ecm-zindi-kp-v4-training-and-prediction-notebook.ipynb

{
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install --upgrade plotly plotnine\n",
    "# !pip install fastinference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d83b08b0",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pickle\n",
    "import random\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from fastai.tabular.all import * \n",
    "# from fastinference.tabular import *\n",
    "from sklearn.model_selection import GroupKFold\n",
    "from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, SplineTransformer\n",
    "\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import Adam, SGD\n",
    "from torch.autograd import Variable\n",
    "from torch.utils.data import TensorDataset, DataLoader,Dataset\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau, CosineAnnealingWarmRestarts\n",
    "\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "71e42b23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:56.280731Z",
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     "exception": false,
     "start_time": "2023-10-12T11:48:56.273517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def periodic_spline_transformer(period, n_splines=None, degree=3):\n",
    "    if n_splines is None:\n",
    "        n_splines = period\n",
    "    n_knots = n_splines + 1  # periodic and include_bias is True\n",
    "    return SplineTransformer(\n",
    "        degree=degree,\n",
    "        n_knots=n_knots,\n",
    "        knots=np.linspace(0, period, n_knots).reshape(n_knots, 1),\n",
    "        extrapolation=\"periodic\",\n",
    "        include_bias=True)\n",
    "\n",
    "def seed_everything(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "#     os.environ['CUDA_LAUNCH_BLOCKING'] = str(1)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "set_seed(42)\n",
    "seed_everything(seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbdaa52e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:56.304909Z",
     "iopub.status.busy": "2023-10-12T11:48:56.304664Z",
     "iopub.status.idle": "2023-10-12T11:48:57.661548Z",
     "shell.execute_reply": "2023-10-12T11:48:57.660662Z"
    },
    "papermill": {
     "duration": 1.364575,
     "end_time": "2023-10-12T11:48:57.663500",
     "exception": false,
     "start_time": "2023-10-12T11:48:56.298925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 42.20it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 328.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118768, 56)\n",
      "(118768, 57)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "df_train = pd.read_csv(\"/kaggle/input/ecm-itu-zindi-kp-data/imgs_2023071012133740345.csv\",parse_dates=['Time'])\n",
    "df_test = pd.read_csv(\"/kaggle/input/ecm-itu-zindi-kp-data/imgs_202307101549519358.csv\",parse_dates=['Time'])\n",
    "df_cell = pd.read_csv(\"/kaggle/input/ecm-itu-zindi-kp-data/imgs_2023071012130978799.csv\",parse_dates=['Time'])\n",
    "df_bs = pd.read_csv(\"/kaggle/input/ecm-itu-zindi-kp-data/imgs_2023071012123392536.csv\")\n",
    "df_features = df_cell.merge(df_bs,on=['BS','CellName'],how='outer')\n",
    "df_features = df_features[df_features['CellName']=='Cell0'].reset_index(drop=True)\n",
    "df_test['split'] = 'test'\n",
    "df_train['split'] = 'train'\n",
    "df_total = pd.concat([df_train,df_test],ignore_index=True)\n",
    "df_total = df_total.merge(df_features,on=['BS','Time'],how='left')\n",
    "df_total['ID'] = df_total['Time'].astype(str)+\"_\"+df_total['BS']\n",
    "df_total['BS'] = df_total['BS'].str.replace(r'[a-zA-Z_]', '', regex=True).astype(int)\n",
    "for col in ['RUType','Mode']:\n",
    "    df_total[col] = df_total[col].str.replace(r'[a-zA-Z]', '', regex=True).astype(int)\n",
    "\n",
    "df_total.sort_values(['BS','Time'], ascending=True,ignore_index=True,inplace=True)\n",
    "df_total['day'] = df_total['Time'].dt.day\n",
    "df_total['weekday_number'] = df_total['Time'].dt.weekday\n",
    "df_total['hour'] = df_total['Time'].dt.hour\n",
    "\n",
    "hour_df = df_total[['hour']].copy()\n",
    "splines = periodic_spline_transformer(24, n_splines=12).fit_transform(hour_df)\n",
    "splines_df = pd.DataFrame(splines,columns=[f\"hour_spline_{i}\" for i in range(splines.shape[1])])\n",
