DeepLearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from sklearn.model_selection import train_test_split,KFold\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Lag1</th>\n",
       "      <th>Lag2</th>\n",
       "      <th>Lag3</th>\n",
       "      <th>Lag4</th>\n",
       "      <th>Lag5</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Today</th>\n",
       "      <th>Direction</th>\n",
       "      <th>Direction2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>-1.055</td>\n",
       "      <td>5.010</td>\n",
       "      <td>1.19130</td>\n",
       "      <td>0.959</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>-1.055</td>\n",
       "      <td>1.29650</td>\n",
       "      <td>1.032</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-2.624</td>\n",
       "      <td>1.41120</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>1.27600</td>\n",
       "      <td>0.614</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.614</td>\n",
       "      <td>-0.623</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.381</td>\n",
       "      <td>1.20570</td>\n",
       "      <td>0.213</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.252</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.584</td>\n",
       "      <td>-0.285</td>\n",
       "      <td>1.88850</td>\n",
       "      <td>0.043</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.252</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.584</td>\n",
       "      <td>1.28581</td>\n",
       "      <td>-0.955</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>2005</td>\n",
       "      <td>-0.955</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.252</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>1.54047</td>\n",
       "      <td>0.130</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.955</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.252</td>\n",
       "      <td>1.42236</td>\n",
       "      <td>-0.298</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>2005</td>\n",
       "      <td>-0.298</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.955</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.422</td>\n",
       "      <td>1.38254</td>\n",
       "      <td>-0.489</td>\n",
       "      <td>Down</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1250 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year   Lag1   Lag2   Lag3   Lag4   Lag5   Volume  Today Direction  \\\n",
       "0     2001  0.381 -0.192 -2.624 -1.055  5.010  1.19130  0.959        Up   \n",
       "1     2001  0.959  0.381 -0.192 -2.624 -1.055  1.29650  1.032        Up   \n",
       "2     2001  1.032  0.959  0.381 -0.192 -2.624  1.41120 -0.623      Down   \n",
       "3     2001 -0.623  1.032  0.959  0.381 -0.192  1.27600  0.614        Up   \n",
       "4     2001  0.614 -0.623  1.032  0.959  0.381  1.20570  0.213        Up   \n",
       "...    ...    ...    ...    ...    ...    ...      ...    ...       ...   \n",
       "1245  2005  0.422  0.252 -0.024 -0.584 -0.285  1.88850  0.043        Up   \n",
       "1246  2005  0.043  0.422  0.252 -0.024 -0.584  1.28581 -0.955      Down   \n",
       "1247  2005 -0.955  0.043  0.422  0.252 -0.024  1.54047  0.130        Up   \n",
       "1248  2005  0.130 -0.955  0.043  0.422  0.252  1.42236 -0.298      Down   \n",
       "1249  2005 -0.298  0.130 -0.955  0.043  0.422  1.38254 -0.489      Down   \n",
       "\n",
       "      Direction2  \n",
       "0              1  \n",
       "1              1  \n",
       "2              0  \n",
       "3              1  \n",
       "4              1  \n",
       "...          ...  \n",
       "1245           1  \n",
       "1246           0  \n",
       "1247           1  \n",
       "1248           0  \n",
       "1249           0  \n",
       "\n",
