{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
    "import random,shutil\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": 26,
   "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', 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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_13 (Conv2D)           (None, 22, 22, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 21, 21, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 19, 19, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 18, 18, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 14400)             0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 128)               1843328   \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 1,871,650\n",
      "Trainable params: 1,871,650\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/skywrace/opt/anaconda3/envs/capstone-design-two/lib/python3.7/site-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.\n",
      "  warnings.warn('`Sequential.model` is deprecated. '\n"
     ]
    }
   ],
   "source": [
    "CNN().model.summary()\n",
    "model = CNN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1236/1236 [==============================] - 85s 68ms/step - loss: 0.2461 - accuracy: 0.8996 - val_loss: 0.2017 - val_accuracy: 0.9126\n",
      "Epoch 2/15\n",
      "1236/1236 [==============================] - 83s 67ms/step - loss: 0.1205 - accuracy: 0.9559 - val_loss: 0.2643 - val_accuracy: 0.9221\n",
      "Epoch 3/15\n",
      "1236/1236 [==============================] - 86s 70ms/step - loss: 0.0918 - accuracy: 0.9667 - val_loss: 0.0291 - val_accuracy: 0.9372\n",
      "Epoch 4/15\n",
      "1236/1236 [==============================] - 86s 70ms/step - loss: 0.0768 - accuracy: 0.9731 - val_loss: 0.0520 - val_accuracy: 0.9517\n",
      "Epoch 5/15\n",
      "1236/1236 [==============================] - 94s 76ms/step - loss: 0.0716 - accuracy: 0.9748 - val_loss: 0.0580 - val_accuracy: 0.9417\n",
      "Epoch 6/15\n",
      "1236/1236 [==============================] - 97s 79ms/step - loss: 0.0630 - accuracy: 0.9788 - val_loss: 0.1348 - val_accuracy: 0.9330\n",
      "Epoch 7/15\n",
      "1236/1236 [==============================] - 101s 82ms/step - loss: 0.0564 - accuracy: 0.9802 - val_loss: 0.0330 - val_accuracy: 0.9417\n",
      "Epoch 8/15\n",
      "1236/1236 [==============================] - 102s 83ms/step - loss: 0.0545 - accuracy: 0.9810 - val_loss: 0.0044 - val_accuracy: 0.9442\n",
      "Epoch 9/15\n",
