New try LSTM old.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,  Dense, Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import yfinance as yf\n",
    "from pandas_datareader.data import DataReader\n",
    "pd.set_option(\"display.max_rows\",200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(prediction,target,grey=None,start=None,end=None):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10, 3), dpi=300)\n",
    "    \n",
    "    # else:\n",
    "    #     ylim = (df.min(),df.max())\n",
    "    \n",
    "\n",
    "    if not isinstance(prediction,pd.DataFrame):\n",
    "        Results=pd.DataFrame(prediction,index=target.index)\n",
    "        Results.plot(ax=ax,legend=True)\n",
    "        ylim = (Results.min().min(),Results.max().max())\n",
    "    else:\n",
    "        prediction.plot(ax=ax,legend=True)\n",
    "        ylim = (0,1)\n",
    "    ax.fill_between(target.index, 0, ylim[1]+1e-2, target,facecolor='k', alpha=0.1)\n",
    "    ax.axvline(x='2022-02-24', color='r')\n",
    "    if not isinstance(prediction,pd.DataFrame):\n",
    "        legend_list=[\"Prediction\", \"NBER recession indicator\"]\n",
    "    else:\n",
    "        legend_list=[\"Prediction\",'lower','upper', \"NBER recession indicator\"]\n",
    "    if '2022-02-24' in target.index:\n",
    "        legend_list+=['Russian invasion of Ukraine']\n",
    "    ax.legend(legend_list)\n",
    "        \n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_data=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r fixed_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(629, 126)\n"
     ]
    },
    {
     "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>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPL</th>\n",
       "      <th>RSXFS</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>UMCSENT</th>\n",
       "      <th>MZMSL</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>S&amp;P: indust</th>\n",
       "      <th>S&amp;P div yield</th>\n",
       "      <th>S&amp;P PE ratio</th>\n",
       "      <th>in a recession</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.009750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.014828</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001843</td>\n",
       "      <td>0.010833</td>\n",
       "      <td>-0.011895</td>\n",
       "      <td>-0.011013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.031962</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-02-01</th>\n",
       "      <td>0.001962</td>\n",
       "      <td>0.001020</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.005569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001908</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>-0.000561</td>\n",
       "      <td>0.022391</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001179</td>\n",
       "      <td>0.010407</td>\n",
       "      <td>0.004643</td>\n",
       "      <td>-0.013202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.032042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-03-01</th>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.002768</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.002941</td>\n",
       "      <td>-0.001829</td>\n",
       "      <td>0.002849</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.019583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-04-01</th>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.002760</td>\n",
       "      <td>0.005983</td>\n",
       "      <td>0.005425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005601</td>\n",
       "      <td>0.005047</td>\n",
       "      <td>0.003638</td>\n",
       "      <td>0.008156</td>\n",
       "      <td>0.006448</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000702</td>\n",
       "      <td>0.007395</td>\n",
       "      <td>0.006602</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.027399</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.004074</td>\n",
       "      <td>-0.000729</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>0.000866</td>\n",
       "      <td>0.015415</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001328</td>\n",
       "      <td>-0.003425</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>-0.025178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.020088</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI   W875RX1  DPCERA3M086SBEA  CMRMTSPL  RSXFS    INDPRO  \\\n",
       "1971-01-01  0.008594  0.009600         0.013628  0.009750    NaN  0.007663   \n",
       "1971-02-01  0.001962  0.001020         0.001887  0.005569    NaN -0.001908   \n",
       "1971-03-01  0.003127  0.002768         0.000489  0.004140    NaN -0.001085   \n",
       "1971-04-01  0.002685  0.002760         0.005983  0.005425    NaN  0.005601   \n",
       "1971-05-01  0.003806  0.004074        -0.000729  0.004872    NaN  0.005062   \n",
       "\n",
       "             IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  UMCSENT     MZMSL  \\\n",
       "1971-01-01  0.003694  0.003908  0.014828  0.039006  ...      NaN  0.001843   \n",
       "1971-02-01  0.000877 -0.001373 -0.000561  0.022391  ...      NaN  0.001179   \n",
       "1971-03-01 -0.002941 -0.001829  0.002849  0.001160  ...      NaN  0.000532   \n",
       "1971-04-01  0.005047  0.003638  0.008156  0.006448  ...      NaN -0.000702   \n",
       "1971-05-01  0.000738 -0.000040  0.000866  0.015415  ...      NaN  0.001328   \n",
       "\n",
       "            DTCOLNVHFNM  DTCTHFNM    INVEST  S&P: indust  S&P div yield  \\\n",
       "1971-01-01     0.010833 -0.011895 -0.011013          NaN          -0.14   \n",
       "1971-02-01     0.010407  0.004643 -0.013202          NaN          -0.14   \n",
       "1971-03-01     0.001600  0.000542  0.003094          NaN          -0.09   \n",
       "1971-04-01     0.007395  0.006602  0.001663          NaN          -0.10   \n",
       "1971-05-01    -0.003425 -0.000199 -0.025178          NaN           0.03   \n",
       "\n",
       "            S&P PE ratio  in a recession  target  \n",
       "1971-01-01      0.031962               0     0.0  \n",
       "1971-02-01      0.032042               0     0.0  \n",
       "1971-03-01      0.019583               0     0.0  \n",
       "1971-04-01      0.027399               0     0.0  \n",
       "1971-05-01     -0.020088               0     0.0  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df=yf.download(\"GE\")\n",
    "# df.head()\n",
    "n_lookback=30\n",
    "n_forecast=1\n",
    "\n",
    "df=pd.read_csv('Data/Transformed HistoricalVariables.csv',index_col=0,parse_dates=True)\n",
    "# df=fixed_data\n",
    "\n",
    "recessions =  DataReader('USRECDM', 'fred', start='1800')\n",
    "df[f'in a recession']=recessions\n",
    "df['target'] = recessions.shift(-250)\n",
    "\n",
    "df.fillna(method='ffill',inplace=True)\n",
    "# df=df.fillna(-1)\n",
    "df=df['1971':] #.resample('MS').last()\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.target.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createXY(dataset,n_lookback = 60,n_forecast = 3,target_col=-1):\n",
    "    X = []\n",
    "    Y = []\n",
    "\n",
    "    for i in range(n_lookback, len(dataset) - n_forecast + 1):\n",
    "        X.append(dataset[i - n_lookback: i, 0:dataset.shape[1]-1])\n",
    "        Y.append(dataset[i: i + n_forecast,target_col])\n",
    "\n",
    "    return np.array(X),np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_for_training shape:  (12419, 126)\n",
      "df_for_testing shape:  (7091, 126)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((12389, 30, 125), (12389, 1))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_training=df[:\"2004\"]\n",
    "df_for_testing=df[\"2004\":]\n",
    "print('df_for_training shape: ',df_for_training.shape)\n",
    "print('df_for_testing shape: ',df_for_testing.shape)\n",
    "\n",
    "# Scalling\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "df_for_training_scaled = scaler.fit_transform(df_for_training.fillna(0))\n",
    "df_for_testing_scaled=scaler.transform(df_for_testing.fillna(0))\n",
    "\n",
    "print()\n",
    "trainX,trainY=createXY(df_for_training_scaled,n_lookback=n_lookback,n_forecast=n_forecast)\n",
    "testX,testY=createXY(df_for_testing_scaled,n_lookback=n_lookback,n_forecast=n_forecast)\n",
    "trainX.shape,trainY.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# with recessions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CustomLoss(labels, pred,w=0.2): \n",
    "    logits=tf.math.log(pred/(1-pred))\n",
    "    return tf.nn.weighted_cross_entropy_with_logits(labels, logits, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import LSTM,  Dense, Dropout, Conv1D, MaxPool1D,Flatten,RepeatVector\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "PT_path='Model/CNN2-LSTM-test'\n",
    "f1 = EarlyStopping(monitor='recall', mode='max', verbose=1, patience=30)\n",
    "f2 = ModelCheckpoint(filepath=PT_path, monitor='recall', mode='max', verbose=0, save_best_only=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_24 (Conv1D)          (None, 28, 32)            12032     \n",
      "                                                                 \n",
      " conv1d_25 (Conv1D)          (None, 26, 32)            3104      \n",
      "                                                                 \n",
      " max_pooling1d_12 (MaxPoolin  (None, 26, 32)           0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 26, 32)            0         \n",
      "                                                                 \n",
      " flatten_12 (Flatten)        (None, 832)               0         \n",
      "                                                                 \n",
      " repeat_vector_12 (RepeatVec  (None, 1, 832)           0         \n",
      " tor)                                                            \n",
      "                                                                 \n",
      " lstm_12 (LSTM)              (None, 100)               373200    \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 388,437\n",
      "Trainable params: 388,437\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.1633 - recall: 0.0273 - precision: 0.1406 - auc: 0.5186"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 16s 335ms/step - loss: 0.1633 - recall: 0.0273 - precision: 0.1406 - auc: 0.5186 - val_loss: 0.0307 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 2/250\n",
