YSA2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d967529",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:228: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler \n",
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9604fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataAudio.csv')\n",
    "\n",
    "#Gereksiz sütunların bırakılması\n",
    "data.head() \n",
    "\n",
    "#Etiketleri Kodlamak\n",
    "data = data.drop(['filename'],axis=1)\n",
    "genre_list = data.iloc[:, -1]\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "#Özellik sütunlarını ölçeklendirme\n",
    "y = encoder.fit_transform(genre_list)\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#Verileri eğitim ve Test setine bölme\n",
    "X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype = float))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "144114f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_dim = features, \n",
    "# units= yapay sinir hücresi, \n",
    "# init=yapay sinir hücresi içerisindeki ağırlıkları rastgele yerine belirli algoritmaya göre verir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb7865d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# 1. Katman - Input\n",
    "model.add(Dense(256, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "\n",
    "# 2. Katman \n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "# 3. Katman\n",
    "model.add(Dense(64, activation='sigmoid'))\n",
    "\n",
    "# 4. Katman - Output\n",
    "model.add(Dense(10, activation='sigmoid'))\n",
    "\n",
    "\n",
    "# sparse_categorical_crossentropy = Etiketler ve tahminler arasındaki çapraz entropi kaybını hesaplar.\n",
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d7988da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# epochs = ağırlıkları yeniden düzenleyerek çözüme en yakın değeri buluncaya kadar yapılacak deneme sayısı\n",
    "# batch_size = tek seferde alınacak veri sayısı\n",
    "# verbose = hatanın gösterilmemesi\n",
    "# validation_split = doğrulama için eğitim verilerinin bir kısmını otomatik olarak ayırmaya izin verir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cbd9f6f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.1213 - accuracy: 0.9698 - val_loss: 2.0092 - val_accuracy: 0.5969\n",
      "Epoch 2/200\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.1137 - accuracy: 0.9753 - val_loss: 2.0292 - val_accuracy: 0.5990\n",
      "Epoch 3/200\n",
      "120/120 [==============================] - 1s 5ms/step - loss: 0.1150 - accuracy: 0.9731 - val_loss: 2.0529 - val_accuracy: 0.6008\n",
      "Epoch 4/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1203 - accuracy: 0.9745 - val_loss: 2.0410 - val_accuracy: 0.6040\n",
      "Epoch 5/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1222 - accuracy: 0.9720 - val_loss: 2.0478 - val_accuracy: 0.6033\n",
      "Epoch 6/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1178 - accuracy: 0.9733 - val_loss: 2.0171 - val_accuracy: 0.6043\n",
      "Epoch 7/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1124 - accuracy: 0.9752 - val_loss: 2.0439 - val_accuracy: 0.6029\n",
      "Epoch 8/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1206 - accuracy: 0.9727 - val_loss: 2.0602 - val_accuracy: 0.6011\n",
      "Epoch 9/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1338 - accuracy: 0.9643 - val_loss: 2.0338 - val_accuracy: 0.5901\n",
      "Epoch 10/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1231 - accuracy: 0.9708 - val_loss: 2.0718 - val_accuracy: 0.6033\n",
      "Epoch 11/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1366 - accuracy: 0.9628 - val_loss: 2.0839 - val_accuracy: 0.6050\n",
      "Epoch 12/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1351 - accuracy: 0.9661 - val_loss: 2.0691 - val_accuracy: 0.5962\n",
      "Epoch 13/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1289 - accuracy: 0.9680 - val_loss: 2.0957 - val_accuracy: 0.5972\n",
      "Epoch 14/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1155 - accuracy: 0.9724 - val_loss: 2.0745 - val_accuracy: 0.6065\n",
      "Epoch 15/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1107 - accuracy: 0.9762 - val_loss: 2.0921 - val_accuracy: 0.6018\n",
      "Epoch 16/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1292 - accuracy: 0.9654 - val_loss: 2.0800 - val_accuracy: 0.6018\n",
      "Epoch 17/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1302 - accuracy: 0.9656 - val_loss: 2.0780 - val_accuracy: 0.6004\n",
