeLj4z06iikpPQd/P6F1U134XinlO1ydgXyj88fJIrIEaAr8r5KnHQLalrod67yvtGRglTGmENgvIruwyWGNK3F5QmJaLm0iQgjwr+4icEopVf9U+YhnjPneGDPf2Q9QkTVAZxHpICJBwG3A/LO2+Qx7VYCIRGObjfZVNSZ3SkrLoZ02ESlfkpcFU4fDqre8HYnyIo+d/hpjioBHgW+B7cAcY8w2EXleRK53bvYtcEJEEoAlwNPGmBOeiskVyek52nmsfMuGaXB4A3zza9g8x9vRKC/x6JoExpivga/Puu+PpX42wK+cX153Kr+I4ycLtPNY+Y7iIlg5BdoOBv8g+OwRaBQNncodNa4aKG0YLyU53c6j02SgKuVweDsC99g+HzIT4aJJcNt0aN4VZt8Fhzd6OzJVyzQZlHJ6WGlkqJcjUXWWMfDlr+DVfpCXWfn2dZkxsOI1aNYJulwFIU3hjrkQGgnTb4G0/d6OUNUiTQalJOocA1WZJX+Dte9A+gH46VVvR1MziSvh0DoY8gj4OQ8FTVrDnZ+Aowg+uglOenZej6o7NBmUkpSeQ1iQP1G6hoEqy5p3YNn/Qb+7oNfNsOJ1yD7q7aiqb8Vr9irg/NvPvL95V7h9DmQdgRm3Qv5J78SnapUmg1KS0nJpGxmGiHg7FFXXbP8Cvn4KuoyCa/8Dl/4Bigvg+396O7LqObEXdnwF8fdDUBlXwm0HwS3vwpGN8PF4KC6s7QhVLdNkUEpSWg5tm2l/gTrLwRUw936IGQC3vAf+AdCsI8TfB+s+gON7vB1h1a18E/wDYVAFlei7XQ3X/hv2LIT5j9k+BtVgaTJwMsaQlJ6j/QXqTMe2w8yxENEWxs0+8yx66NMQEAKL/+y9+KojJw02Tofet0J4JQs4DRgPw38Lm2bAd8/XSnjKOzQZOKWdKiCnoFgnnKmfZR6Cj262B/w7P4VGUWc+3rgFXPhLSPgMktd5JcRqWfsuFObYjmNXDPuNTQrLX4JVUz0amvIeTQZOOpJInSE33SaCvCw73DKyfdnbXfgohEXDoj/VXjNKRmL12/CL8mH1VDuprGVP154jAle/CF2vsbOUt31Wvdeuq9IPNpx5IzWgycApyTnhTOsSKQrzYObtcGKPnYjVuk/52waH2zPnAz/A3u88H1vyWni5L8wcV72EsGUunEyBIY9W7Xn+AXDLO7Zj+dMH4cDyqr92XbRlLrzcp/419XmAJgOnkglnsTrhzLc5iuHTByDxJ7hxCnQcVvlzBoyHyDhYONmzZ5iFufDZwxDUuHqdusbY4bAtelSv3ERgKIybBZEdbLJM2Vb1fdQl+5bCvF+AfzCsfAOyDns7Iq/SZOCUnJ5DVKMgGgV7tFyTqsuMsc0g27+AK/8OvW9x7XkBQXDps5CyBbbO9Vx8S/4Kx3fBre/DsGdsp25Vzmj3LYFj22DIRNv0Ux1hzeyktKAw+OgWyEiq/Dl10ZFNMOtOiO4CDyyyJwFL/+7tqLxKk4FTYpqOJKqS3AxI8tqyE7bDNi/Lvfv84UVY87btFHa1c7VEz5uglbO5oSjfvXEBJK6Cn16DAffas/rhz0D/e2zMrnbq/vQaNG4JvcfULJaItjYhFJyy/So5aTXbX21LP2ATWUhTuHOubQYc+ABs+AhSd3o7urIZY4cx52Z47CU0GTglpeVqMqiKH/8D74yETbNq/7Uzkuxrf/m4+/Z5ZDMs/gv0ugVGVmMIpZ8fjJxsO3fXvue+uAAKcmzzUERbuMJ5JSAC17wEXa92rVM3JcH2aQx6EAKCax5Ty54wbgak74eZt9kmrPrg1HGYdpOdMHjXp9Ckjb1/6FMQ2KhuDp/NTYfZd8IXj8G69z32MpoMgKJiB4czcrVAXVUcXGG/fz4R9tRCx2lpm2aCccDWT9xXXXPRZHumeM2LP9fpqapOl0KHYbZkhTuvWhb/GdL2wujXbYd1Cf8AuPkdiB0In06AAz+Wv4+Vr0NAqJ1x7C5xF8NN/4Wk1XZSXnGR+/btCQWnbHmNrENw+2xbdqNEo2hbuXXHl/b91BVJa2DKUNj1P7jirzZGD9FkABzJzKPIYfTKwFVFBXYxlP53Q/PutuTxofW189oOh72cjx0Ioc3sQbym9n1vz5qHPgWhEdXfj4i9Osg54b4idgd+tLOFB02ADkPPfTwozB7YItvbEUZldepmp9hFa/rebtv83annDXDVP2HnV7ZcR12dpVxcaMtqHN5gy2y0u+DcbYY8Ao1awMJaHCZcHocDfnwF3hsFAty3wA5j9mCpHE0G2AJ1oMNKXXZ0CxTnQ6fLbJtrWJQ940qrhRVLD/wAGQdh0EP24L1vCexdUv39GWPnCDSJhYEP1jy+mP7Q80ZbBC47pWb7yj8Jnz9iD/QjJ5e/XUmnbmBo2Z26a/5rD4ZDJtYsnvIMfggufgLWvQfL/uWZ1yiRMN+eeFR1FNWXj8PuBfbKr9s1ZW8X1AiG/8aOJNv1rVvCrZZTJ2zT28JnoetV8NAPEDvA4y+ryQBITnMuaqOzj12TtMp+bzsYwlvZtldHsW2L9XTJ4w0f2eac7tfaTr+m7ezVQXWHdCZ8Zs8WR/wOAkPcE+Olz9o26WX/V7P9LJpsJ0Td8KY9UFUkop2zU/fkmZ26BTm22mrXqyGqU83iqchlf4Lzx9kRT+s+8MxrHN4Ac+6C/46ANy6A5f+xlVUrs+Sv9u9m2G9sPamK9L/H1p1aNNn+Tde2gz/BlIvtSc7VL8Ct02p2tVoFmgywI4n8BFpHuOlg0NAlr4ambW3te4DozrbkcfZRmDHGcyWPczPsyly9x9iz4IBguPT3trJmwryq76+40HYYNu8O59/mvjijOtm5B+vet9VBq2Pf9/aM/oKHof2Frj2nVS87SS59v20yKsy1w09z0zx3VVBCBK5/Fc5zduzv/Mb9r7F+mi0NctW/ILiJvaL7dw+b/LbMLbsTe83b9mql/922xlJl/APhsj9C6vbaHRzhcMCyF+D9a+1JyQOLbGd/LVZQ1mSAbSZqExFKoL/+OlyStNrORC2t7UAY874dlTPnbs+UPN46F4ry7HoCJXqPgRY94bs/276Mqlj/oW3aGjkZ/PzdGipDf23XFK7OzNb8bPj8UbsC2aXPVu25HYbCjW/Zq7dPHoAVb0Cbfq4nlJrwD4QxH0Dr8+Hje93bEVuYaw/43a+HwRPggYXw6Dq4+FdwbAd8cj+80NVOxEtcaZuGEubDV0/ZVdyu+bfrB9YeN0Cb/nYho8I8972H8pw8ZhcSWvxn28T40DL7O6xlevTDWbq6oTYRFeXbf4r1H7pnf5mH7GiM2EHnPtZ1lC15vPc7mP9L93fCbfgIWvY+8x/Fz98ezNP3w/oqNE/kn4Sl/4B2Q6DLle6NE2w10CGPwrZ59gyzKolqwbOQlexsHqrG32Wvm2DUP+zImLS9No7aOsMMbgy3f2yvGmfcWv0ro7Nt/xLyM6F/qROB6PPgsmfh8S1w93zbvr7lY3j3Sni1v02GsQNth7F/FSaTlgwEyEq2V2eelLjSNgslroDrXoGb3z5zxFgt0mQAJKblNqx1DIyxyxl+9RS82NW2s87/pR0DX1PJzrO9tgPLfnzAPTDi93b453fP1fz1ShzdatuM+9157oGt8+XQ/mK70IyrTVQr34RTx2Dkc547UF74S4g6D+Y9ZD+Hr5+276GiJLnnO9sRO+RRaDe4+q99wS/s59BhqD3TrU2Nm9sqr8VFtr3eHTZ8CBHt7ed8Nj8/WzbkprfgqV0w+g1oEmObzW6fXb2E2nGYHSCx7AXPTfQ6ugWmj7HlRR5cYv93vLiwlkeTgYiMEpGdIrJHRJ4p4/HxIpIqIhudXw94Mp6y5BYUc/xkfsMYSZR1GJb/G14fDP+9FDZMs2Pfr3nJPl6TUTclklbb8eqtKijeNvRpO1N2+b9h5ZSavybYqwL/IOhz67mPicDlz8GpVFt7pzKnTsCPL0O3a2t2wK1MSBN4ZJU9U+44zHasTh0Obwyxr3/2kpl5mbaZI7qLPZDX1LBfwz1fVO2s2F2adbAHt22f1bxkRfoB2L/MNg9WNgckOBz63QHjv4QHF9dsKO3IyZCXYSdYulv6QTvyKzgc7pkPLXu4/zWqyGPJQET8gdeBq4AewDgRKesdzzbG9HV+ve2peMqTnF7PS1cX5MDmj2HajfDvnnYURGgEXPcyPLnTXiLH3wfhre0IhZpKWm3boP0Dy99GxDmE71r43zOw9dOavWZRPmyeZYcElvfPHRsP3a+Dn16xs0wr8sMLUHjKdhR6mn8AdLnC9qc8tdM2owWHw8I/wkvdz+z8/PZ3kH0YbpjivpFN3jT4F/b7qhqeEGycAQj0HVfjkKqkdR+7ANDKKe4tYpeTZj/3olw7AqxprPv2XQOevDIYBOwxxuwzxhQAs4DRHny9akk8Xa20GskgJQG++Y09a6nteuj52bbZ4cWutsrm8d1wyZPwy/Vw/wI7mqVkSJoIdBxuR6jUJM7CPFvgq7wmotL8/G37Z9vBtpnk4E/Vf92dX9sp+aU7jsty2Z/sQbWise7pB+0Ik753nDkDtTaERtrEXGbnZxd79XPR47UyprxWRLS1HaLrP6z+jGxHMWyYbq9wvXHQvPT34Ciy/UvuUJBj+1IyEm0F2Bbd3bNfN/BkMogBSl8fJjvvO9vNIrJZROaKSNuydiQiE0RkrYisTU117zj2ktLV1WomWvo3e9bzwXXw8vm2to27OswqcmSzbW5Y87YdP373fJi02S7SXt5Y8o4j7BDDo5tq8LobwVFoD/CuCAyFcTPtP/GnE6p/QNjwkZ0U1nF4xdtFd7YdjGvegbT9ZW+z5G8gfq4NM/Skczo/r7aLxww/pzW1fhsyEfKzqj+AYd9S25Hbv5ITAU+JjIOB99sm19RdNdtXcRHMvc+uSXHz27UzwqsKvN2B/AUQZ4zpAywEyhwOYoyZaoyJN8bEN2/e3K0BJKXnEhroT3TjoKo98WSqHUs98AG46W37z73sBTuK4Z0r7RjzvEy3xooxNgG8PdJ2lN4933aadRxWeVtqyYG0Jv0GJUMFyxpJVJ6wZrbZIzPZzqisqsxk26na93bXhn8Oewb8AsruuDy6BTbPts0XTcs6L/GC0p2f42a4p4hcXRLTH9pfZE+aqlO7aMM0e0XV9Wr3x+aqoU87i9jVYECEMfDVE7DrG7jmBehxvfvicxNPJoNDQOkz/VjnfacZY04YY0rq/b4N1Pr1cWJaDrGRoUhVe/E3z7aXjwMfgD5j4K558MQ221SRmwZfTLKX/nPvhz2Laj6bMS/T1lb56knocAk8/KP97qrwlnY8/r6l1Y8haZVd2KRxFRNyu8G2rsq696te1G7jTMDYTkFXNGltJ2pt+dg2aZW26DnbqXvx41WLQdXMkEchM8nO9q6KnDTY8RX0GevdJNkoGi56zFnErppl25f+w14dXfKUPWbUQZ5MBmuAziLSQUSCgNuA+aU3EJHWpW5eD2z3YDxlSkrLqXoTkTH2jCUm/sw2v6YxcMmvYOJqeGCxHQa5Z5HtLPp3T9tpeGxH1YM8tA6mXGIXXRk52Y5OaRRd9f10GmHHNVen3LAxkLzm3MlmrhrxeztKZv4vXb9icjjs77nDUHu57qqLJtmzyUWlzuT2/2BXB7vkSfuYqj1dRtkhtiteq9rcky0f27Ie/e70XGyuusBZxO6Lx2yCqsq8kbXvwvf/sO/j0j94LsYa8lgyMMYUAY8C32IP8nOMMdtE5HkRKblGekxEtonIJuAxYLyn4iknRpLTq7GOwaF1kLqj/HZMEdsJeM2LdtzzrR9C6752cZE3BsPUEbD6v5UvCmKMnUH6zpX2yuLeb2xBsOqWWO44whaYq05nbkaiXTs31oXO47IEhtpJVNlH7KgZVxxcbovS9bu7aq8VGmHPwPZ+Z6+ETheji7HVP1Xt8vOzB9PDG+zkKlcYY8tPtO4LrXp7NDyXBDe2I8FOHYdZt8NL3ezgkcMbK05wO76yV/Odr4Br/+PVeQSV8WifgTHma2NMF2NMJ2PMX533/dEYM9/582+NMT2NMecbY0YYY6px2lx9GTmFnMwvqvq6xxum2bH2PW+qfNuAYOgxGm6fBU/ugCv/Zks1fP2UbUaafSfs+Prc8g05afaP7tvf2klVv/ih5mPi2w+xY/WrM8S0pL/A1c7jssTG29EyGz6CXQsq337DRxDsLEpXVQMfsPWTFk2GhM9tAh/xO5uUVO07f5wtOf7Ta65tf2STXUa0LlwVlOh+Lfxqu63DFXexPeOfOgzevNCWmz573kjiStth3KafHVpc0XDsOsCnF/xNrM5IooJTsOUTW8c9pEnVXrBxCzu6YshE25m5cabte9j+BYRF2wlV54+z9Xfm3mf/uK78u20Dd8cZRVAjezDfu7Tqz01ebTvRWtRwcszwZ2zH+xePwSMrym+yyc2wB/G+d1TvAB4YYg/+nz1s6/w072Z/t8o7gsLsqJxlL9gRd5VVUN3gLEpX0yU63c0/wJYv6XKlPWHb9qn9P174rL36PG+k/TuL6gQzxtqRdLd/XHnV2TrA26OJvCqpOhPOEuZDQXblY94r06o3jPqbvVoYN8sOM1vzNrx1CbxzuR3+eP+3dsENd15adhxuz7hOHqva85JW2aavms5mDQiGG9+0r/+/CoZ3bv3EJsWaDCnsM9Ymr4Js27Hv7mJ0qmoGPmjPjiubJV6Ya/sLul9Xa+WbqyWsmb0CffA7mLjGXvUe3Qpz74W3htq/9Ts/gUZR3o7UJb6dDErWMahKMtjwka137q4xwv6BtsDW2Gl2xvDVL9j27oeWQYwHBld1GmG/7/ve9ecUnLJ/5FUZUlqRNv1sR+6mmbaJrCwbpkHLXrbNuLr8/O2yjJc/b3/HyrvCW9oZvRtnVNxftuMrO8igpidctal5Fxj5J3hiqx1ZOPBBW5+pKgMfvMynk0FiWg7NGgXRONjFs90Te22nZlnF0twhrJmtYX7Zs547I2rdF0IiqtZvcHgDmOLqjyQqy9CnbQXSLyade2CoqChdVbXqZUcX1eGOO58yZKItw7D2nfK3Wf+hXawnrgpDp+sKP39nPbAX7N9ePeJbyeCs6oPJ6Tm0rUrn8cbptvmmPrc9+/nbSU57l7g+zK9kZbPqjiQqS0CQbS7KTbNlNUrbON1ZlG6s+15P1Q0te9hqoKv/a2tOnS39IOz/HvreWf1Rc6pafOe3veINeG3gGUXMktJyiHW1iai4