      "35/35 [==============================] - 2s 69ms/step - loss: 0.1261 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.6916 - val_loss: 0.0266 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 3/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.1111 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.8210 - val_loss: 0.0064 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 4/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0959 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.8865 - val_loss: 0.0030 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 5/250\n",
      "35/35 [==============================] - 3s 83ms/step - loss: 0.0901 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9107 - val_loss: 0.0013 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 6/250\n",
      "35/35 [==============================] - 2s 69ms/step - loss: 0.0864 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9218 - val_loss: 7.1905e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 7/250\n",
      "35/35 [==============================] - 2s 63ms/step - loss: 0.0831 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9289 - val_loss: 5.3444e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 8/250\n",
      "35/35 [==============================] - 2s 63ms/step - loss: 0.0824 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9267 - val_loss: 2.0354e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 9/250\n",
      "35/35 [==============================] - 2s 63ms/step - loss: 0.0790 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9354 - val_loss: 1.4869e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 10/250\n",
      "35/35 [==============================] - 2s 65ms/step - loss: 0.0768 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9401 - val_loss: 9.0507e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 11/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0755 - recall: 0.0576 - precision: 1.0000 - auc: 0.9424"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 316ms/step - loss: 0.0755 - recall: 0.0576 - precision: 1.0000 - auc: 0.9424 - val_loss: 6.6992e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 12/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0727 - recall: 0.1117 - precision: 0.9946 - auc: 0.9481"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 270ms/step - loss: 0.0727 - recall: 0.1117 - precision: 0.9946 - auc: 0.9481 - val_loss: 5.0688e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 13/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0701 - recall: 0.1383 - precision: 0.9913 - auc: 0.9538"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 17s 485ms/step - loss: 0.0701 - recall: 0.1383 - precision: 0.9913 - auc: 0.9538 - val_loss: 6.2725e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 14/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0652 - recall: 0.1930 - precision: 0.9969 - auc: 0.9638"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 13s 369ms/step - loss: 0.0652 - recall: 0.1930 - precision: 0.9969 - auc: 0.9638 - val_loss: 3.7246e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 15/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0629 - recall: 0.2603 - precision: 0.9977 - auc: 0.9656"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 12s 353ms/step - loss: 0.0629 - recall: 0.2603 - precision: 0.9977 - auc: 0.9656 - val_loss: 1.6590e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 16/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0628 - recall: 0.3240 - precision: 0.9656 - auc: 0.9660"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 331ms/step - loss: 0.0628 - recall: 0.3240 - precision: 0.9656 - auc: 0.9660 - val_loss: 1.4480e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 17/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0624 - recall: 0.3362 - precision: 0.9668 - auc: 0.9659"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 12s 348ms/step - loss: 0.0624 - recall: 0.3362 - precision: 0.9668 - auc: 0.9659 - val_loss: 1.1266e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 18/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0573 - recall: 0.3865 - precision: 0.9907 - auc: 0.9718"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 13s 389ms/step - loss: 0.0573 - recall: 0.3865 - precision: 0.9907 - auc: 0.9718 - val_loss: 5.3249e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 19/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0562 - recall: 0.4181 - precision: 0.9843 - auc: 0.9711"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 17s 501ms/step - loss: 0.0562 - recall: 0.4181 - precision: 0.9843 - auc: 0.9711 - val_loss: 4.1842e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 20/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0531 - recall: 0.4612 - precision: 0.9769 - auc: 0.9768"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 317ms/step - loss: 0.0531 - recall: 0.4612 - precision: 0.9769 - auc: 0.9768 - val_loss: 2.9511e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 21/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0549 - recall: 0.4788 - precision: 0.9393 - auc: 0.9747"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 15s 448ms/step - loss: 0.0549 - recall: 0.4788 - precision: 0.9393 - auc: 0.9747 - val_loss: 2.1483e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 22/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0557 - recall: 0.4515 - precision: 0.9713 - auc: 0.9709 - val_loss: 2.0751e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 23/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0491 - recall: 0.5231 - precision: 0.9740 - auc: 0.9787"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 16s 456ms/step - loss: 0.0491 - recall: 0.5231 - precision: 0.9740 - auc: 0.9787 - val_loss: 1.7873e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 24/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0462 - recall: 0.5431 - precision: 0.9760 - auc: 0.9823"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 22s 655ms/step - loss: 0.0462 - recall: 0.5431 - precision: 0.9760 - auc: 0.9823 - val_loss: 1.5741e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 25/250\n",
      "35/35 [==============================] - 3s 84ms/step - loss: 0.0473 - recall: 0.5394 - precision: 0.9769 - auc: 0.9799 - val_loss: 1.6934e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 26/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0428 - recall: 0.5643 - precision: 0.9810 - auc: 0.9856"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 14s 403ms/step - loss: 0.0428 - recall: 0.5643 - precision: 0.9810 - auc: 0.9856 - val_loss: 1.4432e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 27/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0386 - recall: 0.6292 - precision: 0.9867 - auc: 0.9880"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 21s 615ms/step - loss: 0.0386 - recall: 0.6292 - precision: 0.9867 - auc: 0.9880 - val_loss: 1.2400e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 28/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0390 - recall: 0.6141 - precision: 0.9778 - auc: 0.9886 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 29/250\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 0.0386 - recall: 0.6189 - precision: 0.9808 - auc: 0.9877 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 30/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0376 - recall: 0.6450 - precision: 0.9770 - auc: 0.9886"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 14s 403ms/step - loss: 0.0376 - recall: 0.6450 - precision: 0.9770 - auc: 0.9886 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 31/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0351 - recall: 0.6644 - precision: 0.9821 - auc: 0.9900"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 16s 480ms/step - loss: 0.0351 - recall: 0.6644 - precision: 0.9821 - auc: 0.9900 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 32/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0366 - recall: 0.6523 - precision: 0.9720 - auc: 0.9880 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 33/250\n",
      "35/35 [==============================] - 2s 70ms/step - loss: 0.0388 - recall: 0.6286 - precision: 0.9728 - auc: 0.9866 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 34/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0360 - recall: 0.6450 - precision: 0.9717 - auc: 0.9895 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 35/250\n",
      "35/35 [==============================] - 3s 98ms/step - loss: 0.0340 - recall: 0.6572 - precision: 0.9863 - auc: 0.9909 - val_loss: 9.5367e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 36/250\n",
      "35/35 [==============================] - 4s 115ms/step - loss: 0.0327 - recall: 0.6602 - precision: 0.9811 - auc: 0.9919 - val_loss: 8.7824e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 37/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0302 - recall: 0.6899 - precision: 0.9853 - auc: 0.9931"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 333ms/step - loss: 0.0302 - recall: 0.6899 - precision: 0.9853 - auc: 0.9931 - val_loss: 8.6362e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 38/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0322 - recall: 0.6960 - precision: 0.9820 - auc: 0.9896"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 27s 782ms/step - loss: 0.0322 - recall: 0.6960 - precision: 0.9820 - auc: 0.9896 - val_loss: 3.5407e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 39/250\n",
      "35/35 [==============================] - 5s 141ms/step - loss: 0.0333 - recall: 0.6742 - precision: 0.9849 - auc: 0.9896 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 40/250\n",
      "35/35 [==============================] - 5s 140ms/step - loss: 0.0328 - recall: 0.6650 - precision: 0.9786 - auc: 0.9909 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 41/250\n",
      "35/35 [==============================] - 5s 138ms/step - loss: 0.0356 - recall: 0.6377 - precision: 0.9731 - auc: 0.9891 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 42/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0269 - recall: 0.7124 - precision: 0.9866 - auc: 0.9952"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 31s 903ms/step - loss: 0.0269 - recall: 0.7124 - precision: 0.9866 - auc: 0.9952 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 43/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0278 - recall: 0.7154 - precision: 0.9858 - auc: 0.9927"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 29s 839ms/step - loss: 0.0278 - recall: 0.7154 - precision: 0.9858 - auc: 0.9927 - val_loss: 4.6952e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 44/250\n",