      "Epoch 18/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1507 - accuracy: 0.9552 - val_loss: 2.1407 - val_accuracy: 0.5880\n",
      "Epoch 19/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1385 - accuracy: 0.9640 - val_loss: 2.0685 - val_accuracy: 0.6004\n",
      "Epoch 20/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1392 - accuracy: 0.9607 - val_loss: 2.1023 - val_accuracy: 0.6008\n",
      "Epoch 21/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1448 - accuracy: 0.9601 - val_loss: 2.0802 - val_accuracy: 0.6026\n",
      "Epoch 22/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1162 - accuracy: 0.9715 - val_loss: 2.0864 - val_accuracy: 0.6008\n",
      "Epoch 23/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1115 - accuracy: 0.9733 - val_loss: 2.1096 - val_accuracy: 0.5951\n",
      "Epoch 24/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1114 - accuracy: 0.9738 - val_loss: 2.0931 - val_accuracy: 0.6047\n",
      "Epoch 25/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1089 - accuracy: 0.9741 - val_loss: 2.1395 - val_accuracy: 0.5955\n",
      "Epoch 26/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1075 - accuracy: 0.9753 - val_loss: 2.1047 - val_accuracy: 0.5947\n",
      "Epoch 27/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1332 - accuracy: 0.9649 - val_loss: 2.1254 - val_accuracy: 0.5916\n",
      "Epoch 28/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1149 - accuracy: 0.9719 - val_loss: 2.1142 - val_accuracy: 0.6043\n",
      "Epoch 29/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1103 - accuracy: 0.9752 - val_loss: 2.1455 - val_accuracy: 0.5990\n",
      "Epoch 30/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1230 - accuracy: 0.9684 - val_loss: 2.1359 - val_accuracy: 0.5958\n",
      "Epoch 31/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1174 - accuracy: 0.9708 - val_loss: 2.1449 - val_accuracy: 0.6043\n",
      "Epoch 32/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1054 - accuracy: 0.9752 - val_loss: 2.1371 - val_accuracy: 0.6075\n",
      "Epoch 33/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1123 - accuracy: 0.9745 - val_loss: 2.1430 - val_accuracy: 0.6008\n",
      "Epoch 34/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1076 - accuracy: 0.9740 - val_loss: 2.1383 - val_accuracy: 0.5976\n",
      "Epoch 35/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1002 - accuracy: 0.9785 - val_loss: 2.1212 - val_accuracy: 0.5965\n",
      "Epoch 36/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1325 - accuracy: 0.9628 - val_loss: 2.1828 - val_accuracy: 0.5955\n",
      "Epoch 37/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1413 - accuracy: 0.9580 - val_loss: 2.1748 - val_accuracy: 0.5965\n",
      "Epoch 38/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1174 - accuracy: 0.9717 - val_loss: 2.1265 - val_accuracy: 0.5983\n",
      "Epoch 39/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1050 - accuracy: 0.9741 - val_loss: 2.1918 - val_accuracy: 0.5958\n",
      "Epoch 40/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1110 - accuracy: 0.9726 - val_loss: 2.1652 - val_accuracy: 0.5979\n",
      "Epoch 41/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0994 - accuracy: 0.9757 - val_loss: 2.1839 - val_accuracy: 0.5951\n",
      "Epoch 42/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1151 - accuracy: 0.9715 - val_loss: 2.1664 - val_accuracy: 0.5955\n",
      "Epoch 43/200\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.1325 - accuracy: 0.9647 - val_loss: 2.1814 - val_accuracy: 0.5916\n",
      "Epoch 44/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1037 - accuracy: 0.9736 - val_loss: 2.1700 - val_accuracy: 0.5916\n",
      "Epoch 45/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0901 - accuracy: 0.9820 - val_loss: 2.1782 - val_accuracy: 0.6018\n",
      "Epoch 46/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1002 - accuracy: 0.9764 - val_loss: 2.2546 - val_accuracy: 0.5891\n",
      "Epoch 47/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1169 - accuracy: 0.9691 - val_loss: 2.1601 - val_accuracy: 0.5994\n",
      "Epoch 48/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0977 - accuracy: 0.9778 - val_loss: 2.2305 - val_accuracy: 0.5901\n",
      "Epoch 49/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1115 - accuracy: 0.9736 - val_loss: 2.2484 - val_accuracy: 0.5873\n",