yF7enjcSmrTxUJC1pOMIWxDt+G7Xtk9aA1Gd3b+YeqvedinCrXNtZzHYA8SmWXbGqbtfT9UNFz5qhylv+fjcx04Xpbu91sPydb6TDDoOt8XOvnoSgGKH4VBGruuL2uxdbMfI16d2zPKc7jdwoanIGDuSyJ1NRKVd/IRdrObLX9lEvfMbe7XgrVo0yvM6jrCz4Ve8fubVqaPYXhV2GmGL3Kla5TvJoGUPGPFbOyV+66cczcqjsNi4Pqx0wzQIi7KzKeu7yDj75UqdorR9kHPCc8nAP9DWLsrPsol6wzQ7OazjCM+8nvI+Edt3cCzBnmSV2P+9LVtRl+YW+BDfSQYAF06yI3S+epKjhw4CuLbCWckZa5/bbFt3Q9BxBBxYXvlaxdUpTldVLXvYKqIJn9nyHa4WpVP1V+9boHFLW6KixHpnUbpu1ZhkqGrMt5KBf4AtiVBwipY//A4wrjUTbZ5tSzc3pDOWTiPs+PvktRVvl7QKgpvYSVuedOFjPw+l7etiUTpVfwUE29IgexdDyjZnUbov7dDThla5tZ7wrWQAdkGTS/9A7NHvuNH/R9pEVHJlUFIjJWZAnViazm06DLUjoyrrN0heY8tIeHpkh38AjJ1uJ+A16+DZ11J1Q/x9tqzLitftam91pSidj/K9ZAAwZCL7Q3vyXOCHBOWkVLztofWQur1hdByXFhppJ39V1G+Ql2XbdT3ZRFRak9Y6OcyXhDWzpck3z7HrHbQ+3y41qbzCN5OBnz8vhj1OCAXw5eMVj7cvKUrXy4WidPVNxxG2gFt5JaUPrQPj8FznsVIXPGLXBUnb2/BOuOoZny1UtzqrGd+0msDoXa/ZsghljWsuyLE1cnqMhpCmtR+kp3UaYReHP7DcLjZ/tuQ1gNhmIqU8IaqT/dvbswh63eyxlyksLCQ5OZm8vDyPvUZdERISQmxsLIGBVauS6pPJIK+wmGPZ+RwcfDcErYdvnoEOw85dCnH7fDvksaGOeY8dBIFhtqmorGSQtNou3tMQE6GqO657xa5z7MFJhsnJyYSHhxMXF1f1VQ3rEWMMJ06cIDk5mQ4dqtb35pPNRMkl1UqjGsENr9uRQl88dm5z0YaP7DKP7S/yQpS1ICDIvreyOpEdDjvZzJ0lKJQqS6Mo21/gQXl5eURFRTXoRAAgIkRFRVXrCsgnk0FJtdJ2zcJsBdLLn7eXqes//HmjtH1w4AfPFaWrKzqNgBN7ICPpzPtP7LZ9CdpfoBqIhp4ISlT3ffpmMii5MiiZYxB/v62Q+O3v7fKOABsaQFE6V3QspzRFSXG6mqxsppSqN3wzGaTlEBzgR/Nw5+QWPz8Y/Tpg7KpYJUXpOl12bj9CQ9OiOzRude4Q06TVdvhp1HneiUupBiQjI4M33nijys+7+uqrycjIcH9AZfDJZJCYlkPbZmFnXk5Ftocr/mLro3xyn63q2VA7jksTsUX89n9v+wlKJK+xHcw+cmmtlCeVlwyKiooqfN7XX39NRESEh6I6k0+OJkpKyy17HYMB420p5YTPnUXpfGQCVKcRsHkWHN0Mbfra6q6pO2z9GKUamOe+2EbC4Sy37rNHmyb86bqe5T7+zDPPsHfvXvr27UtgYCAhISFERkayY8cOdu3axQ033EBSUhJ5eXlMmjSJCRMmABAXF8fatWs5efIkV111FRdffDE//fQTMTExfP7554SGVmN98HL43JWBMYYk55XBOURg9Gt2JbB+dzWconSV6Tjcfi/pN0heZ7/X1sxjpRq4f/zjH3Tq1ImNGzfyr3/9i/Xr1/Pyyy+za9cuAN59913WrVvH2rVreeWVVzhx4sQ5+9i9ezcTJ05k27ZtRERE8Mknn7g1Ro9eGYjIKOBlwB942xjzj3K2uxmYCww0xlRSOa1mMnMLyc4vKr90ddNYeHwzBDbyZBh1S3graN4d9i216wskr7ad555Yg1kpL6voDL62DBo06Ix5AK+88grz5s0DICkpid27dxMVFXXGczp06EDfvn0BGDBgAAcOHHBrTB67MhARf+B14CqgBzBORM6p9CYi4cAkYJWnYimtZFhpbEXVSkOa2sJpvqTTCDi4Agpz7Uiilj0huLG3o1KqQWrU6OeTzaVLl7Jo0SJWrFjBpk2b6NevX5nzBIKDf67m6u/vX2l/Q1V5sploELDHGLPPGFMAzAJGl7Hdn4F/ArUyT/z0sFJX1jHwJR1HQHE+HPjRNhNpE5FSbhMeHk52dnaZj2VmZhIZGUlYWBg7duxg5cqVtRyd5cnT3xig9EymZOCMQesi0h9oa4z5SkTOWhX9jO0mABMA2rVrV6OgEtNKkoGLK5z5iriLwC8QVr1p1znQ+QVKuU1UVBQXXXQRvXr1IjQ0lJYtW55+bNSoUUyZMoXu3bvTtWtXLrjgAq/E6LW2EBHxA14Cxle2rTFmKjAVID4+voISo5VLSsshIiyQJiFVK+LU4AU1sglgzyJ7u62WoVDKnWbMmFHm/cHBwXzzzTdlPlbSLxAdHc3WrVtP3//UU0+5PT5PNhMdAkqvah3rvK9EONALWCoiB4ALgPki4tESmUnpua6tbuaLOg233xs1tzWZlFI+w5PJYA3QWUQ6iEgQcBswv+RBY0ymMSbaGBNnjIkDVgLXe3o0kR1Wqv0FZep4qf2uk82U8jkeSwbGmCLgUeBbYDswxxizTUSeF5HrPfW6FXE4DIfSc7W/oDxt+tpE0PNGb0eilKplHu0zMMZ8DXx91n1/LGfb4Z6MBSAlO4+CYoc2E5XHzx8eWOjtKJRSXuBTM5ATT+hIIqWUKotPJYOk9FLrGCillDrNt5JBWg4i0CYixNuhKKVUuRo3trP/Dx8+zC23lF0wcvjw4axd677xNj6XDFo1CSE4wN/boSilVKXatGnD3Llza+W1fKoAT1J6OdVKlVK+45tn4OgW9+6zVW+4qsw6nIAtYd22bVsmTpwIwOTJkwkICGDJkiWkp6dTWFjIX/7yF0aPPrNiz4EDB7j22mvZunUrubm53HvvvWzatIlu3bqRm5vr1rfgY1cGOuFMKVX7xo4dy5w5c07fnjNnDvfccw/z5s1j/fr1LFmyhCeffBJjyi+w8OabbxIWFsb27dt57rnnWLdunVtj9Jkrg7zCYlKy83TCmVK+roIzeE/p168fx44d4/Dhw6SmphIZGUmrVq144oknWLZsGX5+fhw6dIiUlBRatWpV5j6WLVvGY489BkCfPn3o06ePW2P0mWRwKCMXY3QkkVLKO8aMGcPcuXM5evQoY8eOZfr06aSmprJu3ToCAwOJi4srs3R1bfGZZqIkrVaqlPKisWPHMmvWLObOncuYMWPIzMykRYsWBAYGsmTJEg4ePFjh84cOHXq62N3WrVvZvHmzW+PzmSuDkjkG2meglPKGnj17kp2dTUxMDK1bt+aOO+7guuuuo3fv3sTHx9OtW7cKn//www9z77330r17d7p3786AAe5didBnkkHL8GAu79GSFuHBlW+slFIesGXLz6OYoqOjWbFiRZnbnTx5EoC4uLjTpatDQ0OZNWuWx2LzmWRwRc9WXNGz7I4ZpZTydT7TZ6CUUqp8mgyUUj6hojH8DUl136cmA6VUgxcSEsKJEycafEIwxnDixAlCQqpef81n+gyUUr4rNjaW5ORkUlNTvR2Kx4WEhBAbG1vl52kyUEo1eIGBgXTooOt6V0SbiZRSSmkyUEoppclAKaUUIPWtd11EUoGKi3iULxo47sZw6oKG9p4a2vuBhveeGtr7gYb3nsp6P+2NMc3Le0K9SwY1ISJrjTHx3o7DnRrae2po7wca3ntqaO8HGt57qs770WYipZRSmgyUUkr5XjKY6u0APKChvaeG9n6g4b2nhvZ+oOG9pyq/H5/qM1BKKVU2X7syUEopVQZNBkoppXwnGYjIKBHZKSJ7ROQZb8dTUyJyQES2iMhGEVnr7XiqQ0TeFZFjIrK11H3NRGShiOx2fo/0ZoxVUc77mSwih5yf00YRudqbMVaViLQVkSUikiAi20RkkvP+evk5VfB+6u3nJCIhIrJaRDY539Nzzvs7iMgq5zFvtogEVbgfX+gzEBF/YBdwOZAMrAHGGWMSvBpYDYjIASDeGFNvJ8qIyFDgJPChMaaX877/A9KMMf9wJu1IY8xvvBmnq8p5P5OBk8aYF7wZW3WJSGugtTFmvYiEA+uAG4Dx1MPPqYL3cyv19HMSEQEaGWNOikggsByYBPwK+NQYM0tEpgCbjDFvlrcfX7kyGATsMcbsM8YUALOA0V6OyecZY5YBaWfdPRr4wPnzB9h/1HqhnPdTrxljjhhj1jt/zga2AzHU08+pgvdTbxnrpPNmoPPLAJcCc533V/oZ+UoyiAGSSt1Opp7/AWA/7AUisk5EJng7GDdqaYw54vz5KNDSm8G4yaMistnZjFQvmlPKIiJxQD9gFQ3gczrr/UA9/pxExF9ENgLHgIXAXiDDGFPk3KTSY56vJIOG6GJjTH/gKmCis4miQTG2DbO+t2O+CXQC+gJHgBe9Gk01iUhj4BPgcWNMVunH6uPnVMb7qdefkzGm2BjTF4jFtoR0q+o+fCUZHALalrod67yv3jLGHHJ+PwbMw/4BNAQpznbdkvbdY16Op0aMMSnOf1QH8F/q4efkbIf+BJhujPnUeXe9/ZzKej8N4XMCMMZkAEuAIUCEiJQsYFbpMc9XksEaoLOzdz0IuA2Y7+WYqk1EGjk7vxCRRsAVwNaKn1VvzAfucf58D/C5F2OpsZIDptON1LPPydk5+Q6w3RjzUqmH6uXnVN77qc+fk4g0F5EI58+h2IEy27FJ4RbnZpV+Rj4xmgjAOVTsP4A/8K4x5q/ejaj6RKQj9moA7NKlM+rj+xGRmcBwbLndFOBPwGfAHKAdtlT5rcaYetEpW877GY5tejDAAeChUm3tdZ6IXAz8AGwBHM67f4dtZ693n1MF72cc9fRzEpE+2A5if+wJ/hxjzPPO48QsoBmwAbjTGJNf7n58JRkopZQqn680EymllKqAJgOllFKaDJRSSmkyUEophSYDpZRSaDJQqlaJyHAR+dLbcSh1Nk0GSimlNBkoVRYRudNZI36jiLzlLAR2UkT+7awZ/52INHdu21dEVjqLnM0rKXImIueJyCJnnfn1ItLJufvGIjJXRHaIyHTnrFilvEqTgVJnEZHuwFjgImfxr2LgDqARsNYY0xP4HjvDGOBD4DfGmD7Yma0l908HXjfGnA9ciC2ABrZS5uNAD6AjcJGH35JSlQqofBOlfM5lwABgjfOkPRRbiM0BzHZu8xHwqYg0BSKMMd877/8A+NhZOyrGGDMPwBiTB+Dc32pjTLLz9kYgDrsgiVJeo8lAqXMJ8IEx5rdn3Cny7FnbVbeWS+n6MMXo/6GqA7SZSKlzfQfcIiIt4PR6v+2x/y8lVSBvB5YbYzKBdBG5xHn/XcD3zlW0kkXkBuc+gkUkrDbfhFJVoWckSp3FGJMgIn/AriTnBxQCE4FTwCDnY8ew/QpgywNPcR7s9wH3Ou+/C3hLRJ537mNMLb4NpapEq5Yq5SIROWmMaeztOJTyBG0mUkoppVcGSiml9MpAKaUUmgyUUkqhyUAppRSaDJRSSqHJQCmlFPD/lyRiyO6X2hoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6ns//AtGJMP1Wf7fE+4o2QHSyrfbVXZm58NXbdqFWVLxn+28+Aqsfh9MuslNL3TH1Rvj4f+DLv8KVPp4WWnMI9n4Cuz+yJ/66Yvt8v+GQuxiGnwPDzrJXRD4UEgEA7DiALgZTfUZjDXz0G4hLh2m3QCdFwQNW0QYYOOnkaZfuyMoFDJRsgcEzPdv/phegoeL4hV+nEpMMU26ANU/A/F9AUpZn++5KUx28dBPset8+jkuDnLkwfJ79Sx3q/X12ISTGAMBOBdVpoB04UgEv3Wz7S1Xv2fYv20deU2hLJgaT1iYozves+weODQR7OhPI0QYrH4bsaTBkdve2Pf0Ou/2axz3bd1eaj8AL18LuD2HefXDHp/DDXbDoaZh2U6+f/CHEAoCOAXRg1wew5VXY+Z6/WxJa8pbarJRguwCCyeGtNtWypwEgKRti+3k+DvDV21CxG2bf1f0rq37DYczFsPZpe8L2lpZGePF6279/xWMw717nFFn/noJDJgBkJEZTUd+MQ9NBHM/1j6w3KjEpq7YY9n5q0xAkZdu+4GBStNHeZk32bHuRnhWJX/kQpAyBsZd5tv2sf7PdR5tf9Gz7E7U2226f3R/C5Q9B7iLvfK4XhEwASE+Ios1hNB3EiYqd9Up9kYBLdSz/ZVuJKnex7f/d+5mtnBUsijbYq5vUYZ5/RmauvZJoa+nedgVr4MBKmHWnexlIOzL0DPvr/MtHel4roK0FXr4VdrwDl/zJjjH0IaETAI6uBtYAcJQxxwJAcV5wnYT6ss1L7a/j9FEwfK79tVmSd8rNAkbRBtv905OB7axJtnxj6Vfd227lg8cGcz0lYgNI2Ve2i9RTjjZ49Q473nPBb/vkbK/QCQBaHP5ktcVwpAwGTLC5UCr3+rtFwa9sp01MlrvYPs6Za2/3BEk3UEuj/eXuaf+/iydF4iv22pPt9FshOqFn+x9/BSRk2imhnnA44LU77dXegv+GWd/uWXt8RANAKCvJt7euX0s6DuB7m5fa9MMTrrKPk7JstaxgGQgu2QKO1p4HgLQREBnnftekMbDiTyDhMPOOnu0bICIKZn4Tdn8Ah7d3b1uHA964x05FPedncObdPW+Pj4RMAMjQjKAnc53wJy6CsAgdB/A1Y+zsn5w5kJh57PmcubbfujUIuieL1tvbngaAsHB7ZerOj5LGGlh2K6x/xhZ48db8/Wm32jxCL1wL7/3Mdge1NHS9jTHw9n/Ytsz5Ecz9kXfa4iMhEwCSYiOICg/T1cDtFefZgbr4dMgY670MjKpjhWtt5amJi49/fvg8WzilcI0/WuVdRRvt4rbk7tenPUlW7qnHpoo2wqNzYOtrcN5/wYW/6/l+XeLT4MrH7bGsehT+cSXcPxSeucxebRRtPL5txthAseZxuwDtnJ96ry0+EjIrgUWEtIQoTQjXXnEeZE6097Ny7VoAY4JvVWpfsflF+4ty7KXHPz/sLNsttPcTGHamf9rmLd4YAHbJzLWrcqv22fn57RkDqx+zJ9z4DLjlLRgyq+f7PNG4y+xfcz3s/8KZuuEjeP/nwM/tSt7h82zqhrKv7BTU078NC34ZEP+OQiYAgF0LoGMATk11UL772K/RzFxbEKO22DdL4ENdWwtseQVGXwAxSce/FptiZwXt+QTO+Yk/WucdzUegdJtdSOUNWe1qA7QPAA2V8Npdtpbv6Atg4SM+z5lDVDyMWmD/wP472fPxsYCQ/7J9ftotcMH9AXHyhxALAOkJ0ZTUNPq7GX3D4a2AOf4KAGyfqwYA79v9ERwpPzb750TD58IXD9rA3NMZLL3NGHuS3vi8Xd/Q0/5/l/7jjo1Njb/CPle4Fl66BWqL4Pxf20yf/jjZJmbCpGvtnzFweJtt0/BzA+bkDyEXAKLYUuS73NoBxTW45goAAybY20ObYfTX/NOmYOZK/TByQcev58y1/cr7v4DR5/dq0zziaLMD19vegO1vQvUB24016nwbzLwhIhoyxtj/Vx0O+PJh2/WSNBBufc9W8uoLRGDAOPsXYEIsAERTXmfTQYSFBU6U9oniPHtCcg3WxSRBvxFQrAPBXtdUZ0+SuYvt9MKODJkF4dF2HKCvBoCWRtu+ba/bfDtHym2bR5wDc/8DTrvQTijwpkzn2NQL18LOd+34yWUP2W4z1WMhFwBaHYbqhhZS4zv5hxgqivPtr//2l6tZuXBwnf/aFKy+esvO8jlx9k97kbE29XFfXBC291NY+xTsXG4LlUQn2V/6Yy+BkfNtTQNfycqFTc/bfvYLfw8zvxVQXSx9XWgFgHbF4UM6ADja7IKdE5emZ+bazKANlRCb6p+2BaPNS22Zw1OlJh4+Fz78FdSXef5L+oNf2jTT83/heS6c9jYtgX9+x852mXg1jLnUrmPo7ErG28ZeBgWr7WKq3qzSFSJCZh0AHCsOH/KLwcp3Q2vDsf5/l6MDwUGUl8bf6kptFsiJV5869W/OPHu791PP9lW+Gz77g52KuOQ6O3WxJ9Y8aXPZDDsb7t4El/4FRs3vvZM/QHK2zZevJ3+fCKkAcHQ1cKhPBT1xANgl01WII8BXBL//C1ttqy/Y8iqYts5n/7Q3cIrtXvE0PfTKhyE8Es79T9i1HJ651F5NeOLzB+DN78PoC+G6pZ6XZlR9WmgFAM0IahXnQVikzUHTXkIGJGYFdk6gulI7nXLFn221M3/LW2pnWA0Yf+r3hkfA0DM9GweoL7PrOCZdC3N+CNf8w3bzPXm+TZLmLmPgo/+B5f8J46+Ea56FyJjut0cFhJAKAMmxkUSGiy4GK86D/mM6vpTP7EEhjr5g8xJbjaqtyfZf+1PFHpveYWI3CoAMn2uzslYd6N6+1jxh+/5nO2vgjrkYvvG6TTX95PnHirR0xZXK4JP7bYLAq56wVxQqaIVUABAR0uKjKQv1MYDivGPpdk+UNckuafdmObzeYgysfxYGzYTs6bDubz0v6NETecsAsf3/7vIkPXRLg02LMPpCyGh3VTfkdLj1XTuf/m8Xd53b3tFmM1i6Uhlc+qBNyKaCWkgFAID0xKjQvgKoLYH6wyf3/7tk5drVnIe39m67vKFgtQ1eU2+0WSHLvoIDX/qnLcbY2T9Dz+xeYrT+YyG+f/fGATa9YOfkn/HvJ7+WcRrcttwm/Xt+MWzqoMxhWyu8+m0bMM/+gU1l4Odatap3+OxbFpEYEVktIptEZIuI/ML5fI6IrBKRXSLyooj06nxMWxw+hMcAXJWnOgsAme3yrwSaDX+HqATbdz3hSohKtGl5/eHQRijf2f36ryK2G2jvp+5dvTja4IuHYOBUW8qwI0lZzmRps+HV2+34iOuzW5tsvdq8pXDe/7N/Os8+ZPgyzDcB5xpjJgGTgQtEZBbwW+BPxpiRQCVwmw/bcBIbAEL4CsA1xdOV+uFEKUPsCuFAGwhuqoX8V23OmOgEO2sld/GxdQ29bfNLEB4F4y7v/rY5c6GuBErdKETy1dtQsRvO/G7XJ+6YZLjhZVuI5v3/grd/bFcov3CtTap24e/sr38VUnwWAIxV53wY6fwzwLnAMufzzwALfdWGjrgCgPFn37A/FefZk3xnS+lF7NVBoA0E578CLfUw9aZjz0272Q6Mbl7au21xtNnskKPO92xB3fBujAN88SCkDLULtE4lIhqufAJm3wWrH4U/T7QZLS97CE73QhUtFXB82tEnIuEishE4DCwHdgNVxphW51sKgexOtr1dRNaKyNrS0lKvtSk9IYqWNpsOIiQV58GATrp/XLIm2TGAttau39eXbHjWJg4bNP3Yc1m5tmuktweD934KdcXdm/3TXsoQSM05dZnIgtVQ8KXNiOnuqt+wMPjar20mTUebnekz9UbP2qkCnk8DgDGmzRgzGRgEzATGdGPbx4wx040x0zMyMrzWpozEEK4N3Fxvi5J31v/vkplrfzmX7eiddvXU4W12uuWUG0/uBpl2kw1mvVltK+8lu6Br9AWef8bwubD/866D8BcP2O66ydd3//PPuAvu3X+sNrEKSb0y1G+MqQI+AmYDKSLi+rkyCDjYG21wOVYbOAQHgg9v47gaAJ3Jcq0IDpCB4PXP2oVtk649+bUJV9mB4XW9NBjc0gBbX7c5bHqygCpnLjTV2ApbHSnfbVMxz7jN8/oBOtgb8nw5CyhDRFKc92OBBcA2bCBwTYy+CXjNV23oSHooXwF0lgLiROmjICI2MAaCW5vsNMgxF3WcQC060c7Dz38ZGnuhFsSaJ6G51r3UD13JmWNv937c8etf/tUu0pqpfffKc768AsgCPhKRzcAaYLkx5g3gx8D3RWQXkAY86cM2nCQ9IZQDQD5EJ9s+5q6EhdvUBYEwEPzVW3a169RvdP6eaTfb5He+Hgwu322zeY6+4NgJ3FPx6XaspqOB4Ppy2PAc5F4DiQN6th8V0nyWDtoYsxk4qTacMWYPdjzAL1JiIwkPC9F0EK4i8O5c+mflQt7Lfb9I/Pq/21TLw8/p/D0Dp9hurXV/gxnf9M3xOBzw+r/bqZ+X/Mk7+xg+F1Y/bruVImOPPb/mCRvQZt/V832okBZyy/3CwoS0+CjKQm0MwFUD4FTdPy6ZudBUDZX7fNqsHqk6YGvtTrn+1GkLpt4EJflwcL1v2rLmCTtoe8FvbMlCb8iZa3MatV/N7Er7MOprNp+TUj0QcgEAbDdQyKWErthr58lndrIA7ETti8R7ovqg/VXsSxuft7fuzIKZuAgi42Dd095vR+U+W6t2xHmezcjpzNAzbFH09mkhNi2BI2Udp31QqptCMwAk9rHVwDvfh23/8u0+3B0Aduk/HiTcs3GAncvhT+Pgj2Phje/bX+ltXl534WiDDf+A4fMgdeip3x+TZGcE5b8CjTXea4cxtutHwuCyB7zbvRSdYJPaucYBHA6brG3gFBh2lvf2o0JWSAaAjIQ+lhH0vZ/Zk0irD7ulivPsr8kMN7sNImPse7s7FdQY+OS3kJRta9xuegGeXQi/H2kTjm1/03Zj9NSej6G6oOvB3xNNu8VeBeUvO/V73bXuabvw6/xfdi/pm7uGz7N5hRqqYMfbUL7L/vrvy+MyKmCEZACwGUGb+0Y6iIYqKN1m89Xset93+ynOsyf0iGj3t8nK7X4X0L4VdtHVWd+zxUR+tBuufR5Ou9DO2FlyHfxuOLx4o82X4+nUzPV/h9h+Nu+9u7Kn2pk16/7m2T5PVFUA7/0/21c/7WbvfOaJhs+12Vn3rbBpH5KHwFgP8gsp1YGQDAAZCdE0tzmoaewDqQ4K1zrviM3I6CuuGUDdkZlrk5LVlri/zYo/2nTGU26wj6Pi7En6iv+zweDGf8Kkr0PBKnjlm/C7EfDPO21iMnfVl9sridxruhfQROzK4EObOl9g5S5j4F/ftSfnyx703S/y7Ol27OLzv8CBlTD737xT7F0pQjQA9Km1AAWrbP/xpGttZkdv9k+71JXa3DTdDQDdHQg+uN4WQJ995/HTFl3CI2HEOXDJH+H7222e+um3wKbn4fFz4bAb2S/hWNUvT3LY5C62i9x6ehWw4R/2WBf8wr0xCE9FRNnB4MLVNqPnFM3bo7wntANAXxgHKFhlUzNPu8Xm39n+hvf3caoaAJ1xvd/dcYAVf7Qnqem3nvq9YWF2jOCi39urgoYKePycjguWtOeq+pU9zb06uyeKSba1AvKWde+qo72aInj3pzD0LJjeC9nMXVXCpvcg7YNSHQjNAJBoa9D4vTCMow0OroPBp9uTYcpQ36xWPVUNgM7EJNtKUu5cAZR+ZXPTzLzdzrjpjuFz4dsr7OyWV2+Hf90NLY0dv/fgOjtm0p3B3xNNuxma62x6iO4yBt74HrQ121k/vVE5a8JVNrfQrH/z/b5USAnNAHA0IVwnJ5necnirPRENPt32IU9cZOd8d6fP3R3FeXa1bFy/7m/rbpH4FX+23T6nf6f7+wBIzLRFzM/6nu2eeXK+Lap+ovXP2D7x8Vd6th+AQTOg/zjPuoE2L4Ud79jKWWkjPG9DdyRn2wH1BO9lxVUKQjQApMZFESZ94AqgYJW9HezMjJG72A4qevLLtCueDAC7ZE2Cyr1dz9apOmAHsKfeBPFpnu0H7ODm/J/D11+0M2wenWsza7o01dl5/OOv6P5VRnsi9iqgaH33prnWlsDb/2EDthZQUUEgJANAeJiQ1hdKQxashoTMY8nZMk6zJ1xvzgZqabB5/XsSAOBYN1JHvngQEJtj3htOuwDu+BTSRsLSG+Gdn9iFZFtetVdMPen+ccldDBEx7qeJNgbe/L4dp7n84VOnnlAqAITsfLI+URu4YBUMnnH8FMKJi+G9n0LZLkgf2fN9HN5qryo8DQBHi8Rv7nj1ad1hOyd/0rXeXQiVOhRufccukvvyYbu2oLUR0kbZX+A9FZsK4xbaLp2kgXYmVli4Xf189Dbs2OOqA3aAfsF/23TZSgWBEA4AUZT6swuotsTmkJnxzeOfn3CVPenlLYVzftLz/RTn21tPA0DiAEgY0PlA8Jd/tTn5z7zHs8/vSkS0nSU0ZBa8/l3763/BL7035/7022HLK/DhL917/5DZmoFTBZWQDQAZCdHsKa33XwMKV9vbE3/NJmXZXPKbl8K8+3p+sivOs+UJU3owV72zgeCGKlsAZfxC71ytdGbCVbYN65+xC7m8JXsa/LQYHK12RpZps1dLjva3bcdukwZp148KKiEbAFwJ4YwxiD/yqhSssrnjXX3s7eUuhtfutKuEB8/o2X6K8+x8+Z5MV8zKtYueWhqPL3O45glbtvCs7/esje5IHwXn/8r7nxsWrid1FbJCchAYbBdQU6uDuiY/pYMoWG3nvXeUymDspRAe3fPBYIfD5sD3tPvHJTPX/gI+vOXYc81H4MtHYOSCYyuGlVIBJYQDgGstgB8GgluboGjjsemfJ4pJtjNh8l/pWRrlyr2237ynASCr3UCwy4ZnbV76s3/Qs89WSvlNyAcAv6wFOLTZVnrqajbLxMX2BLvnY8/345q62dMAkJpjawm7BoJbm+HzB+yg6NDZPftspZTfhGwAyEj0Y0I41wKwQV2URh61wF4J9CQ1RHGencaYMdbzzwA7EJ058dgVQN5LUFOov/6VCnAhGwD8mhG0YJWdlZM4oPP3RETbeerb34RmD2crFefZxWXtB249lZVrawq3NsOKP9mAMHJ+zz9XKeU3IRsA+sU700H09hiAMc4FYG4sZspdbCtYbX/Ls315YwDYJTMXWhvg8z9D+U4780erUikV0NwKACJyt4gkifWkiKwXkfN93ThfCg8T+sX7YTFY1QFbZKWzAeD2hpxh5557MhuovhxqDnovALgGgj/5HfQbAeO0KpVSgc7dK4BbjTE1wPlAKnAjcL/PWtVL/JIOoqCTBWAdCQuDiVfBrg+gvqx7+3HVAOhuCujOpI+2U1MdLTZjp86dVyrguRsAXNf6FwHPGmO2tHsuYKUnRPf+NNCCVRCVYNMRu2PiYjsHf8ur3duPt2YAuYRH2s9KyralGJVSAc/dALBORN7DBoB3RSQRcPiuWb0jPSGq968AClfbFATu1nXNnGCDRXdmA7W1wJ5PIHEgxKd71s6OLHwEbnjFlilUSgU8dwPAbcC9wAxjzBEgErjFZ63qJa4uIGNM7+ywqc4mZ3On/7+9iYts4KjYe+r3Fqy2efR3LbeDyN6UMRr6j/HuZyql/MbdADAb+MoYUyUiNwA/A7qoEBIYMhKjaWxxUN/c1js7LFpvu3O6m8544tX2Nm9Z5+9pqIR/3QNPLrDFW659wRYsV0qpTrgbAB4BjojIJOAHwG7g7z5rVS/p9eLwRxeATe/edilDYOiZdjbQiVcrxtjA8NBMmy1z9l1w5yoYc5F32qyUClruBoBWY/tJLgceMsY8DCT6rlm9I723VwMXrIaMMbYYSXdNXGQre7UvYVixB/5xJbx8m60b+62P4Gu/hugE77VZKRW03A0AtSJyH3b655siEoYdBwho6Ql2MLO8pg7WPt39qZbd4XDYANDd/n+XcZdDWKRNw9DaDJ/+L/x1NhSsgQt/D9/8AAZO9mqTlVLBzd16ANcA12HXAxSLyBDg975rVu/IcHYBJex8DfJ+Bp/9Aa75h29OpOU7obHK83KGcf1g1PmwaQnseh9Kt8PYy+DC39qShkop1U1uXQEYY4qB54BkEbkEaDTGBPwYQL/4KERg8IF/2vntxsBTX4NNL3p/Z67+/57Us811ZghtroevvwjXPKsnf6WUx9xNBbEYWA0sAhYDq0Tkal82rDdEhIcxLraaIdVrYdrNcPvHkD0dXr0d3v0ptHmxWEzBKtv3n9aD0onjLofrXrKDvKdd4L22KaVCkrtdQD/FrgE4DCAiGcD7QBfzEgPD4sjPoAm7ujUhA77xT3vyX/mQXU179dMQn9bzHRWssemfe5JATQRGB3QKJqVUH+LuIHCY6+TvVN6NbfsuY7ig9SPyInMh1Vk0PTwSLvodXP5XOPAlPD7vWFoFTx2pgLKvPB8AVkopH3D3JP6OiLwrIjeLyM3Am0CXOYpFZLCIfCQiW0Vki4jc7Xy+n4gsF5GdzlsP5kR6yYGVDGg7xOtyzsmvTbkebn3bdgM9saDrRVinUrjW3vak/18ppbzM3UHgHwGPAbnOv8eMMT8+xWatwA+MMeOAWcCdIjIOm1LiA2PMKOAD52P/2PgcTWFxvNI4tePXs6fBHZ/YWUEv3wbv/Sc4PFg1XLDKVubK7mQ/SinlB+6OAWCMeRl4uRvvPwQcct6vFZFtQDZ2Mdk859ueAT4GThVMvK+5Hra8xp7+8ynfF8mR5lbiojr4z5HQH77xOrx7H3