YSA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4f4da8e",
   "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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>chroma_frequency</th>\n",
       "      <th>sp_centroid</th>\n",
       "      <th>spectral_bandwidth</th>\n",
       "      <th>rolloff</th>\n",
       "      <th>zero_crossing_rate</th>\n",
       "      <th>mfcc</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boombap1.wav</td>\n",
       "      <td>0.453958</td>\n",
       "      <td>1958.596101</td>\n",
       "      <td>2242.096760</td>\n",
       "      <td>4128.825034</td>\n",
       "      <td>0.082547</td>\n",
       "      <td>7.353139</td>\n",
       "      <td>boombap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boombap10.wav</td>\n",
       "      <td>0.464496</td>\n",
       "      <td>2986.492353</td>\n",
       "      <td>2837.508148</td>\n",
       "      <td>6292.511950</td>\n",
       "      <td>0.147826</td>\n",
       "      <td>2.782494</td>\n",
       "      <td>boombap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boombap100.wav</td>\n",
       "      <td>0.459968</td>\n",
       "      <td>2377.117125</td>\n",
       "      <td>2708.232787</td>\n",
       "      <td>5411.317279</td>\n",
       "      <td>0.064741</td>\n",
       "      <td>8.356519</td>\n",
       "      <td>boombap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boombap1000.wav</td>\n",
       "      <td>0.493061</td>\n",
       "      <td>2218.901984</td>\n",
       "      <td>2303.409329</td>\n",
       "      <td>4625.305365</td>\n",
       "      <td>0.101993</td>\n",
       "      <td>4.666915</td>\n",
       "      <td>boombap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boombap1001.wav</td>\n",
       "      <td>0.502233</td>\n",
       "      <td>2215.360115</td>\n",
       "      <td>2261.072044</td>\n",
       "      <td>4563.039075</td>\n",
       "      <td>0.105489</td>\n",
       "      <td>4.909902</td>\n",
       "      <td>boombap</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12193</th>\n",
       "      <td>trap995.wav</td>\n",
       "      <td>0.479732</td>\n",
       "      <td>2341.972464</td>\n",
       "      <td>2381.461711</td>\n",
       "      <td>4880.220490</td>\n",
       "      <td>0.098807</td>\n",
       "      <td>6.350958</td>\n",
       "      <td>trap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12194</th>\n",
       "      <td>trap996.wav</td>\n",
       "      <td>0.586347</td>\n",
       "      <td>2309.798019</td>\n",
       "      <td>2479.139534</td>\n",
       "      <td>4966.919966</td>\n",
       "      <td>0.068026</td>\n",
       "      <td>6.352512</td>\n",
       "      <td>trap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12195</th>\n",
       "      <td>trap997.wav</td>\n",
       "      <td>0.572542</td>\n",
       "      <td>2055.582032</td>\n",
       "      <td>2287.343790</td>\n",
       "      <td>4201.607927</td>\n",
       "      <td>0.059735</td>\n",
       "      <td>6.019572</td>\n",
       "      <td>trap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12196</th>\n",
       "      <td>trap998.wav</td>\n",
       "      <td>0.531964</td>\n",
       "      <td>2348.962830</td>\n",
       "      <td>2425.149789</td>\n",
       "      <td>4921.086910</td>\n",
       "      <td>0.083488</td>\n",
       "      <td>6.259667</td>\n",
       "      <td>trap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12197</th>\n",
       "      <td>trap999.wav</td>\n",
       "      <td>0.485520</td>\n",
       "      <td>2315.664751</td>\n",
       "      <td>2374.392025</td>\n",
       "      <td>4841.720723</td>\n",
       "      <td>0.096273</td>\n",
       "      <td>6.413976</td>\n",
       "      <td>trap</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12198 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              filename  chroma_frequency  sp_centroid  spectral_bandwidth  \\\n",
       "0         boombap1.wav          0.453958  1958.596101         2242.096760   \n",
       "1        boombap10.wav          0.464496  2986.492353         2837.508148   \n",
       "2       boombap100.wav          0.459968  2377.117125         2708.232787   \n",
       "3      boombap1000.wav          0.493061  2218.901984         2303.409329   \n",
       "4      boombap1001.wav          0.502233  2215.360115         2261.072044   \n",
       "...                ...               ...          ...                 ...   \n",
       "12193      trap995.wav          0.479732  2341.972464         2381.461711   \n",
       "12194      trap996.wav          0.586347  2309.798019         2479.139534   \n",
       "12195      trap997.wav          0.572542  2055.582032         2287.343790   \n",
       "12196      trap998.wav          0.531964  2348.962830         2425.149789   \n",
       "12197      trap999.wav          0.485520  2315.664751         2374.392025   \n",
       "\n",
       "           rolloff  zero_crossing_rate      mfcc    label  \n",
       "0      4128.825034            0.082547  7.353139  boombap  \n",
       "1      6292.511950            0.147826  2.782494  boombap  \n",
       "2      5411.317279            0.064741  8.356519  boombap  \n",
       "3      4625.305365            0.101993  4.666915  boombap  \n",
       "4      4563.039075            0.105489  4.909902  boombap  \n",
       "...            ...                 ...       ...      ...  \n",
       "12193  4880.220490            0.098807  6.350958     trap  \n",
       "12194  4966.919966            0.068026  6.352512     trap  \n",
       "12195  4201.607927            0.059735  6.019572     trap  \n",
       "12196  4921.086910            0.083488  6.259667     trap  \n",
       "12197  4841.720723            0.096273  6.413976     trap  \n",
       "\n",
       "[12198 rows x 8 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "dataset = pd.read_csv(\"dataAudio.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ab295a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['boombap']\n",
      " ['boombap']\n",
      " ['boombap']\n",
      " ...\n",
      " ['trap']\n",
      " ['trap']\n",
      " ['trap']]\n",
      "       chroma_frequency  sp_centroid  spectral_bandwidth      rolloff  \\\n",
      "0              0.453958  1958.596101         2242.096760  4128.825034   \n",