      "Epoch 50/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1019 - accuracy: 0.9771 - val_loss: 2.2299 - val_accuracy: 0.5997\n",
      "Epoch 51/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0931 - accuracy: 0.9802 - val_loss: 2.2105 - val_accuracy: 0.5912\n",
      "Epoch 52/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1097 - accuracy: 0.9724 - val_loss: 2.2175 - val_accuracy: 0.6022\n",
      "Epoch 53/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1216 - accuracy: 0.9677 - val_loss: 2.2481 - val_accuracy: 0.5930\n",
      "Epoch 54/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1016 - accuracy: 0.9755 - val_loss: 2.2197 - val_accuracy: 0.6043\n",
      "Epoch 55/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0927 - accuracy: 0.9792 - val_loss: 2.2374 - val_accuracy: 0.5965\n",
      "Epoch 56/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0858 - accuracy: 0.9809 - val_loss: 2.2273 - val_accuracy: 0.6029\n",
      "Epoch 57/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1032 - accuracy: 0.9745 - val_loss: 2.2375 - val_accuracy: 0.5983\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1058 - accuracy: 0.9726 - val_loss: 2.2627 - val_accuracy: 0.5937\n",
      "Epoch 59/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1093 - accuracy: 0.9719 - val_loss: 2.2232 - val_accuracy: 0.5951\n",
      "Epoch 60/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0872 - accuracy: 0.9820 - val_loss: 2.2335 - val_accuracy: 0.6026\n",
      "Epoch 61/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0845 - accuracy: 0.9827 - val_loss: 2.2375 - val_accuracy: 0.5933\n",
      "Epoch 62/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0927 - accuracy: 0.9792 - val_loss: 2.2625 - val_accuracy: 0.5951\n",
      "Epoch 63/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1566 - accuracy: 0.9488 - val_loss: 2.3572 - val_accuracy: 0.5923\n",
      "Epoch 64/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1810 - accuracy: 0.9432 - val_loss: 2.2506 - val_accuracy: 0.5997\n",
      "Epoch 65/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1152 - accuracy: 0.9694 - val_loss: 2.2687 - val_accuracy: 0.5990\n",
      "Epoch 66/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1042 - accuracy: 0.9750 - val_loss: 2.2538 - val_accuracy: 0.6004\n",
      "Epoch 67/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0997 - accuracy: 0.9760 - val_loss: 2.2907 - val_accuracy: 0.5976\n",
      "Epoch 68/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0899 - accuracy: 0.9794 - val_loss: 2.2327 - val_accuracy: 0.5990\n",
      "Epoch 69/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0892 - accuracy: 0.9790 - val_loss: 2.2314 - val_accuracy: 0.5947\n",
      "Epoch 70/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0801 - accuracy: 0.9827 - val_loss: 2.2712 - val_accuracy: 0.6001\n",
      "Epoch 71/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1026 - accuracy: 0.9719 - val_loss: 2.3396 - val_accuracy: 0.5951\n",
      "Epoch 72/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1166 - accuracy: 0.9659 - val_loss: 2.2878 - val_accuracy: 0.6026\n",
      "Epoch 73/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0879 - accuracy: 0.9799 - val_loss: 2.2964 - val_accuracy: 0.5976\n",
      "Epoch 74/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0805 - accuracy: 0.9823 - val_loss: 2.2696 - val_accuracy: 0.5937\n",
      "Epoch 75/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0817 - accuracy: 0.9818 - val_loss: 2.2994 - val_accuracy: 0.5983\n",
      "Epoch 76/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0831 - accuracy: 0.9818 - val_loss: 2.3414 - val_accuracy: 0.6001\n",
      "Epoch 77/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0815 - accuracy: 0.9827 - val_loss: 2.2996 - val_accuracy: 0.5955\n",
      "Epoch 78/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0756 - accuracy: 0.9850 - val_loss: 2.2983 - val_accuracy: 0.6022\n",
      "Epoch 79/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0856 - accuracy: 0.9801 - val_loss: 2.3529 - val_accuracy: 0.6029\n",
      "Epoch 80/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1113 - accuracy: 0.9710 - val_loss: 2.3475 - val_accuracy: 0.5919\n",
      "Epoch 81/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1766 - accuracy: 0.9406 - val_loss: 2.4343 - val_accuracy: 0.5905\n",
      "Epoch 82/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.2230 - accuracy: 0.9266 - val_loss: 2.2994 - val_accuracy: 0.5894\n",