zxgHNc4Ck7LdNdBatsJs2oeO+1XymleqjLKwARqRWRmg7+akWkxt2diMgwYAqwChjgDA4AxUAXdRF9aNsb0FxLcc4VAJTVdlEYJiIKLv4DXPYg7P/c5tupKnBvP22tcHCddv8opfqcLgOAMSbRGJPUwV+iMSbJnR2ISAL2yuEeZ1GZ9p9vgA5TcYrI7SKyVkTWlpaWunk43bDxOVuXd+gZAJTWNZ56m6nfsFcDdaXw9IVQvvvU25TkQ8sRHQBWSvU5Pp3JIyKR2JP/c8aYV5xPl4hIlvP1LOBwR9saYx4zxkw3xkzPyMjwbsOqDsDeT2HydWQkxgJQ2tUVQHtDZ8PN/7In9acusIXSu3K0ApgGAKVU3+KzACAiAjwJbDPG/LHdS68DNznv3wS85qs2dGrTi4CBSdceywjanYRwWZPglrchLAKevggK13X+3sLVkJgFyYN71mallPIyX14BnIlNHneuiGx0/l2ErSW8QER2AvPp7drCxsCm52HY2ZA6jDRnQrhuZwTNOM1OE41Ngb9fBns/6/h9Bavsr/+eLABTSikf8FkAMMasMMaIMSbXGDPZ+feWMabcGHOeMWaUMWa+MabCV23oUMEqm0Z58nUARIaHkRoX6VlK6NRhcMs7kDwInrsadrx7/Os1h2x3kw4AK6X6oMBfzdtdG5+DyHibSdMpPSG661lAXUnKgpvfsnn+l1wH+e1myha6+v81ACil+p7QCgDNRyD/VZtUrV3RFFdtYI/Fp8FN/7K5fpbdBuudiVILVkN4NGTm9rDhSinlfW4vBAsK2+3cf1f3j0t6YjR5hVU9++yYJLjhZVh6I7z+77YAfMEqGDjFriNQSqk+JrSuADY+f6y+bjvpCVGUeqMucFScLcY+9jK7crhwrU7/VEr1WaETAKoLYc/HMOk6CDv+sNMToqlvbqOh2YM8PyeKiLIppCdfDxg720gppfqg0AkAm5bgmvt/ogxP1gJ0JTwCLnsI7vgMRi3wzmcqpZSXhUYAMMZ2/ww9E/rlnPRyeqLtoy/1VgAAe5WRlavz/5VSfVZoBIDCNVCx+6TBX5fslDgA9pTW92arlFLKr0IjAGx8DiLj7PTPDozqn0BafBQrdvog6ZxSSvVRwR8AWhog/xXn3P/EDt8SFiacPSqdz3aW4XB0mJxUKaWCTvAHgO1vQlMNTPp6l287e1QG5fXNbD3kdpkDpZQKaMEfADY+ZzNxnmI65tmj0gH4VLuBlFIhIrgDQE2Rc+7/10+a+3+i/kkxjMlM5LMdZb3TNqWU8rPgDgCbloBxwOSuu39c5o7OYO3+CuqbWn3cMKWU8r/gDgD7VsCQM6DfcLfePmd0Bi1thlV7y33cMKWU8r/gTgZ3/TJocL/cwLShqcREhvHpjjLOHeOfWvVKKdVbgvsKICwM4tPdfntMZDizhqfpQLBSKiQEdwDwwNmjMthTWk9h5RF/N0UppXxKA8AJ5o52TgfV2UBKqSCnAeAEIzISGJgcw2faDaSUCnIaAE4gIpw9KoMVu8pobXP4uzlKKeUzGgA6MGd0BrWNrWwqrPZ3U5RSymc0AHTgzJFphAl8ukO7gZRSwUsDQAdS4qLIHZSi4wBKqaCmAaATc0als7GgiuojLf5uilJK+YQGgE7MGZ2Bw8AXu3U6qFIqOGkA6MTkwSkkRkfoqmClVNDSANCJiPAwzhiZxqc7yjBGq4QppYKPBoAuzBmdwcGqBvaUabF4pVTw0QDQhTmjMgCdDqqUCk4aALowuF8cOenxfLZTB4KVUsFHA8ApnD0qnZW7y2lqbfN3U5RSyqs0AJzCnFEZNLS0sW5/pb+bopRSXqUB4BRmj0gjMlw0PbRSKuhoADiF+OgIpg5J1bQQSqmgowHADXNGZ7ClqIbS2iZ/N0UppbxGA4AbXNNBP9+l3UBKqeDhswAgIk+JyGERyW/3XD8RWS4iO523qb7avzeNH5hEWnyUrgdQSgUVX14B/A244ITn7gU+MMaMAj5wPu7zwsKEs0al8+nOMhwOTQuhlAoOPgsAxphPgYoTnr4ceMZ5/xlgoa/2721nj8qgrK6J7cW1/m6KUkp5RW+PAQwwxhxy3i8GBnT2RhG5XUTWisja0lL/d73MGZUOoNlBlVJBw2+DwMam2Oy0P8UY85gxZroxZnpGRkYvtqxj/ZNiGJOZqNNBlVJBo7cDQImIZAE4bw/38v57ZM7oDNbsreRIc6u/m6KUUj3W2wHgdeAm5/2bgNd6ef89cvaodJrbHKzae+LQhlJKBR5fTgN9AVgJnCYihSJyG3A/sEBEdgLznY8Dxoxh/YiJDNPpoEqpoBDhqw82xny9k5fO89U+fS0mMpyzRmawdE0BV04ZxMRByf5uklJKeUxXAnfTrxZOICUuipufXs2e0jp/N0cppTymAaCbMpNjePa2mQDc+ORqSmoa/dwipZTyjAYADwzPSOBvt8yk6kgz33hyNdVHWvzdJKWU6jYNAB6aOCiZx78xnb1l9dz2zBoamrVimFIqsGgA6IEzRqbzl2sns+5AJXc+v56WNoe/m6SUUm7TANBDF07M4lcLJ/Dh9sP8eNlmTRanlAoYPpsGGkquP30oFXXN/GH5DvrFR/HTi8ciIv5ullJKdUkDgJfcde5IyuubeWLFXtISovnOvBH+bpJSSnVJu4C8RET4f5eM47JJA/ntO9t5cc0Br++j6kgzy7eW6FiDUsor9ArAi8LChP9dNImqhhbueyWPlLgovjY+s0ef6XAYVu4p58U1BbyzpZjmVgdXTR3E/y7K1W4mpVSPaADwsqiIMP7vhqlc9/gq/v35DZw3tj+n5/Tj9OFpnDYgkbAw907ah6obWLa2kKXrCiioaCA5NpLrZg4hTISnPt9Ldmos318w2sdHo5QKZhoAfCAuKoKnb57B/W9vZ8WuMt7OLwYgJS6SGcP6cXpOP2YNT2NsVhLh7QJCc6uDD7eX8OKaAj7ZUYrDwBkj0vjh+afxtfGZxESGY4yhtrGFBz7YyaCUWBbPGOyvw1RKBTgNAD6SGh/Fb6/OBaCg4gir9lawak85q/ZWsHxrCQCJMRFHA0J5fTMvryukvL6ZzKQY7jxnJIumDWZIWtxxnysi/ObKiRTXNHLfq3kMSI5h7mj/F8xRSgUesYW5+rbp06ebtWvX+rsZXnOouoFVeypYtbecVXsq2FNWT0SYcN7Y/lw7YwhzRmccd2XQkdrGFhY/+iUHyutZ+u3ZjB+omUmVUscTkXXGmOmdvq4BwP8O1zYSERZGv/iobm1XXN3IFX/9nDaH4dU7zyQ7JdZHLVRKBaJTBQCdBtoH9E+M6fbJH2xm0qdvmUFDcxu3PL2a6gZNSqeUcp8GgAA3JjOJR2+cxt6yer797DqaW3WNgFLKPRoAgsAZI9P57VW5rNxTzo9f3kwgdOsppfxPZwEFiSunDuJgZQN/WL6D7JRYfvi10/zdJKVUH6cBIIjcde5IDlY18NBHu8hOjeXrM4d49fNLahppcxgG6mCzUkFBA0AQERF+uXACh6ob+dk/80mNi+KCCT1LRQF2gdqjn+zmwY920eYwXDdzCN89bxQZidFeaLVSyl90GmgQqmtq5drHVpJ/sIb5Y/vz4wvGMGpAokeftW5/Bfe9kseOkjouzs2iX1wUL6w+QHREGLfPGcE3z84hPlp/RyjVF+k6gBDV2NLGU5/v5ZGPdlPf3Mo1Mwbzvfmj6Z8U49b2NY0t/O6d7Ty36gBZSTH8cuEEzhs7AIA9pXX8/t2veDu/mIzEaL43fzSLpw8iIlznFCjVl2gACHHldU08+OEu/vHlfiLDw/jWnOHcPmc4CV38an8nv5j/ej2f0tombj4jhx+cP7rDX/nr9lfyP29tY+3+SkZkxHPvhWOZP7a/ZilVqo/QAKAA2FdWz+/f/Yo38w6RnhDNPfNHcc2MwUS2+9VeXN3I/3stn/e2ljA2K4n7r5zIpMEpXX6uMYblW0u4/53t7CmtZ+awftx30RimDEn18REppU5FA4A6zoYDlfzmrW2s2VfJ8Ix4fnzBGOaPHcDzq/bz23e+otXh4HvzR3PrWTnHBYdTaW1z8OLaAv60fCdldU1cOCGTO88ZyYRszVGklL9oAFAnMcbw/rbD3P/2NnaX1pOeEEVZXTNnj0rn1wsnnpSBtDvqm1p5/LM9PP7pHuqb25gxLJVbz8xhwbgBITFG0NTaxqaCaiZkJxEXpYPjyr80AKhOuX61v7n5EIumD2Lh5Gyv9d/XNLawdE0Bf/tiH4WVDWSnxPKN2UO5dsYQkuMiPf5cYwxtDkObMRgDbQ6DwxgcDnAY+7zDYUiOiyQ6Itwrx+Juu5ZvLeHXb21jf/kREqIjuCQ3i0XTBzN1SIqOiyi/0ACg/KrNYXh/WwlPf76XL/dUEBsZzlXTsrn5jBxG9k/odLvqhha2FtWwpaiarUU1bD1Uw67DdbQ63Pv/NSo8jImDkpk+LJXpQ/sxbWiqRwn33LHtUA2/fGMrX+wuZ1T/BL41Zzhr9lbwZt4hjjS3MTwjnsXTB3PllGy3Z2H1hpY2R7e6+VTg0QCg+owtRdX87fN9vLapiOZWB3NHZ3DLmcM4LTPRebK3J/wtRTUUVjYc3a5/YjTjBiZx2oBEYqPCCRMhPEwQgXARwkQICxPCBPs8UFDZwJp9FeQfrKalzf4/PiIjnhnDbDCYMawfQ9PievTLvKyuiT8u38GS1QdIio3k+wtGc93MIUe7uuqaWnlr8yFeWlfAmn2VhIcJ80ZnsGj6IM4dM4CoCP+cfPMKq/nLBzt5f1sJpw1I5MyR6Zw9Kp2ZOf10TUeQ0QCg+pyyuiaeX3WAZ7/cT2lt03Gv5aTHM25gEuOykhg/MInxA5N7tOK4saWNTQVVrN1fybr9lazdV0FNYysA6QlRTBuayrShqUwdksqE7GRiIk/dbdTc6uCZL/bxwAc7aWhp48bZQ7n7vFGkxHV+hbGntI5l6wp5eX0hJTVN9IuPYuHkbCYNTuZIcxtHmttoaG49er+xpe3Y8y22veeOGcDlkweSnuDZf49NBVX85YOdfLj9MMmxkSycPJDdpfWs3ldBc6uDiDBh6pBUzhqVzpkj05k0KNmtcZuWNgdldU2U1jZR19jKyAEJ9E/sO1c6oUwDgOqzmlsdvLOlmMr6ZsYPTGJMVlKX6xO8weEw7CqtY82+Ctbtq2TN/goKKuzVRmS4MG5gMlOHpDB1iA0M7fMeufr5f/PWNvaVH+Gc0zL46cXjuuzKOlFrm4PPdpXx0toClm8tOXp14hIRJsRGhRMbGU5cVDixURHERYVT39TK9uJaIsKEeadlcNXUQZw7tr9b4xwbDlTylw928vFXpaTERfLNs3K46YxhJMbYsZjGljbW7qtkxa4yPt9VRn5RNcZAYnQEs0akcdbIdNITojlc28jhWnuiP1zbxOGaRkprm6g40syJp5HMpBgmDkomNzuZCYOSmZid7HHgcldjSxs1jS1EhIURHiZEhNkrxfAwsVeKp6iyF4w0ACh1CqW1Taw/UMn6A5Vs2F/F5oNVNLbYugqZSTFMHZrC5MEpfLKjlM93lTOyfwI/u3gs807r36P9Vh9poay+ibiocOIiI4iNCu+yW2hnSS3L1hfyzw0HKalpIiUukssmDeSqqYPIHZR8UnfWuv32xP/pjlJS4yL55tnDuemMYacMshX1zazcXc6KXaV8trPsuO64iDAhIzGajMRo+idGk5EYQ//EaPonRdM/MYa4qHC2F9eSV1jF5oPV7CmtP7ptdkosE7KTyB2UwsTsZHIHJXd51eSO8romPth2mPe2FvPZzjKauqiHIcLRoBAdEU5OejyjByQwekAiowYkMnpAAplJMUE1YK8BQKluamlzsO1QDev3V7L+QBXrD1RSWNlASlwk35s/mutOH+LXwdM2h2HFrjKWrSvkvS3FNLU6GNk/gaumDuKKKdkUVB7hL+/vZMWuMvrFR/Gts4dz4+yhHl9dHSg/Qn1zK/0To0mNi+rWL+naxha2FNWQV1jN5oPV5B+sZm/ZsaAwPCOeqUNsF9zUoSmM6p94ynrY+8rqWb61hPe2FrNufyUOY4PLgnEDGNE/AYfD0OowR2/bHA7aHNDmcNjHxlDf1Mruw/XsPFxLWV3z0c9OjIlgVP/jg8LAlFiSYyNJjo3s0fdujKG+uY3axhYEIS46nPioiFMeb09oAFDKC0pr7S/1vjZIWtPYwpubD/HyukLW7q9EBIyBtPgobp8znBtmDe1zba5uaCH/YDUbC6pYv7+SDQVVVNTbk3BCdASTB6cwdUgKU4amMnVwKkmxEeQdrOa9Lfakv6OkDoCxWUmcP24AC8YNYPzAJI9/uZfXNbGjpI6dh2vZUVJr75fUUnnk5BKrcVHhJMVEHg0ISbERJDnv26462w1V29hKTYPz1vm4trGFjiaxxUSGkRAdQZyzuy8hOoK46Ajinf+//fD808hM9mxMRQOAUiFiX1k9r28qIjEmgmtmDA6YhWjGGPaXHznaDbd+fxXbi2uOniyTYiKoaWwlTGBmTj/OH5fJgnEDGNzP8wWL7rSprK6ZnSW1HK5torqhheqGFmqct9UNLdQ0tlDdYE/0NQ0t1De3Eh8dQVJMJIkx9jYpNoLEmEiSYuxtovMW4EhzK3VNduC/rqmVI02t1De3Ud/u9khTKy/eMdvjY+2TAUBELgD+AoQDTxhj7u/q/RoAlAot9U2tbCqsYsOBKvaX13N6ThrnjulPqo/WcgSrUwWAXv+JICLhwMPAAqAQWCMirxtjtvZ2W5RSfVN8dARnjEjnjBHp/m5KUPPHSNZMYJcxZo8xphlYAlzuh3YopVRI80cAyAYK2j0udD53HBG5XUTWisja0tLSXmucUkqFij6bCMQY85gxZroxZnpGRoa/m6OUUkHHHwHgIDC43eNBzueUUkr1In8EgDXAKBHJEZEo4FrgdT+0QymlQlqvzwIyxrSKyF3Au9hpoE8ZY7b0djuUUirU+WWliDHmLeAtf+xbKaWU1WcHgZVSSvlWQKSCEJFSYL+Hm6cDZV5sTl8QbMekx9P3BdsxBdvxQMfHNNQY0+k0yoAIAD0hImu7WgodiILtmPR4+r5gO6ZgOx7w7Ji0C0gppUKUBgCllApRoRAAHvN3A3wg2I5Jj6fvC7ZjCrbjAQ+OKejHAJRSSnUsFK4AlFJKdUADgFJKhaigDgAicoGIfCUiu0TkXn+3p6dEZJ+I5InIRhEJyBJpIvKUiBwWkfx2z