      "35/35 [==============================] - 5s 150ms/step - loss: 0.0288 - recall: 0.7002 - precision: 0.9838 - auc: 0.9930 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 45/250\n",
      "35/35 [==============================] - 5s 135ms/step - loss: 0.0294 - recall: 0.6960 - precision: 0.9854 - auc: 0.9920 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 46/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0240 - recall: 0.7282 - precision: 0.9893 - auc: 0.9958"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 26s 772ms/step - loss: 0.0240 - recall: 0.7282 - precision: 0.9893 - auc: 0.9958 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 47/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0236 - recall: 0.7397 - precision: 0.9870 - auc: 0.9956"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 31s 903ms/step - loss: 0.0236 - recall: 0.7397 - precision: 0.9870 - auc: 0.9956 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 48/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0229 - recall: 0.7409 - precision: 0.9903 - auc: 0.9951"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 32s 945ms/step - loss: 0.0229 - recall: 0.7409 - precision: 0.9903 - auc: 0.9951 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 49/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0207 - recall: 0.7725 - precision: 0.9861 - auc: 0.9973"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 33s 975ms/step - loss: 0.0207 - recall: 0.7725 - precision: 0.9861 - auc: 0.9973 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 50/250\n",
      "35/35 [==============================] - 6s 176ms/step - loss: 0.0218 - recall: 0.7712 - precision: 0.9891 - auc: 0.9962 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 51/250\n",
      "35/35 [==============================] - 6s 183ms/step - loss: 0.0242 - recall: 0.7451 - precision: 0.9856 - auc: 0.9951 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 52/250\n",
      "35/35 [==============================] - 6s 179ms/step - loss: 0.0324 - recall: 0.7093 - precision: 0.9766 - auc: 0.9881 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 53/250\n",
      "35/35 [==============================] - 6s 168ms/step - loss: 0.0237 - recall: 0.7379 - precision: 0.9862 - auc: 0.9960 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 54/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0209 - recall: 0.7761 - precision: 0.9869 - auc: 0.9967"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 39s 1s/step - loss: 0.0209 - recall: 0.7761 - precision: 0.9869 - auc: 0.9967 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 55/250\n",
      "35/35 [==============================] - 6s 162ms/step - loss: 0.0299 - recall: 0.7221 - precision: 0.9810 - auc: 0.9868 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 56/250\n",
      "35/35 [==============================] - 6s 172ms/step - loss: 0.0306 - recall: 0.7015 - precision: 0.9755 - auc: 0.9908 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 57/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0203 - recall: 0.8064 - precision: 0.9852 - auc: 0.9970"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 35s 1s/step - loss: 0.0203 - recall: 0.8064 - precision: 0.9852 - auc: 0.9970 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 58/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0183 - recall: 0.8155 - precision: 0.9882 - auc: 0.9978"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 34s 996ms/step - loss: 0.0183 - recall: 0.8155 - precision: 0.9882 - auc: 0.9978 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 59/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0178 - recall: 0.8325 - precision: 0.9856 - auc: 0.9979"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 36s 1s/step - loss: 0.0178 - recall: 0.8325 - precision: 0.9856 - auc: 0.9979 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 60/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0150 - recall: 0.8592 - precision: 0.9930 - auc: 0.9987"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 33s 966ms/step - loss: 0.0150 - recall: 0.8592 - precision: 0.9930 - auc: 0.9987 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 61/250\n",
      "35/35 [==============================] - 6s 162ms/step - loss: 0.0174 - recall: 0.8447 - precision: 0.9858 - auc: 0.9980 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 62/250\n",
      "35/35 [==============================] - 6s 162ms/step - loss: 0.0173 - recall: 0.8398 - precision: 0.9900 - auc: 0.9979 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 63/250\n",
      "35/35 [==============================] - 5s 151ms/step - loss: 0.0155 - recall: 0.8483 - precision: 0.9929 - auc: 0.9984 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 64/250\n",
      "35/35 [==============================] - 5s 143ms/step - loss: 0.0238 - recall: 0.8180 - precision: 0.9825 - auc: 0.9878 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 65/250\n",
      "35/35 [==============================] - 5s 149ms/step - loss: 0.0328 - recall: 0.6978 - precision: 0.9705 - auc: 0.9903 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 66/250\n",
      "35/35 [==============================] - 5s 150ms/step - loss: 0.0167 - recall: 0.8434 - precision: 0.9893 - auc: 0.9984 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 67/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0135 - recall: 0.8744 - precision: 0.9938 - auc: 0.9990"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 34s 1000ms/step - loss: 0.0135 - recall: 0.8744 - precision: 0.9938 - auc: 0.9990 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 68/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0126 - recall: 0.8926 - precision: 0.9912 - auc: 0.9988"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 33s 980ms/step - loss: 0.0126 - recall: 0.8926 - precision: 0.9912 - auc: 0.9988 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 69/250\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 0.0136 - recall: 0.8768 - precision: 0.9911 - auc: 0.9988 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 70/250\n",
      "35/35 [==============================] - 3s 96ms/step - loss: 0.0163 - recall: 0.8738 - precision: 0.9863 - auc: 0.9976 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 71/250\n",
      "35/35 [==============================] - 4s 106ms/step - loss: 0.0137 - recall: 0.8738 - precision: 0.9883 - auc: 0.9986 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 72/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0104 - recall: 0.9066 - precision: 0.9960 - auc: 0.9995"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 15s 453ms/step - loss: 0.0104 - recall: 0.9066 - precision: 0.9960 - auc: 0.9995 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 73/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0144 - recall: 0.8768 - precision: 0.9911 - auc: 0.9986 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 74/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0212 - recall: 0.8240 - precision: 0.9784 - auc: 0.9914 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 75/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0109 - recall: 0.9072 - precision: 0.9953 - auc: 0.9994"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 263ms/step - loss: 0.0109 - recall: 0.9072 - precision: 0.9953 - auc: 0.9994 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 76/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0106 - recall: 0.9169 - precision: 0.9921 - auc: 0.9994"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 268ms/step - loss: 0.0106 - recall: 0.9169 - precision: 0.9921 - auc: 0.9994 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 77/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0095 - recall: 0.9199 - precision: 0.9954 - auc: 0.9996"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 10s 290ms/step - loss: 0.0095 - recall: 0.9199 - precision: 0.9954 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 78/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0134 - recall: 0.8847 - precision: 0.9898 - auc: 0.9981 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 79/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0118 - recall: 0.9047 - precision: 0.9881 - auc: 0.9991 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 80/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0104 - recall: 0.9078 - precision: 0.9960 - auc: 0.9993 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 81/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0085 - recall: 0.9272 - precision: 0.9961 - auc: 0.9996"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 256ms/step - loss: 0.0085 - recall: 0.9272 - precision: 0.9961 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 82/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0076 - recall: 0.9351 - precision: 0.9968 - auc: 0.9997"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 333ms/step - loss: 0.0076 - recall: 0.9351 - precision: 0.9968 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 83/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0066 - recall: 0.9478 - precision: 0.9987 - auc: 0.9998"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 251ms/step - loss: 0.0066 - recall: 0.9478 - precision: 0.9987 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 84/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0295 - recall: 0.8289 - precision: 0.9688 - auc: 0.9778 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 85/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0180 - recall: 0.8228 - precision: 0.9876 - auc: 0.9978 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 86/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0093 - recall: 0.9211 - precision: 0.9967 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 87/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0083 - recall: 0.9333 - precision: 0.9961 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 88/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0072 - recall: 0.9357 - precision: 0.9961 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 89/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0078 - recall: 0.9369 - precision: 0.9968 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 90/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0144 - recall: 0.8786 - precision: 0.9897 - auc: 0.9960 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 91/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0085 - recall: 0.9266 - precision: 0.9967 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 92/250\n",