      "Epoch 83/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1101 - accuracy: 0.9703 - val_loss: 2.2836 - val_accuracy: 0.5958\n",
      "Epoch 84/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0717 - accuracy: 0.9862 - val_loss: 2.2878 - val_accuracy: 0.5962\n",
      "Epoch 85/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0726 - accuracy: 0.9865 - val_loss: 2.2989 - val_accuracy: 0.6022\n",
      "Epoch 86/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0730 - accuracy: 0.9850 - val_loss: 2.2995 - val_accuracy: 0.6026\n",
      "Epoch 87/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0709 - accuracy: 0.9862 - val_loss: 2.2856 - val_accuracy: 0.5997\n",
      "Epoch 88/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0702 - accuracy: 0.9851 - val_loss: 2.2948 - val_accuracy: 0.5930\n",
      "Epoch 89/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0799 - accuracy: 0.9809 - val_loss: 2.3197 - val_accuracy: 0.5997\n",
      "Epoch 90/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0659 - accuracy: 0.9874 - val_loss: 2.3129 - val_accuracy: 0.6001\n",
      "Epoch 91/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0742 - accuracy: 0.9843 - val_loss: 2.3892 - val_accuracy: 0.6033\n",
      "Epoch 92/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0851 - accuracy: 0.9816 - val_loss: 2.3230 - val_accuracy: 0.6029\n",
      "Epoch 93/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0747 - accuracy: 0.9844 - val_loss: 2.3658 - val_accuracy: 0.5919\n",
      "Epoch 94/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1109 - accuracy: 0.9713 - val_loss: 2.3800 - val_accuracy: 0.5791\n",
      "Epoch 95/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1281 - accuracy: 0.9610 - val_loss: 2.3931 - val_accuracy: 0.5827\n",
      "Epoch 96/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1202 - accuracy: 0.9675 - val_loss: 2.3799 - val_accuracy: 0.5969\n",
      "Epoch 97/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1225 - accuracy: 0.9629 - val_loss: 2.3147 - val_accuracy: 0.5947\n",
      "Epoch 98/200\n",
      "120/120 [==============================] - 1s 4ms/step - loss: 0.1100 - accuracy: 0.9715 - val_loss: 2.3201 - val_accuracy: 0.6054\n",
      "Epoch 99/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0752 - accuracy: 0.9846 - val_loss: 2.3246 - val_accuracy: 0.6001\n",
      "Epoch 100/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0788 - accuracy: 0.9802 - val_loss: 2.3195 - val_accuracy: 0.5983\n",
      "Epoch 101/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0746 - accuracy: 0.9850 - val_loss: 2.3071 - val_accuracy: 0.6033\n",
      "Epoch 102/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0640 - accuracy: 0.9883 - val_loss: 2.3257 - val_accuracy: 0.5979\n",
      "Epoch 103/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0676 - accuracy: 0.9860 - val_loss: 2.3679 - val_accuracy: 0.6011\n",
      "Epoch 104/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0720 - accuracy: 0.9836 - val_loss: 2.3623 - val_accuracy: 0.5965\n",
      "Epoch 105/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0821 - accuracy: 0.9809 - val_loss: 2.3991 - val_accuracy: 0.5987\n",
      "Epoch 106/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9671 - val_loss: 2.3559 - val_accuracy: 0.5905\n",
      "Epoch 107/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1440 - accuracy: 0.9533 - val_loss: 2.4180 - val_accuracy: 0.5972\n",
      "Epoch 108/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0931 - accuracy: 0.9757 - val_loss: 2.3624 - val_accuracy: 0.6001\n",
      "Epoch 109/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0762 - accuracy: 0.9829 - val_loss: 2.3698 - val_accuracy: 0.5979\n",
      "Epoch 110/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0695 - accuracy: 0.9846 - val_loss: 2.3830 - val_accuracy: 0.5972\n",
      "Epoch 111/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0599 - accuracy: 0.9885 - val_loss: 2.3853 - val_accuracy: 0.6036\n",
      "Epoch 112/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0597 - accuracy: 0.9871 - val_loss: 2.3691 - val_accuracy: 0.5994\n",
      "Epoch 113/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0598 - accuracy: 0.9883 - val_loss: 2.3983 - val_accuracy: 0.5976\n",
      "Epoch 114/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0706 - accuracy: 0.9851 - val_loss: 2.3952 - val_accuracy: 0.5990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 115/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0889 - accuracy: 0.9787 - val_loss: 2.4357 - val_accuracy: 0.5951\n",