      "1236/1236 [==============================] - 104s 84ms/step - loss: 0.0504 - accuracy: 0.9820 - val_loss: 0.3029 - val_accuracy: 0.9473\n",
      "Epoch 10/15\n",
      "1236/1236 [==============================] - 98s 80ms/step - loss: 0.0501 - accuracy: 0.9833 - val_loss: 0.2540 - val_accuracy: 0.9554\n",
      "Epoch 11/15\n",
      "1236/1236 [==============================] - 98s 79ms/step - loss: 0.0463 - accuracy: 0.9843 - val_loss: 0.0140 - val_accuracy: 0.9482\n",
      "Epoch 12/15\n",
      "1236/1236 [==============================] - 95s 76ms/step - loss: 0.0413 - accuracy: 0.9858 - val_loss: 0.1042 - val_accuracy: 0.9407\n",
      "Epoch 13/15\n",
      "1236/1236 [==============================] - 89s 72ms/step - loss: 0.0433 - accuracy: 0.9854 - val_loss: 0.0831 - val_accuracy: 0.9452\n",
      "Epoch 14/15\n",
      "1236/1236 [==============================] - 92s 74ms/step - loss: 0.0392 - accuracy: 0.9875 - val_loss: 0.0284 - val_accuracy: 0.9569\n",
      "Epoch 15/15\n",
      "1236/1236 [==============================] - 92s 75ms/step - loss: 0.0402 - accuracy: 0.9865 - val_loss: 0.0038 - val_accuracy: 0.9604\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 32,
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'epoch')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JYgjW7DrI8ncaue6c44ZUslpji35mBI50kov9Hoigao+Lzp5eGpr9o/aaJvFYghiC25ZvwuPO4TNnTRrSdcG9EJYgzHCM5h6IIG9fqautQ6QzSxARennLPl7esp9/OW8yBblD2z4SLBu0hWozHA3No7cHIshKXQ1YgoiIqvKjZZsYX5zHJ06tHvL17twsygtz7dOYGZa6Q84xG6OYIMaX5JOdaUfVpztLEBF45p1G1u0+xFfOnzLsjUreUhc77M1mhqHB56c4PxtXTkwPPjhKZoZQNcbFLjvVNa1ZgjiGnl7ltuWbOK7MzYdPGn4/Izsh0wzXaO6BCFVdauXZ6c4SxDE8sW4Pmxtb+doFU8nKHP4/V43HRUOzn/ZOOyHTDE1Dc3tcEoTX42LX/jY7qj6NWYIYRGd3L7c/8y4zKou4aFbliJ7LWxaoCrHjC8xQNfj8VIxiiWtQdamLlo5uDtpR9WnLEsQgHl61m10H2vj6ohPIyAjXIC9yNVbJZIaho7uHfa2ju0ku6Eh5tv3MpitLEANo7+zhzr+/yyk1Y3jf1PIRP5/VlZvhaPR1AKNbwRQULHW1UW/6sgQxgPtf2cHelg6+fuE0nMZFI1LsyqbElW2LfmZIgn0gxsdpiglsL0Q6swQRRrO/i1/+YyvvO6GcBZNKo/a8Xo/b3mxmSIJHXcRjBJGXnUlFUZ79zKYxSxBh3PviNnztXdw8SCvR4ajxuGwNwgxJ8JiNeCQICJzJZNOi6csSRD/7Wju496XtXDy7klkTiqP63F6Pm7pD7XR0W6mriUz9oXYK87KGfLxLtNix3+nNEkQ/v3h+K/6uHr62cPBWosPhLXXRq1B7sD3qz21SU73PH5f1hyCvx0VTSwdtnd1xi8HEjyWIEHsOtfOHV3fykZOrOL68IOrPX1PmVIXYnK6J0Gg3Cuqv2mP7d9KZJYgQP3v2XQC+cn70Rw9wpK7c1iFMpOJ1zEaQ1yqZ0polCMfWplYeXV3LJ06rZkJJbIb0HncOBblZ9mYzEens7mVfa0dcRxB9eyHsZzYtWYJw/OSZzeRmZXDDeZNj9hoigtcqmUyEGpv9qI5uo6D+Slw5FOVlsdNOdU1LMU0QIrJIRDaJyBYRuSXM/Z8QkTedPytEZE6k10bT23t8/O3Nej531iTKCnJj+VLU2F4IE6HgHojRbDUaju3fSV8xSxAikgncBSwGZgBXiciMfg/bDpyrqrOB7wL3DOHaqPnx8k0U52dz7dnHxeol+lR7XNQebKO7pzfmr2WSWzxajYbj9bhskTpNxXIEsQDYoqrbVLUTeAhYEvoAVV2hqgedb18FqiK9Nlqa/V3sPNDGF953PMX52bF4iaPUeFx09Wjfm9+YgTT4Rr/VaDhej4vag+102YeatBPLBDEB2B3yfa1z20A+Bzw11GtF5DoRWSUiq5qamoYcZFFeNstvPIfPnFkz5GuHwyqZTKTqDvkpyM2iMC/2H1wG4y1109Or1B2y/TvpJpYJItwJd2E7j4jIeQQSxL8O9VpVvUdV56vq/PLy4Z26mpWZQW7W8FqJDlVNX4KwIbsZXKAPRHxHDxCYFgUrdU1Hsdy/XwtMDPm+Cqjr/yARmQ3cCyxW1f1DuTYZjS3MJS87g537bARhBlffHN89EEHBUlc7ciP9xHIEsRKYIiKTRCQHuBJ4IvQBIlINPAZcraqbh3JtssrIEKpLXTaCMMfU4ItPq9H+xhXmkZOVwS6bFk07MRtBqGq3iHwRWAZkAvep6noRud65/27gO4AH+IXTc6HbmS4Ke22sYh1tgbJBe7OZgXX19LK3pSMurUb7C36osSmm9BPTIyJV9UngyX633R3y9bXAtZFemypqPC5e3NxEb6+OuJWpSU17WzrivkkulLfUSl3Tke2kjgOvx01Hdy+NLVbqasILlrgmSoKodvZCqIatFTEpyhJEHPRVMu2zT2QmvCOb5OI/xQSBn9m2zh6aWjviHYoZRZYg4qCvKsTWIcwAGuLcSa6/aju0Ly1ZgoiDyuI8sjPFygbNgOp9flw5mRTlxaeTXH/BY7+t+i69WIKIg6zMDCaOsV6/ZmD1vnYqivNwqvvirmqMiwzBSl3TjCWIOPF6XLYGYQYU71aj/eVkZVBZnG+j3jRjCSJOgnshrCrEhJMox2yE8npsL0S6sQQRJ16Pi8OdPexr7Yx3KCbBdDub5BKlxDXIjv1OP5Yg4qSmrxm8zemao+1r7aSnVxNuBFFd6ubA4U5a/F3xDsWMEksQcRIsdbV1CNNfXYJtkgvy2qmuaccSRJwEq0Ksksn015Bgm+SCqp1SV5tmSh+WIOIkJyuDCWPyra7cvEeitBrtz0YQ6ccSRBzV2KmuJowGXzt52Rmj0gJ3KArzsvG4c2zdLI1Ygoij6lKX1ZWb96jz+akszk+YTXKhqm3/TlqxBBFHNR43h9q6ONRmpa7miAZfYnSSC8eO/U4vliDiyOZ0TTiJuEkuqNrjps7XTkd3T7xDMaPAEkQc1ZQ5x37bOoRx9PQqjQnSizocb6kLVag92B7vUMwosAQRR8GyQRtBmKD9rR1092pCtBoNx2vHfqcVSxBxlJedSUVRniUI06cuWOJalJgjiGrrZZJWIkoQIvIVESmSgP8RkdUickGsg0sHgQPQ7M1mAvpajZYkZoIoL8jFlZNp1XdpItIRxGdVtRm4ACgHPgP8IGZRpZEaj9s2y5k+idZqtD8RobrUZVNMaSLSBBEsyL4I+I2qrgu5zYyAt8zFvtYOWju64x2KSQANPj85WRmMcSXWJrlQXo/t30kXkSaIN0RkOYEEsUxECoHe2IWVPoKnuto0k4HACKIygTrJheP1uNl1oI3eXutlkuoiTRCfA24BTlHVNiCbwDSTGaG+A9BsyG5wWo0m6AJ1UHWpi87uXhqa/fEOxcRYpAnidGCTqh4SkU8C3wZ8x7pIRBaJyCYR2SIit4S5f5qIvCIiHSJyc7/7dojIWyKyVkRWRRhn0uk79tsShMFpNVqSmOsPQbbBM31EmiB+CbSJyBzgG8BO4HeDXSAimcBdwGJgBnCViMzo97ADwJeB2wZ4mvNUda6qzo8wzqRTmJdNWUGOTTEZep1Ncom6izrIW2rNrtJFpAmiWwPNk5cAd6jqHUDhMa5ZAGxR1W2q2gk85FzfR1X3qupKIK1bVHk9bttNbdh/uJOuHk3YXdRB40vyyMoQG0GkgUgTRIuIfBO4GvibMzo4VpnFBGB3yPe1zm2RUmC5iLwhItcN9CARuU5EVonIqqampiE8feKwZvAGjjQKSvQ1iKzMQC8Tq2RKfZEmiCuADgL7IRoI/KK/9RjXhCvDGErZw5mqehKBKaobROSccA9S1XtUdb6qzi8vLx/C0ycOb6mbep8ff5cdgJbOjrQaTew1CMD2QqSJiBKEkxQeAIpF5BLAr6qDrkEQGDFMDPm+CqiLNDBVrXP+uxdYSmDKKiXVlAUW/XbbJ7K01tdqNEF3UYeyZlfpIdKjNj4GvA58FPgY8JqIfOQYl60EpojIJBHJAa4Enojw9dzOXgtExE1gB/fbkVybjLye4KmuliDSWb3PT05mBqWunHiHckxej4tmf7f1MklxWRE+7lsE9kDsBRCRcuBZ4NGBLlDVbhH5IrAMyATuU9X1InK9c//dIlIBrAKKgF4RuZFAxVMZsNTZLJQF/FFVnx7G3y8p1NgBaIbAOUzjinPJyEjcTXJBoScRlyRBQjPDE2mCyAgmB8d+Ihh9qOqTwJP9brs75OsGAlNP/TUDcyKMLemVuHIozs+2SqY0V+fzU1mU+OsPcGTUu/NAG3MmlsQ3GBMzkSaIp0VkGfCg8/0V9PvFb0bGKplMg8/PvOqSeIcRkb4RxD77UJPKIkoQqvp1EfkwcCaB6qR7VHVpTCNLM16Pm3W7D8U7DBMnqprQrUb7y8/JZGxhrpW6prhIRxCo6p+BP8cwlrRW43Hxtzfr6OzuJSfL+jilmwOHO+ns6U3YRkHheD1W6prqBv1NJCItItIc5k+LiDSPVpDpwOtx06uw55D1+j2W7p5e/vZmPT0pdJposA9EorYaDae61M1OO24jpQ2aIFS1UFWLwvwpVNWi0QoyHdT0Hdpnb7hjeXjVbm7442qefKs+3qFEzZFGQck1gmhs7rANninM5jISRF9ViC36DUpV+e3LOwB4+u2G+AYTRYneajSc4Kmuu2wdImVZgkgQZQU51us3Aiu27ufdva1UFufx/Ka9KfPptd7nJytDKHPnxjuUiPV9qLF1iJRlCSJBiAhej9vebMfw2xU78Lhz+N6ls2jr7OHFzcl5QGN/DT4/44rykmKTXJC31DZ4pjpLEAmkxuOyNYhB7D7QxrMbGrlqQTXnTC2nOD87ZaaZgq1Gk0mJK5vCvCybYkphliASiNfjZveBtpSqzomm372ygwwRPnmal+zMDBbOGMczGxrp7E7+9uj1vvak2QMRFBj1uuwMsRRmCSKB1HhcdPUodVbq+h5tnd08vHI3i2dV9P0iXTSzghZ/Nyu27otzdCOjqknRajQcb6mbXTbqTVmWIBJItVWFDGjpmj00+7u55oyavtvOmlKGOyeTZeuTe5rpUFsXHd29Cd8oKJxqj4vag+109yT/KM68lyWIBFLTd+y3fSILFSxtnTWhiJO9Y/puz8vO5P3Tx7F8fWNST8sl4x6IIG+pi+5e7fs7mNRiCSKBVBTlkZOVYZVM/QRLW685YxLOEfB9Fs+qYP/hTl7ffiBO0Y1cQ3NgSjHZ1iDgyKjXfmZTkyWIBJKRIXhLXeywzXJH+e2KHZS6c7hkduV77jt3ajm5WRk8/Xby7qquOxT49J2UaxB9x37bz2wqsgSRYGwvxNGCpa0fX1BNXnbme+5352Zx7tRylq1vpDdJp5kafH4yM4SyguTZJBdU6Yx67dC+1GQJIsF4PS52HjiManL+sou20NLWgSw+sYKGZj9raw+NXmBRVO/zM64wl8wk2iQXlJEhTByTbx9qUpQliART43Hh7+plb0tHvEOJu2Bp66KQ0tZw3j9tHNmZkrSb5hqak28PRCivx21HxKQoSxAJJjina+sQR0pbPxNS2hpOcX42ZxxfxlNv1yflyKv+kJ/KJDrmu7/qUhe79tuoNxVZgkgwNXYAGjBwaetAFs+qYPeBdt6pT642JcFNcslY4hrk9bg43NnDvtbOeIdioswSRIIZX5JHVoak/V6IwUpbw1k4YxwZknxHgDe3d9Pe1ZPkU0zBDZ7p/TObiixBJJiszAyqxuSn/ZzuYKWt4XgKcjl1koenkixB1Dt7IJJ7islGvanKEkQCCpS6pu+nsWOVtg5k0awKtuxtZcvelhhGF11HWo0m7whiYmk+IpYgUpEliARU43Gxc19b2i76RVLaGs6FMyuA5Jpmqj+UvMdsBOVmZVJZlBeXM8Ra/F0p0zQqEcU0QYjIIhHZJCJbROSWMPdPE5FXRKRDRG4eyrWpzOtx09LRzYHD6bfoF2lpazgVxXmcVF2SVNNMDb52MgTGFibfJrlQ8Rj1qiof+9WrnP2j53kqhfqTJ5KYJQgRyQTuAhYDM4CrRGRGv4cdAL4M3DaMa1NWTVlg0S8dz9mPtLR1IItnVbK+rjlpdvbW+/yMLcwjKzO5B/Nej2vURxBv1vrYUN9Mb6/yhQdW8/nfr6Kx2Q4NjKZY/lQuALao6jZV7QQeApaEPkBV96rqSqBrqNemsiO9ftNrHWKopa3hLJrlTDOtT45PlA3N/qRefwiq9rjY19pJa0f3qL3m0jV7yMnK4Nmvncu/XTSNFzY3cf6PX+CPr+1K2mNXEk0sE8QEYHfI97XObVG9VkSuE5FVIrKqqSk1+hNXjUnPRb9gaeunT6+JqLQ1nImlLmaOL0qadYhk3wMR5HUqmUZr5NbV08sT6+pYOH0cY9w5XHfO8Sy78RxOrCrm35a+xZW/fpWtTa2jEksqi2WCCPcOjzStR3ytqt6jqvNVdX55eXnEwSWy3KxMxhfnp90IIlja+sE540f0PItnVbB61yEaErxHgapSfyi5j9kIGu29EC9sauLA4U4uP+nI50avx80D157Kjz4ym431zSy+45/c9fwWuqyZ0bDFMkHUAhNDvq8C6kbh2pRQU5ZevX6HW9oazqJZgb0Tid5prqWjm8OdPYxP4j0QQcG+EKP1M/vYmlo87hzOmXr0h0IR4WPzJ/LsTeeycPo4bl22iQ/e+RLrdh8albhSTSwTxEpgiohMEpEc4ErgiVG4NiWk216IYGnrJ06rHvFzTR5bwOSxBTyV4D0iGlJgD0RQUV42Y1zZozIt6mvr4tkNe/ngnPFkD7C4P7Ywj7s+cRL3XH0yB9s6uewXL/O9v75DW+forZGkgpglCFXtBr4ILAM2AI+o6noRuV5ErgcQkQoRqQW+BnxbRGpFpGiga2MVayKq8bg42NaFr73/+n3qCS1tjdaO4sWzKnh9+wH2tybuqbjJ3Go0nGqPe1SmmP72Vj2d3b1HTS8N5IKZFTzztXP5+KnV3PvSdi64/UVe3Jwaa5WjIaa1dar6pKpOVdXjVfW/ndvuVtW7na8bVLVKVYtUtcT5unmga9NJ9Sgv+sXTSEtbw1k0q4JehWfeaYzac0Zbgy95W42G4y11jcoIYumaWiaPLeDECcURPb4oL5vvXXoij3z+dHKyMvjUfa/ztUfWcjAN9xkNVXIXX6ewI3shUnuaSVW5f8UOZo4ffmlrODMqi6gudSX0prm6Q35EYFxRiiQIj4u6Q+10dsduUXjX/jZW7jjIZfMmDLnSbcGkUp788tl86f2TeWJtHef/5AUeX7snbU8siIQliARVXRpsBp/aCeKVrfvZ3NjKNWcMv7Q1HBFh0awKVmzdl7DTdA0+P+UFuQPOoycbr8dNr8KeQ+0xe42la/YAcOm8SCvmj5aXnclNF5zAX798FlWlLr7y0Fo+d/+qmMaczFLjJzMFuXKyGFeUm/KVTL+JUmlrOItmVdDVozy3MTGnmeqbU2MPRFCw1DVWH2pUlaVrajn9OA8TSka2VjWtoojHvnAG37lkBq9s3c8FP3mB+1fsoMc22B3FEkQCS/VKpmBp61ULJo64tDWcuVUlVBTl8dRbiTnN1OBLjT0QQd7S4F6I2HyoWb3rEDv2t3FZBIvTkcjMED571iSWf/UcTq4p5T+eWM9H717B5sbkOQ041ixBJLDRWvSLl+Ge2hqpjIzANNMLm5s4PIpHQEQq2VuN9ldemEt+dmbMfmaXrqklLzuDxc5xKtEysdTF/Z85hduvmMP2fYe5+Gf/5PZnNsd0LSVZWIJIYDVlbva2dKRk7XYsSlvDuXBmBR3dvfxjU2KVNrb4u2jp6E6pKSYRoTpGH2o6unv465v1XDCjgsK87Kg/v4hw2bwqnv3auVx0YiV3/P1dfvT0xqi/TrKxBJHAjszppt4oIhalreEsmFSKx52TcJvmgqeOptIUEwR2VMdiWvT5jU0cauuK2vTSQDwFudxx5TyuPGUiv1mxg00N6T3dZAkigdWk6KmusSptDSczQ7hg5jie37g3oRrLHNkklzpTTBCYFt11oC3qp6kuXVNLWUEuZ08ui+rzDuQbi6ZRmJfFvz/+dlqXwVqCSGCjfb7NaIlVaetAFs2q5HBnDy+9uy/mrxWpVNtFHeT1uOjo7mVvS/R2sB883MlzG/eyZO74UeubUerO4RsXTuP17Qd4fG1aHQN3FEsQCawoL5tSd07KTTHFsrQ1nNOP81CYl5VQm+aCrUbHFiV3J7n+qmMw6v3rW/V09WhER2tE0xWnTGROVTH//eQGmv2JuZcm1ixBJDhvjOZ04yXWpa3h5GRlsHD6OJ7d0JgwRz83NLdTVpBLbtbo/BuMlprgulkUS12Xrq7lhHGFzKgsitpzRiIzQ/jupbPY19rBT595d1RfO1FYgkhwNR53So0gfv/qzpiWtg5k0awKfO1dvLpt/6i+7kBSpVFQf+NL8snMkKidIbZ932FW7zrE5ScN/WiNaJhdVcLHF1Rz/ys72FDfPOqvH2+WIBKc1+OizteeUAusw9XW2c1Dr++KeWlrOOdMLceVk5kw00wNvtRoNdpfdmYGE0ryozaCWLpmDyKwZO7oTi+F+vqFJ1CUl8V30nDB2hJEgqvxuFGF2oPJP4oIlrZeE+PS1nDysjM574SxLF/fkBDHKaTqCAICH2p2RWFaNHi0xpnHl8U1mZa4crhl8TRW7jjIY6v3xC2OeLAEkeBSZS9EaGnr/BiXtg5k0awK9rV2smrHgbi8flBbZze+9q6UK3ENqi51RWUEsWrnQXYfaB/1xelwPnryROZOLOH/PbUhYQ9/jAVLEAnO61SFJHup62iXtoZz3rSx5GRl8HScW5GmaolrkNfj4lBbF762kf0ifWx1LfnZmVw4M7pHawxHRobwvUtnsf9wJ7c/szne4YwaSxAJbowrm8K8rKSvZBrt0tZwCnKzOGdKOcvebojrXHIqtRoNJ9jsaucIusv5uwJHayyeVYE7NytaoY3IrAnFfPJUL797ZQfr63zxDmdUWIJIcCJCjced1COIeJS2DmTRrArqfH7W1cbvDZ4OIwgY2bTocxv30uLvjvnRGkN18wUnMMaVw3ceXx/13eKJyBJEEkj2vRDxKm0NZ+H0cWRlSFzPZgq2Gk2VTnL9VUfh2O/HVtcyriiXM44fnaM1IlXsyuaWxdN4Y+dB/ry6Nt7hxJwliCRQ43Gz52B7wmzyGoq+0taZo1/aGk6xK5vTj/fEdZqpzufH486J+2gqVty5WZQX5g77Q83+1g7+samJS+dOIDMjPutVg/nwSVWc7B3DD57aOOJ1lkRnCSIJVHtcdPcqdUnYFrGvtPXMmniH0mfxrEp27G9jY5xO6kzVPRChRtLL5H/X1dHdqwk3vRSUkSH815KZHGzr5Lblm+IdTkxZgkgCNUlUyaSq1B5s45l3GvnZ39/lrue2xLW0NZyFM8YhQtw2zaXyHoigao9r2FNMS9fsYUZlEdMqRvdojaGYOb6YT51ewx9e28lbcVzPirXEKA8wg6o5qtdveXyDCeHv6mFTQwsbG5rZUN/CO/XNbKxvptl/pMHRpDI337poetxKW8MpL8zllJpSnn67nq8tnDrqr9/ga+dkb8mov+5o8pa6WbpmD/6uniFNpW3Z28q6Wh/fvnh6DKOLjq8unMpf36zj3x9/m8e+cAYZCTgdNlKWIJJAsJXjjn3xGUGoKo3NHWyob+ad+mY2OH+27ztMsJDDlZPJtIpCPjhnPNMri5heWcS0isKEKVHsb/GsCv7v/77D1qZWji8vGLXXbe/s4WBb6m6SC/J6XH0nAEweWxjxdUvX1JIh8KG58SuHjlRxfjbfXDydm/60jj+9sZsrTqmOd0hRF9N3r4gsAu4AMoF7VfUH/e4X5/6LgDbgGlVd7dy3A2gBeoBuVZ0fy1gTmYgEji8YQV15pDq6e9iyt5UN9S19iWBDfTMHQxbjJpTkM72yiItPrOxLBtWlrqT6BHXhzECCePrtBm44b/KovW5Dc2qXuAZVh5S6RpogenuVv6yp4+wp5YwtTI5/n8tPmsBDK3fxg6c2cuHMCkpcOfEOKapiliBEJBO4C1gI1AIrReQJVX0n5GGLgSnOn1OBXzr/DTpPVROny0sceT0utjbFJkF0dvdy1/NbWLa+gS17W+l2hgW5WRlMqyjkwpkVR0YFlYUUxaAn8GgbX5LPnIklo54g6p0S13RYpIahrZu9tv0Aew61841FJ8QqrKgTEf5rySwuufMlbl22if++7MR4hxRVsRxBLAC2qOo2ABF5CFgChCaIJcDvNFBv+KqIlIhIpaomVgPhBFDjcfP8xiZ6ejWqpX+bG1u48aG1vFPfzFmTy3j/tLF9yWBSmTshywyjZfGsCn7w1EZ2H2hjovMLLdYaUrTVaH+l7hwKcrOGdGjf0jW1uHMyuWBG/I/WGIrplUV8+vQafrNiO1ecMpHZVSXxDilqYlnFNAHYHfJ9rXNbpI9RYLmIvCEi1w30IiJynYisEpFVTU1NUQg7MXk9bjp7evumKEaqt1f5n5e2c8mdL9HY7OfXn5rPH649lW8smsYH54xn8tiClE4OEEgQAMtG8Wym4C7qihTdJBckIkM6tM/f1cOTbzWw+MRK8nOSb3/IjQunUFaQy7//5e2EOC04WmKZIML9dun/LzfYY85U1ZMITEPdICLnhHsRVb1HVeer6vzy8sSp8Im2vkqmfSOfZqr3tXP1fa/x3b++w9mTy3j6xnNYOGPciJ832Xg9bqZXFvH0KJa71vvaKXFlJ+UvwaEKHPsdWYJY/k4jrR3dCXFy63AU5WXzrYums67Wx8Mrdx/7giQRywRRC0wM+b4K6N/9e8DHqGrwv3uBpQSmrNKWtyw6eyEeX7uHC29/kTW7DvGDy0/k3k/Pp7wwtfoiD8WimRW8sesge6M0MjuWBp8/5aeXgrweN7sPtkX0iXrp6lrGF+dx2iTPKEQWG0vmjmfBpFJ+tGwjBw53xjucqIhlglgJTBGRSSKSA1wJPNHvMU8An5KA0wCfqtaLiFtECgFExA1cALwdw1gTXkVRHjmZGcM+IdPX1sWXHlzDVx5ay+SxBTz55bO5ckF1Qu1PiIfFJ1agOnrTTOmwSS7I63HR1aN9C/MDaWrp4MV393HpvAlJVQnXn4jw3SWzaPF3c+uyjfEOJypiliBUtRv4IrAM2AA8oqrrReR6EbneediTwDZgC/Br4F+c28cBL4nIOuB14G+q+nSsYk0GmRnCxNJ8dg5jL8RL7+7jwp++yFNv1XPzBVN55POnU+OMSNLdlLEFHFfuHrUeEelwzEZQsJLpWNNMT6yro6dXk3Z6KdQJFYV89swaHlq5mzW7DsY7nBGL6T4IVX2SQBIIve3ukK8VuCHMdduAObGMLRkFjv2OfATh7+rhh09v5Dcv7+D4cje//tSZnFhVHMMIk4+IsHhWBXe/sI2DhzsZ445dHbu/q4f9hzupTPEF6qC+vRAH2jhjkMc9trqWEycUD2lDXSL7yvlTeWJdHd95fD1/ueHMpC72sLOYkojX42bn/raITiF9e4+PD975Er95eQfXnFHDX790tiWHASyaWUlPr/LMO40xfZ3G4Ca5kvRYg6gszic7UwY9tG9zYwvr65pTYvQQVJCbxbcunsFbe3w8+PqueIczIpYgkkhNmYv2rh6aWjoGfExPr3LX81u47Bcv0+zv4nefXcB/fmhmWlTNDNesCUVUjcmPeY+IVG8U1F9mhjBxzOAnADy2eg+ZGRLXToOx8MHZlZx+nIdbl21if+vA79dEZwkiiQT7Uw9UW75rfxtX/OoVbl22iQtmVLDsxnM4Z2rqlv5Gi4iwaGYFL2/ZT7M/duf7p3qr0XCqPa4BzxDr6VX+smYP75taTllBalXSBXZYz+RwRzc/fDp5F6wtQSSRvuML+u2FUFUeWbmbxXe8yKbGFn56xVx+/vF5KXcuTCwtPrGCzp5ent+4N2avkS6b5EJ5SwPHfoebFn11234amv0J2/dhpKaMK+RzZ03ikVW1vLEzOResLUEkkQlj8snMOHpOd39rB5///Rt8489vMruqhKdvPIdL501I+/LVoZo3cQxjC3N56q3YVTM1+NopystK2BNuY6Ha46a1ozvsvoA/r66lMDeL86en7ibNL39gChVFeUm7w9oSRBLJzsygakx+XyXTcxsbufCnL/KPTU18++LpPHDtqUxIkwXQaMvICFQz/X1jI397MzZrEXU+P+PT7P9PTUglU6i2zm6efruBi2dXpmzrVQi0X/33S2bwTn0zD7y2M97hDJkliCTj9bjZ3NjCvy19i8/+dhVlBbk88aUzufbs45J6k1EiuPH8qcyuKuGGP67mnhe3Rr1ndTrtgQjyesLvhVi+vpG2zh4um5ea00uhLjqxgrMml3Hrsk3sS7IFa0sQSabG42JzYysPvr6Lz597HI9/8cyEbs2YTMa4c3jg2lO5+MRKvv/kRr7z+Hq6e3qj9vzptIs6qGqMCxHeU+r659W1VI3J55Sa0jhFNnpEhP/80Ez8XT384KnkWrBOn8nQFPH+aWNZV+vjm4uncdpxyXtuTaLKy87kzqvmUTUmn1+9uI26Q+3c+fF5uHJG9lbp7O5lX2sHFUXpNcWUl51JRVHeUUfENDb7eXnLPm44b3LajHonjy3g2rOP45f/2EpFUR43nj+FrMzE/3xuCSLJvO+EsbzvhLHxDiOlZWQI37xoOlVj8vmPJ9Zz5T2vcu+n54+oy1ljmnSSC6e69OhTXR9fu4deJS2ml0J95QNTONDayc+f38LrOw5w51XzGJfgFW2Jn8KMiZOrT6/hnqvn825jK5fdtYIte1uG/Vx9m+RKEvsXQix4PUf3hXhs9R7mTizhuFHsBZ4I8rIz+eFHZvPjj87hrVofF93xT17cnNg9bCxBGDOI82eM4+HPn0ZHdy+X/2IFr2zdP6znCZ5omo4jCK/HTVNLB22d3bxT18zGhpaUOlpjqD58chX/+6Uz8RTk8OnfvM5tyzZFda0rmixBGHMMs6tKWPovZzC2KI9P3fcaf1mzZ8jPcWQXdXqtQUBgigkCC9VL19SSnSlcMju1jtYYqsljC3n8hrP42MkT+fnzW/j4va/1TUMmEksQxkRgYqmLP19/Bid7x3Djw2v5+XPvDqkMtt7npzA3i4I02iQXFCx13b7vMH9ZW8f7ThhLaQxPzU0W+TmBKaeffCxxp5wsQRgToWJXNvd/dgGXzh3Pbcs3883H3qIrwqmBel97Wq4/AHhLA2eIPfj6LppaOvhwGk8vhXP5SYEpp7KC3ISbcrIEYcwQ5GZlcvsVc/nS+yfz0MrdfO7+VbREcMBfYJNc+k0vQSCxlriy+ee7+yjOz+a8aVaF19/ksYX85YYzE27KyRKEMUMkItx0wQn88MMn8vKWfXz07leO2Vaz3udPm0ZB4QQPmrx4diW5Wal7tMZIJOKUkyUIY4bpilOque+aU9h9oI3L7lrBhvrmsI/r6umlqbUj7Y7ZCFXtHFVv00vHlkhTTpYgjBmBc6eW86frAw01P3r3K2E/8e1t6UA1PUtcgxbOGMcFM8ZxUvWYeIeSFBJlyskShDEjNGN8EUtvOIOqMfl89rcreWTl7qPurz/k7IFIs5NcQ31oznju+dR8O4Z+CBJhyskShDFRUFmcz5+uP53Tj/fwjT+/yY+Xb+org023VqMmuuI55WQJwpgoKczL5r5rTuGjJ1dx53NbuOmRdXR296Zlq1ETXfGackq/XTvGxFB2ZgY/+shsqktd/PiZzdT7/EwYk487J5PCNNwkZ6InOOV06nGlfGvp21x0xz+5/Yq5Me07byMIY6JMRPjSB6Zw+xVzWLXzAI++UUtFcZ7Nv5uoGM0pp5gmCBFZJCKbRGSLiNwS5n4RkZ85978pIidFeq0xie6yeVXc/9kFFOZlUeOUeRoTDeGmnA53dEf9dWI25hWRTOAuYCFQC6wUkSdU9Z2Qhy0Gpjh/TgV+CZwa4bXGJLwzji/j7187l8w0aYxjRk9wyum040t5desBXDnR34AYy0nRBcAWVd0GICIPAUuA0F/yS4DfaaDc41URKRGRSqAmgmuNSQpj03gHtYm9y+ZVcdm8qpg8dyynmCYAoQXhtc5tkTwmkmsBEJHrRGSViKxqakqskxCNMSaZxTJBhBtT9z8feaDHRHJt4EbVe1R1vqrOLy+P3Wq+Mcakm1hOMdUCE0O+rwLqInxMTgTXGmOMiaFYjiBWAlNEZJKI5ABXAk/0e8wTwKecaqbTAJ+q1kd4rTHGmBiK2QhCVbtF5IvAMiATuE9V14vI9c79dwNPAhcBW4A24DODXRurWI0xxryXDKVtYqKbP3++rlq1Kt5hGGNM0hCRN1R1frj7bCe1McaYsCxBGGOMCSulpphEpAnYOczLy4B9UQwnlpIpVkiueJMpVkiueJMpVkiueEcSq1dVw+4RSKkEMRIismqgebhEk0yxQnLFm0yxQnLFm0yxQnLFG6tYbYrJGGNMWJYgjDHGhGUJ4oh74h3AECRTrJBc8SZTrJBc8SZTrJBc8cYkVluDMMYYE5aNIIwxxoRlCcIYY0xYaZ8gkqm1qYhMFJHnRWSDiKwXka/EO6ZjEZFMEVkjIn+NdyzH4jSselRENjr/xqfHO6aBiMhXnZ+Bt0XkQRFJqK5EInKfiOwVkbdDbisVkWdE5F3nv2PiGWPQALHe6vwcvCkiS0WkJI4hHiVcvCH33SwiKiJl0XittE4QIa1NFwMzgKtEZEZ8oxpUN3CTqk4HTgNuSPB4Ab4CbIh3EBG6A3haVacBc0jQuEVkAvBlYL6qziJwoOWV8Y3qPX4LLOp32y3A31V1CvB35/tE8FveG+szwCxVnQ1sBr452kEN4re8N15EZCKBNs27ovVCaZ0gCGmLqqqdQLC1aUJS1XpVXe183ULgF1jYTnuJQESqgIuBe+Mdy7GISBFwDvA/AKraqaqH4hrU4LKAfBHJAlwkWL8UVX0RONDv5iXA/c7X9wOXjmZMAwkXq6ouV9Vu59tXCfSkSQgD/NsC3A58gwGaqw1HuieIiFubJhoRqQHmAa/FOZTB/JTAD2xvnOOIxHFAE/AbZ0rsXhFxxzuocFR1D3AbgU+K9QT6qCyPb1QRGef0e8H579g4xxOpzwJPxTuIwYjIh4A9qroums+b7gki4tamiURECoA/AzeqanO84wlHRC4B9qrqG/GOJUJZwEnAL1V1HnCYxJkCOYozd78EmASMB9wi8sn4RpWaRORbBKZ2H4h3LAMRERfwLeA70X7udE8QkbRFTSgikk0gOTygqo/FO55BnAl8SER2EJi6e7+I/CG+IQ2qFqhV1eCI7FECCSMRnQ9sV9UmVe0CHgPOiHNMkWgUkUoA57974xzPoETk08AlwCc0sTeMHU/gw8I65/1WBawWkYqRPnG6J4ikam0qIkJgjnyDqv4k3vEMRlW/qapVqlpD4N/1OVVN2E+5qtoA7BaRE5ybPgC8E8eQBrMLOE1EXM7PxAdI0AX1fp4APu18/Wng8TjGMigRWQT8K/AhVW2LdzyDUdW3VHWsqtY477da4CTnZ3pE0jpBOItQwdamG4BHEry16ZnA1QQ+ja91/lwU76BSyJeAB0TkTWAu8P34hhOeM8p5FFgNvEXgfZxQx0KIyIPAK8AJIlIrIp8DfgAsFJF3CVTb/CCeMQYNEOvPgULgGed9dndcgwwxQLyxea3EHjkZY4yJl7QeQRhjjBmYJQhjjDFhWYIwxhgTliUIY4wxYVmCMMYYE5YlCGMSgIi8LxlOvDXpxRKEMcaYsCxBGDMEIvJJEXnd2Tz1K6ffRauI/FhEVovI30Wk3HnsXBF5NaSnwBjn9ski8qyIrHOuOd55+oKQfhQPOLukjYkbSxDGREhEpgNXAGeq6lygB/gE4AZWq+pJwAvAfziX/A74V6enwFshtz8A3KWqcwicoVTv3D4PuJFAb5LjCOycNyZusuIdgDFJ5APAycBK58N9PoED53qBh53H/AF4TESKgRJVfcG5/X7gTyJSCExQ1aUAquoHcJ7vdVWtdb5fC9QAL8X8b2XMACxBGBM5Ae5X1aO6i4nIv/d73GDn1ww2bdQR8nUP9v40cWZTTMZE7u/AR0RkLPT1WPYSeB99xHnMx4GXVNUHHBSRs53brwZecPp31IrIpc5z5Drn+RuTcOwTijERUtV3ROTbwHIRyQC6gBsINBeaKSJvAD4C6xQQONL6bicBbAM+49x+NfArEfkv5zk+Oop/DWMiZqe5GjNCItKqqgXxjsOYaLMpJmOMMWHZCMIYY0xYNoIwxhgTliUIY4wxYVmCMMYYE5YlCGOMMWFZgjDGGBPW/wcEtPEI16biZAAAAABJRU5ErkJggg==",
      "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": 34,
   "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('../model/extended_weight.h5', overwrite=True)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4a61ec2db57b1fa9fd7ac0fdc692aba24704be6d2d3e06fb94182df6acf95472"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 ('capstone-design-two')",
   "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": "3.7.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}