/UTkeUistN5m+rPNnZHJ8fzcxE56PyeNorIRf5sY3eIyGAR+UhEtorIFhG52/l8IH9HnR1TQH5PIhIjIqtFZJPzeH7hfD5HRFY5z3cvOnOtdf1ZwToG4Kw8toN2lceArwdy5TER2QdMN8YE7AIWEZkD1AF/N8ZMcD73O6DCGHO/M1CnGmN+7M92uquT4/k5UGeM+V9/ts0TIpIFZBlj1otIIrAOWAjcTOB+R50d02IC8HsSEQHijTF1IhIJrADuBr4PvGKMWSIi/wdsMsY80tVnBfMVgFYe64OMMZ8CFSc8fTnwjPP+M9h/nAGhk+MJWMaYQ8aY9c77tcA2bMGmQP6OOjumgGSsOufDSOefAc4Fljmfd+s7CuYA4FblsQBjgPdEZJ2I3O7vxnjRAGPMIef9YmCAPxvjJXeJyGZnF1HAdJe0JyLDgCnAKoLkOzrhmCBAvycRCReRjcBhYDmwG6gyxrQ63+LW+S6YA0AwOssYMxW4ELjT2f0QVIztkwz0fslHgBHAZOAQ8Ae/tsYDIpIAvAzcY4ypaf9aoH5HHRxTwH5Pxpg2Y8xkbEGtmcAYTz4nmANA0FUeM8YcdN4eBl7FfvHBoMTZT+vqrz3s5/b0iDGmxPkP1AE8ToB9T85+5ZeB54wxrzifDujvqKNjCvTvCcAYUwV8BMwGUkTEleLfrfNdMAeAoKo8JiLxzgEsRCQeOB/I73qrgPE6cJPz/k3Aa35sS4+5TpROVxBA35NzgPFJYJsx5o/tXgrY76izYwrU70lEMkQkxXk/FjvRZRs2EFztfJtb31HQzgICcE7r+jPHKo/92r8t8pyIDMf+6gdbyOf5QDweEXkBmIdNXVsC/BfwT2ApMASb9nuxMSYgBlY7OZ552G4FA+wD7mjXf96nichZwGdAHuBwPv0TbJ95oH5HnR3T1wnA70lEcrGDvOHYH/FLjTH/7TxHLAH6ARuAG4wxTV1+VjAHAKWUUp0L5i4gpZRSXdAAoJRSIUoDgFJKhSgNAEopFaI0ACilVIjSAKCUj4nIPBF5w9/tUOpEGgCUUipEaQBQyklEbnDmWd8oIo86E27VicifnHnXPxCRDOd7J4vIl85EYq+6EomJyEgRed+Zq329iIxwfnyCiCwTke0i8pxzdapSfqUBQClARMYC1wBnOpNstQHXA/HAWmPMeOAT7EpfgL8DPzbG5GJXmLqefw542BgzCTgDm2QMbAbKe4BxwHDgTB8fklKnFHHqtygVEs4DpgFrnD/OY7EJzxzAi873/AN4RUSSgRRjzCfO558BXnLmaso2xrwKYIxpBHB+3mpjTKHz8UZgGLaQh1J+owFAKUuAZ4wx9x33pMh/nvA+T3OntM/J0ob+21N9gHYBKWV9AFwtIv3haA3codh/I64Mi9cBK4wx1UCliJztfP5G4BNntalCEVno/IxoEYnrzYNQqjv0V4hSgDFmq4j8DFtxLQxoAe4E6oGZztcOY8cJwKbb/T/nCX4PcIvz+RuBR0Xkv52fsagXD0OpbtFsoEp1QUTqjDEJ/m6HUr6gXUBKKRWi9ApAKaVClF4BKKVUiNIAoJRSIUoDgFJKhSgNAEopFaI0ACilVIj6///Bc94boYC+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 5ms/step - loss: 36.2544 - accuracy: 0.5375\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[36.254432678222656, 0.5375000238418579]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(val_features, val_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reach a validation accuracy of about 52% — much worse than we achieved in the\n",
    "previous section with the small model trained from scratch. \n",
    "\n",
    "The learning curves indicate that we’re overfitting almost from the start—\n",
    "despite using dropout with a fairly large rate. That’s because this technique doesn’t\n",
    "use data augmentation, which is essential for preventing overfitting with small image\n",
    "datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DJT-DgHvGhKu"
   },
   "source": [
    "### 2. Approach : Feature Extraction with Data Augmentation\n",
    "\n",
    "\n",
    "Now let’s review the second technique we mentioned for doing feature extraction,\n",
    "which is much slower and more expensive, but which allows us to use data augmentation\n",
    "during training: creating a model that chains the `conv_base` with a new dense\n",
    "classifier, and training it end to end on the inputs.\n",
    "\n",
    "\n",
    "In order to do this, we will first freeze the convolutional base. Freezing a layer or set of\n",
    "layers means preventing their weights from being updated during training. If we don’t\n",
    "do this, the representations that were previously learned by the convolutional base will\n",
    "be modified during training. Because the Dense layers on top are randomly initialized,\n",
    "very large weight updates would be propagated through the network, effectively\n",
    "destroying the representations previously learned.\n",
    "\n",
    "In Keras, we freeze a layer or model by setting its trainable attribute to `False`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "50DF9pH1GhKw"
   },
   "source": [
    "#### Instantiating and freezing the VGG16 convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=False)\n",
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting trainable to `False` empties the list of trainable weights of the layer or model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing the list of trainable weights before and after freezing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 26\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights before freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after freezing the conv base: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"This is the number of trainable weights after freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a new model that chains together\n",
    "\n",
    "1. A data augmentation stage\n",
    "\n",
    "2. Our frozen convolutional base \n",
    "\n",
    "3. A dense classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding a data augmentation stage and a classifier to the convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 420 images belonging to 8 classes.\n",
      "Found 70 images belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "# This is the augmentation configuration we will use for training\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "# This is the augmentation configuration we will use for validation:\n",
    "# only rescaling\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# This is a generator that will read pictures found in\n",
    "# subfolers of './train', and indefinitely generate\n",
    "# batches of augmented image data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        './train',  # this is the target directory\n",
    "        target_size=(image_size, image_size),  # all images will be resized to 150x150\n",
    "        classes=class_names,\n",
    "        batch_size=batch_size)  \n",
    "\n",
    "# This is a similar generator, for validation data\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        './validation',\n",
    "        target_size = (image_size, image_size),\n",
    "        classes = class_names,\n",
    "        batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(image_size, image_size, 3))\n",
    "x = conv_base(inputs)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=\"rmsprop\",\n",
    "    metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this setup, only the weights from the two Dense layers that we added will be\n",
    "trained. That’s a total of four weight tensors: two per layer (the main weight matrix\n",
    "and the bias vector). \n",
    "\n",
    "Note that in order for these changes to take effect, you must first\n",
    "compile the model. If you ever modify weight trainability after compilation, you\n",
    "should then recompile the model, or these changes will be ignored.\n",
    "\n",
    "Let’s train our model. Thanks to data augmentation, it will take much longer for\n",
    "the model to start overfitting, so we can train for more epochs — let’s do 50.\n",
    "\n",
    "__NOTE__ This technique is expensive enough that you should only attempt it if\n",
    "you have access to a GPU (such as the free GPU available in Colab) — it’s\n",
    "intractable on CPU. If you can’t run your code on GPU, then the previous\n",
    "technique is the way to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = os.path.join(\"logs_feature_extraction_with_augmentation\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction_with_augmentation.h5\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "21/21 [==============================] - 17s 779ms/step - loss: 11.6322 - accuracy: 0.1976 - val_loss: 7.2716 - val_accuracy: 0.3857\n",
