003_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries to Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras import models, layers, optimizers\n",
    "from keras.layers import Dropout\n",
    "\n",
    "import os\n",
    "import gc\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Image Directories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = '/Users/arencarpenter/Desktop/Covid_CT_Classification/Split/train'\n",
    "val_dir = '/Users/arencarpenter/Desktop/Covid_CT_Classification/Split/validation'\n",
    "test_dir = '/Users/arencarpenter/Desktop/Covid_CT_Classification/Split/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n",
      "Found 83 images belonging to 2 classes.\n",
      "Found 78 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_img = image.ImageDataGenerator(rescale = 1/255).flow_from_directory(train_dir,\n",
    "                                                                  target_size = (256,256), \n",
    "                                                                  color_mode='grayscale',\n",
    "                                                                  class_mode='binary')\n",
    "val_img = image.ImageDataGenerator(rescale = 1/255).flow_from_directory(val_dir,\n",
    "                                                                target_size = (256,256), \n",
    "                                                                color_mode='grayscale',\n",
    "                                                                class_mode='binary')\n",
    "test_img = image.ImageDataGenerator(rescale = 1/255).flow_from_directory(test_dir,\n",
    "                                                                target_size = (256,256), \n",
    "                                                                color_mode='grayscale',\n",
    "                                                                class_mode='binary', \n",
    "                                                                shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Objects for Performance Tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot performance\n",
    "def plot_performance(hist):\n",
    "    hist_ = hist.history\n",
    "    epochs = hist.epoch\n",
    "\n",
    "    plt.plot(epochs, hist_['accuracy'], label='Training Accuracy')\n",
    "    plt.plot(epochs, hist_['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.plot(epochs, hist_['loss'], label='Training loss')\n",
    "    plt.plot(epochs, hist_['val_loss'], label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling Iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline CNN: 2 Conv2D, 1 Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy', 'Recall', 'Precision'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "12/12 [==============================] - 14s 1s/step - loss: 11.8517 - accuracy: 0.6632 - recall: 0.4348 - precision: 0.5405 - val_loss: 0.3763 - val_accuracy: 0.8193 - val_recall: 0.5333 - val_precision: 0.9412\n",
      "Epoch 2/20\n",
      "12/12 [==============================] - 14s 1s/step - loss: 0.3403 - accuracy: 0.8538 - recall: 0.6957 - precision: 0.8727 - val_loss: 0.7229 - val_accuracy: 0.5904 - val_recall: 0.9667 - val_precision: 0.4677\n",
      "Epoch 3/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.3330 - accuracy: 0.8616 - recall: 0.7899 - precision: 0.8195 - val_loss: 0.5430 - val_accuracy: 0.6627 - val_recall: 0.9667 - val_precision: 0.5179\n",
      "Epoch 4/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.1784 - accuracy: 0.9399 - recall: 0.9348 - precision: 0.9021 - val_loss: 0.3760 - val_accuracy: 0.8072 - val_recall: 0.5667 - val_precision: 0.8500\n",
      "Epoch 5/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.1011 - accuracy: 0.9634 - recall: 0.9565 - precision: 0.9429 - val_loss: 0.2891 - val_accuracy: 0.8916 - val_recall: 0.8333 - val_precision: 0.8621\n",
      "Epoch 6/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.3506 - accuracy: 0.8930 - recall: 0.8841 - precision: 0.8299 - val_loss: 0.3653 - val_accuracy: 0.8193 - val_recall: 0.9000 - val_precision: 0.6923\n",
      "Epoch 7/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.2347 - accuracy: 0.9321 - recall: 0.8986 - precision: 0.9118 - val_loss: 0.3854 - val_accuracy: 0.7952 - val_recall: 0.7000 - val_precision: 0.7241\n",
      "Epoch 8/20\n",
      "12/12 [==============================] - 14s 1s/step - loss: 0.0203 - accuracy: 0.9948 - recall: 1.0000 - precision: 0.9857 - val_loss: 0.4628 - val_accuracy: 0.8193 - val_recall: 0.7000 - val_precision: 0.7778\n",
      "Epoch 9/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 0.0071 - accuracy: 0.9974 - recall: 1.0000 - precision: 0.9928 - val_loss: 0.4560 - val_accuracy: 0.7952 - val_recall: 0.7333 - val_precision: 0.7097\n",
      "Epoch 10/20\n",
      "12/12 [==============================] - 14s 1s/step - loss: 0.0021 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.5303 - val_accuracy: 0.8313 - val_recall: 0.7000 - val_precision: 0.8077\n",
      "Epoch 11/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 8.1624e-04 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.5472 - val_accuracy: 0.8193 - val_recall: 0.7333 - val_precision: 0.7586\n",
      "Epoch 12/20\n",
      "12/12 [==============================] - 17s 1s/step - loss: 2.3072e-04 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.5965 - val_accuracy: 0.8313 - val_recall: 0.7000 - val_precision: 0.8077\n",
      "Epoch 13/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 1.2172e-04 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.6573 - val_accuracy: 0.8434 - val_recall: 0.7000 - val_precision: 0.8400\n",
      "Epoch 14/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 5.1597e-05 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.6409 - val_accuracy: 0.8193 - val_recall: 0.7333 - val_precision: 0.7586\n",
      "Epoch 15/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 2.3792e-05 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.6757 - val_accuracy: 0.7831 - val_recall: 0.7333 - val_precision: 0.6875\n",
      "Epoch 16/20\n",
      "12/12 [==============================] - 16s 1s/step - loss: 1.9186e-05 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.7489 - val_accuracy: 0.7831 - val_recall: 0.7333 - val_precision: 0.6875\n",
      "Epoch 17/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 6.9284e-06 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.7776 - val_accuracy: 0.7831 - val_recall: 0.7333 - val_precision: 0.6875\n",
      "Epoch 18/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 3.3798e-06 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.8121 - val_accuracy: 0.7831 - val_recall: 0.7333 - val_precision: 0.6875\n",
      "Epoch 19/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 2.2012e-06 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.8320 - val_accuracy: 0.7952 - val_recall: 0.7667 - val_precision: 0.6970\n",
      "Epoch 20/20\n",
      "12/12 [==============================] - 15s 1s/step - loss: 1.3098e-06 - accuracy: 1.0000 - recall: 1.0000 - precision: 1.0000 - val_loss: 0.8533 - val_accuracy: 0.7952 - val_recall: 0.7333 - val_precision: 0.7097\n",
      "Total Time Elapsed:  5  minutes  32  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_baseline = model.fit(train_img, \n",
    "                    epochs=20, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 139ms/step - loss: 0.7861 - accuracy: 0.8333 - recall: 0.7778 - precision: 0.7500\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['2 Conv2D, 1 Dense'] = {'Val_Loss': 0.8533, 'Val_Acc': 0.7952, 'Test_Loss': 0.7861, 'Test_Acc': 0.8333}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad for a first model with some default hyperparameters. Twenty epochs with this small of a dataset led to overfitting fairly quickly. \n",
    "\n",
    "Result: Val_Acc -- 79.5, Val_Loss -- 0.853\n",
    "\n",
    "Next steps: augment data to generate more data to combat overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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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ncsEFF2BmFBUV8eijjzJy5Ejq6uqYPn06Z599drfXhbzzzjspLi5m6dKlLF26tMNwsN/97ncpLy+npaWFk08+maVLl/KlL32JW2+9lXnz5lFZWdlhWYsWLeKee+5hwYIFuDsf/OAHmTFjBqNHj2blypU88MAD/PjHP+b888/nkUce6XF870996lPcfvvtzJgxg29961tcf/313Hbbbdx44428+eabFBYWtnXb3HLLLdxxxx0ce+yxNDQ0UFSU3vDYkWmBjyzKIz9X46GIRE1qN0pq94m7c+211zJ58mROOeUU3n33XdavX9/tcubPn98WpJMnT2by5PZhNR566CGqq6uZNm0ay5cv73WgqhdeeIGPf/zjlJSUkEgkOPfcc/njH/8IwMEHH8zUqVOBnoeshWB88s2bNzNjxgwALrnkEubPn99Wx4svvpj77ruv7YzPY489lquvvpo5c+awefPmtM8EjUwL3MwYXVygFrjIQPXQUh5MH/vYx7j66qtZvHgxjY2NbS3n+++/n9raWhYtWkR+fj7jxo3rcgjZVF21zt98801uueUW/vrXvzJ69GguvfTSXpfT0xhQyaFoIRiOtrculO785je/Yf78+Tz++OP8+7//O8uXL2f27NmceeaZPPnkk0yfPp3f/e53HHHEEQNaPkSoBQ4aD0UkihKJBCeccAKf/vSnO+y83LJlC2PGjCE/P5958+bx1ltv9bic448/vu3CxS+//DJLly4FgqFoS0pKGDVqFOvXr+epp55qe01paWmX/czHH388v/71r9mxYwfbt2/n0Ucf5cMf/nC//7ZRo0YxevTottb7L37xC2bMmEFrayvvvPMOJ554IjfffDObN2+moaGBVatWcdRRR3HNNddQU1PDq6++2u91popMCxyCI1G0E1MkembOnMm5557b4YiUiy++mLPOOouamhqmTp3aa0v0s5/9LJdddhmTJ09m6tSpHH300UBwdZ1p06YxceLEPYaiveKKKzj99NPZb7/9mDdvXtv86upqLr300rZlXH755UybNq3H7pLu3HvvvVx55ZXs2LGD8ePHc88999DS0sKsWbPYsmUL7s5XvvIVysrK+OY3v8m8efPIzc1lwoQJbVcXGqjIDCcLcNXcv7Hknc08/68nZrBWIvGl4WSjpz/DyUaqC6W8ROOhiIgkRSrANR6KiEi7aAV4QuOhiPTXUHaTSnr6+15FKsDbzsZUN4pInxQVFVFfX68QjwB3p76+vl8n9/R6FIqZ3Q18FNjg7pPCeeXAg8A4YDVwvrtvGkCd+6Wi7XR6BbhIX4wdO5Y1a9ZQW1s73FWRPigqKmLs2LF9Lt+Xwwh/BvwX8POUebOB37v7jWY2O3x8TT/qOSDtXSg6lFCkL/Lz8zn44IOHuxoySHrtQnH3+cDGTrPPAe4Np+8FPpbhenVJXSgiIu0G2ge+j7uvAwjvx3RX0MyuMLOFZrYw3Z9xyfFQ1IUiIjIEOzHd/S53r3H3mqqqqrSWZWY6FlxEJDTQAF9vZvsBhPcbMlelnpWXFOp0ehERBh7gjwOXhNOXAHuOxD5IKhMF6kIREaEPAW5mDwB/Bg43szVm9hngRuAjZrYS+Ej4eEiUlxToRB4REfpwGKG7d3fxupMzXJc+KS/RmOAiIhCxMzEBKhOFNGg8FBGR6AV4ua5OLyICRDjA1Y0iItkucgFemdB4KCIiEMEALy/ReCgiIhDJAFcXiogIRDDANR6KiEggcgGeHA+lvkFdKCKS3SIX4BD0g+swQhHJdpEMcI2HIiIS0QDX6fQiIhEN8Ap1oYiIRDTAEwU07GpmZ5PGQxGR7BXJANd4KCIiEQ3wCgW4iEhEA1zjoYiIRDPAk+Oh6GQeEclmkQzwZAtcXSgiks0iGeClhcF4KHU6FlxEslgkAzw5HoqGlBWRbBbJAAedzCMiEt0ATxSoC0VEslp0A7ykQC1wEclqkQ1wDSkrItkurQA3s6+Y2XIze9nMHjCzokxVrDcaD0VEst2AA9zMDgC+BNS4+yQgF7gwUxXrjU6nF5Fsl24XSh4wwszygGJgbfpV6hsNaCUi2W7AAe7u7wK3AG8D64At7v7bzuXM7AozW2hmC2trawde006SZ2PW6XR6EclS6XShjAbOAQ4G9gdKzGxW53Lufpe717h7TVVV1cBr2klFOB6KWuAikq3S6UI5BXjT3WvdvQn4FfChzFSrd+XJEQl1LLiIZKl0AvxtYLqZFZuZAScDKzJTrd4lx0PRkLIikq3S6QNfADwMLAaWhcu6K0P16pWZhafTqw9cRLJTXjovdvdvA9/OUF36TVenF5FsFtkzMSE4EkVdKCKSraId4BoPRUSyWKQDvLykUJdVE5GsFekAr0gUsH13i8ZDEZGsFO0A1+n0IpLFIh3gyfFQdCSKiGSjSAd4RSI4nb5ex4KLSBaKdoCrBS4iWSzSAZ4cD0V94CKSjSId4KWFeRTk5uhkHhHJSpEOcDMLT6dXH7iIZJ9IBzgER6KoC0VEslHkA1zjoYhItop+gJcU6DBCEclK0Q/wRCEbdRihiGShyAd4eYnGQxGR7BT5AG87mUf94CKSZaIf4OHp9OpGEZFsE/kATw5oVacdmSKSZSIf4G1DyqoFLiJZJvoBrvFQRCRLRT7AE+F4KOpCEZFsE/kAT46Hoi4UEck2kQ9wCLpR1IUiItkmFgFeXlJAnQJcRLJMWgFuZmVm9rCZvWpmK8zsmExVrD8qE4VsVB+4iGSZvDRf/wPgaXc/z8wKgOIM1Knf1AcuItlowC1wMxsJHA/8FMDdd7v75kxVrD80HoqIZKN0ulDGA7XAPWb2NzP7iZmVdC5kZleY2UIzW1hbW5vG6rpXmdB4KCKSfdIJ8DygGrjT3acB24HZnQu5+13uXuPuNVVVVWmsrnvlJcF4KLq0mohkk3QCfA2wxt0XhI8fJgj0IVeuEQlFJAsNOMDd/T3gHTM7PJx1MvBKRmrVT8kuFO3IFJFsku5RKF8E7g+PQHkDuCz9KvVfewtcXSgikj3SCnB3XwLUZKguA5YcD0VdKCKSTWJxJqaZBafTqwtFRLJILAIcgm4UtcBFJJsowEVEIio2Aa7xUEQk28QmwMtLCqhXH7iIZJHYBHhFooAdu1to3K3xUEQkO8QnwHUsuIhkmdgEeHI8FF2ZR0SyRWwCvEIjEopIlolPgCe7ULQjU0SyRGwCPDkeig4lFJFsEZsATxTmUZCn8VBEJHvEJsDNjAodCy4iWSQ2AQ7hxY3VAheRLBGrAK9IFKoLRUSyRrwCvKRA18UUkawRuwBXF4qIZItYBXi5xkMRkSwSqwDXeCgikk1iFuAaD0VEskesArw8odPpRSR7xCrA27tQFOAiEn/xCvBEsgtFfeAiEn+xCvCSgtxgPBR1oYhIFkg7wM0s18z+ZmZPZKJCadYlOJlHXSgikgUy0QK/CliRgeVkREVCJ/OISHZIK8DNbCxwJvCTzFQnfeUlhTqdXkSyQrot8NuA/wO0dlfAzK4ws4VmtrC2tjbN1fVOXSgiki0GHOBm9lFgg7sv6qmcu9/l7jXuXlNVVTXQ1fWZxkMRkWyRTgv8WOBsM1sNzAVOMrP7MlKrNGg8FBHJFgMOcHf/uruPdfdxwIXAc+4+K2M1G6DK8HR6jYciInEXq+PAof3ixjoWXETiLi8TC3H3PwB/yMSy0pUcD0X94CISd7Frgbd3oSjARSTeYhfg7SMSqg9cROItdgGeHA9FXSgiEnexC3Azo1In84hIFohdgEPQjaIuFBGJu3gGeEmhulBEJPZiGeDqQhGRbBDLAC8vKdCJPCISe7EM8IpEIY1NLezY3TzcVRERGTTxDHCdTi8iWSCWAZ4cD0U7MkUkzmIZ4BUaD0VEskA8AzwcD6VOx4KLSIzFMsA1IqGIZINYBnhJQS6FGg9FRGIulgFuZlSUFFCno1BEJMZiGeAQdKNs1GXVRCTGYhvgFRoPRURiLsYBri4UEYm32AZ4eUmBWuAiEmuxDXCNhyIicRffANd4KCISc/ENcJ3MIyIxF9sATw5oVa9DCUUkpgYc4Gb2PjObZ2YrzGy5mV2VyYqlKzkeirpQRCSu8tJ4bTPwVXdfbGalwCIze9bdX8lQ3dKiLhQRibsBt8DdfZ27Lw6ntwErgAMyVbF0FYfjoejamCISVxnpAzezccA0YEEmlpcJyfFQ1IUiInGVdoCbWQJ4BPiyu2/t4vkrzGyhmS2sra1Nd3X9UpEo1HgoIhJbaQW4meUThPf97v6rrsq4+13uXuPuNVVVVemsrt/KSwrUhSIisZXOUSgG/BRY4e63Zq5KmaMuFBGJs3Ra4McCnwROMrMl4e2MDNUrIyoSBToOXERia8CHEbr7C4BlsC4ZV15SyM6mVnbsbqa4IJ0jJkVE9j6xPRMT2o8FVzeKiMRRvAO87XR6BbiIxE+sAzw5HooOJRSROIp1gFcmNB6KiMRXrAO8XF0oIhJjsQ7w5HgoGtBKROIo1gFuZlQmCtWFIiKxFOsAh+Tp9NqJKSLxkxUBri4UEYmj2Ad4RULjoYhIPMU/wNWFIiIxFfsATx0PRUQkTmIf4BoPRUSGhTs074ZdDdC4KZjOsNgP0Zc6Hsr7youHuTYikjHu0NIEzY3QtDO8D2/NOztON++Elt1B+ZYmaG0a+HTL7pTHu7uf39rpV/+sR+DQUzK6CeIf4OHp9BoPRWQINe2Exo2wYyPsqIedm8OQDYO0eSc07wpvqfNSnmvZ1bFMajA374SmHeCt6dc1Jx9yCyA3L5wOb3tMFwTT+SPbp1Nfm1sQ3HJSplPnVxyWfl07iXeANzUydu0z/Dj/R7z/iU2wbArsexTsOzm4T+wDtlcPaS4yfNzbA3P39qAbYEd9SjCH4dw2rx52hGWatvd9PXlFkFcY3OcWtk/nFQT3hQkoroD8IsgbAfnhLa+om3kp9/kjgufzCjuGajJ4c3IjnQHxC/DWVnjrf2HpXHjlcSp3beWonNFsKpzEqHcXw/JH28uWVIWBnhLqFYcGb6rIYHEPAnHnlqBlunNLx1tj6rxwencDWG7Y6ssLPqM5KdNt8/PD5/I6lrWcoCWb2npNtojbpsOuiKYd7d0OvSkaBSPKg4BN7ANVRwbTxaOD++RzI8ogvzgM0pSQzs2PdIAOt/gE+IYV8NJcWPYwbF0DBQk48mx88gWc9LPtzDpkPNeecWTwz7F+Oax/Gd5bCu8tg7/cGfyEg+Dbep8JsM+k9mDfZ2LQCpDs1rw7CNJdW4MdU7sbwvttXTxOnbctuE8NZ2/peV0FiSAci0ZBURkUVwbdBa1N0NoCu3cFfaydby3J6abwviXok/XW9hZrslWaXxSEalEZlHZutYbPpbZwi8s7hfLooDUrwybaW3/be0FgL50bBLHlwqEnw0euh8PPgIJiDBhd8hx1DWEf+IgyGHdscEtqaYK6vwfLeG9ZEOyvPAaL7w0LGJQfDGMmBLd9JsCYiVA+Xh/gvUFrSxCMjSk/6XfUp/zE3xi0ZFubwx1SqYHX1B6KXT6XnN7d/iXfm9zC4Au/sBQKSsMugMrg111bKIfB3OHxqCAUC0fqcyV9Er1Pya4GePWJoLX95vNBy2L/ajjtJpj0CUhU7fGSXk+nz80PWtn7TIQpFwbz3GHruymhvixo5b/2ZPuOk9xCqPqH9mBPhvvIA7LzZ2FLc0p41sH2uvbHu7YFZcyCn/OpNzrPs073OUGIJvtgU4N5R30wH++6TnlFQWuxaFT7Tqdk90L+iCAsc/LCftG8lOc7P84PgjgZyAWJMKRHtk8XhKGdmz9UW1yyXDQCvKUZ3vhD0NJ+9TdBH13ZgfDhr8JR5wch2oMBnU5vBqPGBrfDT2+f39QYtNbXvwIbwtubf4SlD7aXKRwFY44MbvtMDIK98rAgSPaG/nX3jq3Plk6HPnU4JKq5/QiBtkBOhvPGjkG9c3P368wvbl+3t7bf8L4fSZBbGPavVgQ/5/edlPI4+dO+vOO8Ah06KvEVjQB//Avw0gNBK2ryBcHtfR+EnL6dh1ReUsDf3t7M95/9Ozlm5Bjk5FjQwCN8bMHjjs+3P1dckMshVQkOqUowYr8psN+Ujitp3BS00De8Eob7Clj+K1h0T3sZy3BeOd0AAAn2SURBVAl2nJaMCX4pdLjfJ2V6TO9h39oaBGZqqCZbu9uD+5btdTRuXk9LQx35TdvIo5lcD25pyckLA7ISSipgv8nBdHEFlFS2h2dJZTi/vPdWaVuwdxPwlhN8CWTjLxuRbkQjwGs+DUecCYedGuy97qdpB47msSVr+cHvV6ZdFTM4oGwEh45JcNiYBIcmb1WljDroQ3DQh9oLu8O2dUGgb1wFDRtg+wZoqA3u614P7rva22857Xv2S6qgoCSl66Au+MLopuXalDOCzTaS9c0l1LWWsolDaCkYyZbdxi7PpYlcmjyPZnLJzS+geMQISotHUFpSzKjECMoSJZQliikfWcKokmIseThXMpiLRmU+SM2CfRgi0mfm3k3f4SCoqanxhQsXDtn6uuLutDq0utPqHvQmpMzz1vbnWr29/JbGJl7f0BDcaoP7VbUN7G5uD9HKRGHHUA9vY0oLse4Czz3oH24L9w2wvXbPsN+9vb3rIGz9enE565pKWLoxjwXrjT++28o7u4rZRQFH7FvKMYdUcOwhlRw9vpyRRfm0tjp123fx7qZG3t3c2OX9tl0dW+cFeTkcUDaCgyqKmTK2jPcfNJqpB5Yxskj9vCJDxcwWuXvNHvOzLcAzqaXVWbNpR3uwb2hg5YYGVm1o6BCEpUV5jKsooSJRQHlxAeUlBYwuKaCiJJhO3ipKCiktyiMnp+uwd3fe2djI/66q40+r6vnzqjrqwr79gyqK+dAhFXzokEqmj6+gqrT/v1QAtu5sCgI9GephsK+qbeC19dtwDxrLh41JUH3g6OB2UBnjKxPd1ltE0jMoAW5mpwE/AHKBn7j7jT2Vj1uAd8fd2bBtVxDo67fxem0D72xsZOP23W23xqaujwPOzTFGFxdQXpLfFuqjS/LZ1dTKn1bV8+7mRgDGlBYGgX1oJR86pIKxowd/Z922nU289M4WFr+9icVvb+Jvb29mS2MTAKNG5DPtwLK2UJ/yvlGUqpUukhEZD3AzywX+DnwEWAP8FZjp7q9095psCfC+aNzdwsYdu9nYsDu4376LjdubOt23Bz7A0QeXc2wY2IdUJbrvlhkira3OG3Xbg0B/Kwj1lRsa2lrph+9TyrQDR1N9YBkT9h9JYV4OuTk55JqRm2vBfU7HW16OkWPhvVr0IsDgBPgxwHXu/o/h468DuPt/dPcaBXj8bd3ZxJK3N4et9M387e1NbNs58KNekkGeGx4RZGYY4T7PlCOHkvMgWa79CKPkF13q911yOnglez7fNs/2mNdBN98xXc0e7i/c4Zbdfz3833OP4gPjygf02u4CPJ2jUA4A3kl5vAb4YBcrvgK4AuDAAw9MY3USBSOL8jn+H6o4/h+CE6paW51V4U7fplantdVpTrlvcaelpZUWh5bWVlpaO917e/lghzM4wbS749C2Izo57eHO6WS55GvaeIe7YDqlIeNt8/Z4SQfdNX66nDt0u5r2Sp7tGwAYkZ/5o6zSCfCuvlD3eJfc/S7gLgha4GmsTyIoJ8c4bJ9SDtundLirIhI76VyRZw3wvpTHY4G16VVHRET6Kp0A/ytwmJkdbGYFwIXA45mploiI9GbAXSju3mxmXwCeITiM8G53X56xmomISI/SOpXe3Z8EnsxQXUREpB9if1V6EZG4UoCLiESUAlxEJKIU4CIiETWkoxGaWS3w1gBfXgnUZbA6mab6pUf1S4/ql769uY4Hufse14sc0gBPh5kt7GosgL2F6pce1S89ql/6olDHztSFIiISUQpwEZGIilKA3zXcFeiF6pce1S89ql/6olDHDiLTBy4iIh1FqQUuIiIpFOAiIhG11wW4mZ1mZq+Z2etmNruL5wvN7MHw+QVmNm4I6/Y+M5tnZivMbLmZXdVFmRPMbIuZLQlv3xqq+oXrX21my8J173H9OgvMCbffUjOrHsK6HZ6yXZaY2VYz+3KnMkO6/czsbjPbYGYvp8wrN7NnzWxleD+6m9deEpZZaWaXDGH9vmdmr4bv36NmVtbNa3v8LAxi/a4zs3dT3sMzunltj//rg1i/B1PqttrMlnTz2kHffmkLLj+1d9wIhqVdBYwHCoCXgAmdynwO+GE4fSHw4BDWbz+gOpwuJbioc+f6nQA8MYzbcDVQ2cPzZwBPEVxRaTqwYBjf6/cITlAYtu0HHA9UAy+nzLsZmB1OzwZu6uJ15cAb4f3ocHr0ENXvVCAvnL6pq/r15bMwiPW7DvhaH97/Hv/XB6t+nZ7/T+Bbw7X90r3tbS3wo4HX3f0Nd98NzAXO6VTmHODecPph4GQboqvFuvs6d18cTm8DVhBcGzRKzgF+7oG/AGVmtt8w1ONkYJW7D/TM3Ixw9/nAxk6zUz9j9wIf6+Kl/wg86+4b3X0T8Cxw2lDUz91/6+7JK0X/heBqWMOim+3XF335X09bT/ULc+N84IFMr3eo7G0B3tWFkjsHZFuZ8EO8BagYktqlCLtupgELunj6GDN7ycyeMrOJQ1qx4LqkvzWzReEFpTvryzYeChfS/T/OcG4/gH3cfR0EX9rAmC7K7C3b8dMEv6i60ttnYTB9IeziububLqi9Yft9GFjv7iu7eX44t1+f7G0B3pcLJffpYsqDycwSwCPAl919a6enFxN0C0wBbgd+PZR1A45192rgdODzZnZ8p+f3hu1XAJwN/L8unh7u7ddXe8N2/DegGbi/myK9fRYGy53AIcBUYB1BN0Vnw779gJn03Poeru3XZ3tbgPflQsltZcwsDxjFwH7CDYiZ5ROE9/3u/qvOz7v7VndvCKefBPLNrHKo6ufua8P7DcCjBD9VU+0NF6M+HVjs7us7PzHc2y+0PtmtFN5v6KLMsG7HcKfpR4GLPeyw7awPn4VB4e7r3b3F3VuBH3ez3uHefnnAucCD3ZUZru3XH3tbgPflQsmPA8k9/ucBz3X3Ac60sM/sp8AKd7+1mzL7Jvvkzexogm1cP0T1KzGz0uQ0wc6ulzsVexz4VHg0ynRgS7K7YAh12/IZzu2XIvUzdgnwWBdlngFONbPRYRfBqeG8QWdmpwHXAGe7+45uyvTlszBY9Uvdp/LxbtY73BdFPwV41d3XdPXkcG6/fhnuvaidbwRHSfydYA/1v4XzbiD4sAIUEfz0fh14ERg/hHU7juBn3lJgSXg7A7gSuDIs8wVgOcFe9b8AHxrC+o0P1/tSWIfk9kutnwF3hNt3GVAzxO9vMUEgj0qZN2zbj+CLZB3QRNAq/AzBPpXfAyvD+/KwbA3wk5TXfjr8HL4OXDaE9XudoP84+RlMHpW1P/BkT5+FIarfL8LP1lKCUN6vc/3Cx3v8rw9F/cL5P0t+5lLKDvn2S/emU+lFRCJqb+tCERGRPlKAi4hElAJcRCSiFOAiIhGlABcRiSgFuIhIRCnARUQi6v8DVqjb74jd84wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(history_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 Conv2D, 2 Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(64, 3, activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(32, 3, activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(32, 3, activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12/12 [==============================] - 16s 1s/step - loss: 1.9259 - accuracy: 0.8903 - val_loss: 0.3388 - val_accuracy: 0.8795\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 17s 1s/step - loss: 0.0584 - accuracy: 0.9791 - val_loss: 0.4254 - val_accuracy: 0.8675\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 17s 1s/step - loss: 0.0220 - accuracy: 0.9974 - val_loss: 0.4985 - val_accuracy: 0.8795\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 17s 1s/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.6851 - val_accuracy: 0.8675\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 19s 2s/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.6922 - val_accuracy: 0.8795\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 3.0583e-04 - accuracy: 1.0000 - val_loss: 0.7452 - val_accuracy: 0.8795\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 18s 2s/step - loss: 1.1081e-04 - accuracy: 1.0000 - val_loss: 0.8164 - val_accuracy: 0.8554\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 18s 2s/step - loss: 4.0661e-05 - accuracy: 1.0000 - val_loss: 0.8542 - val_accuracy: 0.8795\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 18s 2s/step - loss: 2.2850e-05 - accuracy: 1.0000 - val_loss: 0.8400 - val_accuracy: 0.8916\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 19s 2s/step - loss: 1.0453e-05 - accuracy: 1.0000 - val_loss: 0.9695 - val_accuracy: 0.8675\n",
      "Total Time Elapsed:  3  minutes  18  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_2 = model.fit(train_img, \n",
    "                    epochs=10, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 156ms/step - loss: 1.7724 - accuracy: 0.7821\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 Conv2D, 2 Dense'] = {'Val_Loss': 0.9695, 'Val_Acc': 0.8675, 'Test_Loss': 1.7724, 'Test_Acc': 0.7821}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JXinlO7SV0IPiE9NoVDeEnm2bejsUpZQqponeQ4wxrNiRxoCO4YRqLxullA/RjOQhO9OOkpqVw5DOZ3k7FKWUOoUmeg9ZkZAOwODO2qVSKeVbNNF7yIqENM5t2YCIZjpYmVLKt2ii94CcvAJ+2JXBkM7a20Yp5Xs00XvAj79kcjK/kMGa6JVSPkgTvQesSEijTkgQ/TqEezsUpZQ6jSZ6D4hPSKNvh+bUq6PzviqlfI8m+ipKPXyCxINHddgDpZTP0kRfRfEJdlhlrZ9XSvkqTfRVFJ+YRuvGYXRu1dDboSilVKk00VdBfkEhqxLTGdy5BbVwznOllJ/QRF8Fm1IOk52Tr9U2Simfpom+ClYkpBMkMEhnklJK+TBN9FUQn5BGj7ZNaVq/jrdDUUqpMmmiP0OHjuWyKUVnk1JK+T5N9GdoVVI6xqCzSSmlfJ4m+jMUn5BGk3qh9IjQ2aSUUr7NrUQvIiNEZIeIJInItFLWtxeRr0Rks4h8IyIRLuvaicj/RGS7iGwTkUjPhe8dxhjiE9MY1LEFwUHarVIp5dsqTPQiEgy8CowEugITRKRriWIzgLnGmGhgOvCMy7q5wPPGmC5AH+CgJwL3ph0HjnAg+6QOS6yU8gvuXNH3AZKMMbuMMbnAfGBUiTJdga+c18uL1jtfCCHGmC8AjDFHjTHHPRK5FxUNe3CRziallPID7iT6NsAel/cpzjJXm4AxzuvRQCMRCQc6A4dF5AMR2SAizzt3CKcQkdtFZK2IrE1LS6v8p6hhKxLS6NyqIWc3qeftUJRSqkLuJPrSKqFNifdTgSEisgEYAuwF8oEQ4CJn/YXAucCU03ZmzGvGmFhjTGzLlr5dHXI8N581vxzSahullN9wJ9GnAG1d3kcAqa4FjDGpxphrjDG9gMedZVnOthucap98YDEQ45HIveSHXZnkFuhsUkop/+FOol8DdBKRDiJSBxgPLHEtICItRKRoX48Cs122bSYiRVnxEmBb1cP2nhUJaYSFBnFhZHNvh6KUUm6pMNE7V+L3AsuA7cACY8xWEZkuIlc5xYYCO0QkAWgFPO1sW4CttvlKRLZgq4H+6/FPUYPiE9Lod244YaE6m5RSyj+EuFPIGLMUWFpi2V9cXi8EFpax7RdAdBVi9Bl7Mo+zK/0Yk/q193YoSinlNn0ythLiE22PIB32QCnlTzTRV8KKHWm0aVqPc1s08HYoSinlNk30bsorKOS7nRkM7txSZ5NSSvkVTfRu2rD7MEdP5mv/eaWU39FE76YVCQcJDhIGdAz3dihKKVUpmujdFJ+QTky7pjQOC/V2KEopVSma6N2QfvQkW/ZmabWNUsovaaJ3w6rEdAAd9kAp5Zc00bshPiGN5g3qEHVOE2+HopRSlaaJvgKFhYb4xHQu6tSCIJ1NSinlhzTRV2DbvmzSj55kcCettlFK+SdN9BUoGvZAZ5NSSvkrTfQViE9Io+vZjTmrUZi3Q1FKqTOiib4cR0/mszb5kPa2UUr5NU305Vi9M4P8QsNgrbZRSvkxTfTliE9Io36dYGLb62xSSin/pYm+HCsS0hhwXjh1QvQ0KaX8l2awMiSnH2N35nGtn1dK+T23Er2IjBCRHSKSJCLTSlnfXkS+EpHNIvKNiESUWN9YRPaKyCueCry6Fc8mpYleKeXnKkz0IhIMvAqMBLoCE0Ska4liM4C5xphoYDrwTIn1fwVWVD3cmrNiRxrtw+vTPlxnk1JK+Td3ruj7AEnGmF3GmFxgPjCqRJmuwFfO6+Wu60WkN9AK+F/Vw60ZufmFrN6VoU/DKqUCgjuJvg2wx+V9irPM1SZgjPN6NNBIRMJFJAj4J/BweQcQkdtFZK2IrE1LS3Mv8mq09tdMjucWaLWNUioghLhRprSRvEyJ91OBV0RkChAP7AXygbuBpcaYPeXNs2qMeQ14DSA2NrbkvmvcioQ0QoOF/ufpbFLKe/Ly8khJSSEnJ8fboSgfEhYWRkREBKGh7k+C5E6iTwHauryPAFJdCxhjUoFrAESkITDGGJMlIv2Bi0TkbqAhUEdEjhpjTmvQ9SXxCen0bt+MBnXdOT1KVY+UlBQaNWpEZGSkTkivADDGkJGRQUpKCh06dHB7O3eqbtYAnUSkg4jUAcYDS1wLiEgLp5oG4FFgthPU9caYdsaYSOxV/1xfT/IHs3PYvi+bIZ3P8nYoqpbLyckhPDxck7wqJiKEh4dX+i6vwkRvjMkH7gWWAduBBcaYrSIyXUSucooNBXaISAK24fXpSkXhQ+KLZ5PSYQ+U92mSVyWdye+EW/3ojTFLjTGdjTHnGWOedpb9xRizxHm90BjTySlzqzHmZCn7eNMYc2+lI6xh8QlptGhYly6tG3s7FKW8KiMjg549e9KzZ09at25NmzZtit/n5ua6tY+bbrqJHTt2lFvm1Vdf5d133/VEyAAcOHCAkJAQ3njjDY/t099pJbSLgkLDysQ0Lr7gLJ1NStV64eHhbNy4EYCnnnqKhg0bMnXq1FPKGGMwxhAUVPo145w5cyo8zj333FP1YF2899579O/fn7i4OG655RaP7ttVfn4+ISH+kUJ1CAQXP+3N4tDxPO1WqVQ5kpKSiIqK4s477yQmJoZ9+/Zx++23ExsbS7du3Zg+fXpx2UGDBrFx40by8/Np2rQp06ZNo0ePHvTv35+DBw8C8MQTTzBz5szi8tOmTaNPnz6cf/75fPfddwAcO3aMMWPG0KNHDyZMmEBsbGzxl1BJcXFxzJw5k127drF///7i5Z9++ikxMTH06NGD4cOHA3DkyBEmT55M9+7diY6OZvHixcWxFpk/fz633norAJMmTeKhhx7i4osv5rHHHuP777+nf//+9OrVi4EDB5KYmAjYL4EHHniAqKgooqOj+de//sWyZcu49tpri/f72Wefcd1111X55+EO//g6qiHxCWmIwKCOWj+vfMv/+3gr21KzPbrPruc05skru53Rttu2bWPOnDn8+9//BuDZZ5+lefPm5Ofnc/HFFzN27Fi6dj31AfqsrCyGDBnCs88+y4MPPsjs2bOZNu30vhnGGH788UeWLFnC9OnT+fzzz3n55Zdp3bo1ixYtYtOmTcTExJQaV3JyMocOHaJ3796MHTuWBQsWcP/997N//37uuusuVq5cSfv27cnMzATsnUrLli3ZsmULxhgOHz5c4WffuXMnX331FUFBQWRlZbFq1SqCg4P5/PPPeeKJJ3jvvfeYNWsWqampbNq0ieDgYDIzM2natCn3338/GRkZhIeHM2fOHG666abKnvozolf0LuIT0+jepgnhDet6OxSlfNp5553HhRdeWPw+Li6OmJgYYmJi2L59O9u2bTttm3r16jFy5EgAevfuTXJycqn7vuaaa04rs2rVKsaPHw9Ajx496Nat9C+ouLg4xo0bB8D48eOJi4sDYPXq1Vx88cW0b98egObN7dDjX375ZXHVkYjQrFmzCj/7tddeW1xVdfjwYa655hqioqKYOnUqW7duLd7vnXfeSXBwcPHxgoKCmDhxIvPmzSMzM5N169YV31lUN72id2Tn5LF+92HuGnKet0NR6jRneuVdXRo0+G0MqMTERF588UV+/PFHmjZtyqRJk0rt/lenTp3i18HBweTn55e677p1655Wxhj3nqOMi4sjIyODt956C4DU1FR++eUXjDGl9lYpbXlQUNApxyv5WVw/++OPP87vfvc77r77bpKSkhgxYkSZ+wW4+eabGTPGDiIwbty44i+C6qZX9I7vktIpKDQ6LLFSlZSdnU2jRo1o3Lgx+/btY9myZR4/xqBBg1iwYAEAW7ZsKfWOYdu2bRQUFLB3716Sk5NJTk7m4YcfZv78+QwcOJCvv/6aX3/9FaC46mb48OG88oodVNcYw6FDhwgKCqJZs2YkJiZSWFjIhx9+WGZcWVlZtGljR4R58803i5cPHz6cWbNmUVBQcMrx2rZtS4sWLXj22WeZMmVK1U5KJWiid6xISKdR3RB6tWtacWGlVLGYmBi6du1KVFQUt912GwMHDvT4Me677z727t1LdHQ0//znP4mKiqJJkyanlJk3bx6jR48+ZdmYMWOYN28erVq1YtasWYwaNYoePXpw/fXXA/Dkk09y4MABoqKi6NmzJytXrgTgH//4ByNGjGDYsGFERJwy6vop/vSnP/Hwww+f9pnvuOMOWrduTXR0ND169Cj+kgKYOHEiHTp0oHPnzlU6J5Uh7t4S1ZTY2Fizdu3aGj2mMYZB/1hOVJvG/OeG2Bo9tlJl2b59O126dPF2GD4hPz+f/Px8wsLCSExMZPjw4SQmJvpN90ZXd955J/3792fy5MlnvI/SfjdEZJ0xptQE5n9nqRrsTDvG3sMnuPtirZ9XyhcdPXqUYcOGkZ+fjzGG//znP36Z5Hv27EmzZs146aWXavS4/nemqkF8gh0aWcefV8o3NW3alHXr1nk7jCorq+9/ddM6euywxOe2bEDb5vW9HYpSSnlcrU/0OXkF/PCLziallApctT7Rr0nOJCevkCHna6JXSgWmWp/oV+xIo05IEP066GxSSqnAVOsTfXxiGn0im1OvTs08oaaUvxg6dOhpDz/NnDmTu+++u9ztGjZsCNinUseOHVvmvivqRj1z5kyOHz9e/P7yyy93aywadxUNkFYb1OpEvy/rBAkHjupolUqVYsKECcyfP/+UZfPnz3c7OZ5zzjksXLjwjI9fMtEvXbr0lFElq2L79u0UFhYSHx/PsWPHPLLP0pQ1zENNq9WJvrhbpSZ6pU4zduxYPvnkE06etPMIJScnk5qayqBBg4r7tcfExNC9e3c++uij07ZPTk4mKioKgBMnTjB+/Hiio6MZN24cJ06cKC531113FQ9x/OSTTwLw0ksvkZqaysUXX8zFF18MQGRkJOnpdga4F154gaioKKKiooqHOE5OTqZLly7cdtttdOvWjeHDh59yHFfz5s3jhhtuYPjw4SxZ8tvMqElJSVx66aX06NGDmJgYdu7cCcBzzz1H9+7d6dGjR/GIm653Jenp6URGRgJ2KIRrr72WK6+8kuHDh5d7rubOnVv89OwNN9zAkSNH6NChA3l5eYAdXiIyMrL4/Zmq1f3o4xPSad04jM6tGno7FKXK99k02L/Fs/ts3R1GPlvm6vDwcPr06cPnn3/OqFGjmD9/PuPGjUNECAsL48MPP6Rx48akp6fTr18/rrrqqjKnuZs1axb169dn8+bNbN68+ZRhhp9++mmaN29OQUEBw4YNY/Pmzdx///288MILLF++nBYtTh02fN26dcyZM4cffvgBYwx9+/ZlyJAhxePTxMXF8d///pfrrruORYsWMWnSpNPiee+99/jiiy/YsWMHr7zySvFdyvXXX8+0adMYPXo0OTk5FBYW8tlnn7F48WJ++OEH6tevXzxuTXlWr17N5s2bi4duLu1cbdu2jaeffppvv/2WFi1akJmZSaNGjRg6dCiffvopV199NfPnz2fMmDGEhoZWeMzyuHVFLyIjRGSHiCSJyGkDSItIexH5SkQ2i8g3IhLhLO8pIqtFZKuzblyVovWg/IJCViWlM7hzC52XU6kyuFbfuFbbGGN47LHHiI6O5tJLL2Xv3r0cOHCgzP3Ex8cXJ9zo6Giio6OL1y1YsICYmBh69erF1q1bSx2wzNWqVasYPXo0DRo0oGHDhlxzzTXFY9R06NCBnj17AmUPhbxmzRpatmxJ+/btGTZsGOvXr+fQoUMcOXKEvXv3Fo+XExYWRv369fnyyy+56aabqF/fPmdTNMRxeS677LLicmWdq6+//pqxY8cWf5EVlb/11luLZ+by1Jj1FV7Ri0gw8CpwGZACrBGRJcYY15/GDGCuMeYtEbkEeAa4ATgO3GiMSRSRc4B1IrLMGOO5FpUztCkli6wTeVpto/xDOVfe1enqq6/mwQcfZP369Zw4caL4Svzdd98lLS2NdevWERoaSmRkZKlDE7sq7YLql19+YcaMGaxZs4ZmzZoxZcqUCvdT3vhcRUMcgx3muLSqm7i4OH7++efiqpbs7GwWLVpU5mxPZQ05HBISQmFhIVD+UMZlnauy9jtw4ECSk5NZsWIFBQUFxdVfVeHOFX0fIMkYs8sYkwvMB0aVKNMV+Mp5vdg0dFcAABrVSURBVLxovTEmwRiT6LxOBQ4CPpFZ4xPSCNLZpJQqV8OGDRk6dCg333zzKY2wWVlZnHXWWYSGhrJ8+fLi4X/LMnjw4OIJwH/66Sc2b94M2CTboEEDmjRpwoEDB/jss8+Kt2nUqBFHjhwpdV+LFy/m+PHjHDt2jA8//JCLLrrIrc9TWFjI+++/z+bNm4uHMv7oo4+Ii4ujcePGREREsHjxYgBOnjzJ8ePHGT58OLNnzy5uGC6quomMjCwelqG8RueyztWwYcNYsGABGRkZp+wX4MYbb2TChAkem4HKnUTfBtjj8j7FWeZqEzDGeT0aaCQip3RMF5E+QB1g55mF6lnxiWn0aNuUpvXrVFxYqVpswoQJbNq0qXiGJ7B12WvXriU2NpZ3332XCy64oNx93HXXXRw9epTo6Giee+45+vTpA9gujr169aJbt27cfPPNpwz3e/vttzNy5MjixtgiMTExTJkyhT59+tC3b19uvfVWevXq5dZniY+Pp02bNsVjyIP94ti2bRv79u3j7bff5qWXXiI6OpoBAwawf/9+RowYwVVXXUVsbCw9e/ZkxowZAEydOpVZs2YxYMCA4kbi0pR1rrp168bjjz/OkCFD6NGjBw8++OAp2xw6dMhj3T8rHKZYRK4FfmeMudV5fwPQxxhzn0uZc4BXgA5APDbpdzPGZDnrzwa+ASYbY74v5Ri3A7cDtGvXrndFVwdVdfh4LjF//YL7LunEA5fV3JjQSlWGDlNcey1cuJCPPvqIt99+u9T11TFMcQrQ1uV9BJDqWsCplrnGOVhDYIxLkm8MfAo8UVqSd7Z/DXgN7Hj0bsRUJauS0ik02q1SKeV77rvvPj777DOWLl3qsX26k+jXAJ1EpAOwFxgPTHQtICItgExjTCHwKDDbWV4H+BDbUPu+x6KuoviENJrUC6VHRJOKCyulVA16+eWXPb7PCuvojTH5wL3AMmA7sMAYs1VEpovIVU6xocAOEUkAWgFPO8uvAwYDU0Rko/Ovp6c/RGUYY1iRkMagji0ICa7Vz4sppWoJtx6YMsYsBZaWWPYXl9cLgdOanY0x7wDvVDFGj0o4cJQD2ScZ3Fl72yjfV1YXPFV7ncn0r7XuklaHPVD+IiwsjIyMjDP6w1aByRhDRkYGYWFhldqu1g2BsCIhjc6tGnJ2k3reDkWpckVERJCSkkJaWpq3Q1E+JCwsjIiIiEptU6sS/YncAn5MzuTGfu29HYpSFQoNDaVDhw7eDkMFgFpVdfP9Lxnk5utsUkqp2qVWJfoVO9IICw3iwsiKByVSSqlAUasSfXxiGn07hBMWqrNJKaVqj1qT6PdkHmdX2jGdTUopVevUmkQfn6jdKpVStVPtSfQJabRpWo/zWjaouLBSSgWQWpHo8woK+S4pg8GdW+pThkqpWqdWJPoNuw9z5GQ+Q3TYA6VULVQrEn18QhrBQcIAnU1KKVUL1Y5En5hGTLumNA6r2kzqSinljwI+0WccPcmWvVkM7qS9bZRStVPAJ/pVSekYnU1KKVWLBXyiX5GQRvMGdejeRmeTUkrVTgGd6AsLDfEJ6Qzq2IKgIO1WqZSqnQI60W/fn0360ZNabaOUqtUCOtHHJ6QDMLiTdqtUStVebiV6ERkhIjtEJElEppWyvr2IfCUim0XkGxGJcFk3WUQSnX+TPRl8RVYkHKTL2Y05q3Hlpt1SSqlAUmGiF5Fg4FVgJNAVmCAiXUsUmwHMNcZEA9OBZ5xtmwNPAn2BPsCTItLMc+GX7djJfNb9ekgnAVdK1XruXNH3AZKMMbuMMbnAfGBUiTJdga+c18td1v8O+MIYk2mMOQR8AYyoetgVW70zg7wCo8MSK6VqPXcSfRtgj8v7FGeZq03AGOf1aKCRiIS7uS0icruIrBWRtZ6aCHlFQhr16wQT215nk1JK1W7uJPrS+iWaEu+nAkNEZAMwBNgL5Lu5LcaY14wxscaY2JYtPXMFHp+YRv9zw6kTEtDtzUopVSF3smAK0NblfQSQ6lrAGJNqjLnGGNMLeNxZluXOttUhOf0Yv2Yc10nAlVIK9xL9GqCTiHQQkTrAeGCJawERaSEiRft6FJjtvF4GDBeRZk4j7HBnWbUqnk1Kx7dRSqmKE70xJh+4F5ugtwMLjDFbRWS6iFzlFBsK7BCRBKAV8LSzbSbwV+yXxRpgurOsWsUnpNGueX0iW+hsUkopFeJOIWPMUmBpiWV/cXm9EFhYxraz+e0Kv9rl5hfy3c4MxsREVFxYKaVqgYBrqVz7aybHcwt02AOllHIEXKKPT0gnNFjof164t0NRSimfEICJPo3e7ZvRsK5btVJKKRXwAirRHzySw7Z92Vpto5RSLgIq0a90RqvUYQ+UUm7LyYbMX7wdRbUKqEQfn5hGi4Z16dK6sbdDUUr5uuOZsPzvMDMKXrkQtn/s7YiqTcBUZBcWGlYmpjO0c0udTUopVbajabD6FVjzOuQehS5XQvY+WDAZxvwXosZUvA8/EzCJPjXrBMFBosMeKKVKl70PvnsZ1s6G/ByIugYumgqtusLJI/DutbDoVijIhx7jvB2tRwVMoo9oVp8fHxtGQeFpY6YppWqzw3vg25mw/m0ozIfocXDRg9Ci029l6jaCSYtg3jj48A4oyIWYG7wXs4cFTKIHEBFCgrXaRikFZO6ClS/ApjhAoOdEGPQANO9Qevk6DWDiAnjvelhyr032F95SoyFXl4BK9EopRVoCrPwnbHkfgkIg9mYY+Ado4sawKHXqw/g4WHAjfPogFORBvzurP+ZqpoleKRUYDmyF+Odh62IIrQf97oIB90Gj1pXbT2gYjHsHFt4En//JXtkPvL96Yq4hmuiVUv4tdQOseB52fAp1Gtnqmf73QIMqzBcdUgeufRM+uA2++DMUnITBD3ss5JqmiV4p5Z/2/AgrnoOkLyCsCQx9FPrcDvU9NH1ocChc8zoE14Gv/2arcYY+CuJ/7YCa6JVS/sMYSF4F8c/BL/FQPxyG/QUuvA3CquFByeAQuHoWBIXCin9A/km49Cm/S/aa6JVSvs8Y2PkVxM+A3auhYSsY/jTE3mR7y1SnoGC46mVbnfPtTHtl/7un/SrZa6JXSvkuYyDhc9vIuncdNG4DI5+3fdxD69VcHEFB8PsXbDXO96/aBtqRz9nlfkATvVLK9xQWwvYl9gr+wBZo2h6ufBF6TLRX1t4gAiOetXX3371sG2iveNEvkr1biV5ERgAvAsHA68aYZ0usbwe8BTR1ykwzxiwVkVDgdSDGOdZcY8wzHoxfKRVICvJh6wc2wafvgPCOto68+7U2wXqbCFz2VwiuCytn2HhHvWKrd3xYhYleRIKBV4HLgBRgjYgsMcZscyn2BHbS8Fki0hU7v2wkcC1Q1xjTXUTqA9tEJM4Yk+zhz6GU8mcFebD5PfugU+YuaNkFxrwB3Ub7XhIVgWF/hpC6sPxpW40z+j+24dZHuRNZHyDJGLMLQETmA6MA10RvgKIm7yZAqsvyBiISAtQDcoFsD8RdOmP8qoFEqVov/yRseAdWzYSs3dA62j6sdP7vfb9KZMgj9i7jy6dssh872zfuOkrhTqJvA+xxeZ8C9C1R5ingfyJyH9AAuNRZvhD7pbAPqA88YIzJLHkAEbkduB2gXbt2lQjfRd4JeP1Se4t34S12kKLaKicb1vwXUjdCzI3Q8dLa/QV4YBt89xJkJHk7EpAg6HtHQA6FW2nJq2DRbXAkFdrEwu9nQKfh/vW7OugB20C77DE7bMK1b9orfR/jTqIv7ayXHCJyAvCmMeafItIfeFtEorB3AwXAOUAzYKWIfFl0d1C8M2NeA14DiI2NPbPhJ49nQsOz4MsnbReofnfbhyfqNT2j3fml45nww3/gh1mQkwX1mtkGrXN62af6Oo/0/askT0rdaHtr/PwJ1GkIERd6P4lk7YWFt0Du8YAaHbHSdi6HuAnQtC1cvRjOHer9n82Z6n+PTfZLp8L862Hc2zXbI8gN7iT6FKCty/sIfquaKXILMALAGLNaRMKAFsBE4HNjTB5wUES+BWKBXXhakzZww4eQstb+cS9/2raM973DJn1PPS3ni46l24kUfnwdco/ABVfA4KlwVjfYPN/We86fCK2i4KKHoOso36v39KQ9a+zvQOIyqNsEBj9ixz3xhd+BvBM2GSy51/bauPBWb0dU8xL+B+9Nsg2tN34EDQNgDok+t9lqm4//CHHj7cBodep7O6piYkz5F9BO/XoCMAzYC6wBJhpjtrqU+Qx4zxjzpoh0Ab7CVvk8AlwA3IytulkDjDfGbC7reLGxsWbt2rVV+lAA7NtkW+63L4HQBrY6Z8B99qo/UBzZ/9tECnknbMPV4KnQqtup5Qry4aeF9nxkJEKLznbChagxPt2AVGnJ39onJnd9A/WaQ3/nri6sibcjO1VeDrw/2fYPH/Gs/RKqLX7+1M7k1Kor3LDYN758PWnjPPjoHmg3ACa+B3Ub1tihRWSdMSa21HUVJXpnB5cDM7FdJ2cbY54WkenAWmPMEqenzX+BhthqnUeMMf8TkYbAHKArtgpojjHm+fKO5bFEX+Tgdpvgtn5gu0T1nmJHomt8jueOUdOyUmzj1fq5zkQK18GgB6Fl5/K3KyyAbR/Z83FwKzTrYCdgiB7vvb7JVWUM7FpuB7Xa/R00OMt+ocfeXKN/ZJWWnwuLbrbzlF423Q6jG+i2LoZFt8DZPWDSB4FbrbplIXxwu60qvP796hmaoRRVTvQ1yeOJvkh6Eqx6ATbNt9UWvSbZhpSmZ9j46w2Zv9jPsDHOvu85wZlI4dzK7aewEBI+swNC7dsITdrCoD9Cz0l2iFZ/YAwkLHOemFwLjc6xybL3ZJ+rHy1TQZ6dzeinRXDxEzDEf0dHrNDm9+1njYiF6xfWWPLzmm0fwcKbnS+1Rba9rJppond1KNleDW94BzD2avaiByH8vOo7ZlWlJ9p69s0L7EQKMTfapNa0bcXblscYSPrSJvyUH6HR2TDgfnvX40P1i6coLLSNq/HPw/7N9ot60APQ83qf7O1QoYJ8e6u/eb5tS7j4Mf9tlCzLxnmw+G5oP7DGqzO86ueltifOWV1sW0Q1V1Npoi9N1l749kVY/5btAxs11jZUnnVB9R/bXQe2OtVOH0JImK2OGHAfND7bs8cxxo4EGP88JK+E+i1gwL22odBXuqkWFtjzED8D0rZD8/Pszyv6Op/tu+y2wgL4+H578THwD3Dp/wucZL/uTdtAee4Qn2ugrBGJX9jG9xpoeNZEX54jB2D1y7BmNuQdh65X2a6IrbvXXAwllewW2Oc26HdPzfRO+HW1PfbOr+ztpre7qRbk2SnhVv7T9oNveYFtSO42OrAakgsLbfe8tW9A37tgxDP+n+x//K/9TB0vsw9B+Uu1oKcVdyVtB5OXVH7GKzdponfHsQw7Kt0Pr9kuiudfbnuwtOldczHsWWN7jST+z/YU6XuX7R7qjZ4JKevsWB47lkLdxjbZ97sbGoTXzPHzT9pb/lX/B4d/tV+8gx+GC64M3GcBjIHPH7XPQcTeApfP8N/PuvpV+xDR+b+Ha+f4Z7WaJyWvgnevs3fjkz+uls4gmugr48Qhm+y//xfkHIbzhtlHndv1q75jJq+y9eS/rHC6Bd5jr+J9oVvgvs024W9bAqH14cKbof990KhV9Rwv74TtTfTti5C9137RDn4EOv/O/69w3WGM89Dfi7Ytxk9GRzzFyhfgq/9nn9cY84b/V615yu7v4Z2x9mJp8sce7wiiif5M5GTb2+jvXoHj6RB5kb2i7DDYMwnHGNj5tTORgtMtcOD90Psm32ysOvizrT75aaF9CrD3FFuf7Kkrk5NHYd0c+1zA0QPQrr893+ddUjsSvCtjYPnf7d1djwkw6lX/eMDNGHvB8s3fbZuXjw/05RUpa+Hta2yvo8kfQ/MOHtu1JvqqyD1mG5S+fQmO7oe2fW0COtPxY0rrFjjoj/bqzR+6BWbstFdsm+fbcVt6Xm97vTRrf2b7y8mGH1+zt/onMqHDEHsHFTnIs3H7oxXP2Se8o8Y4SdOHr4yNsfOqrpxhx4z3g6F7vSZ1I7x9NYTUgymfeKzHnyZ6T8jLgQ1v266Z2SmVHz+msBB+/tjpFrjF6Rb4IPSc6J/1l4d+tWMKbXgHTGHlu6mWHJen03B7Ptv2qd64/c2qmbYqp8uVMGa2bz7YZgx88Wd7NxYzGa6Y6X/VTTVt/08w1xmKZPLH0PL8Ku9SE70n5ef+Nn7MoeSKx48prVvg4Km+M5FCVWXttSNDrnvT6aY6xvaKKaub6tE02+hdclyec3rVaNh+ZfW/YNmj9qLiurd868LAGPh8GvzwbztBtx9Nr+d1B3+GuVfZHDF5yelDl1SSJvrqUOr4MQ/ZusngEGcihQXORAo77UQKg6f65kQKnnD0oL2iW/OG7aba5Up7hX52tF1/ZL+t/lo7G/Jzyh6XR5Vuzevw6UO2ynDcO75RzVdYCJ8+aNtW+t3jdxNm+4T0JHjrSvs3ceNi+yTtGdJEX51KGz8magxsWQCHdzvdAh+xV6614UrnWIbtsfTja3Ay216FNj7HVvEUjctz0UPQopO3I/U/6+fCkvtth4AJcVCngfdiKSywsWx8x7bRDHtSk/yZytwFb11l/14mfQgRZ9alWxN9TSgstH3O45+348fUtm6BJZ04bJP99/+yPWrOdFwedapN82HxXbZX0sT3vPPkckE+fHS3nfpvyDQYOq12/o570uHd8OYV9k7tru/O6K5fE31NMgaO7LPjxugvv51gIz8n8Iaj9aai0RHb9IZJC2v2eYuCPPjgNtvudMmfbfWb8oysvfY5khYdz2jz8hJ9LahLqGEitqpCk7xVp74meU/rPtY+bZq6HuZebR/yqwn5ufD+FJvkL/urJnlPa9LmjJN8RTTRK+WPuo6yjbIHfrL1u8cyqvd4eTl2VqifP4ER/7AP9ym/oYleKX91/kg7ImR6gu25cTSteo6TdwLmT7BTM17xf9Dvzuo5jqo2muiV8medLrWNspm74M3f226snpR7DN691o7AOOpVO1S28jua6JXyd+cOtY2yWSkw53LbqOcJJ4/YQbh+/dYOwdBrkmf2q2qcW4leREaIyA4RSRKRaaWsbyciy0Vkg4hsduaYLVoXLSKrRWSriGwRkVo6KLVS1ShyENzwIRxLgzcvt931qiInC94eDXt+sCNQ9hjnmTiVV1SY6EUkGHgVGImd5HuCMxm4qyeABcaYXsB44F/OtiHAO8CdxphuwFAgz2PRK6V+066vfbryxCF7ZZ+568z2czzTjsOSutEOuRB1jWfjVDXOnSv6PkCSMWaXMSYXmA+MKlHGAEWz/TYBUp3Xw4HNxphNAMaYDGNMQdXDVkqVqk1vO0hW7jGY83s733BlHMuw468c2Gp79XS5snriVDXKnUTfBtjj8j7FWebqKWCSiKQAS4H7nOWdASMiy0RkvYg8UsV4lVIVObuHHf62MM820B782b3tjh605dMTYcJ8OH9E9capaow7ib60J39KPk47AXjTGBMBXA68LSJBQAgwCLje+X+0iAw77QAit4vIWhFZm5ZWTV3ElKpNWnWDKZ/a12/+3g6LW57sfbbc4V9h4gLoeNqfqfJj7iT6FKCty/sIfquaKXILsADAGLMaCANaONuuMMakG2OOY6/2Y0oewBjzmjEm1hgT27JlDUyArVRt0PJ8mLLUzgj21hW2zr00WSm2ATc7FSYtgnOH1Gycqtq5k+jXAJ1EpIOI1ME2ti4pUWY3MAxARLpgE30asAyIFpH6TsPsEGCbp4JXSlWgRUe4aSnUaWTr3lPWnbr+0K+24fZYuu21036Ad+JU1arCRG+MyQfuxSbt7djeNVtFZLqIXOUUewi4TUQ2AXHAFGMdAl7AfllsBNYbYz6tjg+ilCpD8w5w06dQr5ntTbP7e7s8Y6dN8jlZcONHOrtXANPRK5WqLbJT7VAJ2ftg5D/sfLQFuXDD4t8miFF+S0evVErZUVWnLIWmbWHJvXYimMmfaJKvBUK8HYBSqgY1amWT+8p/2nFrWnb2dkSqBmiiV6q2adgSRj7r7ShUDdKqG6WUCnCa6JVSKsBpoldKqQCniV4ppQKcJnqllApwmuiVUirAaaJXSqkAp4leKaUCnM+NdSMiacCvVdhFCyDdQ+H4Oz0Xp9LzcSo9H78JhHPR3hhT6jjvPpfoq0pE1pY1sE9to+fiVHo+TqXn4zeBfi606kYppQKcJnqllApwgZjoX/N2AD5Ez8Wp9HycSs/HbwL6XARcHb1SSqlTBeIVvVJKKRea6JVSKsAFTKIXkREiskNEkkRkmrfj8SYRaSsiy0Vku4hsFZE/eDsmbxORYBHZICKfeDsWbxORpiKyUER+dn5H+ns7Jm8SkQecv5OfRCRORMK8HZOnBUSiF5Fg4FVgJNAVmCAiXb0blVflAw8ZY7oA/YB7avn5APgDsN3bQfiIF4HPjTEXAD2oxedFRNoA9wOxxpgoIBgY792oPC8gEj3QB0gyxuwyxuQC84FRXo7Ja4wx+4wx653XR7B/yG28G5X3iEgE8HvgdW/H4m0i0hgYDLwBYIzJNcYc9m5UXhcC1BOREKA+kOrleDwuUBJ9G2CPy/sUanFicyUikUAv4AfvRuJVM4FHgEJvB+IDzgXSgDlOVdbrItLA20F5izFmLzAD2A3sA7KMMf/zblSeFyiJXkpZVuv7jYpIQ2AR8EdjTLa34/EGEbkCOGiMWeftWHxECBADzDLG9AKOAbW2TUtEmmHv/jsA5wANRGSSd6PyvEBJ9ClAW5f3EQTg7VdliEgoNsm/a4z5wNvxeNFA4CoRScZW6V0iIu94NySvSgFSjDFFd3gLsYm/troU+MUYk2aMyQM+AAZ4OSaPC5REvwboJCIdRKQOtjFliZdj8hoREWwd7HZjzAvejsebjDGPGmMijDGR2N+Lr40xAXfF5i5jzH5gj4ic7ywaBmzzYkjethvoJyL1nb+bYQRg43SItwPwBGNMvojcCyzDtprPNsZs9XJY3jQQuAHYIiIbnWWPGWOWejEm5TvuA951Lop2ATd5OR6vMcb8ICILgfXY3mobCMDhEHQIBKWUCnCBUnWjlFKqDJrolVIqwGmiV0qpAKeJXimlApwmeqWUCnCa6JVSKsBpoldKqQD3/wHLHJiddlPRXgAAAABJRU5ErkJggg==\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": [