      "35/35 [==============================] - 3s 83ms/step - loss: 0.0135 - recall: 0.9047 - precision: 0.9881 - auc: 0.9959 - val_loss: 1.1546e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 93/250\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 0.0140 - recall: 0.8883 - precision: 0.9919 - auc: 0.9970 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 94/250\n",
      "35/35 [==============================] - 3s 97ms/step - loss: 0.0071 - recall: 0.9417 - precision: 0.9955 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 95/250\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 0.0072 - recall: 0.9411 - precision: 0.9942 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 96/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0125 - recall: 0.8987 - precision: 0.9926 - auc: 0.9958 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 97/250\n",
      "35/35 [==============================] - 3s 77ms/step - loss: 0.0076 - recall: 0.9302 - precision: 0.9942 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 98/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0111 - recall: 0.9066 - precision: 0.9907 - auc: 0.9982 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 99/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0105 - recall: 0.9199 - precision: 0.9902 - auc: 0.9986 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 100/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0105 - recall: 0.9035 - precision: 0.9933 - auc: 0.9984 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 101/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0062 - recall: 0.9545 - precision: 0.9968 - auc: 0.9998"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 277ms/step - loss: 0.0062 - recall: 0.9545 - precision: 0.9968 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 102/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0062 - recall: 0.9490 - precision: 0.9981 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 103/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0095 - recall: 0.9272 - precision: 0.9909 - auc: 0.9976 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 104/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0088 - recall: 0.9320 - precision: 0.9929 - auc: 0.9983 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 105/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0055 - recall: 0.9593 - precision: 0.9962 - auc: 0.9999"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 251ms/step - loss: 0.0055 - recall: 0.9593 - precision: 0.9962 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 106/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0059 - recall: 0.9508 - precision: 0.9956 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 107/250\n",
      "35/35 [==============================] - 3s 87ms/step - loss: 0.0083 - recall: 0.9345 - precision: 0.9923 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 108/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0064 - recall: 0.9478 - precision: 0.9962 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 109/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0054 - recall: 0.9636 - precision: 0.9944 - auc: 0.9995"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 255ms/step - loss: 0.0054 - recall: 0.9636 - precision: 0.9944 - auc: 0.9995 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 110/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0053 - recall: 0.9630 - precision: 0.9950 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 111/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0061 - recall: 0.9539 - precision: 0.9968 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 112/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0049 - recall: 0.9587 - precision: 0.9968 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 113/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0046 - recall: 0.9666 - precision: 0.9962 - auc: 0.9999"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 270ms/step - loss: 0.0046 - recall: 0.9666 - precision: 0.9962 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 114/250\n",
      "35/35 [==============================] - 3s 85ms/step - loss: 0.0125 - recall: 0.9157 - precision: 0.9869 - auc: 0.9925 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 115/250\n",
      "35/35 [==============================] - 3s 81ms/step - loss: 0.0211 - recall: 0.8228 - precision: 0.9791 - auc: 0.9936 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 116/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0062 - recall: 0.9405 - precision: 0.9968 - auc: 0.9995 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 117/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0097 - recall: 0.9223 - precision: 0.9909 - auc: 0.9975 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 118/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0088 - recall: 0.9339 - precision: 0.9923 - auc: 0.9989 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 119/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0057 - recall: 0.9593 - precision: 0.9968 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 120/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0038 - recall: 0.9769 - precision: 0.9988 - auc: 0.9999"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 10s 306ms/step - loss: 0.0038 - recall: 0.9769 - precision: 0.9988 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 121/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0058 - recall: 0.9551 - precision: 0.9962 - auc: 0.9986 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 122/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0061 - recall: 0.9551 - precision: 0.9987 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 123/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0045 - recall: 0.9763 - precision: 0.9957 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 124/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0182 - recall: 0.8817 - precision: 0.9851 - auc: 0.9921 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 125/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0162 - recall: 0.8768 - precision: 0.9904 - auc: 0.9915 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 126/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0059 - recall: 0.9575 - precision: 0.9962 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 127/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0050 - recall: 0.9630 - precision: 0.9987 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 128/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0043 - recall: 0.9727 - precision: 0.9969 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 129/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0111 - recall: 0.8999 - precision: 0.9893 - auc: 0.9986 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 130/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0049 - recall: 0.9618 - precision: 0.9944 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 131/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0067 - recall: 0.9502 - precision: 0.9924 - auc: 0.9994 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 132/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0132 - recall: 0.8987 - precision: 0.9867 - auc: 0.9945 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 133/250\n",
      "35/35 [==============================] - 3s 87ms/step - loss: 0.0100 - recall: 0.9272 - precision: 0.9909 - auc: 0.9972 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 134/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0080 - recall: 0.9290 - precision: 0.9929 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 135/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0136 - recall: 0.9187 - precision: 0.9915 - auc: 0.9879 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 136/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0157 - recall: 0.8768 - precision: 0.9823 - auc: 0.9960 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 137/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0035 - recall: 0.9775 - precision: 1.0000 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 8s 247ms/step - loss: 0.0035 - recall: 0.9775 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 138/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0041 - recall: 0.9739 - precision: 0.9963 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 139/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0032 - recall: 0.9806 - precision: 0.9981 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 271ms/step - loss: 0.0032 - recall: 0.9806 - precision: 0.9981 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 140/250\n",
      "35/35 [==============================] - 2s 71ms/step - loss: 0.0046 - recall: 0.9666 - precision: 0.9962 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 141/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0045 - recall: 0.9666 - precision: 0.9969 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 142/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0038 - recall: 0.9733 - precision: 0.9963 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 143/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0029 - recall: 0.9806 - precision: 0.9981 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 144/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0055 - recall: 0.9563 - precision: 0.9981 - auc: 0.9992 - val_loss: 7.6202e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 145/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0075 - recall: 0.9417 - precision: 0.9942 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 146/250\n",
      "35/35 [==============================] - 3s 72ms/step - loss: 0.0040 - recall: 0.9727 - precision: 0.9975 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 147/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0029 - recall: 0.9782 - precision: 0.9981 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 148/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0029 - recall: 0.9836 - precision: 0.9975 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 266ms/step - loss: 0.0029 - recall: 0.9836 - precision: 0.9975 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 149/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0054 - recall: 0.9557 - precision: 0.9956 - auc: 0.9995 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 150/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0023 - recall: 0.9854 - precision: 0.9988 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 10s 278ms/step - loss: 0.0023 - recall: 0.9854 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 151/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0073 - recall: 0.9484 - precision: 0.9918 - auc: 0.9978 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 152/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0061 - recall: 0.9545 - precision: 0.9943 - auc: 0.9992 - val_loss: 9.2366e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 153/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0254 - recall: 0.8216 - precision: 0.9699 - auc: 0.9871 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 154/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0137 - recall: 0.8920 - precision: 0.9906 - auc: 0.9956 - val_loss: 1.3855e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 155/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0040 - recall: 0.9715 - precision: 0.9963 - auc: 0.9999 - val_loss: 1.5394e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 156/250\n",