      "Epoch 2/50\n",
      "21/21 [==============================] - 16s 752ms/step - loss: 4.2603 - accuracy: 0.4571 - val_loss: 4.1719 - val_accuracy: 0.4000\n",
      "Epoch 3/50\n",
      "21/21 [==============================] - 16s 767ms/step - loss: 3.6926 - accuracy: 0.5143 - val_loss: 4.4105 - val_accuracy: 0.3143\n",
      "Epoch 4/50\n",
      "21/21 [==============================] - 15s 727ms/step - loss: 3.1450 - accuracy: 0.5690 - val_loss: 3.5688 - val_accuracy: 0.4714\n",
      "Epoch 5/50\n",
      "21/21 [==============================] - 16s 770ms/step - loss: 2.6020 - accuracy: 0.5857 - val_loss: 3.0999 - val_accuracy: 0.6000\n",
      "Epoch 6/50\n",
      "21/21 [==============================] - 16s 740ms/step - loss: 1.7227 - accuracy: 0.6976 - val_loss: 3.0574 - val_accuracy: 0.6571\n",
      "Epoch 7/50\n",
      "21/21 [==============================] - 16s 744ms/step - loss: 1.6670 - accuracy: 0.7262 - val_loss: 1.9948 - val_accuracy: 0.6286\n",
      "Epoch 8/50\n",
      "21/21 [==============================] - 16s 741ms/step - loss: 1.5795 - accuracy: 0.7357 - val_loss: 2.1614 - val_accuracy: 0.6571\n",
      "Epoch 9/50\n",
      "21/21 [==============================] - 16s 737ms/step - loss: 1.6403 - accuracy: 0.7595 - val_loss: 1.9888 - val_accuracy: 0.6286\n",
      "Epoch 10/50\n",
      "21/21 [==============================] - 17s 807ms/step - loss: 1.5998 - accuracy: 0.7524 - val_loss: 2.6838 - val_accuracy: 0.6571\n",
      "Epoch 11/50\n",
      "21/21 [==============================] - 16s 770ms/step - loss: 0.9330 - accuracy: 0.8214 - val_loss: 1.6577 - val_accuracy: 0.7286\n",
      "Epoch 12/50\n",
      "21/21 [==============================] - 16s 782ms/step - loss: 1.0512 - accuracy: 0.7881 - val_loss: 1.7717 - val_accuracy: 0.6857\n",
      "Epoch 13/50\n",
      "21/21 [==============================] - 16s 761ms/step - loss: 0.9523 - accuracy: 0.8333 - val_loss: 2.4486 - val_accuracy: 0.6143\n",
      "Epoch 14/50\n",
      "21/21 [==============================] - 17s 811ms/step - loss: 0.8774 - accuracy: 0.8476 - val_loss: 3.4850 - val_accuracy: 0.6571\n",
      "Epoch 15/50\n",
      "21/21 [==============================] - 16s 768ms/step - loss: 1.0266 - accuracy: 0.8190 - val_loss: 2.3278 - val_accuracy: 0.6571\n",
      "Epoch 16/50\n",
      "21/21 [==============================] - 16s 773ms/step - loss: 0.8246 - accuracy: 0.8500 - val_loss: 2.2152 - val_accuracy: 0.6714\n",
      "Epoch 17/50\n",
      "21/21 [==============================] - 16s 757ms/step - loss: 0.8190 - accuracy: 0.8405 - val_loss: 1.8512 - val_accuracy: 0.7286\n",
      "Epoch 18/50\n",
      "21/21 [==============================] - 16s 767ms/step - loss: 1.0406 - accuracy: 0.8429 - val_loss: 3.3484 - val_accuracy: 0.6000\n",
      "Epoch 19/50\n",
      "21/21 [==============================] - 16s 772ms/step - loss: 0.7508 - accuracy: 0.8595 - val_loss: 2.7499 - val_accuracy: 0.6286\n",
      "Epoch 20/50\n",
      "21/21 [==============================] - 16s 749ms/step - loss: 0.7002 - accuracy: 0.8667 - val_loss: 2.1313 - val_accuracy: 0.6714\n",
      "Epoch 21/50\n",
      "21/21 [==============================] - 16s 747ms/step - loss: 0.4617 - accuracy: 0.8810 - val_loss: 4.2319 - val_accuracy: 0.6143\n",
      "Epoch 22/50\n",
      "21/21 [==============================] - 16s 745ms/step - loss: 0.8230 - accuracy: 0.8643 - val_loss: 2.1207 - val_accuracy: 0.6857\n",
      "Epoch 23/50\n",
      "21/21 [==============================] - 17s 827ms/step - loss: 0.4906 - accuracy: 0.9048 - val_loss: 2.3895 - val_accuracy: 0.6714\n",
      "Epoch 24/50\n",
      "21/21 [==============================] - 17s 796ms/step - loss: 0.4801 - accuracy: 0.8881 - val_loss: 1.9410 - val_accuracy: 0.7429\n",
      "Epoch 25/50\n",
      "21/21 [==============================] - 17s 824ms/step - loss: 0.5383 - accuracy: 0.8714 - val_loss: 2.5354 - val_accuracy: 0.7429\n",
      "Epoch 26/50\n",
      "21/21 [==============================] - 16s 778ms/step - loss: 0.3463 - accuracy: 0.9381 - val_loss: 7.8247 - val_accuracy: 0.4429\n",
      "Epoch 27/50\n",
      "21/21 [==============================] - 17s 797ms/step - loss: 0.6099 - accuracy: 0.8976 - val_loss: 3.2540 - val_accuracy: 0.6571\n",
      "Epoch 28/50\n",
      "21/21 [==============================] - 16s 760ms/step - loss: 0.3002 - accuracy: 0.9238 - val_loss: 2.9259 - val_accuracy: 0.6857\n",
      "Epoch 29/50\n",
      "21/21 [==============================] - 17s 792ms/step - loss: 0.5603 - accuracy: 0.8810 - val_loss: 3.5017 - val_accuracy: 0.6571\n",
      "Epoch 30/50\n",
      "21/21 [==============================] - 17s 787ms/step - loss: 0.4138 - accuracy: 0.9071 - val_loss: 2.8042 - val_accuracy: 0.7143\n",
      "Epoch 31/50\n",
      "21/21 [==============================] - 16s 758ms/step - loss: 0.4523 - accuracy: 0.9024 - val_loss: 3.3092 - val_accuracy: 0.6571\n",
      "Epoch 32/50\n",
      "21/21 [==============================] - 16s 777ms/step - loss: 0.3770 - accuracy: 0.9310 - val_loss: 2.6923 - val_accuracy: 0.6571\n",
      "Epoch 33/50\n",
      "21/21 [==============================] - 16s 749ms/step - loss: 0.3377 - accuracy: 0.9214 - val_loss: 2.3103 - val_accuracy: 0.6857\n",
      "Epoch 34/50\n",
      "21/21 [==============================] - 17s 798ms/step - loss: 0.3697 - accuracy: 0.9357 - val_loss: 2.4706 - val_accuracy: 0.7429\n",
      "Epoch 35/50\n",
      "21/21 [==============================] - 16s 778ms/step - loss: 0.3270 - accuracy: 0.9238 - val_loss: 3.1356 - val_accuracy: 0.7143\n",
      "Epoch 36/50\n",
      "21/21 [==============================] - 18s 875ms/step - loss: 0.5009 - accuracy: 0.8929 - val_loss: 2.4079 - val_accuracy: 0.7000\n",
      "Epoch 37/50\n",
      "21/21 [==============================] - 18s 836ms/step - loss: 0.3155 - accuracy: 0.9429 - val_loss: 2.4206 - val_accuracy: 0.7286\n",
      "Epoch 38/50\n",
      "21/21 [==============================] - 17s 829ms/step - loss: 0.5126 - accuracy: 0.9262 - val_loss: 2.4771 - val_accuracy: 0.7143\n",
      "Epoch 39/50\n",
      "21/21 [==============================] - 17s 795ms/step - loss: 0.2267 - accuracy: 0.9500 - val_loss: 2.5547 - val_accuracy: 0.7571\n",
      "Epoch 40/50\n",
      "21/21 [==============================] - 17s 801ms/step - loss: 0.3562 - accuracy: 0.9286 - val_loss: 2.9861 - val_accuracy: 0.7000\n",
      "Epoch 41/50\n",
      "21/21 [==============================] - 16s 769ms/step - loss: 0.4196 - accuracy: 0.9262 - val_loss: 1.9966 - val_accuracy: 0.7286\n",
      "Epoch 42/50\n",
      "21/21 [==============================] - 16s 756ms/step - loss: 0.2527 - accuracy: 0.9595 - val_loss: 1.9996 - val_accuracy: 0.7143\n",
      "Epoch 43/50\n",
      "21/21 [==============================] - 16s 766ms/step - loss: 0.3493 - accuracy: 0.9190 - val_loss: 2.0899 - val_accuracy: 0.7143\n",
      "Epoch 44/50\n",
      "21/21 [==============================] - 16s 746ms/step - loss: 0.2321 - accuracy: 0.9381 - val_loss: 2.1638 - val_accuracy: 0.7429\n",