    "plot_performance(history_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 Conv2D, 2 Dense with Padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12/12 [==============================] - 23s 2s/step - loss: 2.3573 - accuracy: 0.6084 - val_loss: 0.5496 - val_accuracy: 0.6747\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 22s 2s/step - loss: 0.5282 - accuracy: 0.7389 - val_loss: 0.3983 - val_accuracy: 0.8554\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 23s 2s/step - loss: 0.4320 - accuracy: 0.8251 - val_loss: 0.2997 - val_accuracy: 0.9036\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 23s 2s/step - loss: 0.3026 - accuracy: 0.8721 - val_loss: 0.2716 - val_accuracy: 0.9157\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 26s 2s/step - loss: 0.2928 - accuracy: 0.8642 - val_loss: 0.3072 - val_accuracy: 0.8916\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 29s 2s/step - loss: 0.3927 - accuracy: 0.8486 - val_loss: 0.2881 - val_accuracy: 0.9157\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 27s 2s/step - loss: 0.2396 - accuracy: 0.9112 - val_loss: 0.5971 - val_accuracy: 0.7229\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 26s 2s/step - loss: 0.1402 - accuracy: 0.9452 - val_loss: 0.2721 - val_accuracy: 0.8675\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 27s 2s/step - loss: 0.0917 - accuracy: 0.9713 - val_loss: 0.7461 - val_accuracy: 0.7470\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 26s 2s/step - loss: 0.0554 - accuracy: 0.9817 - val_loss: 0.4070 - val_accuracy: 0.8554\n",
      "Total Time Elapsed:  4  minutes  37  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_3 = model.fit(train_img, \n",
    "                    epochs=10, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 248ms/step - loss: 0.8718 - accuracy: 0.7692\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 Conv2D, 2 Dense / Padding'] = {'Val_Loss': 0.4070, \n",
    "                                         'Val_Acc': 0.8554, \n",
    "                                         'Test_Loss': 0.8718, \n",
    "                                         'Test_Acc': 0.7692}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plot_performance(history_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still a lot of overfitting to the train dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 Conv2D, 2 Dense with Padding and Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 10,\n",
    "                                           zoom_range = 0.2)\n",
    "\n",
    "train_img_augmented = train_augment.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 2.6068 - accuracy: 0.6031 - val_loss: 0.5351 - val_accuracy: 0.6386\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.6312 - accuracy: 0.6867 - val_loss: 0.4087 - val_accuracy: 0.7711\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.5999 - accuracy: 0.7285 - val_loss: 0.3805 - val_accuracy: 0.8072\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4592 - accuracy: 0.7781 - val_loss: 0.5072 - val_accuracy: 0.6627\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4398 - accuracy: 0.7885 - val_loss: 0.4303 - val_accuracy: 0.8193\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4039 - accuracy: 0.8303 - val_loss: 0.3378 - val_accuracy: 0.8916\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.4373 - accuracy: 0.7885 - val_loss: 0.3652 - val_accuracy: 0.8675\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.4457 - accuracy: 0.7937 - val_loss: 0.3208 - val_accuracy: 0.8795\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.3659 - accuracy: 0.8512 - val_loss: 0.2565 - val_accuracy: 0.8916\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.3887 - accuracy: 0.8225 - val_loss: 0.3381 - val_accuracy: 0.8554\n",
      "Total Time Elapsed:  3  minutes  50  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_4 = model.fit(train_img_augmented, \n",
    "                    epochs=10, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 218ms/step - loss: 0.3732 - accuracy: 0.8462\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 Conv2D, 2 Dense / Padding / Augmented Data'] = {'Val_Loss': 0.3381, \n",
    "                                         'Val_Acc': 0.8554, \n",
    "                                         'Test_Loss': 0.3732, \n",
    "                                         'Test_Acc': 0.8462}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plot_performance(history_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizing Data Augmentation definitely helped our performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 Conv2D, 2 Dense with Padding and Extra Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "12/12 [==============================] - 20s 2s/step - loss: 1.7670 - accuracy: 0.5614 - val_loss: 0.5780 - val_accuracy: 0.6386\n",
      "Epoch 2/20\n",
      "12/12 [==============================] - 20s 2s/step - loss: 0.5822 - accuracy: 0.6762 - val_loss: 0.7464 - val_accuracy: 0.5663\n",
      "Epoch 3/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.5661 - accuracy: 0.7102 - val_loss: 0.4145 - val_accuracy: 0.8072\n",
      "Epoch 4/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4806 - accuracy: 0.7572 - val_loss: 0.4204 - val_accuracy: 0.8675\n",
      "Epoch 5/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4504 - accuracy: 0.8042 - val_loss: 0.3970 - val_accuracy: 0.8675\n",
      "Epoch 6/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4094 - accuracy: 0.8094 - val_loss: 0.3685 - val_accuracy: 0.8675\n",
      "Epoch 7/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4329 - accuracy: 0.8198 - val_loss: 0.3261 - val_accuracy: 0.8675\n",
      "Epoch 8/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3949 - accuracy: 0.7990 - val_loss: 0.3645 - val_accuracy: 0.8434\n",
      "Epoch 9/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3886 - accuracy: 0.8355 - val_loss: 0.3055 - val_accuracy: 0.8916\n",
      "Epoch 10/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3783 - accuracy: 0.8251 - val_loss: 0.3586 - val_accuracy: 0.8916\n",
      "Epoch 11/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3888 - accuracy: 0.8303 - val_loss: 0.4237 - val_accuracy: 0.7470\n",
      "Epoch 12/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3987 - accuracy: 0.8329 - val_loss: 0.3074 - val_accuracy: 0.8916\n",
      "Epoch 13/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3649 - accuracy: 0.8512 - val_loss: 0.2750 - val_accuracy: 0.9036\n",
      "Epoch 14/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.4096 - accuracy: 0.8433 - val_loss: 0.3212 - val_accuracy: 0.8675\n",
      "Epoch 15/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3682 - accuracy: 0.8538 - val_loss: 0.2703 - val_accuracy: 0.9036\n",
      "Epoch 16/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3273 - accuracy: 0.8381 - val_loss: 0.3529 - val_accuracy: 0.8675\n",
      "Epoch 17/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.2867 - accuracy: 0.8799 - val_loss: 0.4695 - val_accuracy: 0.7590\n",
      "Epoch 18/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3792 - accuracy: 0.8381 - val_loss: 0.2804 - val_accuracy: 0.8916\n",
      "Epoch 19/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.3642 - accuracy: 0.8433 - val_loss: 0.2519 - val_accuracy: 0.8916\n",
      "Epoch 20/20\n",
      "12/12 [==============================] - 21s 2s/step - loss: 0.2969 - accuracy: 0.8851 - val_loss: 0.2974 - val_accuracy: 0.8434\n",
      "Total Time Elapsed:  7  minutes  49  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_5 = model.fit(train_img_augmented_2, \n",
    "                    epochs=20, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 223ms/step - loss: 0.2688 - accuracy: 0.8590\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 Conv2D, 2 Dense / Padding / Extra Augmented Data'] = {'Val_Loss': 0.2974, \n",
    "                                         'Val_Acc': 0.8434, \n",
    "                                         'Test_Loss': 0.2688, \n",
    "                                         'Test_Acc': 0.8590}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plot_performance(history_5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still improving our accuracy. Let's try some more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 2 Conv2D, 2 Dense with Padding and Extra Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12/12 [==============================] - 85s 7s/step - loss: 1.1974 - accuracy: 0.5326 - val_loss: 0.6269 - val_accuracy: 0.6386\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 88s 7s/step - loss: 0.6601 - accuracy: 0.6371 - val_loss: 0.5980 - val_accuracy: 0.6627\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 81s 7s/step - loss: 0.6022 - accuracy: 0.7206 - val_loss: 0.6516 - val_accuracy: 0.8313\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 74s 6s/step - loss: 0.5887 - accuracy: 0.7389 - val_loss: 0.3808 - val_accuracy: 0.8434\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 84s 7s/step - loss: 0.5197 - accuracy: 0.7493 - val_loss: 0.4164 - val_accuracy: 0.8313\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 84s 7s/step - loss: 0.4212 - accuracy: 0.7807 - val_loss: 0.3406 - val_accuracy: 0.8795\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 78s 6s/step - loss: 0.4549 - accuracy: 0.7833 - val_loss: 0.5228 - val_accuracy: 0.6867\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 81s 7s/step - loss: 0.4625 - accuracy: 0.7937 - val_loss: 0.3671 - val_accuracy: 0.8675\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 84s 7s/step - loss: 0.5388 - accuracy: 0.7598 - val_loss: 0.4006 - val_accuracy: 0.8795\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 79s 7s/step - loss: 0.4017 - accuracy: 0.8094 - val_loss: 0.3164 - val_accuracy: 0.8554\n",
      "Total Time Elapsed:  14  minutes  55  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_6 = model.fit(train_img_augmented_2, \n",
    "                    epochs=10, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 603ms/step - loss: 0.3540 - accuracy: 0.8590\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 2 Conv2D, 2 Dense / Padding / Extra Augmented Data'] = {'Val_Loss': 0.3164, \n",
    "                                         'Val_Acc': 0.8554, \n",