      "1              0.464496  2986.492353         2837.508148  6292.511950   \n",
      "2              0.459968  2377.117125         2708.232787  5411.317279   \n",
      "3              0.493061  2218.901984         2303.409329  4625.305365   \n",
      "4              0.502233  2215.360115         2261.072044  4563.039075   \n",
      "...                 ...          ...                 ...          ...   \n",
      "12193          0.479732  2341.972464         2381.461711  4880.220490   \n",
      "12194          0.586347  2309.798019         2479.139534  4966.919966   \n",
      "12195          0.572542  2055.582032         2287.343790  4201.607927   \n",
      "12196          0.531964  2348.962830         2425.149789  4921.086910   \n",
      "12197          0.485520  2315.664751         2374.392025  4841.720723   \n",
      "\n",
      "       zero_crossing_rate      mfcc  \n",
      "0                0.082547  7.353139  \n",
      "1                0.147826  2.782494  \n",
      "2                0.064741  8.356519  \n",
      "3                0.101993  4.666915  \n",
      "4                0.105489  4.909902  \n",
      "...                   ...       ...  \n",
      "12193            0.098807  6.350958  \n",
      "12194            0.068026  6.352512  \n",
      "12195            0.059735  6.019572  \n",
      "12196            0.083488  6.259667  \n",
      "12197            0.096273  6.413976  \n",
      "\n",
      "[12198 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "label = dataset.iloc[:,-1:].values\n",
    "print(label)\n",
    "\n",
    "features = dataset.iloc[:,1:-1]\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81e3cef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       chroma_frequency  sp_centroid  spectral_bandwidth      rolloff  \\\n",
      "5711           0.352739   763.870051         1265.752541  1319.608687   \n",
      "5385           0.345159   717.773610         1073.960838  1287.292216   \n",
      "992            0.395720  1817.129413         1773.583505  3757.193947   \n",
      "154            0.453002  2095.687487         2177.960235  4502.522774   \n",
      "9161           0.445160  2119.744922         2057.178898  4193.441310   \n",
      "...                 ...          ...                 ...          ...   \n",
      "9340           0.424434  3598.660268         3281.748994  7800.219500   \n",
      "11082          0.490640  3115.073891         2793.919759  6460.710933   \n",
      "1124           0.432184  1528.944162         2100.804865  3307.713332   \n",
      "11208          0.418247  2152.265189         2345.530765  4582.088959   \n",
      "5034           0.336113   593.199003          967.074691  1025.393800   \n",
      "\n",
      "       zero_crossing_rate       mfcc  \n",
      "5711             0.031685  -0.762679  \n",
      "5385             0.036801  -0.366283  \n",
      "992              0.085050   2.334519  \n",
      "154              0.094346   0.950030  \n",
      "9161             0.109538   0.890566  \n",
      "...                   ...        ...  \n",
      "9340             0.134942   4.698438  \n",
      "11082            0.116028  10.046340  \n",
      "1124             0.044924   8.444165  \n",
      "11208            0.080502   6.514058  \n",
      "5034             0.028933   1.174055  \n",
      "\n",
      "[3660 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(features, label, test_size = 0.3, random_state =0)\n",
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f93622a6",
   "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": 5,
   "id": "c152fdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# 1. Katman - Input\n",
    "model.add(Dense(20,input_dim=6,kernel_initializer='uniform',activation='relu')) \n",
    "\n",
    "# 2. Katman \n",
    "model.add(Dense(12,kernel_initializer='uniform',activation='relu'))\n",
    "\n",
    "# 3. Katman\n",
    "model.add(Dense(8,kernel_initializer='uniform',activation='sigmoid'))\n",
    "\n",
    "# 4. Katman - Output\n",
    "model.add(Dense(1,kernel_initializer='uniform',activation='sigmoid')) \n",
    "\n",
    "# binary_crossentropy = Gerçek etiketler ve tahmin edilen etiketler arasındaki çapraz entropi kaybını hesaplar.\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15314c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Cast string to float is not supported' hatası\n",
    "# https://www.youtube.com/watch?v=k_VAKyzggJI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf243dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cengiz\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "lb = LabelEncoder()\n",
    "y = lb.fit_transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8ebf16a",
   "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": 9,
   "id": "0532296c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "58/58 - 1s - loss: 0.2390 - accuracy: 0.1904 - val_loss: -4.1340e-02 - val_accuracy: 0.1980 - 571ms/epoch - 10ms/step\n",
      "Epoch 2/200\n",
      "58/58 - 0s - loss: -2.9237e-01 - accuracy: 0.1900 - val_loss: -5.0401e-01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 3/200\n",
      "58/58 - 0s - loss: -7.4123e-01 - accuracy: 0.1900 - val_loss: -9.2406e-01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 4/200\n",
      "58/58 - 0s - loss: -1.1551e+00 - accuracy: 0.1900 - val_loss: -1.3135e+00 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 5/200\n",
      "58/58 - 0s - loss: -1.5420e+00 - accuracy: 0.1900 - val_loss: -1.6795e+00 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 6/200\n",
      "58/58 - 0s - loss: -1.9100e+00 - accuracy: 0.1900 - val_loss: -2.0281e+00 - val_accuracy: 0.1980 - 92ms/epoch - 2ms/step\n",
      "Epoch 7/200\n",
      "58/58 - 0s - loss: -2.2633e+00 - accuracy: 0.1900 - val_loss: -2.3667e+00 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 8/200\n",
      "58/58 - 0s - loss: -2.6054e+00 - accuracy: 0.1900 - val_loss: -2.6960e+00 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 9/200\n",
      "58/58 - 0s - loss: -2.9424e+00 - accuracy: 0.1900 - val_loss: -3.0202e+00 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 10/200\n",
      "58/58 - 0s - loss: -3.2740e+00 - accuracy: 0.1900 - val_loss: -3.3403e+00 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 11/200\n",
      "58/58 - 0s - loss: -3.6018e+00 - accuracy: 0.1900 - val_loss: -3.6593e+00 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 12/200\n",
      "58/58 - 0s - loss: -3.9293e+00 - accuracy: 0.1900 - val_loss: -3.9765e+00 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 13/200\n",
      "58/58 - 0s - loss: -4.2559e+00 - accuracy: 0.1900 - val_loss: -4.2929e+00 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 14/200\n",
      "58/58 - 0s - loss: -4.5827e+00 - accuracy: 0.1900 - val_loss: -4.6115e+00 - val_accuracy: 0.1980 - 89ms/epoch - 2ms/step\n",
      "Epoch 15/200\n",
      "58/58 - 0s - loss: -4.9106e+00 - accuracy: 0.1900 - val_loss: -4.9306e+00 - val_accuracy: 0.1980 - 90ms/epoch - 2ms/step\n",
      "Epoch 16/200\n",
      "58/58 - 0s - loss: -5.2378e+00 - accuracy: 0.1900 - val_loss: -5.2483e+00 - val_accuracy: 0.1980 - 90ms/epoch - 2ms/step\n",
      "Epoch 17/200\n",
      "58/58 - 0s - loss: -5.5667e+00 - accuracy: 0.1900 - val_loss: -5.5675e+00 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 18/200\n",
      "58/58 - 0s - loss: -5.8965e+00 - accuracy: 0.1900 - val_loss: -5.8890e+00 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 19/200\n",
      "58/58 - 0s - loss: -6.2284e+00 - accuracy: 0.1900 - val_loss: -6.2090e+00 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 20/200\n",
      "58/58 - 0s - loss: -6.5597e+00 - accuracy: 0.1900 - val_loss: -6.5329e+00 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 21/200\n",
      "58/58 - 0s - loss: -6.8947e+00 - accuracy: 0.1900 - val_loss: -6.8600e+00 - val_accuracy: 0.1980 - 100ms/epoch - 2ms/step\n",
      "Epoch 22/200\n",
      "58/58 - 0s - loss: -7.2300e+00 - accuracy: 0.1900 - val_loss: -7.1855e+00 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 23/200\n",
      "58/58 - 0s - loss: -7.5647e+00 - accuracy: 0.1900 - val_loss: -7.5115e+00 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 24/200\n",
      "58/58 - 0s - loss: -7.9035e+00 - accuracy: 0.1900 - val_loss: -7.8390e+00 - val_accuracy: 0.1980 - 77ms/epoch - 1ms/step\n",
      "Epoch 25/200\n",
      "58/58 - 0s - loss: -8.2415e+00 - accuracy: 0.1900 - val_loss: -8.1719e+00 - val_accuracy: 0.1980 - 78ms/epoch - 1ms/step\n",
      "Epoch 26/200\n",
      "58/58 - 0s - loss: -8.5805e+00 - accuracy: 0.1900 - val_loss: -8.4961e+00 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 27/200\n",
      "58/58 - 0s - loss: -8.9184e+00 - accuracy: 0.1900 - val_loss: -8.8273e+00 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 28/200\n",
      "58/58 - 0s - loss: -9.2584e+00 - accuracy: 0.1900 - val_loss: -9.1573e+00 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 29/200\n",
      "58/58 - 0s - loss: -9.6012e+00 - accuracy: 0.1900 - val_loss: -9.4903e+00 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 30/200\n",
      "58/58 - 0s - loss: -9.9430e+00 - accuracy: 0.1900 - val_loss: -9.8234e+00 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 31/200\n",
      "58/58 - 0s - loss: -1.0284e+01 - accuracy: 0.1900 - val_loss: -1.0155e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 32/200\n",
      "58/58 - 0s - loss: -1.0628e+01 - accuracy: 0.1900 - val_loss: -1.0488e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 33/200\n",
      "58/58 - 0s - loss: -1.0971e+01 - accuracy: 0.1900 - val_loss: -1.0823e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 34/200\n",
      "58/58 - 0s - loss: -1.1318e+01 - accuracy: 0.1900 - val_loss: -1.1158e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 35/200\n",
      "58/58 - 0s - loss: -1.1663e+01 - accuracy: 0.1900 - val_loss: -1.1492e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 36/200\n",
      "58/58 - 0s - loss: -1.2008e+01 - accuracy: 0.1900 - val_loss: -1.1831e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 37/200\n",
      "58/58 - 0s - loss: -1.2354e+01 - accuracy: 0.1900 - val_loss: -1.2166e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 38/200\n",
      "58/58 - 0s - loss: -1.2702e+01 - accuracy: 0.1900 - val_loss: -1.2501e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 39/200\n",
      "58/58 - 0s - loss: -1.3049e+01 - accuracy: 0.1900 - val_loss: -1.2837e+01 - val_accuracy: 0.1980 - 92ms/epoch - 2ms/step\n",
      "Epoch 40/200\n",
      "58/58 - 0s - loss: -1.3397e+01 - accuracy: 0.1900 - val_loss: -1.3179e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 41/200\n",
      "58/58 - 0s - loss: -1.3745e+01 - accuracy: 0.1900 - val_loss: -1.3518e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 42/200\n",
      "58/58 - 0s - loss: -1.4094e+01 - accuracy: 0.1900 - val_loss: -1.3857e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 43/200\n",
      "58/58 - 0s - loss: -1.4443e+01 - accuracy: 0.1900 - val_loss: -1.4193e+01 - val_accuracy: 0.1980 - 93ms/epoch - 2ms/step\n",
      "Epoch 44/200\n",
      "58/58 - 0s - loss: -1.4791e+01 - accuracy: 0.1900 - val_loss: -1.4533e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 45/200\n",
      "58/58 - 0s - loss: -1.5140e+01 - accuracy: 0.1900 - val_loss: -1.4868e+01 - val_accuracy: 0.1980 - 89ms/epoch - 2ms/step\n",
      "Epoch 46/200\n",
      "58/58 - 0s - loss: -1.5490e+01 - accuracy: 0.1900 - val_loss: -1.5211e+01 - val_accuracy: 0.1980 - 93ms/epoch - 2ms/step\n",
      "Epoch 47/200\n",
      "58/58 - 0s - loss: -1.5840e+01 - accuracy: 0.1900 - val_loss: -1.5550e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 48/200\n",
      "58/58 - 0s - loss: -1.6190e+01 - accuracy: 0.1900 - val_loss: -1.5891e+01 - val_accuracy: 0.1980 - 78ms/epoch - 1ms/step\n",
      "Epoch 49/200\n",
      "58/58 - 0s - loss: -1.6541e+01 - accuracy: 0.1900 - val_loss: -1.6231e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 50/200\n",
      "58/58 - 0s - loss: -1.6891e+01 - accuracy: 0.1900 - val_loss: -1.6571e+01 - val_accuracy: 0.1980 - 78ms/epoch - 1ms/step\n",
      "Epoch 51/200\n",
      "58/58 - 0s - loss: -1.7243e+01 - accuracy: 0.1900 - val_loss: -1.6910e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 52/200\n",
      "58/58 - 0s - loss: -1.7592e+01 - accuracy: 0.1900 - val_loss: -1.7249e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 53/200\n",
      "58/58 - 0s - loss: -1.7946e+01 - accuracy: 0.1900 - val_loss: -1.7592e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 54/200\n",
      "58/58 - 0s - loss: -1.8298e+01 - accuracy: 0.1900 - val_loss: -1.7936e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 55/200\n",
      "58/58 - 0s - loss: -1.8648e+01 - accuracy: 0.1900 - val_loss: -1.8276e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 56/200\n",
      "58/58 - 0s - loss: -1.8999e+01 - accuracy: 0.1900 - val_loss: -1.8619e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 57/200\n",
      "58/58 - 0s - loss: -1.9352e+01 - accuracy: 0.1900 - val_loss: -1.8960e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 58/200\n",
      "58/58 - 0s - loss: -1.9702e+01 - accuracy: 0.1900 - val_loss: -1.9298e+01 - val_accuracy: 0.1980 - 78ms/epoch - 1ms/step\n",
      "Epoch 59/200\n",
      "58/58 - 0s - loss: -2.0054e+01 - accuracy: 0.1900 - val_loss: -1.9639e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 60/200\n",
      "58/58 - 0s - loss: -2.0404e+01 - accuracy: 0.1900 - val_loss: -1.9979e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 61/200\n",
      "58/58 - 0s - loss: -2.0758e+01 - accuracy: 0.1900 - val_loss: -2.0323e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 62/200\n",
      "58/58 - 0s - loss: -2.1109e+01 - accuracy: 0.1900 - val_loss: -2.0662e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 63/200\n",
      "58/58 - 0s - loss: -2.1462e+01 - accuracy: 0.1900 - val_loss: -2.1009e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 64/200\n",
      "58/58 - 0s - loss: -2.1815e+01 - accuracy: 0.1900 - val_loss: -2.1350e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 65/200\n",
      "58/58 - 0s - loss: -2.2168e+01 - accuracy: 0.1900 - val_loss: -2.1689e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 66/200\n",
      "58/58 - 0s - loss: -2.2520e+01 - accuracy: 0.1900 - val_loss: -2.2036e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 67/200\n",
      "58/58 - 0s - loss: -2.2874e+01 - accuracy: 0.1900 - val_loss: -2.2379e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 68/200\n",
      "58/58 - 0s - loss: -2.3228e+01 - accuracy: 0.1900 - val_loss: -2.2722e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 69/200\n",
      "58/58 - 0s - loss: -2.3582e+01 - accuracy: 0.1900 - val_loss: -2.3066e+01 - val_accuracy: 0.1980 - 92ms/epoch - 2ms/step\n",
      "Epoch 70/200\n",
      "58/58 - 0s - loss: -2.3934e+01 - accuracy: 0.1900 - val_loss: -2.3405e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 71/200\n",
      "58/58 - 0s - loss: -2.4286e+01 - accuracy: 0.1900 - val_loss: -2.3745e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 72/200\n",
      "58/58 - 0s - loss: -2.4637e+01 - accuracy: 0.1900 - val_loss: -2.4089e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 73/200\n",
      "58/58 - 0s - loss: -2.4989e+01 - accuracy: 0.1900 - val_loss: -2.4432e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 74/200\n",
      "58/58 - 0s - loss: -2.5342e+01 - accuracy: 0.1900 - val_loss: -2.4773e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 75/200\n",
      "58/58 - 0s - loss: -2.5695e+01 - accuracy: 0.1900 - val_loss: -2.5115e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 76/200\n",
      "58/58 - 0s - loss: -2.6047e+01 - accuracy: 0.1900 - val_loss: -2.5456e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 77/200\n",
      "58/58 - 0s - loss: -2.6399e+01 - accuracy: 0.1900 - val_loss: -2.5800e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 78/200\n",
      "58/58 - 0s - loss: -2.6752e+01 - accuracy: 0.1900 - val_loss: -2.6140e+01 - val_accuracy: 0.1980 - 92ms/epoch - 2ms/step\n",
      "Epoch 79/200\n",
      "58/58 - 0s - loss: -2.7105e+01 - accuracy: 0.1900 - val_loss: -2.6485e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 80/200\n",
      "58/58 - 0s - loss: -2.7459e+01 - accuracy: 0.1900 - val_loss: -2.6828e+01 - val_accuracy: 0.1980 - 78ms/epoch - 1ms/step\n",
      "Epoch 81/200\n",
      "58/58 - 0s - loss: -2.7812e+01 - accuracy: 0.1900 - val_loss: -2.7170e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 82/200\n",
      "58/58 - 0s - loss: -2.8164e+01 - accuracy: 0.1900 - val_loss: -2.7514e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 83/200\n",
      "58/58 - 0s - loss: -2.8516e+01 - accuracy: 0.1900 - val_loss: -2.7856e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 84/200\n",
      "58/58 - 0s - loss: -2.8869e+01 - accuracy: 0.1900 - val_loss: -2.8196e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 85/200\n",
      "58/58 - 0s - loss: -2.9221e+01 - accuracy: 0.1900 - val_loss: -2.8539e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 86/200\n",
      "58/58 - 0s - loss: -2.9575e+01 - accuracy: 0.1900 - val_loss: -2.8881e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 87/200\n",
      "58/58 - 0s - loss: -2.9927e+01 - accuracy: 0.1900 - val_loss: -2.9222e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 88/200\n",
      "58/58 - 0s - loss: -3.0278e+01 - accuracy: 0.1900 - val_loss: -2.9564e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 89/200\n",
      "58/58 - 0s - loss: -3.0632e+01 - accuracy: 0.1900 - val_loss: -2.9908e+01 - val_accuracy: 0.1980 - 87ms/epoch - 1ms/step\n",
      "Epoch 90/200\n",
      "58/58 - 0s - loss: -3.0984e+01 - accuracy: 0.1900 - val_loss: -3.0250e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 91/200\n",
      "58/58 - 0s - loss: -3.1337e+01 - accuracy: 0.1900 - val_loss: -3.0589e+01 - val_accuracy: 0.1980 - 79ms/epoch - 1ms/step\n",
      "Epoch 92/200\n",
      "58/58 - 0s - loss: -3.1690e+01 - accuracy: 0.1900 - val_loss: -3.0934e+01 - val_accuracy: 0.1980 - 99ms/epoch - 2ms/step\n",
      "Epoch 93/200\n",
      "58/58 - 0s - loss: -3.2042e+01 - accuracy: 0.1900 - val_loss: -3.1276e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 94/200\n",
      "58/58 - 0s - loss: -3.2397e+01 - accuracy: 0.1900 - val_loss: -3.1622e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 95/200\n",
      "58/58 - 0s - loss: -3.2752e+01 - accuracy: 0.1900 - val_loss: -3.1968e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 96/200\n",
      "58/58 - 0s - loss: -3.3107e+01 - accuracy: 0.1900 - val_loss: -3.2313e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 97/200\n",
      "58/58 - 0s - loss: -3.3459e+01 - accuracy: 0.1900 - val_loss: -3.2650e+01 - val_accuracy: 0.1980 - 90ms/epoch - 2ms/step\n",
      "Epoch 98/200\n",
      "58/58 - 0s - loss: -3.3812e+01 - accuracy: 0.1900 - val_loss: -3.2995e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 99/200\n",
      "58/58 - 0s - loss: -3.4163e+01 - accuracy: 0.1900 - val_loss: -3.3333e+01 - val_accuracy: 0.1980 - 94ms/epoch - 2ms/step\n",
      "Epoch 100/200\n",
      "58/58 - 0s - loss: -3.4516e+01 - accuracy: 0.1900 - val_loss: -3.3678e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 101/200\n",
      "58/58 - 0s - loss: -3.4869e+01 - accuracy: 0.1900 - val_loss: -3.4022e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 102/200\n",
      "58/58 - 0s - loss: -3.5225e+01 - accuracy: 0.1900 - val_loss: -3.4364e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 103/200\n",
      "58/58 - 0s - loss: -3.5578e+01 - accuracy: 0.1900 - val_loss: -3.4712e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 104/200\n",
      "58/58 - 0s - loss: -3.5931e+01 - accuracy: 0.1900 - val_loss: -3.5051e+01 - val_accuracy: 0.1980 - 94ms/epoch - 2ms/step\n",
      "Epoch 105/200\n",
      "58/58 - 0s - loss: -3.6282e+01 - accuracy: 0.1900 - val_loss: -3.5390e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 106/200\n",
      "58/58 - 0s - loss: -3.6633e+01 - accuracy: 0.1900 - val_loss: -3.5733e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 107/200\n",
      "58/58 - 0s - loss: -3.6985e+01 - accuracy: 0.1900 - val_loss: -3.6076e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 108/200\n",
      "58/58 - 0s - loss: -3.7339e+01 - accuracy: 0.1900 - val_loss: -3.6418e+01 - val_accuracy: 0.1980 - 89ms/epoch - 2ms/step\n",
      "Epoch 109/200\n",
      "58/58 - 0s - loss: -3.7691e+01 - accuracy: 0.1900 - val_loss: -3.6762e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 110/200\n",
      "58/58 - 0s - loss: -3.8043e+01 - accuracy: 0.1900 - val_loss: -3.7101e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 111/200\n",
      "58/58 - 0s - loss: -3.8395e+01 - accuracy: 0.1900 - val_loss: -3.7444e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 112/200\n",
      "58/58 - 0s - loss: -3.8748e+01 - accuracy: 0.1900 - val_loss: -3.7787e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 113/200\n",
      "58/58 - 0s - loss: -3.9103e+01 - accuracy: 0.1900 - val_loss: -3.8131e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 114/200\n",
      "58/58 - 0s - loss: -3.9458e+01 - accuracy: 0.1900 - val_loss: -3.8476e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 115/200\n",
      "58/58 - 0s - loss: -3.9811e+01 - accuracy: 0.1900 - val_loss: -3.8817e+01 - val_accuracy: 0.1980 - 90ms/epoch - 2ms/step\n",
      "Epoch 116/200\n",
      "58/58 - 0s - loss: -4.0163e+01 - accuracy: 0.1900 - val_loss: -3.9158e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 117/200\n",
      "58/58 - 0s - loss: -4.0514e+01 - accuracy: 0.1900 - val_loss: -3.9500e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 118/200\n",
      "58/58 - 0s - loss: -4.0868e+01 - accuracy: 0.1900 - val_loss: -3.9842e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 119/200\n",
      "58/58 - 0s - loss: -4.1221e+01 - accuracy: 0.1900 - val_loss: -4.0187e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 120/200\n",
      "58/58 - 0s - loss: -4.1576e+01 - accuracy: 0.1900 - val_loss: -4.0534e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 121/200\n",
      "58/58 - 0s - loss: -4.1931e+01 - accuracy: 0.1900 - val_loss: -4.0876e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 122/200\n",
      "58/58 - 0s - loss: -4.2283e+01 - accuracy: 0.1900 - val_loss: -4.1216e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 123/200\n",
      "58/58 - 0s - loss: -4.2637e+01 - accuracy: 0.1900 - val_loss: -4.1561e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 124/200\n",
      "58/58 - 0s - loss: -4.2991e+01 - accuracy: 0.1900 - val_loss: -4.1901e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 125/200\n",
      "58/58 - 0s - loss: -4.3345e+01 - accuracy: 0.1900 - val_loss: -4.2247e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 126/200\n",
      "58/58 - 0s - loss: -4.3699e+01 - accuracy: 0.1900 - val_loss: -4.2591e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 127/200\n",
      "58/58 - 0s - loss: -4.4052e+01 - accuracy: 0.1900 - val_loss: -4.2935e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 128/200\n",
      "58/58 - 0s - loss: -4.4405e+01 - accuracy: 0.1900 - val_loss: -4.3276e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 129/200\n",
      "58/58 - 0s - loss: -4.4756e+01 - accuracy: 0.1900 - val_loss: -4.3618e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 130/200\n",
      "58/58 - 0s - loss: -4.5109e+01 - accuracy: 0.1900 - val_loss: -4.3959e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 131/200\n",
      "58/58 - 0s - loss: -4.5464e+01 - accuracy: 0.1900 - val_loss: -4.4305e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 132/200\n",
      "58/58 - 0s - loss: -4.5817e+01 - accuracy: 0.1900 - val_loss: -4.4649e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 133/200\n",
      "58/58 - 0s - loss: -4.6171e+01 - accuracy: 0.1900 - val_loss: -4.4990e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 134/200\n",
      "58/58 - 0s - loss: -4.6523e+01 - accuracy: 0.1900 - val_loss: -4.5335e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 135/200\n",
      "58/58 - 0s - loss: -4.6878e+01 - accuracy: 0.1900 - val_loss: -4.5676e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 136/200\n",
      "58/58 - 0s - loss: -4.7231e+01 - accuracy: 0.1900 - val_loss: -4.6018e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 137/200\n",
      "58/58 - 0s - loss: -4.7585e+01 - accuracy: 0.1900 - val_loss: -4.6362e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 138/200\n",
      "58/58 - 0s - loss: -4.7937e+01 - accuracy: 0.1900 - val_loss: -4.6704e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 139/200\n",
      "58/58 - 0s - loss: -4.8288e+01 - accuracy: 0.1900 - val_loss: -4.7047e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 140/200\n",
      "58/58 - 0s - loss: -4.8642e+01 - accuracy: 0.1900 - val_loss: -4.7389e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 141/200\n",
      "58/58 - 0s - loss: -4.8995e+01 - accuracy: 0.1900 - val_loss: -4.7733e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 142/200\n",
      "58/58 - 0s - loss: -4.9348e+01 - accuracy: 0.1900 - val_loss: -4.8072e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 143/200\n",
      "58/58 - 0s - loss: -4.9700e+01 - accuracy: 0.1900 - val_loss: -4.8416e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 144/200\n",
      "58/58 - 0s - loss: -5.0053e+01 - accuracy: 0.1900 - val_loss: -4.8757e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 145/200\n",
      "58/58 - 0s - loss: -5.0407e+01 - accuracy: 0.1900 - val_loss: -4.9104e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 146/200\n",
      "58/58 - 0s - loss: -5.0762e+01 - accuracy: 0.1900 - val_loss: -4.9444e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 147/200\n",
      "58/58 - 0s - loss: -5.1113e+01 - accuracy: 0.1900 - val_loss: -4.9789e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 148/200\n",
      "58/58 - 0s - loss: -5.1468e+01 - accuracy: 0.1900 - val_loss: -5.0129e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 149/200\n",
      "58/58 - 0s - loss: -5.1820e+01 - accuracy: 0.1900 - val_loss: -5.0473e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 150/200\n",
      "58/58 - 0s - loss: -5.2171e+01 - accuracy: 0.1900 - val_loss: -5.0816e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 151/200\n",
      "58/58 - 0s - loss: -5.2525e+01 - accuracy: 0.1900 - val_loss: -5.1157e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 152/200\n",
      "58/58 - 0s - loss: -5.2878e+01 - accuracy: 0.1900 - val_loss: -5.1502e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 153/200\n",
      "58/58 - 0s - loss: -5.3232e+01 - accuracy: 0.1900 - val_loss: -5.1844e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 154/200\n",
      "58/58 - 0s - loss: -5.3584e+01 - accuracy: 0.1900 - val_loss: -5.2185e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 155/200\n",
      "58/58 - 0s - loss: -5.3937e+01 - accuracy: 0.1900 - val_loss: -5.2527e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 156/200\n",
      "58/58 - 0s - loss: -5.4289e+01 - accuracy: 0.1900 - val_loss: -5.2874e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 157/200\n",
      "58/58 - 0s - loss: -5.4642e+01 - accuracy: 0.1900 - val_loss: -5.3213e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 158/200\n",
      "58/58 - 0s - loss: -5.4993e+01 - accuracy: 0.1900 - val_loss: -5.3556e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 159/200\n",
      "58/58 - 0s - loss: -5.5347e+01 - accuracy: 0.1900 - val_loss: -5.3897e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 160/200\n",
      "58/58 - 0s - loss: -5.5703e+01 - accuracy: 0.1900 - val_loss: -5.4243e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 161/200\n",
      "58/58 - 0s - loss: -5.6057e+01 - accuracy: 0.1900 - val_loss: -5.4588e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 162/200\n",
      "58/58 - 0s - loss: -5.6409e+01 - accuracy: 0.1900 - val_loss: -5.4929e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 163/200\n",
      "58/58 - 0s - loss: -5.6762e+01 - accuracy: 0.1900 - val_loss: -5.5267e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 164/200\n",
      "58/58 - 0s - loss: -5.7115e+01 - accuracy: 0.1900 - val_loss: -5.5615e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 165/200\n",
      "58/58 - 0s - loss: -5.7469e+01 - accuracy: 0.1900 - val_loss: -5.5955e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 166/200\n",
      "58/58 - 0s - loss: -5.7821e+01 - accuracy: 0.1900 - val_loss: -5.6299e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 167/200\n",
      "58/58 - 0s - loss: -5.8174e+01 - accuracy: 0.1900 - val_loss: -5.6641e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 168/200\n",
      "58/58 - 0s - loss: -5.8524e+01 - accuracy: 0.1900 - val_loss: -5.6981e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 169/200\n",
      "58/58 - 0s - loss: -5.8873e+01 - accuracy: 0.1900 - val_loss: -5.7316e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 170/200\n",
      "58/58 - 0s - loss: -5.9223e+01 - accuracy: 0.1900 - val_loss: -5.7659e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 171/200\n",
      "58/58 - 0s - loss: -5.9575e+01 - accuracy: 0.1900 - val_loss: -5.8001e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 172/200\n",
      "58/58 - 0s - loss: -5.9930e+01 - accuracy: 0.1900 - val_loss: -5.8347e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 173/200\n",
      "58/58 - 0s - loss: -6.0282e+01 - accuracy: 0.1900 - val_loss: -5.8687e+01 - val_accuracy: 0.1980 - 80ms/epoch - 1ms/step\n",
      "Epoch 174/200\n",
      "58/58 - 0s - loss: -6.0635e+01 - accuracy: 0.1900 - val_loss: -5.9028e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 175/200\n",
      "58/58 - 0s - loss: -6.0988e+01 - accuracy: 0.1900 - val_loss: -5.9375e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 176/200\n",
      "58/58 - 0s - loss: -6.1342e+01 - accuracy: 0.1900 - val_loss: -5.9714e+01 - val_accuracy: 0.1980 - 89ms/epoch - 2ms/step\n",
      "Epoch 177/200\n",
      "58/58 - 0s - loss: -6.1695e+01 - accuracy: 0.1900 - val_loss: -6.0060e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 178/200\n",
      "58/58 - 0s - loss: -6.2047e+01 - accuracy: 0.1900 - val_loss: -6.0398e+01 - val_accuracy: 0.1980 - 86ms/epoch - 1ms/step\n",
      "Epoch 179/200\n",
      "58/58 - 0s - loss: -6.2398e+01 - accuracy: 0.1900 - val_loss: -6.0740e+01 - val_accuracy: 0.1980 - 89ms/epoch - 2ms/step\n",
      "Epoch 180/200\n",
      "58/58 - 0s - loss: -6.2751e+01 - accuracy: 0.1900 - val_loss: -6.1083e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 181/200\n",
      "58/58 - 0s - loss: -6.3103e+01 - accuracy: 0.1900 - val_loss: -6.1427e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 182/200\n",
      "58/58 - 0s - loss: -6.3457e+01 - accuracy: 0.1900 - val_loss: -6.1771e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 183/200\n",
      "58/58 - 0s - loss: -6.3811e+01 - accuracy: 0.1900 - val_loss: -6.2113e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 184/200\n",
      "58/58 - 0s - loss: -6.4164e+01 - accuracy: 0.1900 - val_loss: -6.2456e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 185/200\n",
      "58/58 - 0s - loss: -6.4518e+01 - accuracy: 0.1900 - val_loss: -6.2797e+01 - val_accuracy: 0.1980 - 88ms/epoch - 2ms/step\n",
      "Epoch 186/200\n",
      "58/58 - 0s - loss: -6.4870e+01 - accuracy: 0.1900 - val_loss: -6.3140e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 187/200\n",
      "58/58 - 0s - loss: -6.5223e+01 - accuracy: 0.1900 - val_loss: -6.3485e+01 - val_accuracy: 0.1980 - 81ms/epoch - 1ms/step\n",
      "Epoch 188/200\n",
      "58/58 - 0s - loss: -6.5577e+01 - accuracy: 0.1900 - val_loss: -6.3829e+01 - val_accuracy: 0.1980 - 94ms/epoch - 2ms/step\n",
      "Epoch 189/200\n",
      "58/58 - 0s - loss: -6.5932e+01 - accuracy: 0.1900 - val_loss: -6.4168e+01 - val_accuracy: 0.1980 - 90ms/epoch - 2ms/step\n",
      "Epoch 190/200\n",
      "58/58 - 0s - loss: -6.6283e+01 - accuracy: 0.1900 - val_loss: -6.4513e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 191/200\n",
      "58/58 - 0s - loss: -6.6636e+01 - accuracy: 0.1900 - val_loss: -6.4856e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 192/200\n",
      "58/58 - 0s - loss: -6.6988e+01 - accuracy: 0.1900 - val_loss: -6.5197e+01 - val_accuracy: 0.1980 - 85ms/epoch - 1ms/step\n",
      "Epoch 193/200\n",
      "58/58 - 0s - loss: -6.7342e+01 - accuracy: 0.1900 - val_loss: -6.5540e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 194/200\n",
      "58/58 - 0s - loss: -6.7696e+01 - accuracy: 0.1900 - val_loss: -6.5884e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 195/200\n",
      "58/58 - 0s - loss: -6.8050e+01 - accuracy: 0.1900 - val_loss: -6.6227e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 196/200\n",
      "58/58 - 0s - loss: -6.8403e+01 - accuracy: 0.1900 - val_loss: -6.6571e+01 - val_accuracy: 0.1980 - 84ms/epoch - 1ms/step\n",
      "Epoch 197/200\n",
      "58/58 - 0s - loss: -6.8756e+01 - accuracy: 0.1900 - val_loss: -6.6912e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n",
      "Epoch 198/200\n",
      "58/58 - 0s - loss: -6.9108e+01 - accuracy: 0.1900 - val_loss: -6.7253e+01 - val_accuracy: 0.1980 - 83ms/epoch - 1ms/step\n",
      "Epoch 199/200\n",
      "58/58 - 0s - loss: -6.9462e+01 - accuracy: 0.1900 - val_loss: -6.7597e+01 - val_accuracy: 0.1980 - 87ms/epoch - 2ms/step\n",
      "Epoch 200/200\n",
      "58/58 - 0s - loss: -6.9816e+01 - accuracy: 0.1900 - val_loss: -6.7940e+01 - val_accuracy: 0.1980 - 82ms/epoch - 1ms/step\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y, validation_split=0.33,batch_size=100, epochs=200, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c717a8cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "267/267 [==============================] - 0s 817us/step - loss: -69.3178 - accuracy: 0.1927\n",
      "The test loss is:  -69.31781005859375\n",
      "The best accuracy is: % 19.266806542873383\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(x_train,y)\n",
    "print(\"The test loss is: \", test_loss)\n",
    "print(\"The best accuracy is: %\", test_acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de66a0a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "# geçmişteki tüm verileri listele\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "37c0cf37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3iqm1Tb/u+zjMWrNiLI/crX+Di6CVlpYyePBgpk+fztlnn83UqVO58MILkcRNN93EwQcfzPbt2znjjDN46aWXOOGEE/LuZ86cOUydOpUXX3yRqqoqysrKOPHEZP7T0aNH552efOTIkZx11lmcd955O+xr8+bNjBs3jieeeIKjjz6af/3Xf+W2227j6quvBuCQQw7hhRde4Gc/+xm33norP//5z3do39qmX/cZh5k1udzuqtxuqgceeICysjIGDRrEggULduhWqut//ud/GDVqFO3bt6dTp06MHDmytm7+/Pmceuqp9O/fn3vvvbd2bqf6LF68mN69e3P00UcDcPHFF/PUU0/V1o8enaw6eOKJJ9ZOjJhr27ZtXHLJJfTv35/zzz+/Nu5Cp1+vqW9I3enX872/P/3pT1x22WXA+9Ov9+rVq3b69T/+8Y+NMv26zzjMWrNmWh75nHPO4ctf/jIvvPAC7733HmVlZfz973/n1ltvZfbs2Rx00EGMGzdupynI66o7tXmNrNOT72rOvpqp2eubur21Tb/uMw4za3IdOnRg6NChjB8/vvZsY8OGDRxwwAF07tyZVatW8dhjjzW4jyFDhvDwww/z3nvvsXHjRn73u9/V1tU3PXnHjh3ZuHHjTvvq27cvy5cvZ+nSpUAyy+1pp51W8PtpbdOvFzVxSBouabGkpZIm5anvK+lZSVskXVOn7ipJ8yUtSFcGrCm/X9Lc9Ge5pLnFfA9mVhxjx45l3rx5jBkzBoABAwYwaNAgjjvuOMaPH8/JJ5/cYPuysjIuvPBCBg4cyLnnnsupp76/9lt905OPGTOG7373uwwaNIhXX321trxdu3b84he/4Pzzz6d///7ss88+TJgwoeD30tqmXy/atOqSSoBXgDOBSpI1yMdGxMKcbboCRwLnAG9HxK1p+fHAVGAwsBWYDlwWEUvqvMb3gPURcWNDsXhadbP3eVr11qeQ6ddbyrTqg4GlEbEsIraSJIKzczeIiNURMZtkXfFcxwLPRcSmiKgCZgGjcjdQ0nF4AXBfsd6AmdmerhjTrxdzcPwIIPf6skrgowW2nQ/cJKkUeA8YAdQ9ZTgVWFX3LKSGpEuBSwF69uyZIWwzs71HMaZfL+YZR77LHQrqF4uIRcDNwAySbqp5QN1LGcbSwNlGRNweEeURUd6lS5fCIjZrJVrDyp9WuKyfh2ImjkqgR87z7sAbhTaOiDsjoiwihgDrgNozC0ltgNFA/hXmzaxe7dq1Y+3atU4eBiRJY+3atbWXEBeimF1Vs4E+knoDrwNjgM8W2lhS14hYLaknSZL4eE71MODliKhszIDNWoPu3btTWVnJmjVrmjsUayHatWtH9+7dC96+aIkjIqokTQQeB0qAKRGxQNKEtH6ypG4kYxedgOr0stt+EbEBeCgd49gGXBERb+fsfgweFDfbLfvuuy+9e/du7jBsD1a0y3FbEl+Oa2aWXXNcjmtmZnshJw4zM8vEicPMzDJx4jAzs0ycOMzMLBMnDjMzy8SJw8zMMnHiMDOzTJw4zMwsEycOMzPLxInDzMwyceIwM7NMnDjMzCwTJw4zM8vEicPMzDJx4jAzs0yKmjgkDZe0WNJSSZPy1PeV9KykLZKuqVN3laT5khakKwPm1n0x3e8CSbcU8z2YmdmOirZ0rKQS4KfAmUAlMFvStIhYmLPZOuBK4Jw6bY8HLgEGA1uB6ZL+EBFLJJ0OnA2cEBFbJHUt1nswM7OdFfOMYzCwNCKWRcRWYCrJF36tiFgdEbNJ1hXPdSzwXERsiogqYBYwKq27DPhORGyp2UcR34OZmdVRzMRxBLAi53llWlaI+cAQSaWS2gMjgB5p3dHAqZL+KmmWpI/k24GkSyVVSKpYs2bNbr4FMzOrq5iJQ3nKopCGEbEIuBmYAUwH5gFVaXUb4CDgY8BXgAck7fRaEXF7RJRHRHmXLl12I3wzM8unmImjkvfPEgC6A28U2jgi7oyIsogYQjIWsiRnv7+JxPNANXBII8VsZma7UMzEMRvoI6m3pP2AMcC0QhvXDHpL6gmMBu5Lqx4B/imtOxrYD3ir8cI2M7OGFO2qqoiokjQReBwoAaZExAJJE9L6yZK6ARVAJ6A6vey2X0RsAB6SVEoycH5FRLyd7noKMEXSfJIrri6OiIK6wMzM7INTa/jOLS8vj4qKiuYOw8xsjyJpTkSU1y33neNmZpaJE4eZmWXixGFmZpk4cZiZWSZOHGZmlokTh5mZZeLEYWZmmThxmJlZJk4cZmaWiROHmZll4sRhZmaZOHGYmVkmThxmZpaJE4eZmWXixGFmZpk4cZiZWSZFTRyShktaLGmppEl56vtKelbSFknX1Km7StJ8SQvSlQFrym+Q9LqkuenPiGK+BzMz21HRlo6VVAL8FDgTqARmS5oWEQtzNlsHXAmcU6ft8cAlwGCS5WGnS/pDRCxJN/l+RNxarNjNzKx+xTzjGAwsjYhlEbEVmAqcnbtBRKyOiNkk64rnOhZ4LiI2RUQVMAsYVcRYzcysQMVMHEcAK3KeV6ZlhZgPDJFUKqk9MALokVM/UdJLkqZIOijfDiRdKqlCUsWaNWt2J34zM8ujmIlDecqikIYRsQi4GZgBTAfmAVVp9W3Ah4CBwJvA9+rZx+0RUR4R5V26dMkWuZmZ1auYiaOSHc8SugNvFNo4Iu6MiLKIGEIyFrIkLV8VEdsjohq4g6RLzMzMmkgxE8dsoI+k3pL2A8YA0wptLKlr+m9PYDRwX/r8sJzNRpF0a5mZWRMp6KoqSQ8BU4DH0r/0dykiqiRNBB4HSoApEbFA0oS0frKkbkAF0AmoTi+77RcRG4CHJJWSDJxfERFvp7u+RdJAkm6v5cAXCnqnZmbWKBSx62EHScOAzwEfAx4E7oqIl4scW6MpLy+PioqK5g7DzGyPImlORJTXLS+oqyoiZkbERUAZyV/5MyQ9I+lzkvZt3FDNzKwlK3iMI+02Ggd8HngR+CFJIplRlMjMzKxFKnSM4zdAX+Ae4DMR8WZadb8k9wGZmbUihU458pOI+FO+inz9X2ZmtvcqtKvqWEkH1jyRdJCky4sTkpmZtWSFJo5LIuKdmifppbGXFCUiMzNr0QpNHPtIqp1CJJ35dr/ihGRmZi1ZoWMcjwMPSJpMcuPdBJI5pMzMrJUpNHF8jeQO7ctIJi/8I/DzYgVlZmYtV0GJI51m5Lb0x8zMWrFC7+PoA/w/oB/QrqY8Io4qUlxmZtZCFTo4/guSs40q4HTglyQ3A5qZWStTaOLYPyKeIJkU8bWIuAH4p+KFZWZmLVWhg+ObJe0DLEmnSn8d6Fq8sMzMrKUq9IzjaqA9cCVwIvDPwMVFisnMzFqwXZ5xpDf7XRARXwHeJVmXw8zMWqldnnFExHbgxNw7xwslabikxZKWSpqUp76vpGclbZF0TZ26qyTNl7QgXRmwbttrJIWkQ7LGZWZmu6/QMY4Xgd9KehD4R01hRPymvgbpmcpPgTOBSmC2pGkRsTBns3Uk3V/n1Gl7PMlcWIOBrcB0SX+IiCVpfY90v/9bYPxmZtZICh3jOBhYS3Il1WfSn7N20WYwsDQilkXEVmAqcHbuBhGxOiJmk6wrnutY4LmI2BQRVcAsYFRO/feBr5JMf2JmZk2o0DvHd2dc4whgRc7zSuCjBbadD9yUrjr4HjACqACQNBJ4PSLmNdR7JulS4FKAnj17Zg7ezMzyK/TO8V+Q56/7iBjfULM8ZQWdIUTEIkk3kyxL+y4wD6iS1B64FvhEAfu4HbgdoLy83GcmZmaNpNAxjt/nPG5H0m30xi7aVAI9cp53L6BNrYi4E7gTQNJ/pPv7ENAbqDnb6A68IGlwRKwsdN9mZrb7Cu2qeij3uaT7gJm7aDYb6COpN8kNg2OAzxYamKSuEbFaUk9gNPDxdAGprjnbLAfKI+KtQvdrZmYfTKFnHHX1ARocOIiIqvQu88eBEmBKRCyQNCGtnyypG8nYRSegOr3stl9EbAAeSsc4tgFXpEnDzMyaWaFjHBvZcXxiJckaHQ2KiEeBR+uUTc55vJKkuylf21ML2H+vXW1jZmaNq9Cuqo7FDsTMzPYMBd3HIWmUpM45zw+UdE7RojIzsxar0BsAr4+I9TVPIuId4PqiRGRmZi1aoYkj33a7O7BuZmZ7sEITR4Wk/5T0IUlHSfo+MKeYgZmZWctUaOL4Islkg/cDD5BMA3JFsYIyM7OWq9Crqv4B7DQtupmZtT6FXlU1Q9KBOc8PkvR40aIyM7MWq9CuqkPSK6kAqDv1h5mZtR6FJo7qdM4oACT1wmthmJm1SoVeUnst8LSkWenzIaRrXZiZWetS6OD4dEnlJMliLvBbkiurzMyslSl0ksPPA1eRTEg4F/gY8CzJUrJmZtaKFDrGcRXwEeC1iDgdGASsKVpUZmbWYhWaODZHxGYASW0j4mXgmOKFZWZmLVWhiaMyvY/jEWCGpN9SwDKwkoZLWixpqaSdbiCU1FfSs5K2SLqmTt1VkuZLWpAu8FRT/m1JL0maK+mPkg4v8D2YmVkjUES2q2olnQZ0BqZHxNYGtisBXgHOJFkvfDYwNiIW5mzTFTgSOAd4OyJuTcuPB6YCg0mmOpkOXBYRSyR1SlcIRNKVJCsGTmgo5vLy8qioqMj0Ps3MWjtJcyKivG55oWcctSJiVkRMayhppAYDSyNiWbrtVODsOvtaHRGzSZaHzXUs8FxEbIqIKmAWMCptsyFnuwPw/SRmZk0qc+LI4AhgRc7zyrSsEPOBIZJKJbUHRgA9aiol3SRpBXAR8K18O5B0qaQKSRVr1ngc38yssRQzcShPWUFnBxGxCLgZmEHSTTUPqMqpvzYiegD3AhPr2cftEVEeEeVdunTJGruZmdWjmImjkpyzBJJ7QHY5oF4jIu6MiLKIGAKsA5bk2exXwLkfKEozM8ukmIljNtBHUm9J+wFjgGmFNk4HzknnyBoN3Jc+75Oz2Ujg5UaL2MzMdqloy79GRJWkicDjQAkwJSIWSJqQ1k+W1A2oADqRTKR4NclVUhuAhySVkgycX5HOyAvwHUnHANXAa0CDV1SZmVnjynw57p7Il+OamWXXaJfjmplZ6+bEYWZmmThxmJlZJk4cZmaWiROHmZll4sRhZmaZOHGYmVkmThxmZpaJE4eZmWXixGFmZpk4cZiZWSZOHGZmlokTh5mZZeLEYWZmmThxmJlZJk4cZmaWSVETh6ThkhZLWippUp76vpKelbRF0jV16q6SNF/SgnRlwJry70p6WdJLkh6WdGAx34OZme2oaIlDUgnwU+BTQD9grKR+dTZbB1wJ3Fqn7fHAJcBgYABwVs5a4zOA4yPiBOAV4OvFeg9mZrazYp5xDAaWRsSyiNgKTAXOzt0gIlZHxGySdcVzHQs8FxGbIqIKmAWMStv8MS0DeA7oXsT3YGZmdRQzcRwBrMh5XpmWFWI+MERSqaT2wAigR57txgOP5duBpEslVUiqWLNmTYawzcysIcVMHMpTFoU0jIhFwM0k3VLTgXlAVe42kq5Ny+6tZx+3R0R5RJR36dIlS9xmZtaAYiaOSnY8S+gOvFFo44i4MyLKImIIyVjIkpo6SRcDZwEXRURBycjMzBpHMRPHbKCPpN6S9gPGANMKbSypa/pvT2A0cF/6fDjwNWBkRGxq9KjNzKxBbYq144iokjQReBwoAaZExAJJE9L6yZK6ARVAJ6A6vey2X0RsAB6SVEoycH5FRLyd7vonQFtghiRIBtEnFOt9mJnZjoqWOAAi4lHg0Tplk3Mer6Seq6Ii4tR6yj/cmDGamVk2vnPczMwyceIwM7NMnDjMzCwTJw4zM8vEicPMzDJx4jAzs0ycOMzMLBMnDjMzy8SJw8zMMnHiMDOzTJw4zMwsEycOMzPLxInDzMwyceIwM7NMnDjMzCwTJw4zM8ukqIlD0nBJiyUtlTQpT31fSc9K2iLpmjp1V0maL2lBujJgTfn5aVm1pPJixm9mZjsrWuKQVAL8FPgU0A8YK6lfnc3WAVcCt9ZpezxwCTAYGACcJalPWj2fZA3yp4oVu5mZ1a+YZxyDgaURsSwitgJTgbNzN4iI1RExm2Rd8VzHkqwlvikiqoBZwKi0zaKIWFzEuM3MrAHFTBxHACtynlemZYWYDwyRVCqpPTAC6JHlxSVdKqlCUsWaNWuyNDUzswYUM3EoT1kU0jAiFgE3AzOA6cA8oCrLi0fE7RFRHhHlXbp0ydLUzMwaUMzEUcmOZwndgTcKbRwRd0ZEWUQMIRkLWdLI8ZmZ2W4oZuKYDfSR1FvSfsAYYFqhjSV1Tf/tSTIYfl9RojQzs0zaFGvHEVElaSLwOFACTImIBZImpPWTJXUDKoBOQHV62W2/iNgAPCSplGTg/IqIeBtA0ijgx0AX4A+S5kbEJ4v1PszMbEeKKGjYYY9WXl4eFRUVzR2GmdkeRdKciNjpfjnfOW5mZpk4cZiZWSZOHGZmlokTh5mZZeLEYWZmmThxmJlZJk4cZmaWiROHmZll4sTRgCcXr2byrFebOwwzsxbFiaMBTy95i+/PeIXt1Xv/3fVmZoVy4mjAMd06sqWqmuVr/9HcoZiZtRhOHA3o260TAItXbmzmSMzMWg4njgb0ObQD+wheduIwM6vlxNGAdvuW0OuQA1i8ckNzh2Jm1mI4cexC324d3VVlZpbDiWMXjjm0E6+t28SmrZmWPDcz22sVNXFIGi5psaSlkiblqe8r6VlJWyRdU6fuKknzJS1IVwasKT9Y0gxJS9J/DyrmezimW0ci4JVV7xbzZczM9hhFWzpWUgnwU+BMoBKYLWlaRCzM2WwdcCVwTp22xwOXAIOBrcB0SX+IiCXAJOCJiPhOmowmAV8r1vvo260jAJf99xw6tC3a4TIzK4r/GN2fj/Q6uFH3WcxvwsHA0ohYBiBpKnA2UJs4ImI1sFrSp+u0PRZ4LiI2pW1nAaOAW9J9DE23uxt4kiImjiNL2/P5U3rzxvr3ivUSZmZFs/++JY2+z2ImjiOAFTnPK4GPFth2PnCTpFLgPWAEULNo+KER8SZARLwpqWu+HUi6FLgUoGfPntmjf38/XHdWv91ub2a2tynmGIfylBU0d0dELAJuBmYA04F5QKbR6Yi4PSLKI6K8S5cuWZqamVkDipk4KoEeOc+7A28U2jgi7oyIsogYQjIWsiStWiXpMID039WNFK+ZmRWgmIljNtBHUm9J+wFjgGmFNq7pgpLUExgN3JdWTQMuTh9fDPy20SI2M7NdKtoYR0RUSZoIPA6UAFMiYoGkCWn9ZEndSMYuOgHV6WW3/SJiA/BQOsaxDbgiIt5Od/0d4AFJ/wb8L3B+sd6DmZntTBF7/5Th5eXlUVFRsesNzcyslqQ5EVFet9x3jpuZWSZOHGZmlokTh5mZZdIqxjgkrQFe283mhwBvNWI4jaWlxgUtNzbHlU1LjQtabmx7W1xHRsRON8K1isTxQUiqyDc41NxaalzQcmNzXNm01Lig5cbWWuJyV5WZmWXixGFmZpk4ceza7c0dQD1aalzQcmNzXNm01Lig5cbWKuLyGIeZmWXiMw4zM8vEicPMzDJx4mjArtZMb8I4ekj6s6RF6RrsV6XlN0h6XdLc9GdEM8S2XNLf0tevSMuadF34PDEdk3NM5kraIOnq5jpekqZIWi1pfk5ZvcdI0tfTz9xiSZ9s4ri+K+llSS9JeljSgWl5L0nv5Ry7yU0cV72/u2Y+XvfnxLRc0ty0vCmPV33fD8X7jEWEf/L8kMzo+ypwFLAfyWJS/ZoplsOAsvRxR+AVoB9wA3BNMx+n5cAhdcpuASaljycBNzfz73ElcGRzHS9gCFAGzN/VMUp/r/OAtkDv9DNY0oRxfQJokz6+OSeuXrnbNcPxyvu7a+7jVaf+e8C3muF41ff9ULTPmM846le7ZnpEbAVq1kxvchHxZkS8kD7eCCwiWZq3pTqbZD140n/Pab5QOAN4NSJ2d+aADywiniJZjCxXfcfobGBqRGyJiL8DS0k+i00SV0T8MSJqVtt8jmQBtiZVz/GqT7MerxqSBFzA++sGNZkGvh+K9hlz4qhfvjXTm/3LWlIvYBDw17RoYtqtMKWpu4RSAfxR0hwl67xDnXXhgbzrwjeRMez4n7m5j1eN+o5RS/rcjQcey3neW9KLkmZJOrUZ4sn3u2spx+tUYFVELMkpa/LjVef7oWifMSeO+u32munFIqkD8BBwdSSLXd0GfAgYCLxJcqrc1E6OiDLgU8AVkoY0Qwx5KVl5ciTwYFrUEo7XrrSIz52ka4Eq4N606E2gZ0QMAr4M/EpSpyYMqb7fXYs4XsBYdvwDpcmPV57vh3o3zVOW6Zg5cdTvA62Z3tgk7Uvyobg3In4DEBGrImJ7RFQDd1CkU/SGRMQb6b+rgYfTGFrKuvCfAl6IiFVpjM1+vHLUd4ya/XMn6WLgLOCiSDvF026NtenjOST94kc3VUwN/O5awvFqQ7K89f01ZU19vPJ9P1DEz5gTR/0+0JrpjSntP70TWBQR/5lTfljOZqOA+XXbFjmuAyR1rHlMMrA6n5azLvwOfwU29/Gqo75jNA0YI6mtpN5AH+D5pgpK0nDga8DIiNiUU95FUkn6+Kg0rmVNGFd9v7tmPV6pYcDLEVFZU9CUx6u+7weK+RlrilH/PfUHGEFyhcKrwLXNGMcpJKeSLwFz058RwD3A39LyacBhTRzXUSRXZ8wDFtQcI6AUeAJYkv57cDMcs/bAWqBzTlmzHC+S5PUmsI3kr71/a+gYAdemn7nFwKeaOK6lJP3fNZ+zyem256a/43nAC8Bnmjiuen93zXm80vK7gAl1tm3K41Xf90PRPmOecsTMzDJxV5WZmWXixGFmZpk4cZiZWSZOHGZmlokTh5mZZeLEYdbCSRoq6ffNHYdZDScOMzPLxInDrJFI+mdJz6frL/yXpBJJ70r6nqQXJD0hqUu67UBJz+n9dS8OSss/LGmmpHlpmw+lu+8g6ddK1sq4N71b2KxZOHGYNQJJxwIXkkz6OBDYDlwEHEAyX1YZMAu4Pm3yS+BrEXECyR3RNeX3Aj+NiAHASSR3KkMy4+nVJGspHAWcXOS3ZFavNs0dgNle4gzgRGB2ejKwP8mkctW8P/ndfwO/kdQZODAiZqXldwMPpvN+HRERDwNExGaAdH/PRzoXUrrKXC/g6aK/K7M8nDjMGoeAuyPi6zsUSt+ss11Dc/w01P20Jefxdvx/15qRu6rMGscTwHmSukLtes9HkvwfOy/d5rPA0xGxHng7Z3GffwFmRbKGQqWkc9J9tJXUvinfhFkh/FeLWSOIiIWSriNZDXEfkhlUrwD+ARwnaQ6wnmQcBJJprieniWEZ8Lm0/F+A/5J0Y7qP85vwbZgVxLPjmhWRpHcjokNzx2HWmNxVZWZmmfiMw8zMMvEZh5mZZeLEYWZmmThxmJlZJk4cZmaWiROHmZll8v8BBX2vQbh1MOkAAAAASUVORK5CYII=\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": 33,
   "id": "efc0a9de",
   "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": "7fe94d15",
   "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
}