       "[1250 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Smarket.csv')\n",
    "df_one = pd.get_dummies(df['Direction'])\n",
    "df['Direction2'] = df_one['Up']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df[['Lag1','Lag2','Lag3','Lag4','Lag5']]\n",
    "y=df['Direction2']\n",
    "kf = KFold(n_splits=2)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the keras model\n",
    "model = Sequential()\n",
    "model.add(Dense(10, activation='sigmoid'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy','AUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "100/100 [==============================] - 2s 6ms/step - loss: 0.7073 - accuracy: 0.4820 - auc: 0.4978 - val_loss: 0.6993 - val_accuracy: 0.4680 - val_auc: 0.4803\n",
      "Epoch 2/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6962 - accuracy: 0.5020 - auc: 0.5071 - val_loss: 0.7016 - val_accuracy: 0.5200 - val_auc: 0.4755\n",
      "Epoch 3/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6938 - accuracy: 0.5250 - auc: 0.5076 - val_loss: 0.7028 - val_accuracy: 0.5240 - val_auc: 0.4719\n",
      "Epoch 4/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6930 - accuracy: 0.5310 - auc: 0.5094 - val_loss: 0.7018 - val_accuracy: 0.5200 - val_auc: 0.4741\n",
      "Epoch 5/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6924 - accuracy: 0.5300 - auc: 0.5131 - val_loss: 0.7012 - val_accuracy: 0.5240 - val_auc: 0.4731\n",
      "Epoch 6/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6920 - accuracy: 0.5250 - auc: 0.5152 - val_loss: 0.7012 - val_accuracy: 0.5200 - val_auc: 0.4715\n",
      "Epoch 7/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6913 - accuracy: 0.5270 - auc: 0.5177 - val_loss: 0.7013 - val_accuracy: 0.5200 - val_auc: 0.4746\n",
      "Epoch 8/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6912 - accuracy: 0.5390 - auc: 0.5176 - val_loss: 0.7007 - val_accuracy: 0.5200 - val_auc: 0.4700\n",
      "Epoch 9/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6909 - accuracy: 0.5350 - auc: 0.5197 - val_loss: 0.7002 - val_accuracy: 0.5160 - val_auc: 0.4758\n",
      "Epoch 10/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6907 - accuracy: 0.5360 - auc: 0.5213 - val_loss: 0.7001 - val_accuracy: 0.5120 - val_auc: 0.4744\n",
      "Epoch 11/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6906 - accuracy: 0.5420 - auc: 0.5204 - val_loss: 0.7005 - val_accuracy: 0.5120 - val_auc: 0.4806\n",
      "Epoch 12/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6901 - accuracy: 0.5350 - auc: 0.5256 - val_loss: 0.6997 - val_accuracy: 0.5120 - val_auc: 0.4825\n",
      "Epoch 13/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6901 - accuracy: 0.5390 - auc: 0.5247 - val_loss: 0.7004 - val_accuracy: 0.4960 - val_auc: 0.4843\n",
      "Epoch 14/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6901 - accuracy: 0.5360 - auc: 0.5270 - val_loss: 0.7007 - val_accuracy: 0.4960 - val_auc: 0.4902\n",
      "Epoch 15/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6900 - accuracy: 0.5330 - auc: 0.5258 - val_loss: 0.6993 - val_accuracy: 0.5080 - val_auc: 0.4861\n",
      "Epoch 16/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6898 - accuracy: 0.5450 - auc: 0.5301 - val_loss: 0.7000 - val_accuracy: 0.4920 - val_auc: 0.4915\n",
      "Epoch 17/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6896 - accuracy: 0.5440 - auc: 0.5313 - val_loss: 0.6991 - val_accuracy: 0.4960 - val_auc: 0.4940\n",
      "Epoch 18/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6895 - accuracy: 0.5460 - auc: 0.5327 - val_loss: 0.7002 - val_accuracy: 0.4840 - val_auc: 0.4943\n",
      "Epoch 19/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6895 - accuracy: 0.5410 - auc: 0.5323 - val_loss: 0.6989 - val_accuracy: 0.4880 - val_auc: 0.4941\n",