    "df_total = pd.concat([df_total,splines_df],axis=1)\n",
    "\n",
    "df_total = df_total.sort_values(['BS','Time'],ascending=True,ignore_index=True)\n",
    "all_shits = list(np.arange(1,4)) # \n",
    "for shift_i in tqdm(all_shits):\n",
    "    for col in ['load','ESMode1','ESMode2','ESMode3','ESMode6','Time','Energy']:\n",
    "        df_total[f'{col}_T-{shift_i}'] = df_total.groupby(['BS'])[col].shift(shift_i)        \n",
    "for shift_i in tqdm(all_shits):\n",
    "    df_total[f'Time_T-{shift_i}_hours_elapsed'] = (df_total[f'Time_T-{shift_i}']-df_total['Time']).dt.total_seconds() / 3600\n",
    "    del df_total[f'Time_T-{shift_i}']\n",
    "print(df_total.shape)\n",
    "\n",
    "num_bins = 100\n",
    "df_total['load_bin'] = pd.cut(df_total['load'],bins=[round(i,2) for i in list(np.arange(0,1.01,0.01))],labels=[f'{i}' for i in range(num_bins)])\n",
    "df_total['load_bin'] = df_total['load_bin'].astype(float).fillna(-1).astype(int)\n",
    "\n",
    "print(df_total.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73ae4f4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:57.675336Z",
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     "exception": false,
     "start_time": "2023-10-12T11:48:57.668720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f9aafb99ae0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_total['Energy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d44ca917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:58.243961Z",
     "iopub.status.busy": "2023-10-12T11:48:58.243692Z",
     "iopub.status.idle": "2023-10-12T11:48:58.503599Z",
     "shell.execute_reply": "2023-10-12T11:48:58.502742Z"
    },
    "papermill": {
     "duration": 0.268094,
     "end_time": "2023-10-12T11:48:58.505511",
     "exception": false,
     "start_time": "2023-10-12T11:48:58.237417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "df_total.groupby(['Time'])[['Energy']].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce67b176",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:58.519116Z",
     "iopub.status.busy": "2023-10-12T11:48:58.518854Z",
     "iopub.status.idle": "2023-10-12T11:48:59.170984Z",
     "shell.execute_reply": "2023-10-12T11:48:59.170112Z"
    },
    "papermill": {
     "duration": 0.661038,
     "end_time": "2023-10-12T11:48:59.172802",
     "exception": false,
     "start_time": "2023-10-12T11:48:58.511764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='load'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "df_total.groupby(['load'])[[\"Energy\"]].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e443bb02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:48:59.188010Z",
     "iopub.status.busy": "2023-10-12T11:48:59.187727Z",
     "iopub.status.idle": "2023-10-12T11:49:10.628938Z",
     "shell.execute_reply": "2023-10-12T11:49:10.627953Z"
    },
    "papermill": {
     "duration": 11.450701,
     "end_time": "2023-10-12T11:49:10.630693",
     "exception": false,
     "start_time": "2023-10-12T11:48:59.179992",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1020/1020 [00:06<00:00, 166.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118768, 61)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1020/1020 [00:05<00:00, 195.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(118768, 63)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "from scipy.signal import savgol_filter as sg\n",
    "from scipy.signal import sosfiltfilt, butter, sosfilt, sosfilt_zi\n",
    "\n",
    "def add_sg(df):\n",
    "    w = 5 #5\n",
    "    p = 3 #2\n",
    "    for si in tqdm(df.BS.unique()):\n",
    "        index = df.BS == si\n",
    "        df.loc[index, 'load_smooth'] = sg(df[index].load, w, p)\n",
    "        df.loc[index, 'load_diff'] = sg(df[index].load, w, p, 1)\n",
    "        df.loc[index, 'load_diff2'] = sg(df[index].load, w, p, 2)\n",
    "        df.loc[index, 'load_diff3'] = sg(df[index].load, w, p, 3)\n",
    "\n",
    "add_sg(df_total)\n",
    "print(df_total.shape)\n",
    "\n",
    "def add_sosfiltfilt(df):\n",
    "    for si in tqdm(df.BS.unique()):\n",
    "        index = df.BS == si\n",
    "        sos=butter(4, 0.125, output='sos')\n",
    "        sos8 = butter(8, 0.125, output='sos')\n",