      "Epoch 45/50\n",
      "21/21 [==============================] - 16s 748ms/step - loss: 0.3123 - accuracy: 0.9429 - val_loss: 2.6326 - val_accuracy: 0.7571\n",
      "Epoch 46/50\n",
      "21/21 [==============================] - 16s 757ms/step - loss: 0.4184 - accuracy: 0.9214 - val_loss: 2.9315 - val_accuracy: 0.6857\n",
      "Epoch 47/50\n",
      "21/21 [==============================] - 19s 934ms/step - loss: 0.2153 - accuracy: 0.9714 - val_loss: 2.2021 - val_accuracy: 0.7286\n",
      "Epoch 48/50\n",
      "21/21 [==============================] - 19s 905ms/step - loss: 0.3776 - accuracy: 0.9333 - val_loss: 3.1044 - val_accuracy: 0.7000\n",
      "Epoch 49/50\n",
      "21/21 [==============================] - 19s 882ms/step - loss: 0.3487 - accuracy: 0.9310 - val_loss: 3.2144 - val_accuracy: 0.7143\n",
      "Epoch 50/50\n",
      "21/21 [==============================] - 18s 839ms/step - loss: 0.2779 - accuracy: 0.9476 - val_loss: 3.0806 - val_accuracy: 0.7000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs = 50,\n",
    "    validation_data = validation_generator,\n",
    "    callbacks = callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s plot the results again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='lower right')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'valid'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 2s 438ms/step - loss: 3.0806 - accuracy: 0.7000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.0806334018707275, 0.699999988079071]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(validation_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we reach a validation accuracy of over 43%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the TensorBoard notebook extension on google colab\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FZYRLtbkGhLV"
   },
   "source": [
    "## 2.2 Fine Tuning\n",
    "\n",
    "Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
    "Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used\n",
    "for feature extraction, and jointly training both the newly added part of the model (in this case, the\n",
    "fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly \n",
    "adjusts the more abstract representations of the model being reused in order to make them more relevant for the problem at hand.\n",
    "\n",
    "I stated earlier that it’s necessary to freeze the convolution base of VGG16 in order to be able to\n",
    "train a randomly initialized classifier on top. For the same reason, it’s only possible to fine-tune the top\n",
    "layers of the convolutional base once the classifier on top has already been trained. If the classifier isn’t\n",
    "already trained, the error signal propagating through the network during training will be too\n",
    "large, and the representations previously learned by the layers being fine-tuned will be destroyed. Thus\n",
    "the steps for fine-tuning a network are as follows:\n",
    "\n",
    "The steps for fine-tuning are as follows:\n",
    "\n",
    "1. Add our custom network on top of an already-trained base network.\n",
    "2. Freeze the base network.\n",
    "3. Train the part we added.\n",
    "4. Unfreeze some layers in the base network. (Note that you should not unfreeze “batch normalization” layers, which are not relevant here since there are no such layers in VGG16. )\n",
    "5. Jointly train both these layers and the part we added.\n",
    "\n",
    "We already completed the first three steps when doing feature extraction. Let’s proceed with step 4:\n",
    "we’ll unfreeze our `conv_base` and then freeze individual layers inside it.\n",
    "\n",
    "As a reminder, this is what our convolutional base looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cnObzTupGhLV",
    "outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_14 (InputLayer)       [(None, None, None, 3)]   0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, None, None, 64)    1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, None, None, 64)    36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, None, None, 64)    0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, None, None, 128)   73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, None, None, 128)   147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, None, None, 128)   0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, None, None, 256)   295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, None, None, 256)   590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, None, None, 256)   590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, None, None, 256)   0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, None, None, 512)   1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aDtcl5X2GhLa"
   },
   "source": [
    "We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable.\n",
    "\n",
    "Why not fine-tune more layers? Why not fine-tune the entire convolutional base?\n",
    "You could. But you need to consider the following:\n",
    "\n",
    "- Earlier layers in the convolutional base encode more generic, reusable features, whereas layers higher up encode more specialized features. It’s more useful to fine-tune the more specialized features, because these are the ones that need to be repurposed on your new problem. There would be fast-decreasing returns in fine-tuning lower layers.\n",
    "\n",
    "- The more parameters you’re training, the more you’re at risk of overfitting. The convolutional base has 15 million parameters, so it would be risky to attempt to train it on your small dataset. \n",
    "\n",
    "Thus, in this situation, it’s a good strategy to fine-tune only the top two or three layers in the convolutional base. Let’s set this up, starting from where we left off in the previous example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Freezing all layers until the fourth from the last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tBXYN1t2GhLc",
    "outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer name = input_14, shape = [(None, None, None, 3)], trainable = False\n",
      "layer name = block1_conv1, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block1_conv2, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block1_pool, shape = (None, None, None, 64), trainable = False\n",
      "layer name = block2_conv1, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block2_conv2, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block2_pool, shape = (None, None, None, 128), trainable = False\n",
      "layer name = block3_conv1, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_conv2, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_conv3, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block3_pool, shape = (None, None, None, 256), trainable = False\n",
      "layer name = block4_conv1, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_conv2, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_conv3, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block4_pool, shape = (None, None, None, 512), trainable = False\n",