      "Epoch 20/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6893 - accuracy: 0.5440 - auc: 0.5352 - val_loss: 0.6994 - val_accuracy: 0.4920 - val_auc: 0.4963\n",
      "Epoch 21/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6897 - accuracy: 0.5390 - auc: 0.5316 - val_loss: 0.7002 - val_accuracy: 0.4880 - val_auc: 0.4961\n",
      "Epoch 22/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6896 - accuracy: 0.5430 - auc: 0.5296 - val_loss: 0.7005 - val_accuracy: 0.4840 - val_auc: 0.4990\n",
      "Epoch 23/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6893 - accuracy: 0.5340 - auc: 0.5333 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5004\n",
      "Epoch 24/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6892 - accuracy: 0.5440 - auc: 0.5350 - val_loss: 0.6993 - val_accuracy: 0.4920 - val_auc: 0.5004\n",
      "Epoch 25/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6889 - accuracy: 0.5400 - auc: 0.5370 - val_loss: 0.6997 - val_accuracy: 0.4920 - val_auc: 0.4986\n",
      "Epoch 26/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6890 - accuracy: 0.5450 - auc: 0.5363 - val_loss: 0.7004 - val_accuracy: 0.4800 - val_auc: 0.5016\n",
      "Epoch 27/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6893 - accuracy: 0.5430 - auc: 0.5347 - val_loss: 0.7020 - val_accuracy: 0.4760 - val_auc: 0.4993\n",
      "Epoch 28/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6888 - accuracy: 0.5400 - auc: 0.5407 - val_loss: 0.6998 - val_accuracy: 0.4960 - val_auc: 0.5016\n",
      "Epoch 29/150\n",
      "100/100 [==============================] - 0s 4ms/step - loss: 0.6889 - accuracy: 0.5370 - auc: 0.5367 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.4965\n",
      "Epoch 30/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6888 - accuracy: 0.5430 - auc: 0.5383 - val_loss: 0.6997 - val_accuracy: 0.4920 - val_auc: 0.5038\n",
      "Epoch 31/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6888 - accuracy: 0.5420 - auc: 0.5395 - val_loss: 0.6986 - val_accuracy: 0.4880 - val_auc: 0.5033\n",
      "Epoch 32/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6887 - accuracy: 0.5380 - auc: 0.5398 - val_loss: 0.6999 - val_accuracy: 0.4960 - val_auc: 0.5049\n",
      "Epoch 33/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6892 - accuracy: 0.5430 - auc: 0.5345 - val_loss: 0.6985 - val_accuracy: 0.4840 - val_auc: 0.5040\n",
      "Epoch 34/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6890 - accuracy: 0.5400 - auc: 0.5350 - val_loss: 0.7003 - val_accuracy: 0.4880 - val_auc: 0.5023\n",
      "Epoch 35/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6888 - accuracy: 0.5420 - auc: 0.5402 - val_loss: 0.7001 - val_accuracy: 0.4880 - val_auc: 0.5033\n",
      "Epoch 36/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6891 - accuracy: 0.5350 - auc: 0.5387 - val_loss: 0.6992 - val_accuracy: 0.4880 - val_auc: 0.5045\n",
      "Epoch 37/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6888 - accuracy: 0.5260 - auc: 0.5389 - val_loss: 0.7003 - val_accuracy: 0.4880 - val_auc: 0.5079\n",
      "Epoch 38/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6886 - accuracy: 0.5490 - auc: 0.5400 - val_loss: 0.7001 - val_accuracy: 0.4960 - val_auc: 0.5027\n",
      "Epoch 39/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6888 - accuracy: 0.5230 - auc: 0.5381 - val_loss: 0.7007 - val_accuracy: 0.4880 - val_auc: 0.5061\n",
      "Epoch 40/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6884 - accuracy: 0.5410 - auc: 0.5428 - val_loss: 0.7003 - val_accuracy: 0.4960 - val_auc: 0.5030\n",
      "Epoch 41/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6886 - accuracy: 0.5410 - auc: 0.5385 - val_loss: 0.6988 - val_accuracy: 0.4880 - val_auc: 0.5012\n",
      "Epoch 42/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6883 - accuracy: 0.5320 - auc: 0.5442 - val_loss: 0.6989 - val_accuracy: 0.4880 - val_auc: 0.5052\n",
      "Epoch 43/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6883 - accuracy: 0.5390 - auc: 0.5414 - val_loss: 0.7004 - val_accuracy: 0.4880 - val_auc: 0.5077\n",
      "Epoch 44/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6883 - accuracy: 0.5400 - auc: 0.5424 - val_loss: 0.6989 - val_accuracy: 0.4840 - val_auc: 0.5051\n",
      "Epoch 45/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6881 - accuracy: 0.5410 - auc: 0.5429 - val_loss: 0.6992 - val_accuracy: 0.4920 - val_auc: 0.5052\n",
      "Epoch 46/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6883 - accuracy: 0.5440 - auc: 0.5414 - val_loss: 0.6991 - val_accuracy: 0.4800 - val_auc: 0.5045\n",
      "Epoch 47/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.5400 - auc: 0.5436 - val_loss: 0.6999 - val_accuracy: 0.4840 - val_auc: 0.5083\n",
      "Epoch 48/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6880 - accuracy: 0.5350 - auc: 0.5459 - val_loss: 0.6997 - val_accuracy: 0.4880 - val_auc: 0.5065\n",
      "Epoch 49/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6881 - accuracy: 0.5380 - auc: 0.5427 - val_loss: 0.6998 - val_accuracy: 0.4880 - val_auc: 0.5065\n",
      "Epoch 50/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6881 - accuracy: 0.5350 - auc: 0.5423 - val_loss: 0.6993 - val_accuracy: 0.4840 - val_auc: 0.5071\n",
      "Epoch 51/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6883 - accuracy: 0.5350 - auc: 0.5424 - val_loss: 0.7000 - val_accuracy: 0.4840 - val_auc: 0.5048\n",
      "Epoch 52/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.5370 - auc: 0.5457 - val_loss: 0.6980 - val_accuracy: 0.4960 - val_auc: 0.5058\n",
      "Epoch 53/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6882 - accuracy: 0.5400 - auc: 0.5429 - val_loss: 0.6988 - val_accuracy: 0.4880 - val_auc: 0.5047\n",
      "Epoch 54/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6877 - accuracy: 0.5410 - auc: 0.5457 - val_loss: 0.7002 - val_accuracy: 0.4880 - val_auc: 0.5039\n",
      "Epoch 55/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6878 - accuracy: 0.5440 - auc: 0.5437 - val_loss: 0.6999 - val_accuracy: 0.4880 - val_auc: 0.5052\n",
      "Epoch 56/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6876 - accuracy: 0.5440 - auc: 0.5468 - val_loss: 0.6996 - val_accuracy: 0.4800 - val_auc: 0.5071\n",
      "Epoch 57/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6880 - accuracy: 0.5430 - auc: 0.5429 - val_loss: 0.6997 - val_accuracy: 0.4760 - val_auc: 0.5064\n",
      "Epoch 58/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6878 - accuracy: 0.5530 - auc: 0.5435 - val_loss: 0.6987 - val_accuracy: 0.4840 - val_auc: 0.5025\n",
      "Epoch 59/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6877 - accuracy: 0.5430 - auc: 0.5461 - val_loss: 0.6995 - val_accuracy: 0.4800 - val_auc: 0.5055\n",
      "Epoch 60/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6875 - accuracy: 0.5430 - auc: 0.5470 - val_loss: 0.6999 - val_accuracy: 0.4920 - val_auc: 0.5054\n",
      "Epoch 61/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6873 - accuracy: 0.5410 - auc: 0.5486 - val_loss: 0.6994 - val_accuracy: 0.4800 - val_auc: 0.5092\n",
      "Epoch 62/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6873 - accuracy: 0.5450 - auc: 0.5485 - val_loss: 0.6999 - val_accuracy: 0.4840 - val_auc: 0.5032\n",
      "Epoch 63/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6876 - accuracy: 0.5410 - auc: 0.5459 - val_loss: 0.7006 - val_accuracy: 0.4840 - val_auc: 0.5059\n",
      "Epoch 64/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6874 - accuracy: 0.5470 - auc: 0.5472 - val_loss: 0.6998 - val_accuracy: 0.4840 - val_auc: 0.5088\n",
      "Epoch 65/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6874 - accuracy: 0.5420 - auc: 0.5456 - val_loss: 0.6997 - val_accuracy: 0.4800 - val_auc: 0.5084\n",
      "Epoch 66/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6873 - accuracy: 0.5400 - auc: 0.5502 - val_loss: 0.6995 - val_accuracy: 0.4760 - val_auc: 0.5078\n",
      "Epoch 67/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6875 - accuracy: 0.5420 - auc: 0.5468 - val_loss: 0.7004 - val_accuracy: 0.4920 - val_auc: 0.5052\n",
      "Epoch 68/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6875 - accuracy: 0.5450 - auc: 0.5467 - val_loss: 0.6985 - val_accuracy: 0.5000 - val_auc: 0.5040\n",
      "Epoch 69/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6870 - accuracy: 0.5410 - auc: 0.5504 - val_loss: 0.6998 - val_accuracy: 0.4760 - val_auc: 0.5077\n",
      "Epoch 70/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6869 - accuracy: 0.5430 - auc: 0.5501 - val_loss: 0.7005 - val_accuracy: 0.4880 - val_auc: 0.5040\n",
      "Epoch 71/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6870 - accuracy: 0.5470 - auc: 0.5511 - val_loss: 0.7006 - val_accuracy: 0.4840 - val_auc: 0.5039\n",
      "Epoch 72/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6872 - accuracy: 0.5440 - auc: 0.5476 - val_loss: 0.7007 - val_accuracy: 0.4840 - val_auc: 0.5066\n",
      "Epoch 73/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6869 - accuracy: 0.5450 - auc: 0.5510 - val_loss: 0.7003 - val_accuracy: 0.4840 - val_auc: 0.5070\n",
      "Epoch 74/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6869 - accuracy: 0.5410 - auc: 0.5506 - val_loss: 0.7004 - val_accuracy: 0.4880 - val_auc: 0.5054\n",
      "Epoch 75/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6867 - accuracy: 0.5470 - auc: 0.5520 - val_loss: 0.7004 - val_accuracy: 0.4920 - val_auc: 0.5035\n",
      "Epoch 76/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6867 - accuracy: 0.5440 - auc: 0.5514 - val_loss: 0.6993 - val_accuracy: 0.4840 - val_auc: 0.5064\n",
      "Epoch 77/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6866 - accuracy: 0.5500 - auc: 0.5524 - val_loss: 0.6988 - val_accuracy: 0.4960 - val_auc: 0.5087\n",
      "Epoch 78/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6866 - accuracy: 0.5470 - auc: 0.5516 - val_loss: 0.6994 - val_accuracy: 0.4920 - val_auc: 0.5063\n",
      "Epoch 79/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6863 - accuracy: 0.5470 - auc: 0.5555 - val_loss: 0.7002 - val_accuracy: 0.4920 - val_auc: 0.5084\n",
      "Epoch 80/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6866 - accuracy: 0.5520 - auc: 0.5537 - val_loss: 0.6996 - val_accuracy: 0.4880 - val_auc: 0.5068\n",
      "Epoch 81/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6866 - accuracy: 0.5510 - auc: 0.5530 - val_loss: 0.6995 - val_accuracy: 0.4880 - val_auc: 0.5064\n",
      "Epoch 82/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6862 - accuracy: 0.5480 - auc: 0.5533 - val_loss: 0.6997 - val_accuracy: 0.4800 - val_auc: 0.5086\n",
      "Epoch 83/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6862 - accuracy: 0.5500 - auc: 0.5529 - val_loss: 0.6990 - val_accuracy: 0.4800 - val_auc: 0.5089\n",
      "Epoch 84/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6869 - accuracy: 0.5430 - auc: 0.5518 - val_loss: 0.7004 - val_accuracy: 0.4920 - val_auc: 0.5080\n",
      "Epoch 85/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6864 - accuracy: 0.5450 - auc: 0.5539 - val_loss: 0.6997 - val_accuracy: 0.4800 - val_auc: 0.5101\n",
      "Epoch 86/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6861 - accuracy: 0.5500 - auc: 0.5550 - val_loss: 0.7007 - val_accuracy: 0.4920 - val_auc: 0.5100\n",
      "Epoch 87/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6864 - accuracy: 0.5500 - auc: 0.5532 - val_loss: 0.7001 - val_accuracy: 0.4880 - val_auc: 0.5095\n",
      "Epoch 88/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6863 - accuracy: 0.5340 - auc: 0.5538 - val_loss: 0.7014 - val_accuracy: 0.4800 - val_auc: 0.5103\n",
      "Epoch 89/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6862 - accuracy: 0.5390 - auc: 0.5539 - val_loss: 0.7014 - val_accuracy: 0.4840 - val_auc: 0.5100\n",
      "Epoch 90/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6860 - accuracy: 0.5510 - auc: 0.5551 - val_loss: 0.7003 - val_accuracy: 0.4920 - val_auc: 0.5091\n",
      "Epoch 91/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6857 - accuracy: 0.5490 - auc: 0.5578 - val_loss: 0.7009 - val_accuracy: 0.4920 - val_auc: 0.5132\n",
      "Epoch 92/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6857 - accuracy: 0.5530 - auc: 0.5561 - val_loss: 0.7005 - val_accuracy: 0.4920 - val_auc: 0.5097\n",
      "Epoch 93/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6856 - accuracy: 0.5450 - auc: 0.5581 - val_loss: 0.7001 - val_accuracy: 0.4840 - val_auc: 0.5092\n",
      "Epoch 94/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6855 - accuracy: 0.5470 - auc: 0.5579 - val_loss: 0.7011 - val_accuracy: 0.4960 - val_auc: 0.5107\n",
      "Epoch 95/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6858 - accuracy: 0.5460 - auc: 0.5554 - val_loss: 0.7002 - val_accuracy: 0.4880 - val_auc: 0.5066\n",
      "Epoch 96/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6854 - accuracy: 0.5520 - auc: 0.5590 - val_loss: 0.7002 - val_accuracy: 0.4880 - val_auc: 0.5107\n",
      "Epoch 97/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6857 - accuracy: 0.5410 - auc: 0.5573 - val_loss: 0.6982 - val_accuracy: 0.4880 - val_auc: 0.5118\n",
      "Epoch 98/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6853 - accuracy: 0.5540 - auc: 0.5592 - val_loss: 0.7000 - val_accuracy: 0.4920 - val_auc: 0.5113\n",
      "Epoch 99/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6853 - accuracy: 0.5510 - auc: 0.5581 - val_loss: 0.6999 - val_accuracy: 0.4920 - val_auc: 0.5112\n",
      "Epoch 100/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6851 - accuracy: 0.5550 - auc: 0.5583 - val_loss: 0.6995 - val_accuracy: 0.4920 - val_auc: 0.5113\n",
      "Epoch 101/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6850 - accuracy: 0.5460 - auc: 0.5605 - val_loss: 0.6992 - val_accuracy: 0.4960 - val_auc: 0.5146\n",
      "Epoch 102/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6851 - accuracy: 0.5440 - auc: 0.5591 - val_loss: 0.7000 - val_accuracy: 0.4880 - val_auc: 0.5116\n",
      "Epoch 103/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6852 - accuracy: 0.5470 - auc: 0.5605 - val_loss: 0.6993 - val_accuracy: 0.4960 - val_auc: 0.5159\n",
      "Epoch 104/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.5460 - auc: 0.5613 - val_loss: 0.6997 - val_accuracy: 0.4920 - val_auc: 0.5123\n",
      "Epoch 105/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.5470 - auc: 0.5604 - val_loss: 0.7001 - val_accuracy: 0.4920 - val_auc: 0.5106\n",
      "Epoch 106/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6849 - accuracy: 0.5480 - auc: 0.5616 - val_loss: 0.7012 - val_accuracy: 0.4920 - val_auc: 0.5126\n",
      "Epoch 107/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6847 - accuracy: 0.5490 - auc: 0.5617 - val_loss: 0.7009 - val_accuracy: 0.4960 - val_auc: 0.5111\n",
      "Epoch 108/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6848 - accuracy: 0.5550 - auc: 0.5630 - val_loss: 0.6995 - val_accuracy: 0.4960 - val_auc: 0.5117\n",
      "Epoch 109/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6846 - accuracy: 0.5490 - auc: 0.5634 - val_loss: 0.6998 - val_accuracy: 0.4840 - val_auc: 0.5109\n",
      "Epoch 110/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6848 - accuracy: 0.5510 - auc: 0.5626 - val_loss: 0.6989 - val_accuracy: 0.4920 - val_auc: 0.5127\n",
      "Epoch 111/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6845 - accuracy: 0.5470 - auc: 0.5623 - val_loss: 0.7004 - val_accuracy: 0.4920 - val_auc: 0.5148\n",
      "Epoch 112/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6845 - accuracy: 0.5540 - auc: 0.5620 - val_loss: 0.6999 - val_accuracy: 0.4920 - val_auc: 0.5129\n",
      "Epoch 113/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6845 - accuracy: 0.5490 - auc: 0.5637 - val_loss: 0.7003 - val_accuracy: 0.4920 - val_auc: 0.5127\n",
      "Epoch 114/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6842 - accuracy: 0.5530 - auc: 0.5637 - val_loss: 0.7001 - val_accuracy: 0.4960 - val_auc: 0.5153\n",
      "Epoch 115/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6844 - accuracy: 0.5500 - auc: 0.5619 - val_loss: 0.6995 - val_accuracy: 0.4960 - val_auc: 0.5133\n",
      "Epoch 116/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6842 - accuracy: 0.5520 - auc: 0.5622 - val_loss: 0.6997 - val_accuracy: 0.4960 - val_auc: 0.5144\n",
      "Epoch 117/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6840 - accuracy: 0.5480 - auc: 0.5651 - val_loss: 0.7008 - val_accuracy: 0.4880 - val_auc: 0.5182\n",
      "Epoch 118/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6841 - accuracy: 0.5540 - auc: 0.5639 - val_loss: 0.7002 - val_accuracy: 0.4960 - val_auc: 0.5135\n",
      "Epoch 119/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6843 - accuracy: 0.5520 - auc: 0.5663 - val_loss: 0.7000 - val_accuracy: 0.4960 - val_auc: 0.5131\n",
      "Epoch 120/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6841 - accuracy: 0.5480 - auc: 0.5652 - val_loss: 0.7012 - val_accuracy: 0.4880 - val_auc: 0.5173\n",
      "Epoch 121/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6840 - accuracy: 0.5550 - auc: 0.5659 - val_loss: 0.6997 - val_accuracy: 0.4920 - val_auc: 0.5186\n",
      "Epoch 122/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6837 - accuracy: 0.5550 - auc: 0.5678 - val_loss: 0.7007 - val_accuracy: 0.4920 - val_auc: 0.5137\n",
      "Epoch 123/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6836 - accuracy: 0.5560 - auc: 0.5665 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5154\n",
      "Epoch 124/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6835 - accuracy: 0.5530 - auc: 0.5672 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5167\n",
      "Epoch 125/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6834 - accuracy: 0.5640 - auc: 0.5694 - val_loss: 0.6992 - val_accuracy: 0.4920 - val_auc: 0.5157\n",
      "Epoch 126/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6832 - accuracy: 0.5550 - auc: 0.5691 - val_loss: 0.6993 - val_accuracy: 0.5000 - val_auc: 0.5178\n",
      "Epoch 127/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6830 - accuracy: 0.5530 - auc: 0.5699 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5172\n",
      "Epoch 128/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6830 - accuracy: 0.5520 - auc: 0.5700 - val_loss: 0.6999 - val_accuracy: 0.5000 - val_auc: 0.5162\n",
      "Epoch 129/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6832 - accuracy: 0.5510 - auc: 0.5682 - val_loss: 0.6988 - val_accuracy: 0.4880 - val_auc: 0.5150\n",
      "Epoch 130/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6830 - accuracy: 0.5460 - auc: 0.5703 - val_loss: 0.6989 - val_accuracy: 0.4840 - val_auc: 0.5169\n",
      "Epoch 131/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6829 - accuracy: 0.5560 - auc: 0.5720 - val_loss: 0.6987 - val_accuracy: 0.4880 - val_auc: 0.5155\n",
      "Epoch 132/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6828 - accuracy: 0.5580 - auc: 0.5731 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5166\n",
      "Epoch 133/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6827 - accuracy: 0.5500 - auc: 0.5713 - val_loss: 0.7001 - val_accuracy: 0.4920 - val_auc: 0.5201\n",
      "Epoch 134/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6829 - accuracy: 0.5580 - auc: 0.5714 - val_loss: 0.6990 - val_accuracy: 0.5000 - val_auc: 0.5191\n",
      "Epoch 135/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6827 - accuracy: 0.5550 - auc: 0.5721 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5187\n",
      "Epoch 136/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6825 - accuracy: 0.5510 - auc: 0.5717 - val_loss: 0.7002 - val_accuracy: 0.5000 - val_auc: 0.5167\n",
      "Epoch 137/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6823 - accuracy: 0.5500 - auc: 0.5742 - val_loss: 0.6986 - val_accuracy: 0.4880 - val_auc: 0.5200\n",
      "Epoch 138/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6822 - accuracy: 0.5540 - auc: 0.5733 - val_loss: 0.6996 - val_accuracy: 0.4960 - val_auc: 0.5158\n",
      "Epoch 139/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6825 - accuracy: 0.5500 - auc: 0.5719 - val_loss: 0.6981 - val_accuracy: 0.4960 - val_auc: 0.5188\n",
      "Epoch 140/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6823 - accuracy: 0.5540 - auc: 0.5744 - val_loss: 0.6999 - val_accuracy: 0.4880 - val_auc: 0.5203\n",
      "Epoch 141/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6820 - accuracy: 0.5580 - auc: 0.5760 - val_loss: 0.6986 - val_accuracy: 0.4920 - val_auc: 0.5194\n",
      "Epoch 142/150\n",
      "100/100 [==============================] - 0s 3ms/step - loss: 0.6821 - accuracy: 0.5540 - auc: 0.5747 - val_loss: 0.6997 - val_accuracy: 0.4920 - val_auc: 0.5227\n",
      "Epoch 143/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6820 - accuracy: 0.5520 - auc: 0.5766 - val_loss: 0.6996 - val_accuracy: 0.4840 - val_auc: 0.5201\n",
      "Epoch 144/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6818 - accuracy: 0.5570 - auc: 0.5751 - val_loss: 0.6988 - val_accuracy: 0.4840 - val_auc: 0.5193\n",
      "Epoch 145/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6817 - accuracy: 0.5560 - auc: 0.5767 - val_loss: 0.6995 - val_accuracy: 0.4880 - val_auc: 0.5189\n",
      "Epoch 146/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6819 - accuracy: 0.5540 - auc: 0.5746 - val_loss: 0.7004 - val_accuracy: 0.5000 - val_auc: 0.5194\n",
      "Epoch 147/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6816 - accuracy: 0.5590 - auc: 0.5770 - val_loss: 0.6992 - val_accuracy: 0.4880 - val_auc: 0.5206\n",
      "Epoch 148/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6814 - accuracy: 0.5490 - auc: 0.5774 - val_loss: 0.6998 - val_accuracy: 0.4920 - val_auc: 0.5198\n",
      "Epoch 149/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6815 - accuracy: 0.5550 - auc: 0.5769 - val_loss: 0.7004 - val_accuracy: 0.4920 - val_auc: 0.5211\n",
      "Epoch 150/150\n",
      "100/100 [==============================] - 0s 2ms/step - loss: 0.6815 - accuracy: 0.5530 - auc: 0.5783 - val_loss: 0.7005 - val_accuracy: 0.5040 - val_auc: 0.5200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x216e1ed9cd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainResults = model.fit(X_train,y_train,epochs=150,batch_size=10, validation_data=(X_test,y_test))\n",
    "trainResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x216e6350be0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainResults.epoch\n",
    "trainResults.history['val_accuracy']\n",
    "trainResults.history['accuracy']\n",
    "\n",
    "plt.plot(trainResults.epoch,trainResults.history['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trainResults.epoch,trainResults.history['accuracy'], label='Accuracy')\n",
    "plt.plot(trainResults.epoch,trainResults.history['val_accuracy'], label='Val_Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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