    "        zi = np.array(df[index].load[:4]).mean() * sosfilt_zi(sos8)\n",
    "        df.loc[index, 'load_sosfiltfilt'] = sosfiltfilt(sos,df[index].load)\n",
    "        df.loc[index, 'load_sosfilt'], _ = sosfilt(sos8, df[index].load, zi=zi)\n",
    "\n",
    "add_sosfiltfilt(df_total)\n",
    "print(df_total.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "970f297e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:49:10.655274Z",
     "iopub.status.busy": "2023-10-12T11:49:10.654728Z",
     "iopub.status.idle": "2023-10-12T11:49:10.816924Z",
     "shell.execute_reply": "2023-10-12T11:49:10.815888Z"
    },
    "papermill": {
     "duration": 0.176732,
     "end_time": "2023-10-12T11:49:10.819189",
     "exception": false,
     "start_time": "2023-10-12T11:49:10.642457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92629, 54) (26139, 54)\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "df_total.drop(columns=['w','CellName','ESMode4','Time'],inplace=True)\n",
    "\n",
    "id_variable = 'ID'\n",
    "version_nb = 'v4'\n",
    "TARGET = 'Energy'\n",
    "\n",
    "train_df = df_total[df_total['split']=='train'].reset_index(drop=True)\n",
    "test_df = df_total[df_total['split']=='test'].reset_index(drop=True)\n",
    "train_cols = [i for i in train_df if i not in ['Time','CellName','ID','Energy','split','w','BS','ESMode6']]\n",
    "\n",
    "categorical_cols = ['RUType','Mode','load_bin']\n",
    "\n",
    "print(train_df[train_cols].shape, test_df[train_cols].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9ba0895",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:49:10.844625Z",
     "iopub.status.busy": "2023-10-12T11:49:10.844339Z",
     "iopub.status.idle": "2023-10-12T11:49:10.959892Z",
     "shell.execute_reply": "2023-10-12T11:49:10.958355Z"
    },
    "papermill": {
     "duration": 0.130431,
     "end_time": "2023-10-12T11:49:10.961947",
     "exception": false,
     "start_time": "2023-10-12T11:49:10.831516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train single values ..\n",
      "test single values ..\n",
      "\n",
      " []\n",
      "54\n",
      "54\n",
      "['load', 'ESMode1', 'ESMode2', 'ESMode3', 'ESMode5', 'RUType', 'Mode', 'Frequency', 'Bandwidth', 'Antennas', 'TXpower', 'day', 'weekday_number', 'hour', 'hour_spline_0', 'hour_spline_1', 'hour_spline_2', 'hour_spline_3', 'hour_spline_4', 'hour_spline_5', 'hour_spline_6', 'hour_spline_7', 'hour_spline_8', 'hour_spline_9', 'hour_spline_10', 'hour_spline_11', 'load_T-1', 'ESMode1_T-1', 'ESMode2_T-1', 'ESMode3_T-1', 'ESMode6_T-1', 'Energy_T-1', 'load_T-2', 'ESMode1_T-2', 'ESMode2_T-2', 'ESMode3_T-2', 'ESMode6_T-2', 'Energy_T-2', 'load_T-3', 'ESMode1_T-3', 'ESMode2_T-3', 'ESMode3_T-3', 'ESMode6_T-3', 'Energy_T-3', 'Time_T-1_hours_elapsed', 'Time_T-2_hours_elapsed', 'Time_T-3_hours_elapsed', 'load_bin', 'load_smooth', 'load_diff', 'load_diff2', 'load_diff3', 'load_sosfiltfilt', 'load_sosfilt']\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "remove_non_unique_cols = []\n",
    "print('train single values ..')\n",
    "for col in train_df:\n",
    "    if col in train_cols and col not in ['ESMode5']:\n",
    "        if train_df[col].nunique()<=1:\n",
    "            remove_non_unique_cols.append(col)\n",
    "            print(col,\":\",train_df[col].nunique())\n",
    "print('test single values ..')\n",
    "for col in test_df:\n",
    "    if col in train_cols and col not in ['ESMode5']:\n",
    "        if test_df[col].nunique()<=1:\n",
    "            remove_non_unique_cols.append(col)\n",
    "            print(col,\":\",test_df[col].nunique())\n",
    "\n",
    "print('\\n',remove_non_unique_cols)\n",
    "\n",
    "for col in train_df:\n",
    "    if col in train_cols:\n",
    "        if train_df[col].isnull().sum()/len(train_df)>=0.95:\n",
    "            print(col,\":\",train_df[col].isnull().sum()/len(train_df))\n",
    "            remove_non_unique_cols.append(col)\n",
    "\n",
    "for col in test_df:\n",
    "    if col in train_cols:\n",
    "        if test_df[col].isnull().sum()/len(test_df)>=0.95:\n",
    "            print(col,\":\",test_df[col].isnull().sum()/len(test_df))\n",
    "            remove_non_unique_cols.append(col)\n",
    "            \n",
    "print(len(train_cols))\n",