      "layer name = block5_conv1, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_conv2, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_conv3, shape = (None, None, None, 512), trainable = True\n",
      "layer name = block5_pool, shape = (None, None, None, 512), trainable = True\n"
     ]
    }
   ],
   "source": [
    "conv_base.trainable = True\n",
    "for layer in conv_base.layers[:-4]:\n",
    "    layer.trainable = False\n",
    "    \n",
    "for layer in conv_base.layers[0:]:\n",
    "    print('layer name = ' + layer.name + ', shape = ' + repr(layer.output_shape)\n",
    "            + ', trainable = ' + repr(layer.trainable))        \n",
    " \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XWw1mYfUGhLg"
   },
   "source": [
    "Now we can begin fine-tuning the model. We’ll do this with the `RMSprop` optimizer, using a very low learning rate. The reason for using a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the three\n",
    "layers we’re fine-tuning. Updates that are too large may harm these representations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fine-tuning the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4YBjFhSVGhLh",
    "outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer name = input_18, shape = [(None, 150, 150, 3)], trainable = True\n",
      "layer name = vgg16, shape = (None, None, None, 512), trainable = True\n",
      "layer name = flatten_12, shape = (None, 8192), trainable = True\n",
      "layer name = dense_24, shape = (None, 256), trainable = True\n",
      "layer name = dropout_12, shape = (None, 256), trainable = True\n",
      "layer name = dense_25, shape = (None, 8), trainable = True\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "    optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
    "    metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "21/21 [==============================] - 20s 935ms/step - loss: 2.5061 - accuracy: 0.1548 - val_loss: 1.8825 - val_accuracy: 0.2429\n",
      "Epoch 2/30\n",
      "21/21 [==============================] - 20s 932ms/step - loss: 2.1597 - accuracy: 0.1952 - val_loss: 1.7301 - val_accuracy: 0.3714\n",
      "Epoch 3/30\n",
      "21/21 [==============================] - 20s 938ms/step - loss: 1.8653 - accuracy: 0.3000 - val_loss: 1.5753 - val_accuracy: 0.4143\n",
      "Epoch 4/30\n",
      "21/21 [==============================] - 20s 963ms/step - loss: 1.6819 - accuracy: 0.3738 - val_loss: 1.4570 - val_accuracy: 0.4429\n",
      "Epoch 5/30\n",
      "21/21 [==============================] - 20s 956ms/step - loss: 1.4900 - accuracy: 0.4238 - val_loss: 1.3590 - val_accuracy: 0.4286\n",
      "Epoch 6/30\n",
      "21/21 [==============================] - 20s 938ms/step - loss: 1.2282 - accuracy: 0.5405 - val_loss: 1.2675 - val_accuracy: 0.5000\n",
      "Epoch 7/30\n",
      "21/21 [==============================] - 20s 960ms/step - loss: 1.1882 - accuracy: 0.5548 - val_loss: 1.1840 - val_accuracy: 0.5429\n",
      "Epoch 8/30\n",
      "21/21 [==============================] - 20s 931ms/step - loss: 1.0866 - accuracy: 0.5857 - val_loss: 1.1269 - val_accuracy: 0.5429\n",
      "Epoch 9/30\n",
      "21/21 [==============================] - 19s 921ms/step - loss: 0.9324 - accuracy: 0.6500 - val_loss: 1.0479 - val_accuracy: 0.5714\n",
      "Epoch 10/30\n",
      "21/21 [==============================] - 19s 925ms/step - loss: 0.8240 - accuracy: 0.6833 - val_loss: 1.0298 - val_accuracy: 0.6286\n",
      "Epoch 11/30\n",
      "21/21 [==============================] - 20s 934ms/step - loss: 0.6968 - accuracy: 0.7595 - val_loss: 0.9499 - val_accuracy: 0.5857\n",
      "Epoch 12/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.6573 - accuracy: 0.7690 - val_loss: 0.9385 - val_accuracy: 0.6286\n",
      "Epoch 13/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.6287 - accuracy: 0.7690 - val_loss: 0.8889 - val_accuracy: 0.6429\n",
      "Epoch 14/30\n",
      "21/21 [==============================] - 21s 1s/step - loss: 0.5531 - accuracy: 0.8119 - val_loss: 0.8967 - val_accuracy: 0.7000\n",
      "Epoch 15/30\n",
      "21/21 [==============================] - 22s 1s/step - loss: 0.4807 - accuracy: 0.8524 - val_loss: 0.8767 - val_accuracy: 0.6857\n",
      "Epoch 16/30\n",
      "21/21 [==============================] - 21s 1s/step - loss: 0.4412 - accuracy: 0.8643 - val_loss: 0.8314 - val_accuracy: 0.7000\n",
      "Epoch 17/30\n",
      "21/21 [==============================] - 20s 950ms/step - loss: 0.4161 - accuracy: 0.8571 - val_loss: 0.8314 - val_accuracy: 0.7143\n",
      "Epoch 18/30\n",
      "21/21 [==============================] - 20s 954ms/step - loss: 0.3725 - accuracy: 0.8786 - val_loss: 0.7916 - val_accuracy: 0.7286\n",
      "Epoch 19/30\n",
      "21/21 [==============================] - 20s 929ms/step - loss: 0.3556 - accuracy: 0.8690 - val_loss: 0.7489 - val_accuracy: 0.6714\n",
      "Epoch 20/30\n",
      "21/21 [==============================] - 20s 958ms/step - loss: 0.2972 - accuracy: 0.8929 - val_loss: 0.7240 - val_accuracy: 0.7143\n",
      "Epoch 21/30\n",
      "21/21 [==============================] - 21s 973ms/step - loss: 0.2663 - accuracy: 0.9167 - val_loss: 0.7122 - val_accuracy: 0.7714\n",
      "Epoch 22/30\n",
      "21/21 [==============================] - 19s 922ms/step - loss: 0.2709 - accuracy: 0.9143 - val_loss: 0.7322 - val_accuracy: 0.7571\n",
      "Epoch 23/30\n",
      "21/21 [==============================] - 20s 936ms/step - loss: 0.2267 - accuracy: 0.9357 - val_loss: 0.6977 - val_accuracy: 0.7429\n",
      "Epoch 24/30\n",
      "21/21 [==============================] - 20s 930ms/step - loss: 0.2592 - accuracy: 0.9214 - val_loss: 0.7409 - val_accuracy: 0.7857\n",
      "Epoch 25/30\n",
      "21/21 [==============================] - 20s 929ms/step - loss: 0.2202 - accuracy: 0.9333 - val_loss: 0.7303 - val_accuracy: 0.7429\n",
      "Epoch 26/30\n",
      "21/21 [==============================] - 19s 923ms/step - loss: 0.2009 - accuracy: 0.9429 - val_loss: 0.7513 - val_accuracy: 0.7571\n",
      "Epoch 27/30\n",
      "21/21 [==============================] - 19s 912ms/step - loss: 0.1688 - accuracy: 0.9667 - val_loss: 0.7185 - val_accuracy: 0.7429\n",
      "Epoch 28/30\n",
      "21/21 [==============================] - 20s 976ms/step - loss: 0.1527 - accuracy: 0.9619 - val_loss: 0.7684 - val_accuracy: 0.7571\n",
      "Epoch 29/30\n",
      "21/21 [==============================] - 19s 917ms/step - loss: 0.1290 - accuracy: 0.9690 - val_loss: 0.8116 - val_accuracy: 0.7429\n",
      "Epoch 30/30\n",
      "21/21 [==============================] - 20s 952ms/step - loss: 0.1121 - accuracy: 0.9667 - val_loss: 0.7612 - val_accuracy: 0.7571\n"
     ]
    }
   ],
   "source": [
    "logdir = os.path.join(\"logs_fine_tuning\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(filepath=\"fine_tuning.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
    "    tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "train_generator,\n",
    "epochs=30,\n",
    "validation_data=validation_generator,\n",
    "callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9rwSMMQaGhLx",
    "outputId": "0a58db5a-0f22-45e8-d1fb-0a664fceaf4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }