{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout,Conv2D,Flatten,Dense,MaxPooling2D,BatchNormalization\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 39562 images belonging to 2 classes.\n",
      "Found 9890 images belonging to 2 classes.\n",
      "steps_per_epoch 1236, validation_steps 309\n"
     ]
    }
   ],
   "source": [
    "batch_size=32\n",
    "\n",
    "train_datagen = image.ImageDataGenerator(\n",
    "  rescale=1./255,\n",
    "  rotation_range=30,\n",
    "  validation_split=0.2,\n",
    "  width_shift_range=0.1,\n",
    "  height_shift_range=0.1,\n",
    "  shear_range=0.1,\n",
    "  fill_mode='wrap'\n",
    ")\n",
    "def generate(subset='training', data_dir='../data/eye', shuffle=False, target_size=(24,24), color_mode='grayscale', class_mode='categorical'):\n",
    "  return train_datagen.flow_from_directory(data_dir, batch_size=batch_size, target_size=target_size, color_mode=color_mode, class_mode=class_mode, shuffle=shuffle, subset=subset)\n",
    "train_data = generate(subset='training', shuffle=True)\n",
    "validation_data = generate(subset='validation')\n",
    "\n",
    "steps_per_epoch = len(train_data.classes) // batch_size\n",
    "validation_steps= len(validation_data.classes) // batch_size\n",
    "print(f\"steps_per_epoch {steps_per_epoch}, validation_steps {validation_steps}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNN():\n",
    "    model = Sequential([\n",
    "        Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(24,24,1)),\n",
    "        MaxPooling2D(pool_size=(2,2),strides=1),\n",
    "        Conv2D(32,(3,3),activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2,2),strides=1),\n",
    " \n",
    "        Conv2D(64, (3, 3), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2,2), strides=1),\n",
    "\n",
    "        Dropout(0.25),\n",
    "   \n",
    "        Flatten(),\n",
    "    \n",
    "        Dense(128, activation='relu'),\n",
    "    \n",
    "        Dropout(0.5),\n",
    "    \n",
    "        Dense(2, activation='softmax')\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1 Pro\n",
      "\n",
      "systemMemory: 32.00 GB\n",
      "maxCacheSize: 10.67 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-06 13:00:05.229936: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-06-06 13:00:05.230383: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 22, 22, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 21, 21, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 19, 19, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 18, 18, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 15, 15, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 15, 15, 64)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 14400)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1843328   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 2)                 258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,871,650\n",