    "train_cols = [col for col in train_cols if col not in remove_non_unique_cols]\n",
    "print(len(train_cols))\n",
    "print(train_cols)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4cb52cf9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:49:10.988227Z",
     "iopub.status.busy": "2023-10-12T11:49:10.987941Z",
     "iopub.status.idle": "2023-10-12T11:49:11.012459Z",
     "shell.execute_reply": "2023-10-12T11:49:11.011649Z"
    },
    "papermill": {
     "duration": 0.03948,
     "end_time": "2023-10-12T11:49:11.014197",
     "exception": false,
     "start_time": "2023-10-12T11:49:10.974717",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "Nfold = 10\n",
    "Inference=False\n",
    "\n",
    "if Inference:\n",
    "    print('Data has been splitted...')\n",
    "else:\n",
    "    train_df['fold'] = 0\n",
    "    strafy_bin = train_df['BS'].astype('int')\n",
    "\n",
    "    skf = GroupKFold(n_splits = Nfold)\n",
    "    for i, (_, train_index) in enumerate(skf.split(train_df.index,train_df.index, strafy_bin)):\n",
    "        train_df.loc[train_index, 'fold'] = i  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49944ca9",
   "metadata": {
    "papermill": {
     "duration": 0.011295,
     "end_time": "2023-10-12T11:49:11.037638",
     "exception": false,
     "start_time": "2023-10-12T11:49:11.026343",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### FastAI model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6cc2c861",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T11:49:11.062861Z",
     "iopub.status.busy": "2023-10-12T11:49:11.062607Z",
     "iopub.status.idle": "2023-10-12T11:49:11.077148Z",
     "shell.execute_reply": "2023-10-12T11:49:11.076402Z"
    },
    "papermill": {
     "duration": 0.029383,
     "end_time": "2023-10-12T11:49:11.079035",
     "exception": false,
     "start_time": "2023-10-12T11:49:11.049652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "def mae(preds, targs):\n",
    "    x = (targs-preds)\n",
    "    return (abs(x)).mean()\n",
    "\n",
    "def mape(preds, targs):\n",
    "    x = (targs-preds)/targs\n",
    "    return (abs(x)).mean()\n",
    "\n",
    "def fit_fastai(Nfolds, train_df, test_df, train_cols, cat_feats, TARGET, model_path):\n",
    "    \n",
    "    oof_pred_fastai = np.zeros(train_df.shape[0], dtype=np.float32)\n",
    "    pred_fastai = np.zeros(test_df.shape[0], dtype=np.float32)\n",
    "    scores = []\n",
    "    scores_pvt = []\n",
    "    \n",
    "    train_df_fast = train_df[train_cols + [TARGET]].copy()\n",
    "    test_df = test_df.copy()\n",
    "    for col in train_df_fast.columns:\n",
    "        if col not in cat_feats:\n",
    "            train_df_fast[col] = train_df_fast[col].fillna(0)\n",
    "            test_df[col] = test_df[col].fillna(0)\n",
    "        else:\n",
    "            # Impute missing categorical values with the most frequent category\n",
    "            most_frequent_category = train_df_fast[col].mode().iloc[0]\n",
    "            train_df_fast[col] = train_df_fast[col].fillna(most_frequent_category)\n",
    "            test_df[col] = test_df[col].fillna(most_frequent_category)\n",
    "\n",
    "    train_df_fast[cat_feats] = train_df_fast[cat_feats].astype('category')\n",
    "    test_df[cat_feats] = test_df[cat_feats].astype('category')\n",
    "    \n",
    "    cont_nn = train_cols.copy()\n",
    "    for col in cat_feats:\n",
    "        cont_nn.remove(col)\n",
    "\n",
    "    cat_nn = cat_feats\n",
    "\n",
    "    layers =  [256, 512, 1024, 512, 256] #[256, 512, 1024, 512, 256]\n",
    "\n",
    "    val_pct, tst_preds = L(), L()\n",
    "\n",
    "    for fold in range(Nfolds):\n",
    "        print(\"*\"*10, f'Fold-{fold+1}', \"*\"*10)\n",
    "        train_idx = train_df.loc[train_df['fold']!=fold, :].index\n",
    "        valid_idx = train_df.loc[train_df['fold']==fold, :].index\n",
    "        splits = (L(list(train_idx)), L(list(valid_idx)))\n",
    "        dls = TabularPandas(train_df_fast, [Categorify, Normalize], cat_nn, cont_nn, splits = splits, y_names=TARGET,reduce_memory=False).dataloaders(1024)\n",
    "        learn = tabular_learner(dls, layers=layers, n_out=1, y_range = (0,100),loss_func = mae, metrics=AccumMetric(mae))\n",
    "#         print(learn.summary())\n",
    "#         learn.lr_find(suggest_funcs=(slide, valley))\n",
    "#         if os.path.isfile(model_path + f'models/nn_model_{fold}.pth'):\n",
    "#             learn = tabular_learner(dls, layers=layers, n_out=1, path = model_path)\n",
    "#             learn.load(f'nn_model_{fold}')\n",
    "#         else:\n",
    "        learn.fit_one_cycle(100, 2e-3, cbs=SaveModelCallback(monitor='mae', comp=np.less, fname=f'nn_model_{fold}'))\n",
    "\n",
    "        val_df = train_df.loc[train_df['fold']==fold]\n",
    "        val_dl = dls.test_dl(val_df[train_cols].fillna(0))\n",
    "        \n",
    "        preds, _ = learn.get_preds(dl=val_dl)\n",
    "        oof_pred_fastai[val_df.index] = preds.squeeze().numpy()\n",
    "       \n",
    "        score = mean_absolute_error(val_df[TARGET], oof_pred_fastai[val_df.index])\n",
    "        score_pvt = mean_absolute_percentage_error(val_df[TARGET], oof_pred_fastai[val_df.index])\n",
    "        \n",
    "        scores.append(score)\n",
    "        scores_pvt.append(score_pvt)\n",
    "        print(f'MAE for Fold-{fold+1}:', np.round(score, 3))\n",
    "        print(f'MAPE for Fold-{fold+1}:', np.round(score_pvt, 3))\n",
    "        \n",
    "        test_dl = dls.test_dl(test_df[train_cols])\n",
    "        preds, _ = learn.get_preds(dl=test_dl)\n",
    "        pred_fastai += preds.squeeze().numpy()/Nfolds\n",
    "\n",
    "#         display(test_df[train_cols].iloc[-5:])\n",
    "        \n",
    "#         exp = ShapInterpretation(learn, test_df[train_cols].iloc[-5:])\n",
    "#         exp.summary_plot()\n",
    "\n",
    "\n",
    "    score = mean_absolute_error(train_df[TARGET],oof_pred_fastai)\n",
    "    score_pvt = mean_absolute_percentage_error(train_df[TARGET],oof_pred_fastai)\n",
    "    \n",
    "    print(f'OOF MAE:', np.round(score, 3))\n",
    "    print(f'Average MAE:', f'{np.round(np.mean(scores), 3)}+/-{np.round(np.std(scores), 3)}')\n",
    "    \n",
    "    print(f'OOF MAPE:', np.round(score_pvt, 3))\n",
    "    print(f'Average MAPE:', f'{np.round(np.mean(scores_pvt), 3)}+/-{np.round(np.std(scores_pvt), 3)}')\n",
    "\n",
    "    return oof_pred_fastai, pred_fastai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2b3b33e",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2023-10-12T11:49:11.102847Z",
     "iopub.status.busy": "2023-10-12T11:49:11.102346Z",
     "iopub.status.idle": "2023-10-12T12:05:59.911101Z",
     "shell.execute_reply": "2023-10-12T12:05:59.910144Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "papermill": {
     "duration": 1008.822813,
     "end_time": "2023-10-12T12:05:59.912892",
     "exception": false,
     "start_time": "2023-10-12T11:49:11.090079",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Fold-1 **********\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9.456817</td>\n",
       "      <td>6.349920</td>\n",
       "      <td>6.349921</td>\n",
       "      <td>00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.526755</td>\n",
       "      <td>4.211842</td>\n",
       "      <td>4.211842</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.179759</td>\n",
       "      <td>3.728648</td>\n",
       "      <td>3.728648</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.304284</td>\n",
       "      <td>3.278872</td>\n",
       "      <td>3.278872</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.755949</td>\n",
       "      <td>3.024884</td>\n",
       "      <td>3.024884</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.723711</td>\n",
       "      <td>3.111871</td>\n",
       "      <td>3.111871</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.607093</td>\n",
       "      <td>2.745358</td>\n",
       "      <td>2.745358</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.538596</td>\n",
       "      <td>2.832667</td>\n",
       "      <td>2.832667</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.357962</td>\n",
       "      <td>2.479171</td>\n",
       "      <td>2.479171</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.207158</td>\n",
       "      <td>2.756423</td>\n",
       "      <td>2.756423</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.050705</td>\n",