      "35/35 [==============================] - 3s 81ms/step - loss: 0.0034 - recall: 0.9794 - precision: 0.9981 - auc: 0.9999 - val_loss: 1.3855e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 157/250\n",
      "35/35 [==============================] - 3s 91ms/step - loss: 0.0062 - recall: 0.9508 - precision: 0.9943 - auc: 0.9994 - val_loss: 2.6170e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 158/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0044 - recall: 0.9666 - precision: 0.9975 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 159/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0041 - recall: 0.9630 - precision: 0.9962 - auc: 0.9999 - val_loss: 1.5394e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 160/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0025 - recall: 0.9830 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 161/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0020 - recall: 0.9836 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 162/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0054 - recall: 0.9624 - precision: 0.9919 - auc: 0.9995 - val_loss: 1.2623e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 163/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0084 - recall: 0.9272 - precision: 0.9941 - auc: 0.9977 - val_loss: 1.5394e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 164/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0030 - recall: 0.9788 - precision: 0.9981 - auc: 1.0000 - val_loss: 8.0820e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 165/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0070 - recall: 0.9478 - precision: 0.9949 - auc: 0.9987 - val_loss: 1.3855e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 166/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0032 - recall: 0.9757 - precision: 0.9981 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 167/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0108 - recall: 0.9242 - precision: 0.9858 - auc: 0.9981 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 168/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0056 - recall: 0.9551 - precision: 0.9956 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 169/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0027 - recall: 0.9806 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 170/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0024 - recall: 0.9860 - precision: 0.9982 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 256ms/step - loss: 0.0024 - recall: 0.9860 - precision: 0.9982 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 171/250\n",
      "35/35 [==============================] - 3s 84ms/step - loss: 0.0032 - recall: 0.9763 - precision: 0.9988 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 172/250\n",
      "35/35 [==============================] - 3s 85ms/step - loss: 0.0033 - recall: 0.9775 - precision: 0.9975 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 173/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0022 - recall: 0.9818 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 174/250\n",
      "35/35 [==============================] - 3s 83ms/step - loss: 0.0019 - recall: 0.9854 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 175/250\n",
      "35/35 [==============================] - 3s 82ms/step - loss: 0.0029 - recall: 0.9794 - precision: 0.9969 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 176/250\n",
      "35/35 [==============================] - 4s 101ms/step - loss: 0.0027 - recall: 0.9830 - precision: 0.9994 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 177/250\n",
      "35/35 [==============================] - 3s 88ms/step - loss: 0.0127 - recall: 0.9059 - precision: 0.9868 - auc: 0.9966 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 178/250\n",
      "35/35 [==============================] - 3s 82ms/step - loss: 0.0054 - recall: 0.9624 - precision: 0.9956 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 179/250\n",
      "35/35 [==============================] - 3s 82ms/step - loss: 0.0024 - recall: 0.9836 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 180/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0018 - recall: 0.9897 - precision: 0.9994 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 314ms/step - loss: 0.0018 - recall: 0.9897 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 181/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0030 - recall: 0.9800 - precision: 0.9988 - auc: 0.9993 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 182/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0079 - recall: 0.9466 - precision: 0.9924 - auc: 0.9980 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 183/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0030 - recall: 0.9775 - precision: 0.9981 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 184/250\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 0.0024 - recall: 0.9836 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 185/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0036 - recall: 0.9727 - precision: 0.9957 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 186/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0035 - recall: 0.9757 - precision: 0.9969 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 187/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0043 - recall: 0.9691 - precision: 0.9969 - auc: 0.9993 - val_loss: 7.6971e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 188/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0035 - recall: 0.9757 - precision: 0.9969 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 189/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0024 - recall: 0.9842 - precision: 0.9969 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 190/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0024 - recall: 0.9830 - precision: 0.9982 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 191/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0031 - recall: 0.9769 - precision: 0.9963 - auc: 0.9999 - val_loss: 7.6971e-10 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 192/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0157 - recall: 0.8956 - precision: 0.9833 - auc: 0.9941 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 193/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0056 - recall: 0.9539 - precision: 0.9968 - auc: 0.9995 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 194/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0017 - recall: 0.9915 - precision: 1.0000 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 10s 278ms/step - loss: 0.0017 - recall: 0.9915 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 195/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0031 - recall: 0.9782 - precision: 0.9975 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 196/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0030 - recall: 0.9800 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 197/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0015 - recall: 0.9897 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 198/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0019 - recall: 0.9885 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 199/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0167 - recall: 0.8975 - precision: 0.9860 - auc: 0.9889 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 200/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0077 - recall: 0.9430 - precision: 0.9917 - auc: 0.9987 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 201/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0022 - recall: 0.9854 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 202/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0025 - recall: 0.9836 - precision: 0.9975 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 203/250\n",
      "35/35 [==============================] - 3s 77ms/step - loss: 0.0015 - recall: 0.9885 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 204/250\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 0.0026 - recall: 0.9824 - precision: 0.9988 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 205/250\n",
      "35/35 [==============================] - 3s 79ms/step - loss: 0.0023 - recall: 0.9818 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 206/250\n",
      "35/35 [==============================] - 3s 78ms/step - loss: 0.0040 - recall: 0.9709 - precision: 0.9956 - auc: 0.9987 - val_loss: 1.1546e-08 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 207/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0058 - recall: 0.9612 - precision: 0.9944 - auc: 0.9998 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 208/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0054 - recall: 0.9618 - precision: 0.9950 - auc: 0.9980 - val_loss: 1.7165e-07 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 209/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0048 - recall: 0.9612 - precision: 0.9969 - auc: 0.9990 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 210/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0021 - recall: 0.9842 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 211/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0017 - recall: 0.9891 - precision: 0.9982 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 212/250\n",
      "35/35 [==============================] - 3s 77ms/step - loss: 0.0024 - recall: 0.9818 - precision: 0.9994 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 213/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0014 - recall: 0.9860 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 214/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0142 - recall: 0.9017 - precision: 0.9854 - auc: 0.9941 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 215/250\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 0.0042 - recall: 0.9733 - precision: 0.9950 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 216/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0021 - recall: 0.9879 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 217/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0020 - recall: 0.9848 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 218/250\n",