      "Epoch 116/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1736 - accuracy: 0.9451 - val_loss: 2.4766 - val_accuracy: 0.5898\n",
      "Epoch 117/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1929 - accuracy: 0.9339 - val_loss: 2.4276 - val_accuracy: 0.5869\n",
      "Epoch 118/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0925 - accuracy: 0.9755 - val_loss: 2.4036 - val_accuracy: 0.5979\n",
      "Epoch 119/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0652 - accuracy: 0.9864 - val_loss: 2.3885 - val_accuracy: 0.6011\n",
      "Epoch 120/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0659 - accuracy: 0.9851 - val_loss: 2.3775 - val_accuracy: 0.6001\n",
      "Epoch 121/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0648 - accuracy: 0.9848 - val_loss: 2.3877 - val_accuracy: 0.5987\n",
      "Epoch 122/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0585 - accuracy: 0.9892 - val_loss: 2.3948 - val_accuracy: 0.5965\n",
      "Epoch 123/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0557 - accuracy: 0.9895 - val_loss: 2.4047 - val_accuracy: 0.5983\n",
      "Epoch 124/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0532 - accuracy: 0.9892 - val_loss: 2.4083 - val_accuracy: 0.5983\n",
      "Epoch 125/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0700 - accuracy: 0.9827 - val_loss: 2.3953 - val_accuracy: 0.5955\n",
      "Epoch 126/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1148 - accuracy: 0.9659 - val_loss: 2.4309 - val_accuracy: 0.5923\n",
      "Epoch 127/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0780 - accuracy: 0.9820 - val_loss: 2.4195 - val_accuracy: 0.5965\n",
      "Epoch 128/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0999 - accuracy: 0.9713 - val_loss: 2.4395 - val_accuracy: 0.5962\n",
      "Epoch 129/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0875 - accuracy: 0.9774 - val_loss: 2.3953 - val_accuracy: 0.5990\n",
      "Epoch 130/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0721 - accuracy: 0.9829 - val_loss: 2.4122 - val_accuracy: 0.5933\n",
      "Epoch 131/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0585 - accuracy: 0.9890 - val_loss: 2.4392 - val_accuracy: 0.6004\n",
      "Epoch 132/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0552 - accuracy: 0.9893 - val_loss: 2.4652 - val_accuracy: 0.5983\n",
      "Epoch 133/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0642 - accuracy: 0.9846 - val_loss: 2.4785 - val_accuracy: 0.5958\n",
      "Epoch 134/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0674 - accuracy: 0.9843 - val_loss: 2.4358 - val_accuracy: 0.6054\n",
      "Epoch 135/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0773 - accuracy: 0.9797 - val_loss: 2.4373 - val_accuracy: 0.5976\n",
      "Epoch 136/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1133 - accuracy: 0.9663 - val_loss: 2.4907 - val_accuracy: 0.5912\n",
      "Epoch 137/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1600 - accuracy: 0.9434 - val_loss: 2.5609 - val_accuracy: 0.5891\n",
      "Epoch 138/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1380 - accuracy: 0.9566 - val_loss: 2.5092 - val_accuracy: 0.5958\n",
      "Epoch 139/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0858 - accuracy: 0.9778 - val_loss: 2.4294 - val_accuracy: 0.6036\n",
      "Epoch 140/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0757 - accuracy: 0.9829 - val_loss: 2.4678 - val_accuracy: 0.6036\n",
      "Epoch 141/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0608 - accuracy: 0.9860 - val_loss: 2.4283 - val_accuracy: 0.5962\n",
      "Epoch 142/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0555 - accuracy: 0.9890 - val_loss: 2.4282 - val_accuracy: 0.6018\n",
      "Epoch 143/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0523 - accuracy: 0.9902 - val_loss: 2.4402 - val_accuracy: 0.6029\n",
      "Epoch 144/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0517 - accuracy: 0.9890 - val_loss: 2.4615 - val_accuracy: 0.5997\n",
      "Epoch 145/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0519 - accuracy: 0.9906 - val_loss: 2.4601 - val_accuracy: 0.5972\n",
      "Epoch 146/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0555 - accuracy: 0.9865 - val_loss: 2.4645 - val_accuracy: 0.5962\n",
      "Epoch 147/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0636 - accuracy: 0.9855 - val_loss: 2.5005 - val_accuracy: 0.5976\n",
      "Epoch 148/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0603 - accuracy: 0.9862 - val_loss: 2.5034 - val_accuracy: 0.5944\n",
      "Epoch 149/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0842 - accuracy: 0.9781 - val_loss: 2.5924 - val_accuracy: 0.5969\n",
      "Epoch 150/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.2345 - accuracy: 0.9210 - val_loss: 2.6253 - val_accuracy: 0.5940\n",
      "Epoch 151/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1437 - accuracy: 0.9491 - val_loss: 2.5129 - val_accuracy: 0.5926\n",
      "Epoch 152/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0805 - accuracy: 0.9778 - val_loss: 2.5043 - val_accuracy: 0.5990\n",
      "Epoch 153/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0563 - accuracy: 0.9895 - val_loss: 2.4823 - val_accuracy: 0.5994\n",
      "Epoch 154/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0501 - accuracy: 0.9909 - val_loss: 2.4843 - val_accuracy: 0.6008\n",
      "Epoch 155/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0751 - accuracy: 0.9802 - val_loss: 2.4658 - val_accuracy: 0.5901\n",
      "Epoch 156/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0644 - accuracy: 0.9839 - val_loss: 2.4818 - val_accuracy: 0.5955\n",
      "Epoch 157/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0533 - accuracy: 0.9899 - val_loss: 2.4968 - val_accuracy: 0.5891\n",
      "Epoch 158/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0760 - accuracy: 0.9809 - val_loss: 2.5565 - val_accuracy: 0.5916\n",
      "Epoch 159/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0850 - accuracy: 0.9788 - val_loss: 2.5386 - val_accuracy: 0.5848\n",
      "Epoch 160/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0680 - accuracy: 0.9834 - val_loss: 2.5251 - val_accuracy: 0.5965\n",
      "Epoch 161/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0764 - accuracy: 0.9780 - val_loss: 2.5102 - val_accuracy: 0.5926\n",
      "Epoch 162/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0638 - accuracy: 0.9857 - val_loss: 2.5247 - val_accuracy: 0.5987\n",
      "Epoch 163/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0980 - accuracy: 0.9712 - val_loss: 2.5184 - val_accuracy: 0.5919\n",
      "Epoch 164/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0969 - accuracy: 0.9722 - val_loss: 2.5492 - val_accuracy: 0.5916\n",
      "Epoch 165/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0776 - accuracy: 0.9804 - val_loss: 2.5596 - val_accuracy: 0.5923\n",
      "Epoch 166/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0637 - accuracy: 0.9869 - val_loss: 2.5480 - val_accuracy: 0.5987\n",
      "Epoch 167/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0814 - accuracy: 0.9781 - val_loss: 2.5319 - val_accuracy: 0.5958\n",
      "Epoch 168/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0507 - accuracy: 0.9893 - val_loss: 2.5237 - val_accuracy: 0.6018\n",
      "Epoch 169/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0452 - accuracy: 0.9909 - val_loss: 2.5551 - val_accuracy: 0.5947\n",
      "Epoch 170/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0469 - accuracy: 0.9904 - val_loss: 2.5483 - val_accuracy: 0.5965\n",
      "Epoch 171/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0576 - accuracy: 0.9864 - val_loss: 2.5542 - val_accuracy: 0.5955\n",
      "Epoch 172/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0535 - accuracy: 0.9888 - val_loss: 2.5520 - val_accuracy: 0.5987\n",
      "Epoch 173/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0890 - accuracy: 0.9750 - val_loss: 2.6133 - val_accuracy: 0.5891\n",
      "Epoch 174/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1752 - accuracy: 0.9441 - val_loss: 2.5543 - val_accuracy: 0.5749\n",
      "Epoch 175/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.1681 - accuracy: 0.9423 - val_loss: 2.5203 - val_accuracy: 0.6004\n",
      "Epoch 176/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0734 - accuracy: 0.9834 - val_loss: 2.5339 - val_accuracy: 0.5955\n",
      "Epoch 177/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0577 - accuracy: 0.9872 - val_loss: 2.5114 - val_accuracy: 0.5937\n",
      "Epoch 178/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0528 - accuracy: 0.9893 - val_loss: 2.5258 - val_accuracy: 0.5951\n",
      "Epoch 179/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0466 - accuracy: 0.9906 - val_loss: 2.5172 - val_accuracy: 0.5983\n",
      "Epoch 180/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0489 - accuracy: 0.9902 - val_loss: 2.5211 - val_accuracy: 0.5944\n",
      "Epoch 181/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0450 - accuracy: 0.9902 - val_loss: 2.5420 - val_accuracy: 0.5987\n",
      "Epoch 182/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0466 - accuracy: 0.9900 - val_loss: 2.5634 - val_accuracy: 0.5873\n",
      "Epoch 183/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0667 - accuracy: 0.9834 - val_loss: 2.6372 - val_accuracy: 0.5813\n",
      "Epoch 184/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1140 - accuracy: 0.9643 - val_loss: 2.7034 - val_accuracy: 0.5855\n",
      "Epoch 185/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1316 - accuracy: 0.9570 - val_loss: 2.5682 - val_accuracy: 0.5859\n",
      "Epoch 186/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1038 - accuracy: 0.9696 - val_loss: 2.6148 - val_accuracy: 0.5905\n",
      "Epoch 187/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.1199 - accuracy: 0.9631 - val_loss: 2.5687 - val_accuracy: 0.5891\n",
      "Epoch 188/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0799 - accuracy: 0.9774 - val_loss: 2.5942 - val_accuracy: 0.5955\n",
      "Epoch 189/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0485 - accuracy: 0.9900 - val_loss: 2.5434 - val_accuracy: 0.5997\n",
      "Epoch 190/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0424 - accuracy: 0.9911 - val_loss: 2.5553 - val_accuracy: 0.5990\n",
      "Epoch 191/200\n",
      "120/120 [==============================] - 0s 3ms/step - loss: 0.0432 - accuracy: 0.9925 - val_loss: 2.5654 - val_accuracy: 0.6001\n",
      "Epoch 192/200\n",
      "120/120 [==============================] - 0s 4ms/step - loss: 0.0462 - accuracy: 0.9900 - val_loss: 2.5600 - val_accuracy: 0.5976\n",
      "Epoch 193/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0447 - accuracy: 0.9906 - val_loss: 2.5834 - val_accuracy: 0.6018\n",
      "Epoch 194/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0439 - accuracy: 0.9907 - val_loss: 2.5785 - val_accuracy: 0.5979\n",
      "Epoch 195/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0495 - accuracy: 0.9883 - val_loss: 2.6247 - val_accuracy: 0.5987\n",
      "Epoch 196/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0881 - accuracy: 0.9733 - val_loss: 2.8126 - val_accuracy: 0.5976\n",
      "Epoch 197/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1667 - accuracy: 0.9442 - val_loss: 2.7035 - val_accuracy: 0.5777\n",
      "Epoch 198/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1362 - accuracy: 0.9573 - val_loss: 2.6702 - val_accuracy: 0.5972\n",
      "Epoch 199/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.1070 - accuracy: 0.9678 - val_loss: 2.5940 - val_accuracy: 0.5972\n",
      "Epoch 200/200\n",
      "120/120 [==============================] - 0s 2ms/step - loss: 0.0638 - accuracy: 0.9839 - val_loss: 2.5740 - val_accuracy: 0.5944\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,y_train,validation_split=0.33,epochs=200,batch_size=48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cee1f638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "267/267 [==============================] - 1s 2ms/step - loss: 0.8820 - accuracy: 0.8597\n",
      "The test loss is:  0.8819581866264343\n",
      "The best accuracy is: % 85.96861362457275\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_train,y_train)\n",
    "print(\"The test loss is: \", test_loss)\n",
    "print(\"The best accuracy is: %\", test_acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0bdc05cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "# tüm verileri listeleme\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ff8d896",
   "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"
    }
   ],
   "source": [
    "# accuracy geçmişi\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Training accuracy', 'Validation accuracy'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3dc8c63",
   "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"
    }
   ],
   "source": [
    "# loss geçmişi\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['Training loss', 'Validation loss'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6624032f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34610842",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}