    "                                         'Test_Loss': 0.3540, \n",
    "                                         'Test_Acc': 0.8590}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "plot_performance(history_6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 3 Conv2D, 2 Dense with Padding and Extra Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 71s 6s/step - loss: 0.5554 - accuracy: 0.7285 - val_loss: 0.3810 - val_accuracy: 0.8434\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 67s 6s/step - loss: 0.4178 - accuracy: 0.7937 - val_loss: 0.4726 - val_accuracy: 0.8313\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.4684 - accuracy: 0.8068 - val_loss: 0.4261 - val_accuracy: 0.8434\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 65s 5s/step - loss: 0.4362 - accuracy: 0.7963 - val_loss: 0.3393 - val_accuracy: 0.8313\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 59s 5s/step - loss: 0.4436 - accuracy: 0.8042 - val_loss: 0.3555 - val_accuracy: 0.8313\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 59s 5s/step - loss: 0.4229 - accuracy: 0.7885 - val_loss: 0.3694 - val_accuracy: 0.8313\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 59s 5s/step - loss: 0.4569 - accuracy: 0.7859 - val_loss: 0.4171 - val_accuracy: 0.7952\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4844 - accuracy: 0.7911 - val_loss: 0.3759 - val_accuracy: 0.8313\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4925 - accuracy: 0.7963 - val_loss: 0.4013 - val_accuracy: 0.8554\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 59s 5s/step - loss: 0.3966 - accuracy: 0.8172 - val_loss: 0.4202 - val_accuracy: 0.7831\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4288 - accuracy: 0.8146 - val_loss: 0.3261 - val_accuracy: 0.8795\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 57s 5s/step - loss: 0.3943 - accuracy: 0.8198 - val_loss: 0.4108 - val_accuracy: 0.8072\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.3885 - accuracy: 0.8172 - val_loss: 0.3485 - val_accuracy: 0.8193\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 59s 5s/step - loss: 0.3528 - accuracy: 0.8616 - val_loss: 0.2612 - val_accuracy: 0.8795\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.3809 - accuracy: 0.8355 - val_loss: 0.2390 - val_accuracy: 0.9036\n",
      "Total Time Elapsed:  16  minutes  41  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_7 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 518ms/step - loss: 0.2213 - accuracy: 0.9231\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data'] = {'Val_Loss': 0.2390, \n",
    "                                         'Val_Acc': 0.9036, \n",
    "                                         'Test_Loss': 0.2213, \n",
    "                                         'Test_Acc': 0.9231}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "plot_performance(history_7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3x3 Conv2D layers really helped!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 4 x 3 Conv2D, 2 Dense with Padding and Extra Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(128, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(128, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(128, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 2.3988 - accuracy: 0.6214 - val_loss: 0.6662 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.6641 - accuracy: 0.6397 - val_loss: 0.6558 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 66s 6s/step - loss: 0.6844 - accuracy: 0.5822 - val_loss: 0.6843 - val_accuracy: 0.6386\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 61s 5s/step - loss: 0.6608 - accuracy: 0.6606 - val_loss: 0.5139 - val_accuracy: 0.8193\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.6385 - accuracy: 0.6789 - val_loss: 0.5546 - val_accuracy: 0.6867\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 63s 5s/step - loss: 0.5906 - accuracy: 0.7076 - val_loss: 0.5067 - val_accuracy: 0.7590\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 65s 5s/step - loss: 0.5385 - accuracy: 0.7389 - val_loss: 0.6443 - val_accuracy: 0.6627\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.5576 - accuracy: 0.7102 - val_loss: 0.4457 - val_accuracy: 0.8072\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.5269 - accuracy: 0.7624 - val_loss: 0.4650 - val_accuracy: 0.8072\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 66s 6s/step - loss: 0.6298 - accuracy: 0.7337 - val_loss: 0.4943 - val_accuracy: 0.8193\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.6315 - accuracy: 0.6789 - val_loss: 0.4319 - val_accuracy: 0.8193\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.4522 - accuracy: 0.7937 - val_loss: 0.4934 - val_accuracy: 0.8193\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.5470 - accuracy: 0.7363 - val_loss: 0.4366 - val_accuracy: 0.8193\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 78s 7s/step - loss: 0.4470 - accuracy: 0.7990 - val_loss: 0.7915 - val_accuracy: 0.8554\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 76s 6s/step - loss: 0.5894 - accuracy: 0.7807 - val_loss: 0.4030 - val_accuracy: 0.7952\n",
      "Total Time Elapsed:  18  minutes  31  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_8 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 568ms/step - loss: 0.4635 - accuracy: 0.7949\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['4 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data'] = {'Val_Loss': 0.4030, \n",
    "                                         'Val_Acc': 0.7952, \n",
    "                                         'Test_Loss': 0.4635, \n",
    "                                         'Test_Acc': 0.7949}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plot_performance(history_8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now gone too far! Overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 63s 5s/step - loss: 0.8641 - accuracy: 0.5535 - val_loss: 0.6620 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.6529 - accuracy: 0.6397 - val_loss: 0.6602 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.6433 - accuracy: 0.6606 - val_loss: 0.6004 - val_accuracy: 0.7711\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.7077 - accuracy: 0.6423 - val_loss: 0.9250 - val_accuracy: 0.6386\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 75s 6s/step - loss: 0.6386 - accuracy: 0.6815 - val_loss: 0.5854 - val_accuracy: 0.8313\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 74s 6s/step - loss: 0.5122 - accuracy: 0.7624 - val_loss: 0.4111 - val_accuracy: 0.9036\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.6376 - accuracy: 0.6632 - val_loss: 0.4558 - val_accuracy: 0.8916\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.5912 - accuracy: 0.7311 - val_loss: 0.4334 - val_accuracy: 0.8554\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 71s 6s/step - loss: 0.5108 - accuracy: 0.7415 - val_loss: 0.4874 - val_accuracy: 0.8554\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 64s 5s/step - loss: 0.4585 - accuracy: 0.7676 - val_loss: 0.4418 - val_accuracy: 0.8675\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 67s 6s/step - loss: 0.4304 - accuracy: 0.8042 - val_loss: 0.3533 - val_accuracy: 0.8795\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 78s 7s/step - loss: 0.4428 - accuracy: 0.7807 - val_loss: 0.3138 - val_accuracy: 0.8795\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.4435 - accuracy: 0.7990 - val_loss: 0.3369 - val_accuracy: 0.8675\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 77s 6s/step - loss: 0.4965 - accuracy: 0.7728 - val_loss: 0.3763 - val_accuracy: 0.8675\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.4468 - accuracy: 0.7990 - val_loss: 0.3642 - val_accuracy: 0.8795\n",
      "Total Time Elapsed:  19  minutes  31  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_9 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 502ms/step - loss: 0.3271 - accuracy: 0.9103\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout'] = {'Val_Loss': 0.3642, \n",
    "                                         'Val_Acc': 0.8795, \n",
    "                                         'Test_Loss': 0.3271, \n",
    "                                         'Test_Acc': 0.9103}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "plot_performance(history_9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.4 Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 63s 5s/step - loss: 0.8667 - accuracy: 0.5744 - val_loss: 0.6720 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 75s 6s/step - loss: 0.6695 - accuracy: 0.6397 - val_loss: 0.6755 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 75s 6s/step - loss: 0.6738 - accuracy: 0.6397 - val_loss: 0.6497 - val_accuracy: 0.6386\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 75s 6s/step - loss: 0.6535 - accuracy: 0.6397 - val_loss: 0.6706 - val_accuracy: 0.6386\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.6470 - accuracy: 0.6319 - val_loss: 0.6362 - val_accuracy: 0.6386\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 76s 6s/step - loss: 0.6910 - accuracy: 0.6397 - val_loss: 0.6493 - val_accuracy: 0.6386\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.6425 - accuracy: 0.6501 - val_loss: 0.6524 - val_accuracy: 0.6386\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.5922 - accuracy: 0.6580 - val_loss: 0.5538 - val_accuracy: 0.6386\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.5682 - accuracy: 0.6606 - val_loss: 0.5964 - val_accuracy: 0.6386\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.5450 - accuracy: 0.6867 - val_loss: 0.4918 - val_accuracy: 0.7229\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 72s 6s/step - loss: 0.6367 - accuracy: 0.7023 - val_loss: 0.4522 - val_accuracy: 0.8193\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.5628 - accuracy: 0.7676 - val_loss: 0.4354 - val_accuracy: 0.8675\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.4742 - accuracy: 0.7572 - val_loss: 0.4058 - val_accuracy: 0.8313\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.4750 - accuracy: 0.7676 - val_loss: 0.4311 - val_accuracy: 0.8313\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 75s 6s/step - loss: 0.4918 - accuracy: 0.7676 - val_loss: 0.4694 - val_accuracy: 0.8554\n",
      "Total Time Elapsed:  19  minutes  44  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_10 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 499ms/step - loss: 0.4554 - accuracy: 0.8462\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.4 Dropout'] = {'Val_Loss': 0.4694, \n",
    "                                         'Val_Acc': 0.8554, \n",
    "                                         'Test_Loss': 0.4554, \n",
    "                                         'Test_Acc': 0.8462}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xB6Z+Dh0vr3sbT2MM/PSONQKpdXe4fz1cxDDtSx5eKSJjRCRZRFJF5LzhISLSSUTWisg2EdkpItfblkeJSIGIbLf9e+uCo1fnOHv2LIMHDyYuLo7x48fz9ttve2SS79+/P8nJyUyaNMnVoaiGsvcr2D4XhvzesSQP4B9kDbtsEW4NuzyVWr8xNrSC07DoLlj+R+gyAu7+4qKSfF3qzBAi4gu8AYwC0oFNIrLUGGNfHH4KWGSMmS0ifYBlQJRt3T5jTH/nht14tWrVii1btrg6jEtW09h/5aUKTsPnM6FtHxj2pwvbtnkbmPIpvDsa5t5qXVDVol39xNmQjmyxhpGeOQKj/i8Meqhekjw41qMfCKQaY/YbY4qBhcBNVdoYINj2uCVw1HkhKqU83son4OwJuPlN8Gt64du37gp3LoK8kzD/NijKdX6MDcUY+HE2vHsdmHK4ZwUMnllvSR4cS/QRwGG75+m2ZfaeAaaISDpWb/5hu3XRtpLOtyJydXUHEJEZIrJZRDafPHnS8eiVUu5v70rYPg+GPAIdBlz8fiIug9v/Axm7YdGvodSxeabcSkG2dUHYilnQfRT8Zl2DnHdwJNFXNw9s1TO4k4D3jTGRwPXAhyLiAxwDOhljBgCPAvNFJLjKthhj3jHGJBhjEtq0aXNhr0Ap5b4Ksq0Lo9r2hWF/vPT9dR8F4161JkJb+rBnXVCVvgXeGgp7V8B1f7Ou/A0MbZBDO3IWLx3oaPc8kvNLM/cCYwCMMRtEJAAIM8acAIpsy7eIyD6gB7AZpZT3W/Fnq9wy+aOLK9lUZ8AU64KqtX+F4PZw7TPO2W99MQY2vAGrn4YWHWDayl+mfGggjvToNwHdRSRaRPyBicDSKm0OASMBRKQ3EACcFJE2tpO5iEgXoDvgcfOQDh8+/LyLn15++WV++9vf1rpd8+bNAeuq1AkTJtS476rDSat6+eWXyc/Pr3x+/fXXOzQXjaMqJkhTyqmSl1tDKa/+A3Rw8niMoY9ZV9Cu/ydsfMe5+3am/CxYOBm+ehJ6jIH71zV4kgcHEr0xphR4CFgJ7MEaXZMoIs+KyDhbsz8A94nIDmABcLexBugPBXbaln8C3G+MyaqPF1KfJk2axMKFC89ZtnDhQoeTY4cOHfjkk08u+vhVE/2yZcvOmVXyUuzZs4fy8nLWrVtHXl79zRhY0zQPyksVZMPnv4d2MTD0cefvXwSufxF6/soamphUte/pBg7/BG8PtS6CGvN3uGOuNQ2zCzh0mtcYs8wY08MY09UY85xt2V+MMUttj5OMMYONMXHGmP7GmK9syz81xvS1LY83xnxefy+l/kyYMIEvvviCoqIiwJoZ8ujRowwZMoSzZ88ycuRI4uPj6devH5999tl526elpRETEwNAQUEBEydOJDY2ljvuuIOCgoLKdg888EDlFMdPP/00YM0pf/ToUUaMGMGIESMAiIqK4tSpUwC89NJLxMTEEBMTUznFcVpaGr179+a+++6jb9++jB49+pzj2Js/fz533XUXo0ePZunSX/5YUlNTufbaa4mLiyM+Pp59+/YB8MILL9CvXz/i4uIqZ9y0/1Zy6tQpoqKiAGsqhNtuu40bb7yR0aNH1/pe/ec//6m8evauu+4iNzeX6OhoSkpKAGt6iaioqMrnys0tnwX5p2yjbPzrbn8xfHxh/L8h8nL4dDoc3FA/x7lQ5eXw/avWjVTEB+5dCVfeb304uYjnXWmzfBZk7HLuPsP7wdjna1zdunVrBg4cyIoVK7jppptYuHAhd9xxByJCQEAAixcvJjg4mFOnTnHllVcybty4Gu9lOnv2bAIDA9m5cyc7d+48Z5rh5557jtDQUMrKyhg5ciQ7d+5k5syZvPTSS6xdu5awsHNvxrBlyxbmzJnDxo0bMcZwxRVXMGzYsMr5aRYsWMC//vUvbr/9dj799FOmTJlyXjwfffQRq1atIjk5mddff73yW8qdd97JrFmzuOWWWygsLKS8vJzly5ezZMkSNm7cSGBgYOW8NbXZsGEDO3furJy6ubr3Kikpieeee47vv/+esLAwsrKyaNGiBcOHD+fLL7/k5ptvZuHChYwfP54mTZrUeUzlYj8vg50LYdgsaB9Xv8fyD7Tq/++OhgV3wLSvoG2v+j1mbfKzYMkD1gnX3jfCuNehmXO+fV8KnevGQfblG/uyjTGGJ554gtjYWK699lqOHDnC8ePHa9zPunXrKhNubGwssbGxlesWLVpEfHw8AwYMIDExsdoJy+ytX7+eW265haCgIJo3b86tt95aOUdNdHQ0/ftbddGapkLetGkTbdq0oXPnzowcOZKtW7eSnZ1Nbm4uR44cqZwvJyAggMDAQFavXs0999xDYGAg8MsUx7UZNWpUZbua3qs1a9YwYcKEyg+yivbTp09nzpw5ADpnvafIz7KmF27Xz6rNN4TAUOuCKr8AmDveunmJKxzaCG9dbY0IGvu/1sycbpDkwRN79LX0vOvTzTffzKOPPsrWrVspKCio7InPmzePkydPsmXLFpo0aUJUVFS1UxPbq663f+DAAV588UU2bdpESEgId999d537qW2eooopjsGa5ri60s2CBQv4+eefK0stZ86c4dNPP63xbk81TTns5+dHeXk5UPtUxjW9VzXtd/DgwaSlpfHtt99SVlZWWf5Sbmz5nyA/E+78pP5KNtUJ6Wwdc871MHcCTFsOAS3r3s4Zysvhh1fh62ehVUe496tLu16gHmiP3kHNmzdn+PDhTJs27ZyTsDk5ObRt25YmTZqwdu3ayul/azJ06NDKG4Dv3r2bnTt3AlaSDQoKomXLlhw/fpzly3+Zra9Fixbk5p5/JeDQoUNZsmQJ+fn55OXlsXjxYq6+utpr0s5TXl7Oxx9/zM6dOyunMv7ss89YsGABwcHBREZGsmTJEgCKiorIz89n9OjRvPfee5UnhitKN1FRUZXTMtR20rmm92rkyJEsWrSIzMzMc/YL8Otf/5pJkyZpb94T7PkCdi2yTr62j627vbO1j4WJc+HUXlh4J5QW1f8x8zKtOXhWP22Van6zzu2SPGiivyCTJk1ix44dlXd4AquWvXnzZhISEpg3bx69etVeH3zggQc4e/YssbGxvPDCCwwcOBCwhjgOGDCAvn37Mm3atHOm+50xYwZjx46tPBlbIT4+nrvvvpuBAwdyxRVXMH36dAYMcOyXbN26dURERFTOIQ/WB0dSUhLHjh3jww8/5NVXXyU2NparrrqKjIwMxowZw7hx40hISKB///68+OKLADz22GPMnj2bq666qvIkcXVqeq/69u3Lk08+ybBhw4iLi+PRRx89Z5vs7Gwd/unu8rOs6YTDG7BkU50uw60TwGnfweL7rd52fTm4Ad4aAge+te6Kddv7Dfct4gLpNMXKrX3yySd89tlnfPjhhzW20d8PN/DJvZD0GcxYayV7V/v+FVj1F7jiAbjyAefvf/cnsOY5q2R02/v1f9LZAbVNU+x5NXrVaDz88MMsX76cZcuWuToUVZs9n1uJb8ST7pHkAa6aaV09u3G29a8+9L0VbnwFAs6b1cXtaKJXbuu1115zdQiqLnmZVsmmfZw1aZm7ELHmk4m+2poi2dlatIOuI106Nv5CeEyir2lkhmrc3K302Ogsf9xKpL/+DHzd7BoHHx/o9StXR+EWPOJkbEBAAJmZmfpHrc5hjCEzM5OAgABXh9I4JX0Guz+1biTSrq+ro1G18IgefWRkJOnp6ehc9aqqgICAWm9arupJ3in44lFo39+6NaByax6R6Js0aUJ0dLSrw1BKVVj2GBSdgZtnu1/JRp3HI0o3Sik3krgEEhfbSjZ9XB2NcoAmeqWU486ehC8fta7+HKwlG0+hiV4p5biv/8e6MffNs8HXIyq/Ck30SilHlRRaZZvY26GtXonsSTTRK6Ucs+9rKM61rghVHkUTvVLKMYlLrFvhRQ91dSTqAmmiV0rVraQAkpdZU/HqcEqPo4leKVW31K+h+Cz0vcXVkaiLoIleKVW3pCXQLBSitGzjiTTRK6VqV1IAycttZRsdUumJNNErpWpXWba52dWRqIukiV4pVbvExVq28XCa6JVSNaso2/QZp2UbD+ZQoheRMSKSLCKpIjKrmvWdRGStiGwTkZ0icr3duj/btksWkeucGbxSqp6lroaSPOijZRtPVudHtIj4Am8Ao4B0YJOILDXGJNk1ewpYZIyZLSJ9gGVAlO3xRKAv0AFYLSI9jDFlzn4hSql6kLgYAltD1NWujkRdAkd69AOBVGPMfmNMMbAQuKlKGwNU3CG3JXDU9vgmYKExpsgYcwBIte1PKeXuSgogeYWOtvECjiT6COCw3fN02zJ7zwBTRCQdqzf/8AVsi4jMEJHNIrJZ7yKllJtIWWWVbfQiKY/nSKKv7o7cVW/eOgl43xgTCVwPfCgiPg5uizHmHWNMgjEmoU2bNg6EpJSqd4mLITAMOg9xdSTqEjmS6NOBjnbPI/mlNFPhXmARgDFmAxAAhDm4rVLK3RTnw96VWrbxEo4k+k1AdxGJFhF/rJOrS6u0OQSMBBCR3liJ/qSt3UQRaSoi0UB34CdnBa+UqiepWrbxJnV+VBtjSkXkIWAl4Au8Z4xJFJFngc3GmKXAH4B/icgjWKWZu40xBkgUkUVAElAKPKgjbpTyAIlLbGWbwa6ORDmBQ9/JjDHLsE6y2i/7i93jJKDa3whjzHPAc5cQo1KqIRXnw94VEDdRyzZeQq+MVUqdK+UrKMnXso0X0USvlDpX0hIIaqNlGy+iiV4p9YvK0TbjwMfX1dEoJ9FEr5T6hZZtvJImeqXULxIXQ1Bb6HyVqyNRTqSJXillKc6zyjZ9tGzjbTTRK6UsKV9BaYFOSeyFNNErpSxatvFamuiVUrayzVfQ5yYt23ghTfRKKas2X1qgNwD3UprolVJW2aZ5O+g0yNWRqHqgiV6pxq7orHWTEb1IymtpoleqsUupKNvoRVLeShO9Uo1d4hJoHg6drnR1JKqe6BykSjVmRWet8fPxv3Zp2eZMYQn+vj4ENHH/0lFJWTkHM/Op5q6ol6ypny8dQwOdvl9N9Eo1ZntXQGmhS8s2uYUljPjfbygqLWdEr7aMjQlneM82BPq7T3oqKi1jfcoplu/OYPWe45zOL6mX4/Tv2IolDzp/1lD3eSeVUg0vyVa26ei6ss0HP6SRmVfMuLgOfJ96is93HCWgiQ/DerRhbEx7rundluCAJg0eV35xKd8mn2T57gzW/HyCs0WltAjwY1TvdgzuFoa/n/Mr3yGB/k7fJ2iiV6rxqhhtEz8VfFxzuu5sUSn/Xn+Aa3q15dVJAygtK2dTWjYrdh9jRWIGKxOP4+/rw+BurRkb055RfdoRElQ/yRCsbxdrfj7B8l0ZfLP3BIUl5YQG+XNDbHvGxIRzVdf6SfD1TRO9Uo2VG5RtPtxwkNP5Jcwc2R0AP18fBnVtzaCurXn6xr5sO3yaFbuPsXx3Bms/3YnvYuHKLqGMiWnPdX3a0TY44JJjyM4rZtWe46zYncH6lFMUl5XTtkVTbk/oyJiYcAZGheLn63nJ3Z5Y9/B2HwkJCWbz5s2uDkMp77fwTjiyBR5JckmPPq+olKtfWEu/iJZ8MG1grW2NMSQePcNyW9LffzIPEbisUwhjYsIZExNOZIjjJzFP5BayMvE4K3Yf48f9WZSVGyJaNWNsTDhj+4UzoGMIPj5yqS+xQYnIFmNMQnXrtEevVGNUlGuVbRLucVnZZt7Gg2TlFVf25msjIsREtCQmoiWPje5JyomzLN+VwfLdx/jrl3v465d7iI1syZiYcMbGtCc6LOi8fRw5XcCK3Rms2H2MzQezMQa6hAXxm6FdGBvTnpiIYEQ8K7k7ShO9Uo3R3pVQVuSysk1BcRnvrNvP1d3DuKxzyAVtKyL0aNeCHu1a8Ltru3PgVF5lAn9hRTIvrEimV3iLypr6loNWzX9Heg4AvcJb8LuR3Rkb054e7Zp7bXK3p4leqcYocTG06ACRtZdM6su8jQc5ddax3nxdosOCeGB4Vx4Y3vWcXvsrX6fw8uoUAGIjW/LHMT0Z0zecLm2aX/IxPY0meqUam8qyzTSXlG0KS8p4e91+ruramsujQp2674hWzZWlajcAABraSURBVLh3SDT3DonmxJlCNh/MJjay5QXV772RQz9lERkjIskikiois6pZ/08R2W77t1dETtutK7Nbt9SZwSulLkLyClvZxjVTEi/46RAnc4uc0puvTdvgAK7v177RJ3lwoEcvIr7AG8AoIB3YJCJLjTFJFW2MMY/YtX8YGGC3iwJjTH/nhayUuiQuLNsUlpTx1rf7GBgdypVdWjf48RsrR3r0A4FUY8x+Y0wxsBC4qZb2k4AFzghOKeVkhWcgdbXVm3dB2WbR5sMcP1PE7+u5N6/O5chPOgI4bPc83bbsPCLSGYgG1tgtDhCRzSLyo4hU+11RRGbY2mw+efKkg6ErpS7YXlvZxgU3AC8qLWP2N/tI6BzCoK7am29IjiT66sYe1XSV1UTgE2NMmd2yTrZB/JOBl0Wk63k7M+YdY0yCMSahTZs2DoSklLooiUsgOAIiL2/wQ3+yJZ1jOYX87trujWJIoztxJNGnAx3tnkcCR2toO5EqZRtjzFHb//uBbzi3fq+UaiiFZyB1le0G4A1btikuLefNtfsY0KkVQ7qFNeixlWOJfhPQXUSiRcQfK5mfN3pGRHoCIcAGu2UhItLU9jgMGAwkVd1WKdUAkpdDWbFLLpL679Z0jpwuYOZI7c27Qp2jbowxpSLyELAS8AXeM8YkisizwGZjTEXSnwQsNOdOntMbeFtEyrE+VJ63H62jlGpASUsgOBIiqp0Opd6UlJXz+tpU4iJbMryHlmZdwaELpowxy4BlVZb9pcrzZ6rZ7geg3yXEp5RyhsIca7TN5fc1eNlm8bYjpGcX8D/j+mpv3kU8e+5NpZRjkle4pGxTWlbOG2tTiYkI5ppebRv02OoXmuiVagwSF1tlm8iGLdt8tv0oBzPzmXmN1uZdSRO9Ut6uMAf2fW1dJNWAybas3PD62lR6tw9mVJ92DXZcdT5N9Ep5OxeNtvl8x1EOnMrjdyO7aW/exTTRK+XtEhdDy44QcVmDHbKs3PDamhR6tmvB6D7hDXZcVT1N9Ep5s4LTsG+NdZFUA/aql+06xr6Tecwc2d3jbsnnjTTRK+XNXFC2Kbf15ru3bc7YGO3NuwNN9Ep5MxeUbVYkZrD3+Fkeuqab9ubdhCZ6pbxVRdmmAUfblJcbXv06hS5tgrghtkODHFPVTRO9Ut4qeRmUl0CfhivbfJV0nJ8zcnn4mm74am/ebWiiV8pbJS6Blp0gIr5BDmeM1ZuPDgviRu3NuxVN9Ep5o4LsBi/brN5zgqRjZ3hwRDf8fDW1uBP9aSjljX7+0irbNNANwCt6851CA7m5v/bm3Y0meqW80fb50LobdGiYss03ySfZdSSHh7Q375b0J6KUt8ncBwe/hwFTGqRsY4zh5a9TiAxpxi3x1d5OWrmYJnqlvM32eSA+EDuxQQ63LuUUOw6f5sER3WiivXm3pD8VpbxJeZlVtuk2CoLb1/vhjDG8snovHVoGMD4+st6Ppy6OJnqlvMm+NZB7zCrbNIDvUzPZeug0D4zohr+fphN3pT8ZpbzJtrkQ2Bp6jKn3QxljeOXrvYQHB3B7gvbm3ZkmeqW8RV6mNawydiL4+df74Tbsz2RTWjYPDO9KUz/fej+eunia6JXyFrs+tsbON1DZ5tWvU2jboil3XN6xQY6nLp4meqW8gTGw7UPoMADa9an3w23cn8mP+7O4f1hXAppob97daaJXyhsc2wHHdzdcb35NCmHNmzL5ik4Ncjx1aTTRK+UNts0FvwCImVDvh9qclsX3qZncP6yL9uY9hCZ6pTxdSaFVn+99IzRrVe+He+XrFFoH+Wtv3oM4lOhFZIyIJItIqojMqmb9P0Vku+3fXhE5bbduqoik2P5NdWbwSikg+UsoPN0gZZuth7L5LuUUM4Z2IdDfr96Pp5yjzp+UiPgCbwCjgHRgk4gsNcYkVbQxxjxi1/5hYIDtcSjwNJAAGGCLbdtsp74KpRqzbXOteeejhtbbIfKKSpm38SDvrDtASGATplzZud6OpZzPkY/kgUCqMWY/gIgsBG4CkmpoPwkruQNcB6wyxmTZtl0FjAEWXErQSimb04dh31oY9ifwcX4l9kxhCf/5IY131x8gO7+Eq7uH8cfrehHUVHvznsSRn1YEcNjueTpwRXUNRaQzEA2sqWXb86a3E5EZwAyATp207qeUw3YsAAz0n+zU3WbnFTPn+wPM+SGN3MJSRvZqy4PXdCO+U4hTj6MahiOJvrp5Tk0NbScCnxhjyi5kW2PMO8A7AAkJCTXtWyllr7zcKttED4MQ55RSTuYW8e/v9vPhjwfJLy5jbEw4D47oRkxES6fsX7mGI4k+HbC/9C0SOFpD24nAg1W2HV5l228cD08pVaOD38Ppg3DN/7nkXWXkFPLWt/tY8NMhSsrKuTGuAw+O6EaPdi2cEKhyNUcS/Sagu4hEA0ewkvl53xNFpCcQAmywW7wS+JuIVHzfGw38+ZIiVkpZts2Fpi2h9w0XvYvDWfnM/nYfn2xOp9wYbhkQwW9HdCM6LMiJgSpXqzPRG2NKReQhrKTtC7xnjEkUkWeBzcaYpbamk4CFxhhjt22WiPxfrA8LgGcrTswqpS5BYQ4kfWbV5ps0u+DN9588y5vf7GPxtiP4inBbQiT3D+tKx9DAeghWuZpDp86NMcuAZVWW/aXK82dq2PY94L2LjE8pVZ3d/4XSAhhw5wVtlpyRyxtrU/li51H8/XyYOiiKGUO7EN4yoJ4CVe5Ax0gp5Ym2zYW2fRy++ffuIzm8viaVFYkZBPn7ct/QLkwf0oU2LZrWc6DKHWiiV8rTnPgZjmyG6/5W582/tx7K5vU1qaz5+QQtAvyYObI791wVRUhQ/c9Xr9yHJnqlPM32ueDjB7F31Ngk9UQuTy9N5PvUTEICm/D4dT25a1BnggOaNGCgyl1oolfKk5SVwI6F0HMsBIVV22RV0nEe+Wg7/n4+PHl9byZf0UmvZG3k9KevlCdJ+QryTsKAu85bVV5ueH1tKi+t2ktsZEvevusy2re88BE5yvtoolfKk2ybC83DoevIcxbnFZXy2Mc7WL47g1sHRPC3W/vpXPGqkiZ6pTxF7nHYuxKuehh8f/nTPZSZz4wPN7P3eC5P/ao39w6JRuo4SasaF030SnmKnQvBlJ0z7/z6lFM8tGArxsAH0wZydfc2LgxQuStN9Ep5AmNg2zzoeCWEdccYw7vrD/C3ZXvo1rY5//p1Ap1b67QFqnqa6JXyBOmb4VQyjHudwpIynli8i/9uPcJ1fdvxj9v701xH1aha6G+HUp5g24fQJIjjHccy4+0N7EjP4ZFre/DwNd3w8dF6vKqdJnql3F1xHuz+L6eirueGd7aTX1TK23ddxnV9w10dmfIQmuiVcndJS6E4l4f39CGwpS/zpl+h88SrC6KJXik3VlJWTvrqt6G8HX5RV/HZ5HhaBeo8NerCOP9uwkopp8g8W8Qf3lpM9NltpHW6lTn3DNQkry6KJnql3FDi0RzGvf49vY5/jsGHEbfPxM9X/1zVxdHfHKXczOc7jjJ+9g9QXsr0Fj8i3a+F4A6uDkt5ME30SrmJsnLD31f8zMMLthHToSVf3liGf96xc66EVepi6MlYpdxATkEJv1u4jW+STzL5ik48c2Nf/BdPg2ah0GOsq8NTHk4TvVIulnriLDP+s5lDWfn89eYYplzZGfKz4OcvIeFe8NMTsOrSaKJXysmMMeQWlZJ1tpis/GLr/7xiMvOKyc4vJvNsMVl5RWTlWeuP5xTRIsCP+fddycDoUGsnuz6GsuILvvm3UtXRRK+UA4wxpGcXkJ5dYCXrvIoEXkRWfglZeUW2BG4l85IyU+1+mvr50DrIn9Dm/oQGNSU6LIh2wQH8+qooIlrZ3SRk21xo3x/C+zXQK1TeTBO9UtUwxrDvZB4/Hchi44FMfjqQxbGcwvPaBQf40bp5U0ICmxAZEkhcZCsriQf6E1qR0G2PWzf3J9DfgT+5YzsgYydc/2I9vDLVGGmiVwrrNnw/Z+RWJvWfDmSRmVcMQJsWTbkiOpSB0aF0a9Pc1hv3JyTQnyb1MbZ92zzwbQr9Jjh/36pR0kSvGqWSsnJ2H8mpTOqb0rI4U1gKQGRIM4b1bMMV0aFcEd2azq0DG+6OTSWFsPMj6H0DNAtpmGMqr+dQoheRMcArgC/wb2PM89W0uR14BjDADmPMZNvyMmCXrdkhY8w4J8St1AUpLCljx+HTVmJPy2LLwWzyi8sA6NImiF/FtmdgdCgDo1ufWytvaMnLoPC0jp1XTlVnohcRX+ANYBSQDmwSkaXGmCS7Nt2BPwODjTHZItLWbhcFxpj+To5bqVrlFZWy9VC2rcaexfbDpykuLQegV3gLbrsskoHRrRkYHUqbFk1dHK2dbXOhZUeIHubqSJQXcaRHPxBINcbsBxCRhcBNQJJdm/uAN4wx2QDGmBPODlSpuhzLKWDl7gyW785gy8FsSssNvj5CTIdgpg7qzMDo1lweFeK+E4PlpMO+NTDsj+Dj6+polBdxJNFHAIftnqcDV1Rp0wNARL7HKu88Y4xZYVsXICKbgVLgeWPMkqoHEJEZwAyATp06XdALUI3bocx8lu8+xvLdGWw/fBqAHu2ac9/QLlzZpTWXdQ7xnNvs7VgAGOg/2dWRKC/jyF9AdWehqg4S9gO6A8OBSOA7EYkxxpwGOhljjopIF2CNiOwyxuw7Z2fGvAO8A5CQkFD9AGSlbFJP5LJ8l9VzTzp2BoCYiGAev64nY2LC6dqmuYsjvAjl5VbZJnoohES5OhrlZRxJ9OlAR7vnkcDRatr8aIwpAQ6ISDJW4t9kjDkKYIzZLyLfAAOAfSjlIGMMScfOsMJWlkk9cRaA+E6tePL63oyJCadjaKCLo7xEh36A7DQY/oSrI1FeyJFEvwnoLiLRwBFgIlD1u+USYBLwvoiEYZVy9otICJBvjCmyLR8MvOC06JXXKi837Eg/XZncD2Xl4yMwMDqUu67sy3V9wwlvGeDqMJ1n21xoGgy9b3R1JMoL1ZnojTGlIvIQsBKr/v6eMSZRRJ4FNhtjltrWjRaRJKAMeNwYkykiVwFvi0g51pTIz9uP1lHKXlm5YVNaFit2Z7AyMYNjOYU08RWu6hrGb4d3ZVSfdrRu7kYjZJyl8AwkLoG4ieDv4d9MlFty6CyVMWYZsKzKsr/YPTbAo7Z/9m1+AHSyDlWjkrJyNuzLZPnuDFYlZXDqbDH+fj4M69GGx6/rycje7WjZrImrw6xfiYuhtAAG3OXqSJSX8pDhCA4oL4O8k07fbeLRHJ5ZmkhpueecI27fMoABnUIZ0LEVMRHBBDRxn6F6ZeWGlBO5bDt0mm2HTrPlYDZnCkto5u/LqG5hXNMzgsHdwn6ZE6Y0E3JdG3O92/ofaNMbIuJdHYnyUt6T6Auy4R89nb7bvsDHTt9rPSsAMoCfXB3I+XyBXrZ/kyoWVpTa99v+NUaj/woNNc2CanS8J9H7B8EN/3TqLtMy83ln3X7GxoRzdfcwp+67vhWUlHEwM58Dp/JIy8zjSHYB5cYaK9uhVTOiwwKJah1EVFgQgf7O6/GXlBkOZ+eTdiqPA6fyOJSVXzllb5vm/kSFBREdFkRU6yBaBXp5ScZRvv4QM97VUSgvJlZ53X0kJCSYzZs3uzoMAO56dyNJR8/w3Z9GODa9rBvLLy5l26HTbNyfycYDWWyrMiXAQNvsjAOjQ2nbwvHRLGeLStl6MLtyOt8dh3MoLitHBHqFB9smBgslIcrNphpQysuIyBZjTEJ16zw7e9WjrYey+S7lFLPG9vL4JA8Q6O/H4G5hDO5mfTMpKi1jZ7o1e+OP+zP5ZEs6/9lwEIAuYUHnJP7IkF9GguTkl7Ap7Zc52ncfPUNZxVQDES25e3CUldg7h9JSe+xKuQXt0dfg7jk/sePwadb/6RqCPOUS+ktQWlZO4tEz59xoo2La3ohWzYiNbMmBU3kkH8/FGPD386F/x1aV87THdwppFO+TUu5Ke/QXaMfh03yTfJLHr+vZaJKXn68PcR1bEdexFfcN7UJ5uSH5eG7lfO070k8THRbEr/pZ0/nGdWzlVqN5lFI1axxZ7AK9+nUKLZs14deDOrs6FJfx8RF6tw+md/tgpl4V5epwlFKXoB7ug+bZdh/J4eufTzB9SDQtArTGrJTyfJroq3j16xSCA/yYOjjK1aEopZRTaKK3k3T0DF8lHWfakGiCtTevlPISmujtvLYmhRZN/bjnqmhXh6KUUk6jid4mOSOX5bszuGdwlI7/Vkp5FU30Nq+uSSHI35dpQ7Q3r5TyLprogZTjuSzbdYypV0W5742jlVLqImmiB15bk0qzJr5Mv7qLq0NRSimna/SJPvXEWT7feZS7BnUmNEh780op79PoE/2ba1MJ8PPlPu3NK6W8VKNO9AdO5bFk+xGmXNmJMG+8F6lSStHIE/0ba1Np4uvDfUO1N6+U8l6NNtEfzMxj8bYj3HlF5wu60YZSSnmaRpvo31y7D18f4TfDtDevlPJujTLRH87K59Ot6Uwe2Il2wdqbV0p5t0aZ6N/8Zh8+or15pVTj4FCiF5ExIpIsIqkiMquGNreLSJKIJIrIfLvlU0UkxfZvqrMCv1hHThfwyZbD3H55JO1bNnN1OEopVe/qvMOUiPgCbwCjgHRgk4gsNcYk2bXpDvwZGGyMyRaRtrblocDTQAJggC22bbOd/1IcM/ubVAAeGN7NVSEopVSDcqRHPxBINcbsN8YUAwuBm6q0uQ94oyKBG2NO2JZfB6wyxmTZ1q0Cxjgn9At3LKeARZvSmXBZRyJaaW9eKdU4OJLoI4DDds/Tbcvs9QB6iMj3IvKjiIy5gG0bzNvf7qfcGH47vKurQlBKqQbnyM3BpZplppr9dAeGA5HAdyIS4+C2iMgMYAZAp06dHAjpwh0/U8j8nw4xPj6SjqGB9XIMpZRyR4706NOBjnbPI4Gj1bT5zBhTYow5ACRjJX5HtsUY844xJsEYk9CmTZsLid9hb3+7n7Jyw4MjtDavlGpcHEn0m4DuIhItIv7ARGBplTZLgBEAIhKGVcrZD6wERotIiIiEAKNtyxrUidxC5m08yC0DIujUWnvzSqnGpc7SjTGmVEQewkrQvsB7xphEEXkW2GyMWcovCT0JKAMeN8ZkAojI/8X6sAB41hiTVR8vpDb/WrefkrJy7c0rpRolMea8krlLJSQkmM2bNzttf6fOFnH139cyNiacl+7o77T9KqWUOxGRLcaYhOrWef2Vsf/6bj+FpWU8eI325pVSjZNXJ/qsvGI+3HCQG2M70LVNc1eHo5RSLuHVif7d9fspKCnjYe3NK6UaMa9N9Kfzi/ngh4Nc36893du1cHU4SinlMl6b6N9bf4CzRaXMvKa7q0NRSimX8spEn5Nfwpzv0xgbE07PcO3NK6UaN69M9HN+OEBuUSkPa29eKaW8L9GfKSzhvfUHGN2nHX06BLs6HKWUcjmvS/QffJ/GmcJSZo7U3rxSSoGXJfqzRaX8e/0Bru3dlpiIlq4ORyml3IJXJfoPfkgjp6BEe/NKKWXHaxJ9XlEp//5uPyN6tiE2spWrw1FKKbfhyI1HPMLZolKu7NKa+4Z2cXUoSinlVrwm0bcLDmD2lMtcHYZSSrkdryndKKWUqp4meqWU8nKa6JVSystpoldKKS+niV4ppbycJnqllPJymuiVUsrLaaJXSikvJ8YYV8dwDhE5CRy8hF2EAaecFE5986RYwbPi9aRYwbPi9aRYwbPivZRYOxtj2lS3wu0S/aUSkc3GmARXx+EIT4oVPCteT4oVPCteT4oVPCve+opVSzdKKeXlNNErpZSX88ZE/46rA7gAnhQreFa8nhQreFa8nhQreFa89RKr19XolVJKncsbe/RKKaXsaKJXSikv5zWJXkTGiEiyiKSKyCxXx1MbEekoImtFZI+IJIrI71wdU11ExFdEtonIF66OpS4i0kpEPhGRn23v8SBXx1QTEXnE9juwW0QWiEiAq2OyJyLvicgJEdlttyxURFaJSIrt/xBXxlihhlj/1/Z7sFNEFouI29xntLp47dY9JiJGRMKccSyvSPQi4gu8AYwF+gCTRKSPa6OqVSnwB2NMb+BK4EE3jxfgd8AeVwfhoFeAFcaYXkAcbhq3iEQAM4EEY0wM4AtMdG1U53kfGFNl2Szga2NMd+Br23N38D7nx7oKiDHGxAJ7gT83dFC1eJ/z40VEOgKjgEPOOpBXJHpgIJBqjNlvjCkGFgI3uTimGhljjhljttoe52IlogjXRlUzEYkEfgX829Wx1EVEgoGhwLsAxphiY8xp10ZVKz+gmYj4AYHAURfHcw5jzDogq8rim4APbI8/AG5u0KBqUF2sxpivjDGltqc/ApENHlgNanhvAf4J/BFw2kgZb0n0EcBhu+fpuHHitCciUcAAYKNrI6nVy1i/eOWuDsQBXYCTwBxbqenfIhLk6qCqY4w5AryI1XM7BuQYY75ybVQOaWeMOQZWpwVo6+J4HDUNWO7qIGojIuOAI8aYHc7cr7ckeqlmmduPGxWR5sCnwO+NMWdcHU91ROQG4IQxZourY3GQHxAPzDbGDADycJ/Swjlste2bgGigAxAkIlNcG5V3EpEnsUqm81wdS01EJBB4EviLs/ftLYk+Heho9zwSN/sKXJWINMFK8vOMMf91dTy1GAyME5E0rJLYNSIy17Uh1SodSDfGVHxD+gQr8buja4EDxpiTxpgS4L/AVS6OyRHHRaQ9gO3/Ey6Op1YiMhW4AbjTuPeFQ12xPvR32P7eIoGtIhJ+qTv2lkS/CeguItEi4o91Qmupi2OqkYgIVg15jzHmJVfHUxtjzJ+NMZHGmCis93WNMcZte53GmAzgsIj0tC0aCSS5MKTaHAKuFJFA2+/ESNz0xHEVS4GptsdTgc9cGEutRGQM8CdgnDEm39Xx1MYYs8sY09YYE2X7e0sH4m2/05fEKxK97WTLQ8BKrD+URcaYRNdGVavBwF1YvePttn/XuzooL/IwME9EdgL9gb+5OJ5q2b51fAJsBXZh/T261eX6IrIA2AD0FJF0EbkXeB4YJSIpWKNDnndljBVqiPV1oAWwyvZ39pZLg7RTQ7z1cyz3/iajlFLqUnlFj14ppVTNNNErpZSX00SvlFJeThO9Ukp5OU30Sinl5TTRK6WUl9NEr5RSXu7/AyM6TjZ7zgzoAAAAAElFTkSuQmCC\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": [
    "plot_performance(history_10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "May benefit from some additional epochs but val acc was plateuing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.2 Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 1.1843 - accuracy: 0.6005 - val_loss: 0.7038 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.6730 - accuracy: 0.6397 - val_loss: 0.6630 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 56s 5s/step - loss: 0.6920 - accuracy: 0.6162 - val_loss: 0.6473 - val_accuracy: 0.6386\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.6533 - accuracy: 0.6397 - val_loss: 0.5661 - val_accuracy: 0.8434\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.5781 - accuracy: 0.7363 - val_loss: 0.4811 - val_accuracy: 0.8193\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.5237 - accuracy: 0.7415 - val_loss: 0.4506 - val_accuracy: 0.8313\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.5359 - accuracy: 0.7389 - val_loss: 0.5303 - val_accuracy: 0.6747\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.5224 - accuracy: 0.7154 - val_loss: 0.5334 - val_accuracy: 0.6506\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.5308 - accuracy: 0.6997 - val_loss: 0.3971 - val_accuracy: 0.8072\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.4896 - accuracy: 0.7546 - val_loss: 0.3808 - val_accuracy: 0.8554\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.4598 - accuracy: 0.7598 - val_loss: 0.4412 - val_accuracy: 0.7952\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4797 - accuracy: 0.7520 - val_loss: 0.5629 - val_accuracy: 0.6024\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 57s 5s/step - loss: 0.4627 - accuracy: 0.7546 - val_loss: 0.3923 - val_accuracy: 0.8554\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4815 - accuracy: 0.7807 - val_loss: 0.3811 - val_accuracy: 0.8313\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.4510 - accuracy: 0.7598 - val_loss: 0.3895 - val_accuracy: 0.8554\n",
      "Total Time Elapsed:  15  minutes  21  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_11 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 481ms/step - loss: 0.4265 - accuracy: 0.8077\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.2 Dropout'] = {'Val_Loss': 0.3895, \n",
    "                                         'Val_Acc': 0.8554, \n",
    "                                         'Test_Loss': 0.4265, \n",
    "                                         'Test_Acc': 0.8077}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "plot_performance(history_11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN: 3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout / Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.Adam(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.7836 - accuracy: 0.6319 - val_loss: 0.6404 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 54s 5s/step - loss: 0.6412 - accuracy: 0.6397 - val_loss: 0.6760 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6058 - accuracy: 0.6423 - val_loss: 0.5057 - val_accuracy: 0.7590\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 56s 5s/step - loss: 0.6646 - accuracy: 0.6266 - val_loss: 0.6865 - val_accuracy: 0.6386\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 54s 5s/step - loss: 0.6822 - accuracy: 0.6397 - val_loss: 0.6754 - val_accuracy: 0.6386\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6707 - accuracy: 0.6397 - val_loss: 0.6657 - val_accuracy: 0.6386\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 53s 4s/step - loss: 0.6611 - accuracy: 0.6397 - val_loss: 0.6583 - val_accuracy: 0.6386\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 54s 5s/step - loss: 0.6581 - accuracy: 0.6397 - val_loss: 0.6545 - val_accuracy: 0.6386\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 55s 5s/step - loss: 0.6544 - accuracy: 0.6397 - val_loss: 0.6543 - val_accuracy: 0.6386\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6532 - accuracy: 0.6397 - val_loss: 0.6544 - val_accuracy: 0.6386\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6520 - accuracy: 0.6397 - val_loss: 0.6543 - val_accuracy: 0.6386\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6545 - accuracy: 0.6397 - val_loss: 0.6543 - val_accuracy: 0.6386\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 54s 4s/step - loss: 0.6552 - accuracy: 0.6397 - val_loss: 0.6543 - val_accuracy: 0.6386\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 53s 4s/step - loss: 0.6555 - accuracy: 0.6397 - val_loss: 0.6542 - val_accuracy: 0.6386\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 54s 5s/step - loss: 0.6548 - accuracy: 0.6397 - val_loss: 0.6542 - val_accuracy: 0.6386\n",
      "Total Time Elapsed:  14  minutes  52  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_12 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 1s 485ms/step - loss: 0.6455 - accuracy: 0.6538\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores['3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout / Adam'] = {'Val_Loss': 0.6542, \n",
    "                                         'Val_Acc': 0.6386, \n",
    "                                         'Test_Loss': 0.6455, \n",
    "                                         'Test_Acc': 0.6538}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "plot_performance(history_12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scores Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores_df = pd.DataFrame.from_dict(scores, orient='index',columns = ['Val_Loss', 'Val_Acc', 'Test_Loss', 'Test_Acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Val_Loss</th>\n",
       "      <th>Val_Acc</th>\n",
       "      <th>Test_Loss</th>\n",
       "      <th>Test_Acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2 Conv2D, 1 Dense</th>\n",
       "      <td>0.8533</td>\n",
       "      <td>0.7952</td>\n",
       "      <td>0.7861</td>\n",
       "      <td>0.8333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Conv2D, 2 Dense</th>\n",
       "      <td>0.9695</td>\n",
       "      <td>0.8675</td>\n",
       "      <td>1.7724</td>\n",
       "      <td>0.7821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Conv2D, 2 Dense / Padding</th>\n",
       "      <td>0.4070</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.8718</td>\n",
       "      <td>0.7692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Conv2D, 2 Dense / Padding / Augmented Data</th>\n",
       "      <td>0.3381</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.3732</td>\n",
       "      <td>0.8462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 Conv2D, 2 Dense / Padding / Extra Augmented Data</th>\n",
       "      <td>0.2974</td>\n",
       "      <td>0.8434</td>\n",
       "      <td>0.2688</td>\n",
       "      <td>0.8590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 2 Conv2D, 2 Dense / Padding / Extra Augmented Data</th>\n",
       "      <td>0.3164</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.3540</td>\n",
       "      <td>0.8590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data</th>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.9036</td>\n",
       "      <td>0.2213</td>\n",
       "      <td>0.9231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data</th>\n",
       "      <td>0.4030</td>\n",
       "      <td>0.7952</td>\n",
       "      <td>0.4635</td>\n",
       "      <td>0.7949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout</th>\n",
       "      <td>0.3642</td>\n",
       "      <td>0.8795</td>\n",
       "      <td>0.3271</td>\n",
       "      <td>0.9103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.4 Dropout</th>\n",
       "      <td>0.4694</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.4554</td>\n",
       "      <td>0.8462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.2 Dropout</th>\n",
       "      <td>0.3895</td>\n",
       "      <td>0.8554</td>\n",
       "      <td>0.4265</td>\n",
       "      <td>0.8077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 x 3 Conv2D, 2 Dense / Padding / Extra Augmented Data / 0.25 Dropout / Adam</th>\n",
       "      <td>0.6542</td>\n",
       "      <td>0.6386</td>\n",
       "      <td>0.6455</td>\n",
       "      <td>0.6538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Val_Loss  Val_Acc  \\\n",
       "2 Conv2D, 1 Dense                                     0.8533   0.7952   \n",
       "3 Conv2D, 2 Dense                                     0.9695   0.8675   \n",
       "3 Conv2D, 2 Dense / Padding                           0.4070   0.8554   \n",
       "3 Conv2D, 2 Dense / Padding / Augmented Data          0.3381   0.8554   \n",
       "3 Conv2D, 2 Dense / Padding / Extra Augmented Data    0.2974   0.8434   \n",
       "3 x 2 Conv2D, 2 Dense / Padding / Extra Augment...    0.3164   0.8554   \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.2390   0.9036   \n",
       "4 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.4030   0.7952   \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.3642   0.8795   \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.4694   0.8554   \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.3895   0.8554   \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...    0.6542   0.6386   \n",
       "\n",
       "                                                    Test_Loss  Test_Acc  \n",
       "2 Conv2D, 1 Dense                                      0.7861    0.8333  \n",
       "3 Conv2D, 2 Dense                                      1.7724    0.7821  \n",
       "3 Conv2D, 2 Dense / Padding                            0.8718    0.7692  \n",
       "3 Conv2D, 2 Dense / Padding / Augmented Data           0.3732    0.8462  \n",
       "3 Conv2D, 2 Dense / Padding / Extra Augmented Data     0.2688    0.8590  \n",
       "3 x 2 Conv2D, 2 Dense / Padding / Extra Augment...     0.3540    0.8590  \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.2213    0.9231  \n",
       "4 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.4635    0.7949  \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.3271    0.9103  \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.4554    0.8462  \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.4265    0.8077  \n",
       "3 x 3 Conv2D, 2 Dense / Padding / Extra Augment...     0.6455    0.6538  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Model\n",
    "### CNN: 3 x 3 Conv2D, 2 Dense with Padding and Extra Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 383 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# try adding slight rotation\n",
    "train_augment_2 = image.ImageDataGenerator(rescale = 1/255,\n",
    "                                           brightness_range = [0.6, 1.0],\n",
    "                                           rotation_range = 15,\n",
    "                                           zoom_range = 0.2, horizontal_flip=True)\n",
    "\n",
    "train_img_augmented_2 = train_augment_2.flow_from_directory(train_dir,\n",
    "                                                    target_size = (256,256), \n",
    "                                                    color_mode='grayscale',\n",
    "                                                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model fit\n",
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu', input_shape=(256, 256, 1)))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(32, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.Conv2D(64, 3, padding ='same', activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(512, activation = 'relu'))\n",
    "model.add(layers.Dense(256, activation = 'relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling models\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer=optimizers.RMSprop(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "12/12 [==============================] - 67s 6s/step - loss: 1.9311 - accuracy: 0.5744 - val_loss: 0.6858 - val_accuracy: 0.6386\n",
      "Epoch 2/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.6694 - accuracy: 0.6397 - val_loss: 0.7157 - val_accuracy: 0.6386\n",
      "Epoch 3/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.7041 - accuracy: 0.6475 - val_loss: 0.6400 - val_accuracy: 0.6386\n",
      "Epoch 4/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.6107 - accuracy: 0.6945 - val_loss: 0.5422 - val_accuracy: 0.6747\n",
      "Epoch 5/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.6031 - accuracy: 0.7206 - val_loss: 0.5101 - val_accuracy: 0.7590\n",
      "Epoch 6/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.5329 - accuracy: 0.7389 - val_loss: 0.4615 - val_accuracy: 0.8193\n",
      "Epoch 7/15\n",
      "12/12 [==============================] - 69s 6s/step - loss: 0.5273 - accuracy: 0.7650 - val_loss: 0.5121 - val_accuracy: 0.7711\n",
      "Epoch 8/15\n",
      "12/12 [==============================] - 72s 6s/step - loss: 0.4817 - accuracy: 0.7755 - val_loss: 0.4312 - val_accuracy: 0.7711\n",
      "Epoch 9/15\n",
      "12/12 [==============================] - 70s 6s/step - loss: 0.5100 - accuracy: 0.7702 - val_loss: 0.4590 - val_accuracy: 0.8193\n",
      "Epoch 10/15\n",
      "12/12 [==============================] - 67s 6s/step - loss: 0.5359 - accuracy: 0.7807 - val_loss: 0.3935 - val_accuracy: 0.7711\n",
      "Epoch 11/15\n",
      "12/12 [==============================] - 73s 6s/step - loss: 0.4144 - accuracy: 0.7885 - val_loss: 0.4242 - val_accuracy: 0.8675\n",
      "Epoch 12/15\n",
      "12/12 [==============================] - 71s 6s/step - loss: 0.4977 - accuracy: 0.7650 - val_loss: 0.4081 - val_accuracy: 0.8193\n",
      "Epoch 13/15\n",
      "12/12 [==============================] - 74s 6s/step - loss: 0.4469 - accuracy: 0.7963 - val_loss: 0.4769 - val_accuracy: 0.6988\n",
      "Epoch 14/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.4354 - accuracy: 0.8172 - val_loss: 0.3441 - val_accuracy: 0.8675\n",
      "Epoch 15/15\n",
      "12/12 [==============================] - 68s 6s/step - loss: 0.4636 - accuracy: 0.8016 - val_loss: 0.4105 - val_accuracy: 0.8795\n",
      "Total Time Elapsed:  19  minutes  8  seconds\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "start = timer()\n",
    "history_7 = model.fit(train_img_augmented_2, \n",
    "                    epochs=15, \n",
    "                    validation_data=val_img, \n",
    "                    workers = 7)\n",
    "end = timer()\n",
    "elapsed = end - start\n",
    "print('Total Time Elapsed: ', int(elapsed//60), ' minutes ', (round(elapsed%60)), ' seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 2s 635ms/step - loss: 0.3856 - accuracy: 0.9103\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(test_img, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "Y_pred = model.predict_generator(test_img)\n",
    "predictedClasses = np.where(Y_pred>0.5, 1, 0)\n",
    "array = confusion_matrix(test_img.classes, predictedClasses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZouoTnORwgjYzwxcJzcySlV797ARtZga4gjYzS1at0quhnaDNzPAUh5lZsjzFYWaWKC+zMzNLVHrp2QnazAxIc4rDb1QxMwPqiKJbIZJulrRK0sIGfV+X9Kqk+Vk7vtA4TtBmZpTulVeZW4DjGum/LiKGZe3eQoM4QSdo1Kc/wazfTWfF8md4b91SXl46l9tv+wkHHDCk0qFZM9lr9BEc8LNL+OjcH3Pkn2/lI499n32/9k9U7dZ5h8cM/k41R70+g6E//EozRtp6RBP+KzhWxCPA27sak+egE7THnrszb94CfvzTqbz15mr6D+jLZZf+K48/+huGHfZJli9/tdIhWpn1Pf8ENrz6Fi9/6zY2rlzNbgcPYuAlp9FzxME8O/oKiK2TRPfDh9L7lKOoXftehSJu+ZoyBy2pGqhu0FUTETVFHHqBpLOAucDFEfFOvp2doBM0bdpMpk3b+oW/Tz01n+cXPsIpJ4/muut/WqHIrLk8f9YkNq1eu+X7mieep/bd9QydciE9jzyINY9vmdpE7asYcu15LL/+Tvqc9elKhNsqNGWZXZaMi0nIDf0YuJrcgpGrge8C5+Q7wFMcLcTq1bl/LW3atKnCkVhzaJicN1s3/0UAOvXZc6v+fl8+EbVrx6s/+U2zxNZaRRPaTo0f8UZE1EVEPXAjMLzQMa6gE9auXTuqqqoYOLAf37rmclaufINp02cWPtBapZ4fOwiAvyz56xRX54F703/8KSz6wreITbWVCq1VqC3zSmhJfSJiZfb1s8DCfPuDE3TS/vD4PRz+kUMAWLLkz3z62NN4883VFY7KKqHjB/Zk4GWn887vn2X9sy9t6R/8nWpW3zuHNY8vqmB0rUMxF/+KJel24GhgL0krgCuBoyUNI1eEvwycV2icZk/Qkv45In6+g21bJt5V1ZN27XZr1thSc/Y/f4Ue3bszaL8BXPTVL3HfvXfwiWNOYtmyFZUOzZpRu66dOXDqBKK2jv8d/6Mt/b1OOYruwwYz9+PjKhhd61HKG1UiYkwj3Tc1dZxKzEFftaMNEVETEYdHxOFtPTkDvPDCizz51DNMmzaTUceeTrduXbns0gsqHZY1I3XqwEG/mEDnAb1ZeMY32bgydy2iXdfO7HfV2bzyw/+mfsNGqnp0papHV5BQhyqqenRF7asqHH3LUspldqVSlgpa0oIdbQL2Lsc5W7s1a9by4ksvM/iD+1Y6FGsmal/FgTddQvdDB/Pc577BX15YvmVbh7/pTse9ejLoijMZdMWZWx3XuV8vep04gufPnszq+55q7rBbrBRv9S7XFMfewLHAtmv8BPyhTOds1Xr33ov9hw7mttvvqnQo1hwkht4wjt0//mEWff7brJu3ZKvNG1e9y4KTr9zusP1/Mp73Fi/nle/fyXsNEroVVhfpPS6pXAn6HqBbRMzfdoOkh8t0zlZjxq9+xjPPLGTBc8+zbu16hgzZj3Hjvkhtba3XQLcRgyf9C71OOJLl182g7i//R/fD/noX6YaVq9m48m3W/GH7C4P1Gzax6c01jW6z/NrM40Yj4tw82/6pHOdsTebMmcepp/4jXx1fTceOHXllxWv8/vdPMPk7U3yBsI3YY+ShAAz46qkM+OqpW21bdu10ll87vRJhtWrNObdcLEWCZT1A+4590wzMKuqhPT9W6RAsQUe9PkO7OsbpA08qOudMW/bfu3y+YngdtJkZbWiKw8yspUlxisMJ2syMtrWKw8ysRfEUh5lZotrSjSpmZi2K56DNzBLlKQ4zs0SleE+IE7SZGVDnCtrMLE0pTnH4nYRmZuSmOIpthUi6WdIqSQsb9O0paZakJdmvexQaxwnazIxcBV1sK8ItwHHb9E0EZkfEEGB29j0vJ2gzM0r7RpWIeAR4e5vuE4Gp2eepwEmFxvEctJkZTbvVu+H7UzM1EVFT4LC9N7/VOyJWSupd6DxO0GZmNO0iYZaMCyXkXeYEbWZGs6zieENSn6x67gOsKnSA56DNzCjtKo4duBsYm30eC8wsdIAraDMzSltBS7odOBrYS9IK4EpgEjBd0rnAcuBzhcZxgjYzo7QPS4qIMTvY9MmmjOMEbWYG1EV6Dxx1gjYzww9LMjNLVorP4nCCNjPDD+w3M0tWvac4zMzS5ArazCxRXsVhZpYoT3GYmSXKUxxmZolyBW1mlihX0GZmiaqLukqHsB0naDMzfKu3mVmyfKu3mVmiXEGbmSXKqzjMzBJVylUckl4G1gF1QG1EHL4z4zhBm5lRllu9j4mIt3ZlACdoMzPSnIP2W73NzMjNQRfbJFVLmtugVW8zXAD3S3q6kW1FcwVtZkbTKuiIqAFq8uwyIiJek9QbmCXphYh4pKkxuYI2MyO3DrrYVkhEvJb9ugq4Cxi+MzE5QZuZkaugi235SNpNUvfNn4FRwMKdiclTHGZmlHQVx97AXZIgl2Nvi4j7dmYgJ2gzM0p3o0pELAUOKcVYTtBmZqS5zM4J2swMPw/azCxZrqDNzBKV4sOSlOLfGrY1SdXZwnizLfxz0fp5HXTLsNO3ilqr5p+LVs4J2swsUU7QZmaJcoJuGTzPaI3xz0Ur54uEZmaJcgVtZpYoJ2gzs0Q5QSdO0nGS/iTpRUkTKx2PVZ6kmyWtkrRTj7C0lsMJOmGSqoAfAZ8BDgTGSDqwslFZAm4Bjqt0EFZ+TtBpGw68GBFLI2IjcAdwYoVjsgrLXp30dqXjsPJzgk5bX+CVBt9XZH1m1gY4QadNjfR5XaRZG+EEnbYVQP8G3/sBr1UoFjNrZk7QaXsKGCJpkKSOwBnA3RWOycyaiRN0wiKiFrgA+B2wGJgeEYsqG5VVmqTbgSeAoZJWSDq30jFZefhWbzOzRLmCNjNLlBO0mVminKDNzBLlBG1mlignaDOzRDlBW8lJqpM0X9JCSb+S1HUXxjpa0j3Z5xPyPdFP0u6SvrwT5/i6pEt2NkazcnGCtnJ4PyKGRcTBwEbgSw03KqfJP3sRcXdETMqzy+5AkxO0WaqcoK3cHgUGS9pX0mJJNwDzgP6SRkl6QtK8rNLuBluegf2CpMeAkzcPJOlsST/MPu8t6S5Jz2btSGAS8MGsev/PbL9LJT0laYGkqxqMdUX2nO0HgKHN9qdh1gRO0FY2ktqTe5b1c1nXUOAXEXEo8B7wb8CnIuIwYC5wkaTOwI3APwJHAR/YwfA/AH4fEYcAhwGLgInAS1n1fqmkUcAQco9tHQZ8RNLfS/oIudvmDyX3F8BHS/xbNyuJ9pUOwFqlLpLmZ58fBW4C9gGWRcQfs/4jyL2E4HFJAB3J3b68P/DniFgCIOmXQHUj5xgJnAUQEXXAGkl7bLPPqKw9k33vRi5hdwfuioi/ZOfw800sSU7QVg7vR8Swhh1ZEn6vYRcwKyLGbLPfMEr3SFUB346In25zjvElPIdZ2XiKwyrlj8AISYMBJHWV9CHgBWCQpA9m+43ZwfGzgfOzY6sk9QDWkauON/sdcE6Due2+knoDjwCfldRFUndy0ylmyXGCtoqIiDeBs4HbJS0gl7D3j4j/Izel8T/ZRcJlOxhiHHCMpOeAp4GDImI1uSmThZL+MyLuB24Dnsj2mwF0j4h5wDRgPvBrctMwZsnx0+zMzBLlCtrMLFFO0GZmiXKCNjNLlBO0mVminKDNzBLlBG1mlignaDOzRP0/2MTdUixYbBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(array, annot=True, annot_kws={\"size\": 16})\n",
    "ax.set(xlabel='Predicted', ylabel='True')\n",
    "plt.savefig(\"confusion_matrix.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.92      0.93        51\n",
      "           1       0.86      0.89      0.87        27\n",
      "\n",
      "    accuracy                           0.91        78\n",
      "   macro avg       0.90      0.91      0.90        78\n",
      "weighted avg       0.91      0.91      0.91        78\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Classification Report')\n",
    "print(classification_report(test_img.classes, predictedClasses))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}