      "Trainable params: 1,871,650\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "CNN().summary()\n",
    "model = CNN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-06 13:00:05.990412: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2022-06-06 13:00:06.261311: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1235/1236 [============================>.] - ETA: 0s - loss: 0.2607 - accuracy: 0.8920"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-06 13:00:26.303513: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1236/1236 [==============================] - 25s 18ms/step - loss: 0.2606 - accuracy: 0.8921 - val_loss: 0.3078 - val_accuracy: 0.8819\n",
      "Epoch 2/15\n",
      "1236/1236 [==============================] - 21s 17ms/step - loss: 0.1429 - accuracy: 0.9453 - val_loss: 0.2039 - val_accuracy: 0.9325\n",
      "Epoch 3/15\n",
      "1236/1236 [==============================] - 20s 16ms/step - loss: 0.1056 - accuracy: 0.9619 - val_loss: 0.1910 - val_accuracy: 0.9418\n",
      "Epoch 4/15\n",
      "1236/1236 [==============================] - 20s 16ms/step - loss: 0.0838 - accuracy: 0.9694 - val_loss: 0.1767 - val_accuracy: 0.9369\n",
      "Epoch 5/15\n",
      "1236/1236 [==============================] - 20s 16ms/step - loss: 0.0724 - accuracy: 0.9737 - val_loss: 0.2012 - val_accuracy: 0.9295\n",
      "Epoch 6/15\n",
      "1236/1236 [==============================] - 22s 18ms/step - loss: 0.0660 - accuracy: 0.9763 - val_loss: 0.1745 - val_accuracy: 0.9412\n",
      "Epoch 7/15\n",
      "1236/1236 [==============================] - 21s 17ms/step - loss: 0.0584 - accuracy: 0.9794 - val_loss: 0.1815 - val_accuracy: 0.9411\n",
      "Epoch 8/15\n",
      "1236/1236 [==============================] - 24s 20ms/step - loss: 0.0539 - accuracy: 0.9808 - val_loss: 0.1846 - val_accuracy: 0.9333\n",
      "Epoch 9/15\n",
      "1236/1236 [==============================] - 27s 22ms/step - loss: 0.0525 - accuracy: 0.9816 - val_loss: 0.2680 - val_accuracy: 0.9203\n",
      "Epoch 10/15\n",
      "1236/1236 [==============================] - 18s 15ms/step - loss: 0.0498 - accuracy: 0.9830 - val_loss: 0.1682 - val_accuracy: 0.9484\n",
      "Epoch 11/15\n",
      "1236/1236 [==============================] - 19s 15ms/step - loss: 0.0476 - accuracy: 0.9831 - val_loss: 0.1784 - val_accuracy: 0.9432\n",
      "Epoch 12/15\n",
      "1236/1236 [==============================] - 18s 15ms/step - loss: 0.0442 - accuracy: 0.9849 - val_loss: 0.2411 - val_accuracy: 0.9311\n",
      "Epoch 13/15\n",
      "1236/1236 [==============================] - 18s 14ms/step - loss: 0.0431 - accuracy: 0.9848 - val_loss: 0.1483 - val_accuracy: 0.9532\n",
      "Epoch 14/15\n",
      "1236/1236 [==============================] - 18s 15ms/step - loss: 0.0439 - accuracy: 0.9852 - val_loss: 0.1833 - val_accuracy: 0.9338\n",
      "Epoch 15/15\n",
      "1236/1236 [==============================] - 18s 15ms/step - loss: 0.0391 - accuracy: 0.9866 - val_loss: 0.2265 - val_accuracy: 0.9295\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_data, validation_data=validation_data, epochs=15, steps_per_epoch=steps_per_epoch, validation_steps=validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('accuracy')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('val_acuuracy')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6eqmqb2VBvwJ1QFmBx+6FMBFnCWKISoIK1caMli0HmlE9tf4QUF7gYVd9G722eJCJIEsQQ/TWSCb7tmZGT2CJ0QXFOSH3lxVk093rY19j+yhGZcY6SxBDVJSTRlZqkt1RbUZVZW0zUyelMzkrNeR+m3LDuMESxBCJOHMyWReTGUWbaptOuUEu2Jz8LBLERjKZyLIEMQyl3izrYjKjpqG1iwNNHaetPwCkJScyIzfTRjKZiLIEMQylXg+NJ7ppbOuKdihmHHir/jBhwOMCU24YEymWIIah1EYymVFUWdtEgsAZU7IHPK7Mm03NsXbau23xIBMZliCGIZAgdtmkfWYUbKprPrnE6EDKCjyo2hcXEzmWIIbBm52KJy3J6hDGdf4lRpsGrD8E2JQbJtIsQQyDiFDq9VB12L6pGXfVHGunqb3nlBlcQ5k2KYP05EQrVJuIsQQxTKVeD1X1Nge/cdem2iaAAYe4BiQkCKXeLLsXwkSMJYhhKvVm0dTeQ4ONZDIuqqxtJi05gVJvVljH25xMJpJcTRAicqWI7BSRahG5I8T+ZSKy2flZLSIL++1PFJE3ROSvbsY5HCcL1VYQNC6qrGvijKK3lhgdTFlBNo0numlotS8uZuRcSxAikgjcDVwFVADXiUhFv8P2Apeo6gLgTuC+fvu/jH+50phT4nyjs0K1cUtPn48tB5rDqj8EjLcpN370zA7+sHpftMMYs9xsQSwGqlV1j6p2Aw8DS4MPUNXVqnrceboGKA7sE5Fi4GrgfhdjHLa8rFQmZCRbgjCuqToSeonRgZQ5CWLHOBjJ1N7dy29f2cvD62ujHcqY5WaCmAIE/5+rc7adzmeBZ4Ke/xfwTWDA+YtF5GYR2SAiGxoaGoYZ6tCdHMlkXUzGJZW1zQCcGUaBOiA3K5XcrJRx0YJYs6eR7j4f1fWtdPfaNOducDNBSIhtIYf8iMhl+BPE7c7z9wH1qvr6YC+iqvep6iJVXZSXlzeSeIcsMCeTjWQybqisbWJiRjJTJ6UPfnCQsnEy5cbKnf4vhD19ajetusTNBFEHTA16Xgwc7H+QiCzA3420VFUbnc0XAB8QkX34u6beKSIPuBjrsJR6PbR29nKkxQqCJvIq65pYOHXCKUuMDqbMm03VkVb6fGP7i8uqXUeZlZsJwNaDY79LLRrcTBDrgRIRmSkiKcC1wFPBB4jINOAJ4HpVrQpsV9VvqWqxqs5wzntBVT/pYqzDUpJviwcZd5zo6qXqyOmXGB1IeYGHzh4fNcfG7uJB+xtPsPfoCT65ZDoZKYlsswThCtcShKr2ArcCz+EfifSoqm4VkVtE5BbnsO8Ck4F7RGSTiGxwKx43lNpIJuOSLQea8SmcOTX0CnIDKRsHU26sqvJ3L11Wnk95gccShEsGnv1rhFR1BbCi37Z7gx7fBNw0yDVeAl5yIbwRm+wUBC1BmEjbXOcvUA+nBVHq9SACOw63cuUZhRGOLDasrDrKtEkZzJicwbyiHJ584wA+n5KQMLTuODMwu5N6hErybSSTibxNdU0UT0wn9zRLjA4kPSWR6ZMyxuxIpu5eH6/tPsrFpbmICBVF2bR19VJ3vCPaoY05liBGqNSbRXV9m41kMhFVWds0pPsf+hvLU268vv84J7r7uKQ0H4CKQv86GVsPNkczrDHJEsQIlXg9tHX1crC5M9qhmDHiaFsXdcc7hnT/Q39lBdnsazxBZ09f5AKLESurGkhKEM6bPRnwJ8PEBGHbIatDRFpYCUJEviwi2eL3GxHZKCLvdju4eHBydbkx+m3NjL63lhgdeoE6oLzAg0/H5lxhK6saWDRjIlmp/hJqWnIis/MyrVDtgnBbEJ9R1Rbg3UAe8GngR65FFUdsJJOJtE21zc4So8NPEGN1yo36lk62H2o52b0UMK8ox+6FcEG4CSIwNOC9wO9UtZLQd0qPOxMyUsj3pFqh2kRMZW0TpV4PmanDH2Q4Y3ImqUkJY64OsWrXUQAuLs192/aKwmwOt3TSaNPvR1S4CeJ1Efk7/gTxnIh4GGSOpPGk1OuxW/1NRASWGA1ngaCBJCYIJd6sMTflxsqqBvI8qScL0wEVRf7nVoeIrHATxGeBO4B3qGo7kIy/m8ngn/p715E2fGN8agPjvtpjHRwPc4nRwZR5s8fU8qN9PuWVXQ1cXJJ3yvQjgYRhdYjICjdBnAfsVNUmEfkk8B3AxpQ5Sr0eOnr6bBy2GbFNToF64TDuoO6vvMBDQ2sXx050j/haseDNA80cb+85pXsJYGJmCkU5adaCiLBwE8SvgHZnxbdvAvuBP7oWVZyxQrWJlMraJlKTEk6OjhuJsVaoXrmzARG4qCT0rM0VRdnjslDt5j1Y4SaIXvVHsRT4uar+HBj5O3iMKAkMdbU6hBmhytomzpiSQ3KYS4wOZKytLrdqVwMLiicwKTMl5P6Kohz2NLTR0T327v0YyH+/vIfP/n49Xb2R/73DfRe2isi3gOuBvznLiSZHPJo4lZ2WTGFO2pgcc25GT2+fjy0Hm0dcoA7I86QyMSN5TCSI5vYe3qg5ziUlp3YvBVQUZuPTsdNiCofPp/xpzX7aunpJTUqM+PXDTRAfB7rw3w9xGP/KcD+JeDRxrMTrsS4mMyJVR9ro7PFFpP4A/lUPywo8Y6JQ/Ur1UXwKl5SdflGweeNwJNPL1UepPdbBsiXTXbl+WAnCSQoPAjnOam+dqmo1iCCl+f45mcb6Ii3GPZVOgfrMCIxgCigv8C8eFO8j7FZVNeBJSxqwdVU8MR1PWtK4Gsn00Nr9TMpM4T3zvK5cP9ypNj4GrAM+CnwMWCsi17gSUZwq9Xro6h3bi7QYd1XWNjEhI5lpkzIids2yAg/t3fE9wk5VWVnVwEUluSQNUJsRESoKx0+h+nBzJ/+7vZ6PLip2pXsJwu9i+jb+eyBuUNVPAYuBf3YlojhVWmCry5mR2VTbxILioS8xOpCxMJJpV30bh1s6uaR08DXn5xXlsONwy7hoyT+yvpY+n/KJxdNce41wE0SCqtYHPW8M51wRuVJEdopItYjcEWL/MhHZ7PysdobRIiJTReRFEdkuIltF5Mthxhk1Jfn+oa67LEGYYWjv9i8xeuYIJugLJTBcNp4L1St3+lePuziMBFFRlE1nj4+9R8f2gJHePh8Pr6/hopJcpk/OdO11wk0Qz4rIcyJyo4jcCPyNfivF9eeMdLobuAqoAK4TkYp+h+0FLlHVBcCdwH3O9l7ga6o6F1gCfDHEuTElMzWJKRPSbU4mMyxbDrTgUyJyB3WwrNQkpk5KZ0ccf3FZWdVAqTeLwpz0QY8NFKrHejfTSzsbONTcybJz3Ws9QPhF6m/g//BeACwE7lPV2wc5bTFQrap7VLUbeBj/fRTB112tqsedp2uAYmf7IVXd6Dxuxb+m9ZTwfqXoKfVmWReTGZa3pvieEPFrl3mz47YF0d7dy7q9x8LqXgKYnZdFSmLCmB/J9ODa/eR7Url8rjvF6YCwp4tU1T8Dfx7CtacAtUHP64BzBzj+s8Az/TeKyAzgLGBtqJNE5GbgZoBp09zNpoMp9Xp4tbqR3j7fgMU0Y/rbVNvElAnp5HmGvsToYMoLPLy4s56u3j7XipluWbvnGN19vrC6lwBSkhIo8WaN6ZFMtcfaeamqgS9dNiciN1QOZMCri0iriLSE+GkVkcH+D4SqtIWsHInIZfgTxO39tmfhT0pfcdajOPWCqvep6iJVXZSXF96byC0lXg/dfT72NdpIJjM0lXVNER3eGqyswEOfT6muj7/uz5VVDaQlJ/COGZPCPmdeUTbbDraM2WWAH15fgwAfd7E4HTBgglBVj6pmh/jxqGr2QOfibzFMDXpeDBzsf5CILADuB5aqamPQ9mT8yeFBVX0i3F8omsqcgqAVqs1QNLZ1UXusI2I3yPUXz1NurKpqYMmsyaQlh9/yqSjMpvFEN/WtY29tiJ4+H4+sr+OysnymTBi8JjNSbrZP1gMlIjJTRFKAa4Gngg8QkWnAE8D1qloVtF2A3wDbVfVnLsYYUXPysxDBCtVmSDbX+SdGdqP+ADAjN5OUxPhbPKimsZ09R0+EXX8ImOesxLf14NibcPof245wtK2LZUtGpzvdtQShqr3ArcBz+IvMj6rqVhG5RURucQ77LjAZuEdENonIBmf7BfjnfXqns32TiLzXrVgjJT0lkakTM6xQbYZkU20TCQLzR7DE6ECSExOYnZ8Vd1NurNzlH9461AQRaDGNxTrEg2v3M2VC+ilLrrpl+GsahkFVV9BvOKyq3hv0+CbgphDnvUKcLmlqI5nMUFXWNVGSP7IlRgdTXuDhtd2Ngx8YQ1ZVNVA8MZ2ZuUMb5+9JS2b65IwxN9R179ETvFrdyNffXUpiwuh8PNpQmwgr8XrYe/QE3b22IqsZnH+J0WbX6g8BZQUeDrd00tze4+rrREp3r4/V1Ue5pPTU1ePCMa8oe8wNdV2+roakBOFji6YOfnCEWIKIsDKvh16fsq/xRLRDMXGg7ngHx050R/wGuf7ibcqN1/cf50R335C7lwIqCrPZ39hOa2d8JMTBdPb08diGWq6o8JKfnTZqr2sJIsJKbHU5MwSbapsAIrYGxOmcHMkUJ+/LVbsaSEoQzps9eVjnVzh3VG8/FB+/72Ce3XKY4+09LDvXnWm9T8cSRITNzssiQaAqzgqCJjoCS4wGvuG7pSA7jey0pLgpVK/c2cA50yfiSRveumTzivxddtvGyEimh9bWMH1yBucPM2EOlyWICEtLTmT65Ewb6mrCUlnXxLyibNfviBURygviY8qN+tZOth1qCfvu6VDyPalMzkwZE4XqqiOtrNt3jE8snkbCKBWnAyxBuKAkP8vWpzaD6u3z8eaBZtfrDwFlBR6qDrfG/B3GL1cdBYY+vDWYiFAxRgrVD62tISUxgWvOKR7117YE4YKyAg/7G9tdWUTcjB276v1LjLo1xUZ/ZQUeWrt6OdAU24sHraxqIDcrlYrCwSZrGFhFUTa7jrTF9YjCju4+/ryxjqvmFzA5K/LzdA3GEoQLSrz+uW/2NNhIJnN6laNUoA6Ihyk3+nzKy7sauLgkd8TdKRWF2XT3+eJyDqqApzcfpLWz19VFgQZiCcIFpTaSyYShsq6JnHT/TV2jofTkUNfYfV9uOdDM8fYeLikb+cSbJwvVcdzN9ODaGubkZ7F4ZviTFUaSJQgXzMzNJDFBLEGYAW2qbWZBcU5ElxgdSHZaMlMmpMd0C2JlVQMicOGc3BFfa2ZuJunJiXE75caWA81U1jax7Nxpo/Ye6c8ShAtSkxKZMTnDRjKZ0zq5xOgo1R8Cygo8MZ0gVlU1MH9KTkT62xMThPJCT9xO2vfQuhrSkhP48FmjX5wOsAThkrICj037bU5r68EW+nw6avWHgLICD7sbYrNw29zew8aa4yMavdRfRaF/JFOsj9zqr62rl7+8cYD3LSgiJ2N494JEgiUIl5Tke9h/rJ3OHhvJZE4VKFAvcHkOpv7KC/xTwew5Gnut21d3H8WnIxve2l9FUTatnb3UHY/tkVv9/c8bBzjR3ef6mtODsQThklKvB1XYuP/44AebcaeyrpkpE9LJ94zevDrw1pxMsdjNtKqqAU9aUkS73QKF6ni6YU5VeXBtDRWF2aPeBdmfJQiXnDtrErlZKXzmD+t5eF1N3DVxjbsqa5tcn8E1lFm5WSQlSMyNZFJVVlY1cOGc3Iiu517m9ZAg8TWSaVNtE9sPtbBsSfSK0wGWIFySm5XKitsu4pzpE7njiTe5dfkbtIyRmSXNyBw70U3NsfZRrz8ApCQlMDsvK+ZaELvq2zjU3Dmi6TVCSU9JZFZeVlzNyfTg2hoyUxJZeuaUaIfiboIQkStFZKeIVIvIHSH2LxORzc7PahFZGO658SA/O40/feZcvvGeMp7dcpirf/Eyb9SMry6njTXHLTH2U1nXBLi3xOhgYnEk06oq/+pxkU4Q4KwNESddTM3tPTxdeZClZ00hy8UFpMLlWoIQkUTgbuAqoAK4TkQq+h22F7hEVRcAdwL3DeHcuJCQIHzxsjk8+vkl+Hzw0Xtf49crd+Pzjf0up1VVDXz4ntXc8qfXrYstSGVtEyIwv3j0u5gAygs9HGjqiKnEvbKqgZL8LKZMSI/4tSsKsznY3MnxE90Rv3ak/XljHV29vqjdOd2fmy2IxUC1qu5R1W7gYWBp8AGqulpVA1+p1wDF4Z4bb86ZPokVt13EFRVe/u2ZHdz4+/U0tHZFOyzXNLZ18bXHKvGkJbF6dyMPr6+Ndkgxo7K2iZL8rKh9QwxMuRErU9J3dPexdu8xV1oPED93VKsqD62rYeHUCZzh0vrkQ+VmgpgCBH8q1DnbTuezwDNDPVdEbhaRDSKyoaGhYQThui8nI5l7lp3Nv37wDNbuaeSqn7/MK7uORjusiFNVvvn4Zprbe3jk5vM4b9ZkfvC37RyM8UniRsPJJUaj1L0EUFbgnwQvVgrVa/Y20t3ri+jw1mBzC/0JMda7mdbtPUZ1fVvUh7YGczNBhCq/h+xnEJHL8CeI24d6rqrep6qLVHVRXp47b7BIEhE+uWQ6f7n1AiZkJHP9b9fy78/uoKcv9m5cGq4H1uzn+R313HFVORVF2fzoI/Pp8ynffvLNcd/VVHe8g8ZRWGJ0IEU5aXjSkmKmDrFyZwNpyQmuzTc0OSuVguy0mL+j+sG1NXjSknj/gqJoh3KSmwmiDgheXbsYONj/IBFZANwPLFXVxqGcG8/KC7J5+tYLufYdU7nnpd18/NevUXusPdphjVjVkVb+9W/buaQ0j09fMAOA6ZMz+cZ7ynhxZwNPvnEgugFGWaBAHc3x7SJCmTd2CtWrdjVw7szJpCUnuvYa82J8bYjGti6e3XKYj5xdTHqKe/8OQ+VmglgPlIjITBFJAa4Fngo+QESmAU8A16tq1VDOHQvSUxL5tw8v4JfXncWuI2289xcv88ybh6Id1rB19vRx2/I3yEpN4qcfXfi2Mdw3nD+Dc6ZP5PtPb6O+tTOKUUZXZW0TKaOwxOhgygo87Dgc/Skoao+1s6fhhGvdSwEVRdnsbjgRszMbPP56Hd19vpjqXgIXE4Sq9gK3As8B24FHVXWriNwiIrc4h30XmAzcIyKbRGTDQOe6FWu0vX9hEX+77SJm5WXxhQc38u0n34zZN/JA/v3Znew43MpPPrqAPM/bJ1tLTBB+/JEFdPT08c//syXqH0zRUlnbPCpLjA6mvMBDS2cvh1uim6xXuji8Ndi8omz6fBozraZgPp+/OL14xiRKvNH94tCfq+9SVV2hqqWqOltVf+Bsu1dV73Ue36SqE1X1TOdn0UDnjmXTJmfw2OfP4/MXz+LBtTUsvevVuJrs76Wd9fz21b3ccN503lnuDXnMnPwsvvquUp7beoQVbx4e5Qij7+QSo1EsUAfESqF6VVUDUyakMzsv09XXqSiM3ZFMr+4+yv7GdpYtia3WA9id1DElJSmBb713Ln/4zGKOtnXx/rteiYtpOo62dfH1xzZT5vXwrffOHfDYz100k/lTcvjuX7ZwLA7GpUdSdUMbHT19UZ9fB/xTUEB052Tq7vWxencjl5TluT6lRPHEdDypSTFZqH5obQ2TMlO48oyCaIdyCksQMeiS0jye+fJb03R8KYan6VBVvvFYJS2dPfz8ujMHLTQmJSbwk48uoKWzh+8/PWZ7DUM6ucRoDCSInIxkCnPSopogNtYcp62rl4tL3B99mJAgzI3BO6qPtHTy921HuOacYlKTYqc4HWAJIkYFT9PxzJbDvO8Xr5z8gIklf3xtPy/ubOCfriqnvCC8RebLC7L54mVz+Mumg/xj2xGXI4wdm2qbyU5LYsYoLTE6GH+hOnoJYlVVA0kJwvlzJo/K61UUZrPjcCt9MTSLwaPra+nzKdfFyJ3T/VmCiGHB03T0+ZSP/Go1962KnWk6dh5u5QcrtnNZWR43nD9jSOf+n0vnUF7g4dtPvklzR2y2jiLNP4PrhKjP0BlQVuBhd31b1O7BWVnVwNnTJ5KdNjoL4swryqa9u499jSdG5fUG0+dTlq+r4cI5uczMdbcGM1yWIOJA8DQdP1yxg8/8YT1N7dHtvw8Mac1OS+In/Ya0hiMlKYGfXLOQxhPd/OBv21yKMnZ0dPex80hrTBSoA8oLPHT3+dh3dPQ/MBtau9h6sMX14a3BKor8LdxY6WZaWVXPweZOPhFjQ1uDWYKIE4FpOu784Bmsrm7kg3dHd5TTj57Zwc4jrfzkowvJHeb6wfOLc7j54lk8uqHu5GyeY9XWg83+JUZjoP4QUOaN3kiml3f5/3+PZoIoyfeQnCgxs3jQg2tqyPOkckVF6FF/scASRBwREa5fMp3lNy+hrauPD979alT68F/cUc/vV+/jxvNncFlZ/oiu9eXLS5iVl8m3nniTtq7eCEUYeyrr/KNnFkZpBtdQZudnkpggUSlUr6xqIDcrhYrC8OpWkZCSlEBJvicmhroeaOrgxZ31fHzR1KjfEzOQ2I3MnNY50yfy9JcuYHZ+Fjf/aQN3vbBr1IbCNrR28Y3HKykv8HDHVeUjvl5aciI/uWYBB5s7+PEzOyIQYWyqrG2iKCeN/OzRXWJ0IKlJiczKzRz1FoTPp7y86ygXleSRkDC69ZiKGBnJ9Mi6GhS4dvHUQY+NJksQcaowJ51HP38eSxcW8dO/V3HrQ2/Q3u3uN3BV5RuPV9LS2cvPrz0rYnPnnDN9Ep8+fyZ/WrOfNXsaBz8hDlXWNcVU91JAWYGHnUdG9wNzy8Fmjp3oHtXupYCKwmyOtnVRH8U7yHv6fDy8vpZLS/MonhgbI9pOxxJEHEtLTuQ/P34m//Tecp7ZcohrfvUadcfdm/Dv96v38dLOBr5z9dyIzyX09feUMm1SBrf/eTMd3fE3zchAjp/oZn9je0wmiPICD7XHOka1e2/lTn/94cKS3FF7zYB5TqF6axS7mZ7ffoT61i6WnTs9ajGEyxJEnBMRbr54Nr+98R3UHm/nA3e9yloXvoVvP9TCvz2zg8vL87l+SeTf2BkpSfzoI/PZ39jOf/x9Z8SvH01vLTEaO/WHgMCUG1WjOOBh1a4G5k/JGfbghpGYGwMjmR5cW0NRThqXlY+sfjcaLEGMEZeW5fOXL/rXmFh2/1oeWLM/Ytd+a0hrMj++ZoFr4/jPn53LsnOn8ZtX97JxDK3dXVnb7F9iNEZWCQsWWF1utArVzR09bKxpikr3EkB2WjLTJmVELUHsbzzBy7uOcu3iaSSOcv1lOCxBjCGz8rL4ny9ewEUluXznf7bwT0++SXfvyG+C+uGK7eyqb+M/Pjb8Ia3huuOqcgqz0/jm45vjckZb8CfULQeaeWxDLXf+dRuPrK9hTl4WnlG6IWwopkxIJzMlcdQSxOrqo/T51PXZWwdSURi9tSEeWldDYoLw8XfEdnE6IDqL4hrXZKclc/8N7+Cnf9/Jr17aTfWRNu755NnD/mB/fvsR/vjafj5zwcxR+dbnSUvmhx+ez42/W88vX9jFN94z8pFSblFVDjZ3suNQCzsOt7Ld+e/eoydOTueQlpxAqdfDDefNiG6wp5GQIJQ6a0OMhlW7GvCkJnHWtAmj8nqhVBRl8+zWw7R19Y7quuBdvX08tqGOd83NxxtDo9kGYgliDEpMEG6/spzyAg/ffHwzS+96lV9ff86QF0Kvb+3kG49v9l/nyjKXoj3VpWX5XHNOMfeu3MNVZxTGxALubV297Dzcys7Drew43MKOQ61sP9xCa+dbxd2pk9IpL8jmvWcUUF6YTXmBh+mTM2O+K6G8wMOzWw6jqq5OA6KqrNzZwAVzcqM69j9QqN5xqIVFM9xZ5jSUv1Ye4tiJbj4RB8XpAEsQY9jSM6cwK9d/r8Q1967mJ9cs5P0Lw1vv1udTvv7YZk509fLLm5e4uhxkKP98dQWrqhr4+mOVPHXrhaQkjc4HSp9PqTnWzo5DLWw/3HqydVATtBxsVmoS5QUelp5ZRHlBNnMLPZR6PTHZhRSOMq+H5etqaWjtcvU+jer6Ng42d3LrO6O7dvzJKTdGMUH09vn45Qu7qCjM5uIojN4aLlcThIhcCfwcSATuV9Uf9dtfDvwOOBv4tqr+NGjfV4GbAAXeBD6tquN3rcphml+cw1O3XsgXHnidLy1/gx2HW/jaFWWD3qD0u9X7WFXVwJ0fPCMqq1zlZCTzgw/N53N/3MC9K3dz2+Ulrr2Wz6e8Un2Uh9fX8OKOBjqc2keCwMzcTOYX5/CxRcWUF2RTXuhhyoT0mJlwLxKCFw9yM0G8tXpcdD8gC7LTmJSZwtYDo1eH+Mumg+xrbOfX158TV+8d1xKEiCQCdwNXAHXAehF5SlWDZ2Y7BtwGfLDfuVOc7RWq2iEij+Jfl/r3bsU7luV5Unnoc0v43lNbuPvF3ew41Mp/XXvmab/xbjvYwo+f2cG75nr5ZBQnEruiwssHFhbxyxd28Z55BRG/96K+pZPHXq/j4fU11B7rYFJmCtecU8z84hzmFmRT4s0a9ZZTNASPZHKzeLyyqoHZeZlRvzlMREa1UB3cenh3DM+7FIqbLYjFQLWq7gEQkYeBpcDJBKGq9UC9iFx9mtjSRaQHyAAOuhjrmJeSlMAPPzSfuYXZfP/pbXzontX896cWnTLNcEd3H7c9/AY5Gcn8+CPzo/5t518+MI9Xq4/yzccr+fMXzidphH3XfT5l1a4Glq+t4fkd9fT5lPNnT+ab7ynn3fO8Mbloi9smZqaQ70l1dcqNzp4+1u09FjM3h1UUZfP7V/fR0+dzvR7y5BsH2NfYzn1x1noAd4e5TgFqg57XOdsGpaoHgJ8CNcAhoFlV/x7qWBG5WUQ2iMiGhoaxPSPoSIkInzpvBg989lwa27pYetcrp8yi+oMV26iub+NnH1vI5CjcyNTfpMwUvr90HpV1zfzmlb3Dvs7h5k5+8fwuLv73F/n079azseY4n7toFi99/VIe+twS3r+waFwmhwC3p9xYs6eRrl4fl5RFt/4QMK8om+4+H7sb2lx9nd4+H3e9WM28ouyYnrX1dNxMEKFSZVgzyonIRPytjZlAEZApIp8Mdayq3qeqi1R1UV5ebLz5Yt15syfz1K0XUjQhnRt/t477X96DqvKPbUd4YE0NN104k4tGYRnIcF09v5D3zPPyH/+oGtIfdG+fj+e3H+GmP6zn/B89z8/+UcWsvEzuWXY2q++4nDuuKmdGjC7UMtrKCzzsOtIW8dXWVJXntx/he09tJT05kXNnjt6ooYEEZpF1+4a5J944wP7Gdr7yrtK4az2Au11MdUDw3SDFhN9N9C5gr6o2AIjIE8D5wAMRjXAcmzopgz9/4Xy+/lgl//q37VTWNfNq9VEqCrP5xigOaQ2HiHDn0jO44j9Xcfvjm3n08+cNWGQ/0NTBI+treWxDLYeaO8nzpHLLJbO59h3TmBYjy33GmrKCbLp6fexrPMHsvKyIXLO6vo07/7qNlVUNzMrL5Dc3LIqZms6svCzSkhPYerCFD5/tzmv09Pm464VqzpiSzbvmxv60GqG4mSDWAyUiMhM4gL/I/Ikwz60BlohIBtABXA5scCXKcSwzNYm7P3E2d71Yzc/+UUVacgK/uO7MmOxqyc9O47vvq+Brj1Xyx9f2ceMFM9+2v7fPxws76lm+roaXAqNlSvL43vvncfnc/Jiecz8WBBeqR5ogWjp7+MX/7uL3q/eRnpzId66eyw3nz4ip/weJCUJZgbtTfz+58QA1x9q5/1OL4rL1AC4mCFXtFZFbgefwD3P9rapuFZFbnP33ikgB/g/+bMAnIl/BP3JprYg8DmwEeoE3gPvcinU8S0gQbru8hEUzJpIgwpz80R/SGq4Pnz2Fpzcf5MfP7uTyuV6mTsqg9lg7j6yv5dENtdS3duHNTuXWy+bwsUVTmTrJWgvhmpOfRYL4h7q+d37hsK7h8ymPvV7LT57bSeOJbj6+aCpff09ZVCblC0dFYTZ/23zQlRsEe/p8/PLFXcyfksPlcdp6AJfvg1DVFcCKftvuDXp8GH/XU6hzvwd8z834zFvOnx37N++ICD/80Hze/Z+ruHX5G+SkJ/PyrgYE/93X1y2exmVleSMe6TQepSUnMiM3k53DnHJjw75jfP/pbbx5oJlzpk/kdzcuZn4Mzl4bbF5RNsvX1XCgqSPiQ2+f2FhH7bEO/uWGeXHbegC7k9rEmaIJ6Xz76rl864k3KcxJ47Z3lvCxd0xlyoT0aIcW98oLPEPucjnU3MGPntnBXzYdpCA7jZ9feyYfWFgUFx+KFUFTf0cyQXT3+vjlC9UsLM7hnXEwpfdALEGYuHPd4mm8Y8ZEZuZmxfw8R/GkzJvNM1sO097dS0bKwB8NnT193P/yHu5+cTd9qnzpnXP4wqWzBz0vlpQXeBCBrQdbePe8gohd94mNddQd7+DOpWfERaIcSPz83zQmSCzXSuJVWYEHVdh1pO20q9+pKs9tPcIPVmyj9lgHV84r4NtXz43Lek9GShKzcjMjekf1ydbD1AlcGiP3fIyEJQhjDPD2kUyhEkTVkVa+//RWXq1upNSbxYM3ncsFc2K/djWQiqIcNu6P3OJUf95Yx4GmDv71Q/HfegBLEMYYx7RJGaQnJ54y5UZzew//+b9V/GnNfrJSk/j+B+ax7NxpY2IwwLyibJ6uPEhTezcTMlJGdK3uXv99D2dOncClUVwQKZIsQRhjAGfxIG/WySk3+nzKQ+tq+Nnfd9Lc0cMnzp3G/72ijEmZI/sgjSUn76g+1DLikXyPv+5vPfxgjLQewBKEMSZIWYGHF3bUs2ZPI//y1FZ2HG5lyaxJfO/985jrfJiOJcEjmUaSILp7fdz9or/1EK31tt0Q/21EY0zElBVkc7Stm2vvW0NrZy/3LDub5Z9bMiaTA0BuVire7NQR31H92Ou1HGjq4KtXxOecS6djLQhjzEkXleQyZUI6H1s0lc9fMitm5k5y00jXhujq7ePuF6o5a9qEuFotLhyWIIwxJ5V6Pbx6xzujHcaomleUw6pdR+ns6RtWQnx0Qx0Hmzv50UcWjKnWA1gXkzFmnKsoyqbPp+w6MvS1Ibp6+7jnxWrOnjaBi8ZY6wEsQRhjxrnASKatB5uHfO6j6/1Tyo+12kOAJQhjzLg2bVIGWalJQ65DdPX2cfeLuzln+kQujPMbBk/HEoQxZlxLSBDmFg59osJH1tdyuKWTr8bpanHhsARhjBn3Kgqz2X6oBV+YS6529vRxz4u7eceMiVwwZ7LL0UWPqwlCRK4UkZ0iUi0id4TYXy4ir4lIl4h8vd++CSLyuIjsEJHtInKem7EaY8aveUU5nOjuY/+x9rCOD7Qe4nWt6XC5liBEJBG4G7gKqACuE5GKfocdA24DfhriEj8HnlXVcmAhsN2tWI0x41vgjupwCtWdPX3c81I1i2dM4vzZY7f1AO62IBYD1aq6R1W7gYeBpcEHqGq9qq4HeoK3i0g2cDHwG+e4blVtcjFWY8w4VuLNIilBwqpDPLyuhiMtXXzlXSVjuvUA7iaIKUBt0PM6Z1s4ZgENwO9E5A0RuV9EMkMdKCI3i8gGEdnQ0NAwsoiNMeNSalIic/KzBh3J5G897GbxzEmcN8ZbD+BuggiVWsOrAPnv8D4b+JWqngWcAE6pYQCo6n2qukhVF+XljZ1Jsowxo6uiKJutg7Qglq+rob51fLQewN0EUQdMDXpeDBwcwrl1qrrWef44/oRhjDGumFeUQ0NrF/WtnSH3B1oP586cNOKpweOFmwliPVAiIjNFJAW4FngqnBNV9TBQKyJlzqbLgW3uhGmMMW/dUb39UGvI/Q+traGhtYuvvKt0NMOKKtcm61PVXhG5FXgOSAR+q6pbReQWZ/+9IlIAbACyAZ+IfAWoUNUW4EvAg05y2QN82q1YjTEmeCRT/zUdOnv6+NXK3SyZNT5qDwGuzuaqqiuAFf223Rv0+DD+rqdQ524CFrkZnzHGBOSkJ1M8MT3kSKYHndbDL687KwqRRY/dSW2MMY6KwuxTEkRHdx+/emk3582azJJZ46f1AJYgjDHmpHlFOextPMGJrt6T2x5cu5+jbf6RS+ONJQhjjHFUFGWjCjsO+wvVHd193LtyD+fPnsy546z1AJYgjDHmpHlOoXqbM+XGA2sCrYfxM3IpmCUIY4xxFOakMSEjmW2HWmjv7uXXq3ZzwZzJLJ45KdqhRYWtSW2MMQ4RoaLQf0e1v/XQza/GaesBLEEYY8zbzCvK5g+v7efA8Q4unJPLO2aMz9YDWIIwxpi3qSjKprvXR2Nv97gcuRTMahDGGBOkojAHgItKclk0jlsPYC0IY4x5m5L8LL5w6Ww+cnbISR7GFUsQxhgTJCFBuP3K8miHEROsi8kYY0xIliCMMcaEZAnCGGNMSJYgjDHGhGQJwhhjTEiWIIwxxoRkCcIYY0xIliCMMcaEJKoa7RgiRkQagP3DPD0XOBrBcNwUT7FCfMUbT7FCfMUbT7FCfMU7klinq2peqB1jKkGMhIhsUNVF0Y4jHPEUK8RXvPEUK8RXvPEUK8RXvG7Fal1MxhhjQrIEYYwxJiRLEG+5L9oBDEE8xQrxFW88xQrxFW88xQrxFa8rsVoNwhhjTEjWgjDGGBOSJQhjjDEhjfsEISJXishOEakWkTuiHc9ARGSqiLwoIttFZKuIfDnaMQ1GRBJF5A0R+Wu0YxmMiEwQkcdFZIfzb3xetGM6HRH5qvMe2CIiy0UkLdoxBROR34pIvYhsCdo2SUT+ISK7nP9OjGaMAaeJ9SfO+2CziDwpIhOiGOLbhIo3aN/XRURFJDcSrzWuE4SIJAJ3A1cBFcB1IlIR3agG1At8TVXnAkuAL8Z4vABfBrZHO4gw/Rx4VlXLgYXEaNwiMgW4DVikqmcAicC10Y3qFL8Hruy37Q7geVUtAZ53nseC33NqrP8AzlDVBUAV8K3RDmoAv+fUeBGRqcAVQE2kXmhcJwhgMVCtqntUtRt4GFga5ZhOS1UPqepG53Er/g+wKdGN6vREpBi4Grg/2rEMRkSygYuB3wCoareqNkU1qIElAekikgRkAAejHM/bqOoq4Fi/zUuBPziP/wB8cDRjOp1Qsarq31W113m6BoiZBapP828L8J/AN4GIjTwa7wliClAb9LyOGP7ADSYiM4CzgLVRDmUg/4X/DeuLchzhmAU0AL9zusTuF5HMaAcViqoeAH6K/5viIaBZVf8e3ajC4lXVQ+D/sgPkRzmecH0GeCbaQQxERD4AHFDVykhed7wnCAmxLebH/YpIFvBn4Cuq2hLteEIRkfcB9ar6erRjCVMScDbwK1U9CzhB7HSBvI3Td78UmAkUAZki8snoRjU2ici38XftPhjtWE5HRDKAbwPfjfS1x3uCqAOmBj0vJsaa6v2JSDL+5PCgqj4R7XgGcAHwARHZh7/r7p0i8kB0QxpQHVCnqoEW2eP4E0YsehewV1UbVLUHeAI4P8oxheOIiBQCOP+tj3I8AxKRG4D3Acs0tm8Ym43/y0Kl8/dWDGwUkYKRXni8J4j1QImIzBSRFPyFvqeiHNNpiYjg7yPfrqo/i3Y8A1HVb6lqsarOwP/v+oKqxuy3XFU9DNSKSJmz6XJgWxRDGkgNsEREMpz3xOXEaEG9n6eAG5zHNwB/iWIsAxKRK4HbgQ+oanu04xmIqr6pqvmqOsP5e6sDznbe0yMyrhOEU4S6FXgO/x/Yo6q6NbpRDegC4Hr838Y3OT/vjXZQY8iXgAdFZDNwJvDD6IYTmtPKeRzYCLyJ/+84pqaFEJHlwGtAmYjUichngR8BV4jILvyjbX4UzRgDThPrXYAH+Ifzd3ZvVIMMcpp43Xmt2G45GWOMiZZx3YIwxhhzepYgjDHGhGQJwhhjTEiWIIwxxoRkCcIYY0xIliCMiQEicmk8zHhrxhdLEMYYY0KyBGHMEIjIJ0VknXPz1K+d9S7aROQ/RGSjiDwvInnOsWeKyJqgNQUmOtvniMj/ikilc85s5/JZQetRPOjcJW1M1FiCMCZMIjIX+DhwgaqeCfQBy4BMYKOqng2sBL7nnPJH4HZnTYE3g7Y/CNytqgvxz6F0yNl+FvAV/GuTzMJ/57wxUZMU7QCMiSOXA+cA650v9+n4J5zzAY84xzwAPCEiOcAEVV3pbP8D8JiIeIApqvokgKp2AjjXW6eqdc7zTcAM4BXXfytjTsMShDHhE+APqvq21cVE5J/7HTfQ/DUDdRt1BT3uw/4+TZRZF5Mx4XseuEZE8uHkGsvT8f8dXeMc8wngFVVtBo6LyEXO9uuBlc76HXUi8kHnGqnOfP7GxBz7hmJMmFR1m4h8B/i7iCQAPcAX8S8uNE9EXgea8dcpwD+l9b1OAtgDfNrZfj3waxH5f841PjqKv4YxYbPZXI0ZIRFpU9WsaMdhTKRZF5MxxpiQrAVhjDEmJGtBGGOMCckShDHGmJAsQRhjjAnJEoQxxpiQLEEYY4wJ6f8DDqObBjMfhUkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model val loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['loss', 'val_loss', 'accuracy', 'val_accuracy'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('../model/extended_weight.h5', overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflowjs as tfjs\n",
    "\n",
    "tfjs.converters.save_keras_model(model, '../../DetectApp/model')"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.9.12 ('drowsiness_detector')",
   "language": "python",
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