      "35/35 [==============================] - 3s 77ms/step - loss: 0.0059 - recall: 0.9703 - precision: 0.9938 - auc: 0.9968 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 219/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0195 - recall: 0.8805 - precision: 0.9817 - auc: 0.9877 - val_loss: 6.9274e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 220/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0022 - recall: 0.9812 - precision: 1.0000 - auc: 1.0000 - val_loss: 6.1577e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 221/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0017 - recall: 0.9867 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 222/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0023 - recall: 0.9854 - precision: 0.9963 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 223/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0019 - recall: 0.9842 - precision: 1.0000 - auc: 1.0000 - val_loss: 3.0789e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 224/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0028 - recall: 0.9806 - precision: 0.9988 - auc: 1.0000 - val_loss: 5.3880e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 225/250\n",
      "35/35 [==============================] - 3s 76ms/step - loss: 0.0018 - recall: 0.9860 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 226/250\n",
      "35/35 [==============================] - 3s 85ms/step - loss: 0.0013 - recall: 0.9897 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 227/250\n",
      "35/35 [==============================] - 3s 83ms/step - loss: 0.0035 - recall: 0.9709 - precision: 0.9950 - auc: 0.9996 - val_loss: 3.0789e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 228/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0024 - recall: 0.9836 - precision: 0.9969 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 229/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 0.0010 - recall: 0.9939 - precision: 1.0000 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 250ms/step - loss: 0.0010 - recall: 0.9939 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 230/250\n",
      "35/35 [==============================] - ETA: 0s - loss: 8.4259e-04 - recall: 0.9945 - precision: 1.0000 - auc: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, lstm_cell_12_layer_call_fn, lstm_cell_12_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Model\\CNN2-LSTM\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 9s 254ms/step - loss: 8.4259e-04 - recall: 0.9945 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 231/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0013 - recall: 0.9885 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 232/250\n",
      "35/35 [==============================] - 3s 77ms/step - loss: 0.0028 - recall: 0.9830 - precision: 0.9969 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 233/250\n",
      "35/35 [==============================] - 3s 89ms/step - loss: 0.0011 - recall: 0.9897 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 234/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0142 - recall: 0.9138 - precision: 0.9869 - auc: 0.9903 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 235/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0041 - recall: 0.9721 - precision: 0.9975 - auc: 0.9999 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 236/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0019 - recall: 0.9873 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 237/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0015 - recall: 0.9891 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 238/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 9.5239e-04 - recall: 0.9939 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 239/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0021 - recall: 0.9860 - precision: 0.9982 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 240/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0024 - recall: 0.9818 - precision: 0.9981 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 241/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0013 - recall: 0.9927 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 242/250\n",
      "35/35 [==============================] - 3s 73ms/step - loss: 0.0013 - recall: 0.9879 - precision: 0.9994 - auc: 0.9997 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 243/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0013 - recall: 0.9915 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 244/250\n",
      "35/35 [==============================] - 3s 94ms/step - loss: 0.0013 - recall: 0.9897 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 245/250\n",
      "35/35 [==============================] - 3s 80ms/step - loss: 0.0013 - recall: 0.9891 - precision: 0.9994 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 246/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0094 - recall: 0.9448 - precision: 0.9905 - auc: 0.9941 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 247/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0202 - recall: 0.8556 - precision: 0.9765 - auc: 0.9914 - val_loss: 6.1577e-09 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 248/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0047 - recall: 0.9763 - precision: 0.9963 - auc: 0.9996 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 249/250\n",
      "35/35 [==============================] - 3s 74ms/step - loss: 0.0025 - recall: 0.9782 - precision: 0.9988 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 250/250\n",
      "35/35 [==============================] - 3s 75ms/step - loss: 0.0011 - recall: 0.9945 - precision: 1.0000 - auc: 1.0000 - val_loss: 0.0000e+00 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=32,kernel_size=(3,),activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]),))\n",
    "model.add(Conv1D(filters=32,kernel_size=(3,),activation='relu'))\n",
    "# model.add(Conv1D(filters=32,kernel_size=(3,),activation='relu'))\n",
    "model.add(MaxPool1D(1))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Flatten())\n",
    "model.add(RepeatVector(1))\n",
    "model.add(LSTM(100, return_sequences=False))\n",
    "model.add(Dense((n_forecast)\n",
    "            ,activation='sigmoid'\n",
    "            ))\n",
    "model.summary()\n",
    "# fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              metrics=['Recall','Precision',\"AUC\"],\n",
    "              loss=CustomLoss\n",
    "            # loss='BinaryCrossentropy'\n",
    "              )\n",
    "# fit the model\n",
    "history = model.fit(trainX, trainY, epochs=250, batch_size=320, \n",
    "                    validation_split=0.1, \n",
    "                    verbose=1,  \n",
    "                    callbacks=[\n",
    "                                #  f1,\n",
    "                                f2\n",
    "                               ]\n",
    "                  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fe03d08d60>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.legend()\n",
    "plt.show();\n",
    "plt.plot(history.history['auc'], label='AUC')\n",
    "plt.plot(history.history['recall'], label='recall')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model/CNN2-LSTM'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PT_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x1fdc99a92d0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(PT_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "388/388 [==============================] - 3s 9ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction_train=model.predict(trainX).mean(axis=1)\n",
    "trainResults= pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 3000x900 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nm/bNnDkzZucHgLYQz361oKDAtN0yY2U4rct27drVsmxFRYXmzp1r2nfqqac6qsfj8ejkk0827XvttdccthIAmqRi3woAiRavvrV1PaNGjXLRyuB2tb7R19IHH3ygysrKwPaRRx5pyoRhp3///jriiCMC2+Xl5UGBKAAQTir2rQAQa4WFhUH7Dhw4YFl+9erVpgznubm5mjx5sqO6Wpc1DEOvv/66ZfnWrzmdA5WkU045xbRtNwcaz/cEAB0FAWwA0M7t27fPtF1UVOTq+C5dupi2v/jii6jb1Kx19rWxY8e6nowBgHiLZ786ZswY0/Znn33meHnOhQsXmrYnTpxoWXb58uWqr68PbA8YMEA9evRwVI8kTZkyxbS9ZMkSx8cCgJSafSsAJFq8+tZ49uGtx5lObwI2Y9wKIFqp2LcCQKxt27YtaF/rfqml1mOyiRMnulqpx80YL5rx5Lhx45SZmRnY3r59u/bs2eOonrZ8TwDQURDABgDtnNdr7sobGxtdHd8yoEGSVq5cGXWbJMnv9+tf//qXaV+02dcMw9D69ev16aef6uOPP9bKlSu1f//+qM4JAK3Fs1899thjTcvVbd26Vc8880zYOkpLS/WPf/wjsO3xeIJS3Nu1Yfjw4WHrsCsfq88KAB1HKvatdnbs2KHFixdr7ty5Wrp0qXbs2BHReQDATrz61ljX8/XXX1ueg3ErgERLxb7VCnOtACLVOrtkv379lJGRYVk+XmO8+vp6U1Y0t3VlZmZq0KBBjupi3AoAsUcAGwC0c62fztu9e7er41s/PRKrQfLs2bNNqfAzMjJ06aWXRny+xx9/XF27dtWgQYN07LHH6hvf+IaGDx+uoqIiDRs2TNddd52WL18ei6YD6ODi2a/6fD79/ve/N+377ne/q7feesvymG3btmn69Onau3dvYN91111nu6zJ6tWrTdt9+vSxLBtK6/KbNm1STU2Nq3MA6NhSsW8NZenSpRo4cKBKSko0fvx4TZ06VaNGjVJJSYl69uypiy66yLYdAOBGvPrWaOtpXb62tlYbNmwIWTbW49bW5wOAcFKxbw2FuVYA0Zg1a5Zpe/r06bbl4zXGW79+vRoaGgLb2dnZ6tq1a5vUxbgVAGKPADYAaOcGDhxo2l60aJHjY9esWaMDBw6Y9u3atSsm7Wq9fOhZZ51lm0I6nI0bN6q0tDTka6tWrdKf//xnHXXUUbrwwgstywGAE/HuVy+55BLddNNNge2KigqdccYZOumkk/TAAw/ov//9r95880099thjuuaaa3TkkUdqwYIFgfKXX365HnzwQds6Wk9s9+7d2+E7atK9e3dTCny/3x+03AkA2EnFvjWU0tJSyxuHO3fu1LPPPqszzjhDRx99tJYuXer6/ADQUrz61tb1fP755/L7/Y7r+uyzz4L2WdUV7bi1V69etucDgHBSsW8NhblWAJF64403NHfuXNO+cKvvxHqMZ7WsZ+t6Wh8XSV1W48l4vScA6EgIYAOAdu64444zbb/wwgtBKeStPP3000H7Kioqom5TeXm5XnzxRdO+aJcPdcIwDD3//PMaO3asVqxY0eb1AUhNiehX77rrLj377LPq169fYN/s2bP105/+VOeee66mT5+uq6++WrNmzVJlZaUkadSoUfrPf/6jJ554Qj6fz/b8rduQm5vr5O0EeDweZWdn254TAOykYt8ajS+++EKTJk3Sc88912Z1AEh98epbjznmGKWnpwe2Dx48qDfeeMNRPRs3btSnn37quK5ox62tyzNmBeBWKvatkWKuFUBrpaWl+t73vmfad95552nixIm2x8V6jFdfX6/a2tqY1xPqmHiNW63eEwB0JASwAUA7N336dNNkx86dO/XQQw+FPW7nzp16+OGHg/bHYqLjP//5j6qqqgLb3bt31xlnnBHRuYYMGaKf/vSneuWVV7R27VodPHhQ9fX12r17t+bNm6fbbrtNPXr0MB2zefNmTZ8+PWbZ5AB0LInqV2fMmKEvvvjCUcDvyJEjdeONN+rcc891dO7WbcjKynJ0XEsEsAGIRir2rS117dpVM2fO1L///W999dVXKi0tVX19vfbv368vv/xSf/zjH4OWI62urtZll10W9OQ6ADgVr741JydHp5xyimnfr3/9a0cBHTfffHPIjEJObwS6HbcyZgUQrVTsW1tirhVApPx+vy677DJt3bo1sK+goCBk39darMd4oc4Zi3pC1RWvcatdXQDQURDABgDtXK9evXTRRReZ9t188816+eWXLY/Zv3+/zjvvvJDLv1VXV0fdptbLh1566aWmpeec6N+/v+bMmaPVq1frvvvu0znnnKNBgwYpPz9faWlpKi4u1pQpU3T77bdrw4YNQU/9bNq0Sddee23U7wVAx5OIfrWhoUF33nmnBg4cqH/+859hyy9btkxXXHGFBg4cqNdeey1s+ZqaGtN2RkZG2GNay8zMNG3H4vMCQMeRin1rs3//+9/atm2bHnvsMV166aU66qijVFhYqLS0NHXu3FmjRo3SddddpyVLluivf/2rqT+tq6vTJZdcEtRPA4AT8exbb7jhBtP2F198oauuukoNDQ2Wx/y///f/9NRTT4V8zaquaMetjFkBRCsV+1aJuVYA0fvZz36mN99807Tvb3/7m/r06RP22FiP8aTQfV4850Dj9Z4AoCMhgA0AUsDvf/97FRcXB7br6up0wQUX6Morr9TcuXNVVlam2tparVu3Tg8//LBGjBihBQsWSJI6d+5sOldeXl5UbVm/fr3mzZtn2hfJ8qH9+/fXtGnTHJXNysrSX//6V/34xz827X/xxRf12Wefua4bAOLZrx48eFDTpk3TLbfcorKyMklS7969de+99+rLL7/UwYMHVVtbq82bN+vZZ5/ViSeeGDh227ZtOvvss/XHP/7Rto7WTwDW1dWFuwRBWqewj+QJRgAdW6r1rc0uvfRSxxPV3/ve9/TUU0/J6z08HbNt2zb96U9/cnQ8ALQWr771xBNP1CWXXGLa9+STT2rs2LGBQN76+nrt2bNHr776qk4++WTdfPPNkppu5rXOMGFVV7TjVsasAGIh1fpWiblWANF5+OGHdf/995v2/fznP9e3v/1tR8fHeowX6pyxqCdUXVbjyXi9JwDoUAwAQMLMmTPHkBT4r1+/fhGfa/bs2UZubq7pfOH+69Wrl/H3v/89Zm0wDMP49a9/bTrf0UcfHdX53KivrzcGDx5sqv/666+PW/0AEq+99at+v984+eSTTeXPPPNM4+DBg7Zt+8tf/mJ4PJ7AMR6Px3j//fcty8+YMcNUxwMPPOD6euTn55vOsXLlStfnANA+0bfG3pVXXmlq3+jRo9ukHgDJq731rYZhGOXl5caECRNc1SPJePzxx40uXbqY9s2ZMydkHd26dTOV++KLL1xdi8WLF5uO7969u6vjAbRv9K1zIn6/oTDXCsAwDOPJJ580fVeWZMycOdPw+/2OzzFx4kTT8S+99JKrNuzbty+oH6yurg4q98Ybb0T9XfuHP/yh6Rw33XRTQt8TAHQkZGADgBRxwgkn6KOPPtKgQYMclR87dqxmz56t/Px80/7WTwm6YRiGnnjiCdO+SLKvRSotLU0/+tGPTPveeeeduNUPILXEo1994okn9N577wW2hw0bpueeey7oHK19//vf1y233BLYNgxDP/zhDy3Lt34Ku7Ky0vb8rRmGEZTCPtqMnQA6plTqW6PRermor776Srt27WqTugCkvnjNB+Tl5enDDz8MyhZkV37WrFm64oorVFFR4aiuaMetrcszZgUQqVTqWyPFXCuA1157TVdeeaUMwwjsu+CCC/TII4/I4/E4Pk+sx3hpaWkhs5VFW0+oY6zGk/F6TwDQkRDABgApZOzYsVq5cqX+9re/6Rvf+IbS0tKCyowePVoPPfSQPv30Uw0ZMkT79u0zvT548OCI6//ggw+0cePGwHZGRobjyZdYOemkk0zba9asMX25AgA32rpffeihh0zbt956a9ASIFZuuukm0+T0ihUr9PHHH4cs261bN9P21q1bHdXRbNeuXWpoaAhse71ede3a1dU5AKBZqvSt0TjqqKNMfbNhGPr6669jXg+AjiNe8wHZ2dl68skntWDBAl188cUhx4RFRUW69tprtWTJEl111VWqrKw0LZHk8Xg0cODAkOePdty6bds22/MBgBup0rdGg7lWoOOaM2eOLrzwQtOc4CmnnKKnn35aPp/P1bliPcZrucyzXT2tj4ukLqvxZLzeEwB0JMGjbQBAu5aenq7vfve7+u53v6vKykpt2bJFe/fuVW5urvr06RM0AbJixQrT9vjx4yOu+/HHHzdtn3XWWerSpUvE54tEnz59TNsNDQ3av3+/ioqK4toOAKmjrfrV0tJSLVmyJLDt9Xo1ffp0x+3Kzc3VCSecoJdeeimwb+7cuZoyZUpQ2SOPPNK0vXnzZsf1hCrfr18/nggEEJVU6Fuj1bt3b+3evTuwvWfPnpjXAaBjied8wMSJE/XUU0/J7/dr8+bN2rNnj+rq6tS7d2/16tXLFOTRup4hQ4aoU6dOIc975JFH6tNPPw1sRztuHTp0qKvjAaC1VOhbo8FcK9AxLViwQOecc45qamoC+yZPnqyXXnpJGRkZrs8X67lJqzHewIEDlZaWFgi6q66u1p49e1wFhzmtK17vCQA6EgLYACCF5ebmhh30Ll261LQdaQBbRUWFnn/+edO+q666KqJzRSM9PT1oX319fdzbASA1xbJf3bhxo+mp5aKiIhUUFLhqz4ABA0zbVk8Vtm5z64nucFauXGl7PgCIRnvtW6PVetzKmBVALMVrPsDr9ap///7q379/TOph3AogmbXXvjUazLUCHc9XX32lM844w7RM8dixY/XGG28oNzc3onPGa4yXnp6uQYMGafXq1aa6pk6d6qie2tparV+/3lFdjFsBIPZYQhQAOrDS0lLTk80FBQWaPHlyROd6/vnnVVlZGdju0aOHTj/99Kjb6NbOnTtN2x6PJ+5Z4AB0XG761ZZLfUgKuRRJOK0nkhsbG0OWGzFihKnsxo0btWPHDsf1tF4+b8yYMc4bCQBRSta+NVqtx60sFwIgnmI5HxDO66+/bto+44wzLMu2HmfOnz/fVV2MWwEkUrL2rdFgrhXoWFavXq1TTjlF+/fvD+wbNmyY3n77bdcPh7XUeky2aNEi09Kk4bgZ40Uznly8eLFpXqFnz56WS4jG8z0BQEdBABsAdGAvvPCC6Ym5iy++WDk5ORGdq/XyoZdeemlENwyjNW/ePNN2z549E9IOAB2Tm3619YTvvn37VFdX56q+1lmBrIIf8vPzdfzxx5v2vfvuu47qMAxD7733nmnf2Wef7aKVABCdZO1bo7F161Zt2rTJtK/18kwA0JZiOR9g5+DBg3rjjTcC24WFhfrmN79pWX7atGmmzB5ff/11UH9pZePGjVqzZk1gOz8/X9OmTXPfaACIULL2rdFgrhXoODZt2qSTTz5Zu3fvDuwbMGCA3n333ai/Fw8dOlSDBg0KbFdWVjoOLKusrNQnn3wS2PZ4PDrrrLMsy7d+zekcaKiydnOg8XxPANBREMAGAB1UTU2N7rzzTtO+//mf/4noXBs3btSHH35o2jdz5sxImxaVRx991LR90kknJaQdADoet/1qr169TJO+9fX1+uijjxzX5/f79cEHH5j2tZw0ae2cc84xbbfuL63MmTNHGzZsCGx3795dkyZNctxOAIhGsvetkWrdB/fp00dHHHFEzOsBgFBiOR8Qzp133qmamprA9mWXXaasrCzL8llZWTr11FNN+2bNmuWortblTj/9dGVkZLhoLQBELpn71mgw1wp0DDt27NBJJ52krVu3Bvb16tVL77//vnr16hWTOiKdm3z22WdNy5mOHz9eJSUlluWnT59umhf44IMPgpYFDcUwDP3zn/807Tv33HNtj4nXewKAjoIANgDooH7xi1+YnmK+5JJLdPTRR0d0rieeeEKGYQS2x40bp5EjR0bdRrf+/e9/B91wPO+88+LeDgAdk9t+NTc3V8ccc4xp3//93/85ru9f//qXaVJJsp9Ivuiii0zZLObOnavZs2fb1mEYhu644w7TvquuukpeL18jAMRHsvetkVi5cqXuu+8+0z7GrADiKZbzAXYWLFigBx98MLBdWFioW265Jexx11xzjWn7T3/6k/bs2WN7zO7du/XnP//Z9jwA0JaSvW+NBHOtQMdQWlqqU045RevWrQvsKy4u1rvvvqsBAwbErJ6rr75aHo8nsP3MM89o5cqVtsfU1NTorrvuMu0LN8YrKioy9VWGYej2228P275Zs2Zp48aNge1+/frp5JNPtj0mXu8JADoK7jwBQIpYtWqVo3KNjY361a9+pYceeiiwr7CwUA888EBE9RqGoSeeeMK0L9rsa88884xefPFFU1BcOE8//bS+853vmPaNGTNG559/flRtAdBxxaNfveyyy0zbb731ln7729+GPW7hwoX64Q9/aNp34oknqnfv3pbHdOvWTf/7v/9r2ved73xH27dvtzzm97//vebOnRvYLigo0M9+9rOw7QMAK6nUty5ZskQPPPCAqqqqwp675TGnn366ysvLA/uys7P1i1/8wvE5AKC1eM0HrFu3Tg0NDY7KfvzxxzrjjDNMyzjffffd6tatW9hjzzzzTFMw8r59+3TNNdeYluVrqa6uTtdcc4327dsX2HfcccfptNNOc9RWAAgllfpW5loBWCkvL9fpp5+u5cuXB/Z17txZ77zzjoYNGxbTukaOHKkZM2YEtuvq6nTllVfq4MGDIcsbhqEf//jHpiXiBw4cqKuvvjpsXXfccYfpAdx//etfevrppy3Lr1ixQjfeeKNp36233ho2m2883xMAdAQew82IFQAQkY8//ljV1dVB+7/88kvToLh79+7697//HfIcJSUlGj58uGUdxcXFGjFihC6++GKdccYZ6tu3r+n1iooKvfHGG7rrrrv0xRdfBPZ7vV49//zzEU8+zJ07V1OnTg1sZ2RkaMeOHSoqKorofJJ0++2364477tDgwYM1Y8YMnXXWWRo1apQpc5DU9GVg3rx5euihh/Tf//7X9FpWVpbmzJkTlIEDQGpIlX61vr5eI0eO1Ndff23af/bZZ+vmm2/WxIkTTU/xbdu2TX/72990zz33qLa21lTn/Pnzwy7tWVpaqhEjRmjnzp2Bff369dPDDz+ss88+O1DX1q1b9bvf/U5/+9vfTMffc889BLABKYy+1V3f+sEHH+iEE05Qly5ddMEFF+j888/XhAkT1LVrV1M5wzC0bNky/eMf/9Df//53Ux2S9OCDD+r6668P+74AtE+p0rdK0o033qhnn31Wl19+uc477zyNHTtW6enpgdcNw9DChQv16KOP6tFHH5Xf7w+8dsEFF+j555839b925s2bp6lTp5rOMW3aNN13332mjEaLFy/WDTfcoA8//DCwz+fzad68ecwHACmMvrWJ076VuVYAVk444YSgTIu/+c1vdOyxx7o+17hx41RYWGhbZu3atRo9erTpQbDRo0frwQcf1LRp0wL7vv76a/3yl7/Uiy++aDr+P//5jy688EJH7fne976nv//974Ftr9erm2++WT/5yU8C7ayvr9eTTz6pn/70p9q/f3+g7KhRo7R48WLTUqTJ8J4AIOUZAIA2169fP0NSVP9deeWVtnV06dLFVL6oqMgYNWqUMXHiRKN///5GRkZG0Dl9Pp8xa9asqN7b1VdfbTrnN7/5zajOZxiGcdtttwW11ev1Gn379jVGjRplTJo0yRg6dKiRlZUV8lqlp6cbL7zwQtTtAJC8UqlfXbFihVFUVBSyjYWFhcbo0aONCRMmGH379rV8L3/4wx8c1/fhhx+G7D87d+5sjB071hgwYIDh8/mCXj/33HMNv9/v6r0BaF/oW931rXPmzAl5XPfu3Y0RI0YYxxxzjDFy5EijsLDQso4bbrjB1fsC0P6kUt96ww03mM6RkZFhHHnkkcbEiRONYcOGGQUFBSHbP336dKO6utr1tbv77rtDnq+kpMQYN26c0bNnz5Cv33fffa7rAtC+0Le661uZawVgJdq+tOV/c+bMcVTn008/bXg8nqDji4uLjXHjxhl9+vQJ+foPf/hDV++tsrLSGD9+fNB5mvvZUaNGGXl5eUGvd+3a1Vi9erWruuL1ngAg1YUPGwYAtEulpaUqLS21fL137956/PHHdeKJJ0ZcR1VVlZ577jnTvmiXD7Xi9/u1efNmbd682bbckCFD9NRTT2ncuHFt0g4AHVdb9avDhg3T3Llzdckll+irr74yvbZ//37T03+t5efn6+GHH3bV9x5//PF6/fXXdeGFF5reT1lZmemp8ZYuueQSzZo1y3HGDABwKlX61pZ27dqlXbt22Zbp1KmT/vznP+vSSy+NqA4AsBOP+QCpKVPP6tWrLV9PS0vTz3/+c91xxx2Osle09vOf/1w+n0833XSTGhsbA/u3b9+u7du3B5X3+Xz6v//7P/34xz92XRcAhJMqfWsz5loBJMpFF10kwzB0zTXXmLJp7tmzR3v27Al5zI033qh77rnHVT05OTl6++23deGFF2r27NmB/Xb9bP/+/fXf//5XQ4YMcVVXvN4TAKQ6b/giAID24Fe/+pWOPfbYsBMXgwcP1j333KPVq1dHPaHy4osvqry8PLDdo0cPnX766VGdU5JmzJihX/7ylzr22GOVnZ0dtnxaWpqOO+44PfXUU1q2bBkTKgBiIp796ogRI7Ro0SI98sgjjvqw7t2766abbtKKFSsiCrA48cQTtWLFCv3gBz9QTk6OZbmxY8fqhRde0JNPPqnMzEzX9QBAa6nUtx511FG6++67dfrpp6uoqMhRm4YOHap77rlHGzduJHgNQMzEq289//zzdeGFF4ZdGio3N1czZ87U0qVLdeedd0YVYHHDDTfos88+05lnnimvN/RUttfr1VlnnaXFixcTvAYgZlKpb2WuFUCyufjii7Vs2TJdcsklpmWTWzv++OP1wQcf6N57743owdqioiK9++67+vvf/67BgwfblvvVr36lpUuX6qijjnJdjxS/9wQAqcxjGIaR6EYAAGKnqqpKX3zxhdauXavdu3erpqZGOTk56t27t44++mgdccQRiW6iK42NjVq9erXWr1+vrVu36uDBg6qrq1NeXp4KCws1YMAATZgwwdHkCwBEIhH9amlpqRYtWqTNmzerrKxM9fX1KigoUHFxscaOHRvTOqurqzV//nytXLlSZWVlysjIUK9evTRp0iTbiR0AiEYq9q2bNm3SmjVrtHnzZu3fv1/V1dXKyspSYWGhevbsqUmTJqlLly4xejcAECxefathGFq1apVWrFihrVu3qry8XD6fT8XFxRo2bJgmTJigjIyMmNTV0t69ezVv3jytX79elZWVys3N1aBBgzRlyhR17do15vUBgJR6fStzrQCSzcGDBzVv3jytWbNG5eXlysrKUt++fTVlyhT16tUrpnUtXbpUn3/+uXbs2KHGxkZ16dJFI0eO1KRJk2yDztyK53sCgFRCABsAAAAAAAAAAAAAAAAAICFYQhQAAAAAAAAAAAAAAAAAkBAEsAEAAAAAAAAAAAAAAAAAEoIANgAAAAAAAAAAAAAAAABAQhDABgAAAAAAAAAAAAAAAABICALYAAAAAAAAAAAAAAAAAAAJQQAbAAAAAAAAAAAAAAAAACAhCGADAAAAAAAAAAAAAAAAACQEAWwAAAAAAAAAAAAAAAAAgIQggA0AAAAAAAAAAAAAAAAAkBAEsAEAAAAAAAAAAAAAAAAAEoIANgAAAAAAAAAAAAAAAABAQhDABgAAAAAAAAAAAAAAAABICALYAAAAAAAAAAAAAAAAAAAJQQAbAAAAAAAAAAAAAAAAACAhCGADAAAAAAAAAAAAAAAAACQEAWwAAAAAAAAAAAAAAAAAgIQggA0AAAAAAAAAAAAAAAAAkBAEsAEAAAAAAAAAAAAAAAAAEoIANgAAAAAAAAAAAAAAAABAQhDABgAAAAAAAAAAAAAAAABICALYAAAAAAAAAAAAAAAAAAAJQQAbAAAAAAAAAAAAAAAAACAhCGADAAAAAAAAAAAAAAAAACQEAWwAAAAAAAAAAAAAAAAAgIQggA0AAAAAAAAAAAAAAAAAkBAEsAEAAAAAAAAAAAAAAAAAEoIANgAAAAAAAAAAAAAAAABAQhDABgAAAAAAAAAAAAAAAABICALYAAAAAAAAAAAAAAAAAAAJQQAbAAAAAAAAAAAAAAAAACAhCGADAAAAAAAAAAAAAAAAACQEAWwAAAAAAAAAAAAAAAAAgIQggA0AAAAAAAAAAAAAAAAAkBAEsAEAAAAAAAAAAAAAAAAAEoIANgAAAAAAAAAAAAAAAABAQhDABgAAAAAAAAAAAAAAAABICALYAAAAAAAAAAAAAAAAAAAJ8f8Bjgq+855B6EUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainResults['Actual']=df_for_training.iloc[-prediction_train.shape[0]:,-2]\n",
    "trainResults['Pred']=prediction_train\n",
    "plot((trainResults['Pred']),df_for_training.iloc[-prediction_train.shape[0]:,-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "221/221 [==============================] - 2s 9ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction=model.predict(testX)\n",
    "min_length = min(len(df_for_testing), len(prediction))\n",
    "testResults = pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 3000x900 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2VVu3bk2rfUApYjgbn8Fm3njjjcRNCK/Xq0pLS5PaJoENmSB+sxO/jGUhX4jhzGKYsSzkkxvjt2vyy8CBA9X3v/999eqrr6q2trak7cLhsHrmmWfUUUcd1aOd6667zvI5vfnmmz2ub2fNmqWWLl2atN17772nZs6cmbSd3+9X77zzjuW2gO6I4fRjuGsCWyAQUOeff776y1/+Yjg2tWrVKvXv//7vPdoZNmyYamlpSdkWkC4S2AA43uTJk9WIESPUAw88oFpbW1NuH41G1Te+8Y0eH7rdv2zo/Nu//VvSflOnTtWWnI3H4z3aGjVqlKVM+FWrVvW4CHjssce02y9fvlxVV1cnbf/QQw9ZOiellDrllFOS9r3wwgtVc3OzpX0jkYjtJxaATsRwh3Rj+K677kraJxgMqtdff910n8bGRjVp0qSk/caMGZOyLcCIW2O4c+C+X79+6vTTT1c33XSTmj9/vtq+fbt6+OGHszZwn8tzArojfjNLYAsGg+rb3/622rBhg6V9jKpMfO1rX0urfUApYjgbn8E67e3t6vDDD0+08+1vf1sNHz48qW0S2JAJ4jc78ctYFvKFGM4shhnLQj65MX5ra2vV+PHj1ZNPPmkp1tva2tTZZ5+d1E4gEFCrV6+2dE7Tp09P2vecc87RTmEYCoXUWWedlbT9iSeeaKkdwAgxnH4MX3fddapPnz7q5ptvVp9//nnKdpRS6tFHH+1RXe7HP/6xpX2BdJDABsDxFixYYHl+707RaFRNmTIl6QP34osvTrnf8uXLk8rEBoPBlOVS29ra1GGHHZbU1v3335+yrblz5ybtc+mll6bc54EHHkjaZ/jw4SocDqfc75FHHulxwWGnehuQCWL4oHRiuPsTRFdffXXKdpRS6u233+5x0WZ1kALoyq0xvHz5crV27VrD17I5cJ/LcwK6I37Tj9+lS5eqjRs32t7v3nvvTWq/pKSEm+dIGzHcewlsP/rRjxJtDBkyRDU2NpLAhqwifjOPX8aykE/EcGYxzFgW8smN8fv000+rWCxm65yam5vVkCFDktq58cYbU+73/PPPJ+1TW1urdu7cabrPjh07ekyR+PLLL9vqL9CJGO6QTgz/4x//ULt27bLVjlJKffe7301qZ/DgwbaPAVhFAhuAovXEE0/0+KKdyve+972kfaxWTHjwwQeT9ps2bZrp9nv37lV+vz+xvcfjMZyOqLtYLNZjUP3555833ScUCqmBAwcmtu/bt6/hNAtAoSGGOwwYMCBp+4ULF1o6J6VUj6lWnnvuOcv7Apkq5BhOJZsD94VyToAdxG/6wuGwqqqqSurDs88+m9M+AMSwuQ8//DDpu/z8+fOVUooENhQE4rcDY1lwKmK4A2NZcCInx6/OHXfckdTO5MmTU+7TfYpEq5WYbr755qT95s6dm2n3AVuI4fR9/vnnPaqwLVu2rFfaArwCAEXqhBNOSFres2ePtLa2mu7zzDPPJC1ffvnlltq68MILpaKiIrH83nvvybZt27TbP/fccxKNRhPLs2bNkpEjR6Zsx+v1yr//+78nrXv66adN95k/f77s2LEjsfyNb3xDBg0alLItIN+I4Q579+5NWh46dGjKdjoNGzYsaXn//v2W9wUyVcgxnEtuPCe4H/GbvkAgIMcdd1zSus2bN+epNyhWxLBeNBqVyy+/PPFdfs6cOTJ79uw89wo4iPjtwFgWnIoY7sBYFpzIjfHb/ZxSXZuGQiF56aWXktZ9/etft9RW9+1eeOEFCYfDlvYFsoEYTt/AgQNlzJgxOWkLIIENQNGqqanpsa6hoUG7/erVq2Xt2rWJ5YqKCpk+fbqltrpvq5SS5557Trt999dOO+00S+2IiJx66qlJywsWLDDd/sEHH0xavuSSSyy3BeQTMdyhqqoqabmtrc1yW923raurs7wvkKlCjuFcceM5oTgQv5np/vMz+9kBvYEY1rvrrrvkgw8+EJGO79m//e1v89wjIBnx24GxLDgVMdyBsSw4kRvj1+616eLFi6WlpSWxfPjhh8vw4cMttTVixAg57LDDEstNTU2yZMkSG70FMkMMO6ctFDcS2AAUra1bt/ZYV1tbq93+o48+SlqeNm2a+P1+y+3NmDHD9Hhmr1n9UiQiMnnyZCkpKUksb9u2TXbt2mW4bXt7u7z22muJ5ZqaGpk4caLltoB8IoY7TJo0KWn5vffes9ROU1OTrFq1KrHs9XplypQplvsJZKqQYzhX3HhOKA7Eb2a6//zMfnZAbyCGja1Zs0Z++tOfJpZvu+02Kjqh4BC/jGXB2YjhDoxlwYncGL92r00zGfMWKdz3JBQHYtg5baG4kcAGoGj94x//SFoePny4BINB7fZdL45FRMaNG2erve7bdz9ep0gkkpTVb7etkpISGTVqlKW2PvroI4lEIonlrgN+e/bskd/85jcyc+ZMGTJkiJSUlMghhxwixxxzjHz3u9+V119/3XKfgN5ADHe48MILk5bvvffepLjW+fWvf51Upv28886Tfv36We4nkKlCjeFccuM5oTgQv+lraWlJVHfq1H0aBqC3EcM9KaXkiiuukPb2dhHpuNlw5ZVX5rlXQE/EL2NZcDZiuANjWXAiN8Zv93NKdW3qhHMCdIjh9G3YsEG2bNmStK5rRUUgm0hgA1C0HnrooaTlM88803T71atXJy0PHTrUVnvdt+9+vE7r16+XaDSaWC4rK7NdCt1qW92fbhs5cqSIiDz++OMyatQoueaaa+T111+XrVu3Sjgclh07dsiHH34o99xzj8ycOVNOOeUUWbNmja2+AdlCDHe47LLLZPz48Ynl5cuXy0UXXSRNTU3afe6//3659dZbE8t9+/aVu+66y1YfgUwVagznkhvPCcWB+E3fE088kTTlSt++fXs8lQv0NmK4p/vuuy8x+B8MBuX+++8Xj8eT514BPRG/jGXB2YjhDoxlwYncFr+xWEweffTRpHVOPyfADDGcvkceeUSUUonlsWPHyqGHHtorbQHW6xwCgIs8//zzPZ66nDdvnuk+O3fuTFoeMmSIrTYHDx6ctKybErB7O933S6et7sfs1L1KVN++feUXv/iF/Md//Ieldl599VU5/vjj5ZlnnuHGG3KKGD4oGAzK008/LbNmzUo8BfP3v/9dXn/9dbn44otl2rRpUltbK21tbbJq1Sp56qmn5MMPP0zsX1dXJ/Pnz+eCAzlVyDGcS248J7gf8Zu+1tbWpJtuIiJf/epXJRAI5KlHKEbEcE+fffaZ/PCHP0ws33DDDbafrgdygfjtwFgWnIoYPoixLDiNG+P3j3/8o6xfvz6xHAgE5OKLLzbdJ9vnZDbmDWQTMZy+7du3yz333JO0LtXPDsgECWwAis7evXt7TAVy3nnnybRp00z3a25uTlquqKiw1W737SORiIRCISkpKclqO0b7dD9mp/379yctL1myJGke9sMPP1wuvvhiGTdunJSUlMj69esTgwmd9u7dK+ecc4589NFHMmzYMNt9BewihnsaNWqUfPDBB/Ld735XHn/8cYnH47J79275zW9+o92nvLxcLr30Urnllltk0KBBtvsIpKvQYziX3HhOcDfiNzPXX3+9bNy4MbFcUVEhN910U/46hKJDDBu76qqrEhVfDj/8cMtJMEAuEb8HMZYFJyKGe2IsC07hxvhdt25d0gMcIiLf/OY3UyboZPucUo15A9lADKdPKSVXXHGFNDY2JtYNHjxYvvnNb2a1HaArphAFUFTi8bhccsklSXN1V1VVmV4Yd+r+ZaW0tNRW22VlZSmPmY12jNqymsD24YcfJsrA3njjjbJ8+XL58Y9/LF/5ylfknHPOkWuuuUaWLFkijz/+eNLc8Pv27SPjHjlBDOv1799f/vznP8vTTz+d8kLF7/fL5ZdfLt/5zncY8ENOOSGGc8mN5wT3In4z8/jjj8t9992XtO6Xv/yl1NfX56lHKDbEsLE///nP8vzzz4uIiMfjkT/+8Y8FcVMf6Ir4TcZYFpyGGNZjLAuFzo3x29raKl/5yleSpuwdPnx4j2rhRrJ9ToX0fgR3IoYzc9tttyWulzv9/ve/T6toA2AVCWwAisr3v/99eeGFF5LW/fGPf7Q0d3l7e3vSctdBLyuMBsHb2tqy3o5RW0btiOi/LH3zm9+Un//85+L3GxfqvOiii+TBBx9MWrdo0SJ56623bPcVsIMY1vvoo49k1qxZMnv27KQLMiPRaFR++9vfyvjx4+VrX/uaNDQ02O4jkA4nxHAuufGc4F7Eb/reffddufzyy5PWnXnmmfKtb30rTz1CMSKGe9q5c6dce+21ieWvf/3rMnPmzPx1CNAgfpMxlgWnIYb1GMtCoXNb/Cql5LLLLkuqXOr3++Uvf/mL9OnTJ+X+2T6nQno/gjsRw+mbP39+j1kDrrrqKpk9e3ZW2wG6I4ENQNH4zW9+I7/61a+S1v3gBz+QCy+80NL+3bPrw+GwrfZDoVDKY2ajHaO2dE8GGK3v16+f3H777SnbuOSSS2TWrFlJ6+6//37rnQRsIob1Hn/8cTn22GNlyZIlItJRPWLOnDny7LPPyvbt2yUcDsu+ffvknXfekZtvvllqampEpOOC509/+pNMnz5ddu3aZbufgB1OieFccuM5wZ2I3/StXbtWzjnnnKRByiOOOEL+9Kc/icfjyWPPUEyIYWPf/va3Zc+ePSIiMnDgQLnzzjvz3COgJ+K3J8ay4CTEsB5jWSh0bozf6667Tp566qmkdffee6/MmDHD0v7ZPqdCeT+COxHD6fvnP/8pF198scTj8cS6E044Qe65556stgMYIYENQFF47LHHkp6sFhGZN2+e3HbbbZaPUVlZmbTcPfs+FaPM+u7HzEY7Rm0ZtaNbf/HFF1su//qNb3wjablzwAHINmLYOIZFRBYvXiyXXnpp4gKqrKxMnn32WXnqqafk7LPPlkMOOUQCgYBUV1fLtGnT5NZbb5Xly5fLlClTEsdYuXKlzJ07NzHtCpBtTorhXHLjOcF9iN/0bdu2TU477TTZuXNnYt3QoUPl5Zdfln79+uWxZygmxLCxZ555Rp544onE8j333JO4MQ4UCuLXGGNZcApiWI+xLBQ6N8bvbbfdJr/+9a+T1t1yyy1y9dVXWz5Gts+pEN6P4E7EcPpWrFghZ511lrS2tibWTZw4UZ599lnDqnJAtpHABsD1FixYIJdddlnSxeyXv/xleeCBB2xVPej+xaKlpcVWP7pv7/f7DbPtM23HaB87CWx2pkzpvu3GjRsTT7AD2UIM62M4FovJFVdcIbFYLLHuvvvuk7POOsv0+PX19bJgwQKpq6tLrFu8eLE8+eSTtvsKpOK0GM4lN54T3IX4Td/evXvltNNOkw0bNiTW9e/fX1555RVLU1UA2UAMG2toaJD/7//7/xLLX/rSl+Siiy7KW38AI8SvHmNZcAJiWI+xLBQ6N8bvH//4R/nRj36UtO473/mO/OQnP7F1nGyfEwls6A3EcPo2bNggp512muzduzex7rDDDpOXXnpJqqqqstoWoEMCGwBXW7RokVxwwQUSjUYT60499VR5/PHHxefz2TrWgAEDkpa3bNlia/+tW7cmLffv399SO933S6et7sfsNHDgwB7rxowZY7md+vr6Hl/iulaYADJFDBsfs9Nzzz0n69atSywfccQR8rWvfc1SGwMHDuzxFNJ///d/2+sokIITYziX3HhOcA/iN31NTU1yxhlnyIoVKxLrqqqq5KWXXpLDDz88jz1DMSGG9X7961/Ltm3bRESkoqJC7rvvvrz2B+iO+DXHWBYKHTFsjrEsFDI3xu9jjz3Wo0LTZZddltZUgNk+J92YN5AuYjh927Ztk1NOOSVxrSzSMYvAwoULDb9/A72FBDYArvXOO+/I7Nmzk8q6Tp8+Xf7v//5PgsGg7eN1v9m0efNmW/t33/6II44w3G7kyJHi9/sTy21tbbJr165eaWvs2LE91vXt29dWW92337dvn639AR1iOHVbr776atLyWWedZesponPOOSdp+c0330x6AhbIhFNjOJfceE5wB+I3fW1tbXL22WfLe++9l1hXXl4uzz33nBx99NF57BmKCTFsbv/+/Yn/b2lpkREjRojH40n5b9OmTUnHOemkk5Je735DHUgH8ZsaY1koZMRwaoxloVC5MX7nz58vl112mcTj8cS6OXPmyIMPPmgr7joVwjkBOsRw+nbv3i2nnHKKrF+/PrFuwIABsnDhQhk2bFjW2gGsIIENgCt98skn8qUvfUmam5sT644++mh5/vnnpaKiIq1jdv9ysXLlSlv7r1q1yvR4nQKBgIwaNSrttkKhUNKXDLO2xo0bZ7i/Hd3nfS8vL7e1P2CEGLYWw12nJRMROfTQQy23Y7R9W1sbA/fICifHcC658ZzgfMRv+sLhsMyZM0def/31xLqSkhJ5+umnZcaMGXnsGYoJMQw4F/FrDWNZKFTEsDWMZaEQuTF+Fy5cKBdeeGFSJarTTz9dHnvsMduVqHR9cMN7EtyBGE5fQ0ODnH766Un9ra6ulpdfftlWlWMgW0hgA+A6q1evllNPPTXpwnXs2LEZz9E9adKkpOX33nsv6YtDKm+++abp8cxee+uttyy3s3Tp0qSBu0GDBmlLMY8bN67HnOs7duyw3FYoFJKGhoakdbW1tZb3B4wQw9ZjuPsgfdfKb1YEAoEe63hqFZlyQwznihvPCc5G/KYvFovJxRdfLC+88EJind/vl//93/+VU089NY89QzEhhgHnIn6tYywLhYgYto6xLBQaN8bvm2++Keeee25SvJ1wwglpV6LS9cHOmHdnv8yOB6SDGE5fS0uLnHXWWfLBBx8k1lVWVsoLL7wgEydOzFo7gB0ksAFwlU2bNskpp5wiO3fuTKw79NBD5ZVXXkl7fvFORxxxRFJVpZaWFstf0FtaWuTtt99OLHs8Hjn77LO123d/7ZVXXrHcz+7bdi+r3lVZWZmccsopSeuWLl1qua2PP/44aYCgT58+Ul9fb3l/oDti2F4Mdx9k37Ztm+V2RES2bt2atOzxeBi4R0bcEsO54sZzgnMRv+lTSsnXv/51+dvf/pZY5/V65eGHH5bzzjsvfx1DUSGGrbv66qvllVdesf1v4MCBSce56667kl6/+uqr83RGcDri1x7GslBoiGF7GMtCIXFj/H7wwQdy1llnSWtra2LdlClTZMGCBVJWVmax98ZmzZqVVM1qzZo1smnTJkv7bty4UT799NPEcp8+fWTWrFkZ9QcghtMXCoXkvPPOS0q0Ky0tlfnz58txxx2XtXYAu0hgA+Aa27dvl5NPPlm2bNmSWDd48GB59dVXZfDgwVlpY/bs2UnLDz74oKX9/vrXvyaVrp0yZYrp4NiZZ56Z9PTZ4sWLe0wpaEQpJY888kjSunPPPdd0n/PPPz9pueuNt1SefPLJpOUvfOELWS1di+JCDNuP4REjRiQtv/baaynb6OrVV19NWh4+fLjtJ1+BTm6K4Vxy4znBeYjfzHzrW9+SRx99NGnd73//e7nkkkvy1CMUG2LYnjFjxsgpp5xi+1/3ik+TJ09Oep3pVZAO4jc9jGWhUBDD9jGWhULhxvhduXKlnH766UmVRsePHy8vvvii9O3b12Kv9UpLS+W0005LWvfQQw9Z2rf7dmeccUZWK0mh+BDD6YtGozJ37lxZuHBhYl0gEJAnn3xSvvjFL2atHSAtCgBcYM+ePerII49UIpL4179/f7Vy5cqstrNs2TLl8XgSbQSDwZRttLW1qcMOOyypb3/4wx9StvWVr3wlaZ9LL7005T4PPPBA0j7Dhw9XoVDIdJ+GhgbVr1+/pP1ee+21lG1t27ZNVVdXJ+338MMPp9wPMEIMd7AbwwsXLkzaXkTU22+/nbIdpZQKhULq8MMPT9r38ssvt7Qv0J0bY9jMww8/nHS8mTNnpn2sQjknFC/id2ZGx/vhD3/Y47P4rrvuyuiYgB3E8MyMjmfH8OHDk9petGhRztqGOxG/M9M+FmNZKATE8My0jsNYFgqBG+N3/fr1qr6+Pmm/0aNHq+3bt2frdJRSSi1YsCCpjdraWrVz507TfXbs2KFqa2uT9nvxxRez2i8UF2I4fbFYTH31q19Nasfr9aq//vWvWW0HSBcJbAAcr7GxUU2dOjXpw7a6ulp9+OGHvdLehRdemNTW1KlTVUNDg+G28XhcXXnllUnbjxw5UoXD4ZTtrFixQnm93qR9H3vsMdPtuw/CPfDAA5bO6c4770zab+jQoWrDhg3a7VtaWtTMmTN77GPlvIDuiOGD29uN4VAopIYNG5a0z4gRI9SmTZtM9wuHwz0uUkRELVmyJOV5Ad25NYbNZPvmeSGcE4oT8ZtZ/N522209PktvueWWjPoH2EEMk8AG5yJ+M49fxrKQT8Rw+jHMWBbyzY3xu3XrVjVy5Mik/YYNG5YyrtJ13HHHJbV1zjnnaPsYCoXU2WefnbT9CSec0Cv9QnEghjNz1VVXJbXj8XjUQw89lPV2gHR5lFJKAMDBTjrpJFm8eHHSultvvVWOP/5428eaPHmy1NTUmG6zdu1amThxYtL84xMnTpR77rlHZs2alVi3Zs0a+dGPfiR///vfk/Z/4okn5IILLrDUnyuvvFLuv//+xLLX65X/+I//kO9+97uJfkYiEfnLX/4i3/ve92Tfvn2JbSdMmCBLly61VEK9vb1dJk2aJKtXr06sO+SQQ+T222+XuXPnJqZKicfjsnjxYrnuuuvko48+Smzr8Xjk+eeflzPOOMPSeQFdEcOZxfCjjz4ql112WdK62tpaueWWW+SSSy5J+nlEIhF59dVX5ZZbbpF33303aZ+zzjpLFixYYOm8gK7cHMNdy6h39fLLL8udd96ZWJ4wYYLcfffdhtuOHDlSRo4cadpOLs8J6Ir4TT9+/+d//kfmzZuXtO4LX/iC3HLLLZb611V9fb2MGzfO9n4AMZz5Z7AdI0aMkE2bNiWWFy1alHTegB3Eb+bxy1gW8okYziyGGctCPrktfltbW2XatGmyYsWKxDqfzye//e1v5bDDDrN5Rh3XtZ2foTpvvPGGzJw5U+LxeGLdrFmz5O6775ZjjjkmsW7p0qVy3XXXyZIlS5L69sYbb8hxxx1nu2+ACDGcilkM//SnP5Wf/OQnSeu+8pWvyJVXXmm7nWxfbwMJ+c6gA4BMSbenrjL5Z/UJ6scffzypbGznv/79+6vJkyeroUOHGr7+7W9/29a5tbS0qClTpvQ4TjAYVIcffriaMGGCqqys7PF6XV2dWr16ta22VqxY0aOMs4io8vJyNX78eHXMMccYvi4i6uc//7mttoCuiOHMY7j7UzOd/7xerxo9erSaNm2aGj9+vKqoqDDcbsyYMWr37t22zg3o5OYYzsY5Wa3GlKtzAroiftOP38suuyxrP7vLLrvM1rkBnYjh7HwGW0UFNmQT8Zud+GUsC/lCDGcew4xlIV/cFr8bNmzI6jmZVTPt6vbbbzfcv76+Xk2ePFkNGjTI8PW7777b0vEBHWI4/RjuXo04k3/MQIDekrosDwCgh4suukiUUnL55ZdLW1tbYv2uXbtk165dhvtcf/31cscdd9hqp7y8XF566SW54IIL5LXXXkusD4fDSU+YdjVixAh55plnZMyYMbbaGjdunCxevFjOP/98Wbt2bWJ9a2urLF++3HCfYDAov/vd7+SKK66w1RaQb26L4d/97ndyyCGHyM9+9jOJRqOJ9fF4PCmejZx22mnyyCOPSG1treX2gHzLVQznkhvPCTDC3zrgbMQw4FxujF/GslBM3BbDjGWhmLgtfkVEfvCDH4jP55MbbrhBYrFYYv22bdtk27ZtPbb3+Xxy1113ybXXXpvDXgLZ4cYYBgqVN98dAACn+rd/+zdZvny5XHzxxRIIBLTbnXjiibJ48WK58847xePx2G6nX79+8sorr8j9998vo0ePNt3uxhtvlGXLlslRRx1lux0RkfHjx8snn3wiP/vZz2TQoEHa7SorK2XevHmyZs0aBvzgWG6KYa/XK7fccot8/PHHcuWVV0rfvn1Nt/f5fPLFL35R/v73v8uLL75oGu9AocpVDOeSG88JMMLfOuBsxDDgXG6MX8ayUEzcFMOMZaHYuCl+O1133XXy/vvvy1lnnSVer3HKgdfrlbPPPluWLl1K8hoczY0xDBQij1JK5bsTAOB0jY2N8sYbb8inn34qTU1NUlpaKsOGDZMZM2bI4MGDs9rWsmXL5IMPPpDt27dLLBaT2tpaGT9+vBx77LGmX5rsisfj8sEHH8gnn3wiO3bsEK/XK/3795dRo0bJ8ccfL8FgMGttAfnmthiOx+OyYsUK+eSTT2Tv3r3S2NgopaWlUl1dLaNGjZIpU6ZIZWVlVtoCCkEuYzhX3HhOgBH+1gFnI4YB53Jj/DKWhWLithhmLAvFxG3xKyKye/dueeONN2T9+vXS0tIiFRUVMmrUKJkxY4bU1dXlu3tAVrkxhoFCQQIbAAAAAAAAAAAAAAAAACAvmEIUAAAAAAAAAAAAAAAAAJAXJLABAAAAAAAAAAAAAAAAAPKCBDYAAAAAAAAAAAAAAAAAQF6QwAYAAAAAAAAAAAAAAAAAyAsS2AAAAAAAAAAAAAAAAAAAeUECGwAAAAAAAAAAAAAAAAAgL0hgAwAAAAAAAAAAAAAAAADkBQlsAAAAAAAAAAAAAAAAAIC8IIENAAAAAAAAAAAAAAAAAJAXJLABAAAAAAAAAAAAAAAAAPKCBDYAAAAAAAAAAAAAAAAAQF6QwAYAAAAAAAAAAAAAAAAAyAsS2AAAAAAAAAAAAAAAAAAAeUECGwAAAAAAAAAAAAAAAAAgL0hgAwAAAAAAAAAAAAAAAADkBQlsAAAAAAAAAAAAAAAAAIC8IIENAAAAAAAAAAAAAAAAAJAXJLABAAAAAAAAAAAAAAAAAPKCBDYAAAAAAAAAAAAAAAAAQF6QwAYAAAAAAAAAAAAAAAAAyAsS2AAAAAAAAAAAAAAAAAAAeUECGwAAAAAAAAAAAAAAAAAgL0hgAwAAAAAAAAAAAAAAAADkBQlsAAAAAAAAAAAAAAAAAIC8IIENAAAAAAAAAAAAAAAAAJAXJLABAAAAAAAAAAAAAAAAAPKCBDYAAAAAAAAAAAAAAAAAQF6QwAYAAAAAAAAAAAAAAAAAyAsS2AAAAAAAAAAAAAAAAAAAeUECGwAAAAAAAAAAAAAAAAAgL0hgAwAAAAAAAAAAAAAAAADkBQlsAAAAAAAAAAAAAAAAAIC8IIENAAAAAAAAAAAAAAAAAJAXJLABAAAAAAAAAAAAAAAAAPKCBDYAAAAAAAAAAAAAAAAAQF6QwAYAAAAAAAAAAAAAAAAAyAsS2AAAAAAAAAAAAAAAAAAAefH/A/4uHIEzSDopAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testResults['Actual']=df_for_testing.iloc[-min_length:, -2]\n",
    "testResults[\"Pred\"]=prediction[-min_length:]\n",
    "plot((testResults['Pred']).rolling(1).mean(),testResults[\"Actual\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'2004-01-02' in df_for_testing.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 3000x900 with 1 Axes>, <Axes: >)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pred=prediction\n",
    "origin=df_for_testing\n",
    "\n",
    "\n",
    "min_length = min(len(origin), len(prediction))\n",
    "pred=pred[-min_length:]\n",
    "origin=origin.iloc[-min_length:, -2]\n",
    "\n",
    "aligned = pd.DataFrame(columns=[f'pred{i}' for i in range(n_forecast)], index=pd.date_range(\n",
    "    start=origin.index[0], freq='D', periods=pred.shape[0]+n_forecast-1))\n",
    "for i in range(len(prediction)):\n",
    "    col=i % n_forecast\n",
    "    try:\n",
    "        aligned.iloc[i:i+n_forecast,col]=prediction[i]\n",
    "    except IndexError:\n",
    "        break\n",
    "CI=aligned.std(axis=1)* 1.96 / np.sqrt(aligned.shape[1])\n",
    "mean=aligned.mean(axis=1)\n",
    "aligned=pd.concat([mean,mean-CI,mean+CI],keys=['m','lc',\"uc\"],axis=1)\n",
    "plot(aligned,testResults[\"Actual\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}