       "      <td>2.199598</td>\n",
       "      <td>2.199598</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.694715</td>\n",
       "      <td>2.128818</td>\n",
       "      <td>2.128818</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.304376</td>\n",
       "      <td>1.907617</td>\n",
       "      <td>1.907617</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.948409</td>\n",
       "      <td>2.135629</td>\n",
       "      <td>2.135629</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.692899</td>\n",
       "      <td>1.388263</td>\n",
       "      <td>1.388263</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.427960</td>\n",
       "      <td>1.319002</td>\n",
       "      <td>1.319002</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.337882</td>\n",
       "      <td>0.991907</td>\n",
       "      <td>0.991907</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.290303</td>\n",
       "      <td>1.331691</td>\n",
       "      <td>1.331691</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.346402</td>\n",
       "      <td>1.245452</td>\n",
       "      <td>1.245452</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.236902</td>\n",
       "      <td>1.137814</td>\n",
       "      <td>1.137814</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.259490</td>\n",
       "      <td>1.077210</td>\n",
       "      <td>1.077210</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.270741</td>\n",
       "      <td>1.206021</td>\n",
       "      <td>1.206021</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.227288</td>\n",
       "      <td>1.086869</td>\n",
       "      <td>1.086869</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.293678</td>\n",
       "      <td>1.208295</td>\n",
       "      <td>1.208295</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.191420</td>\n",
       "      <td>0.947581</td>\n",
       "      <td>0.947581</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.256577</td>\n",
       "      <td>1.098251</td>\n",
       "      <td>1.098251</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.147675</td>\n",
       "      <td>1.123763</td>\n",
       "      <td>1.123763</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.137789</td>\n",
       "      <td>0.983911</td>\n",
       "      <td>0.983911</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.198502</td>\n",
       "      <td>1.928072</td>\n",
       "      <td>1.928072</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.269977</td>\n",
       "      <td>1.124112</td>\n",
       "      <td>1.124112</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.241395</td>\n",
       "      <td>0.933075</td>\n",
       "      <td>0.933075</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.219755</td>\n",
       "      <td>1.294602</td>\n",
       "      <td>1.294602</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.139998</td>\n",
       "      <td>1.093005</td>\n",
       "      <td>1.093005</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.115636</td>\n",
       "      <td>1.199052</td>\n",
       "      <td>1.199052</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.087988</td>\n",
       "      <td>0.922500</td>\n",
       "      <td>0.922500</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.060492</td>\n",
       "      <td>1.398217</td>\n",
       "      <td>1.398216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.075835</td>\n",
       "      <td>0.878960</td>\n",
       "      <td>0.878960</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.154817</td>\n",
       "      <td>1.516069</td>\n",
       "      <td>1.516069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.184346</td>\n",
       "      <td>1.010882</td>\n",
       "      <td>1.010882</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.197495</td>\n",
       "      <td>1.273591</td>\n",
       "      <td>1.273591</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.159901</td>\n",
       "      <td>1.014838</td>\n",
       "      <td>1.014838</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.085670</td>\n",
       "      <td>0.981784</td>\n",
       "      <td>0.981784</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.191049</td>\n",
       "      <td>1.410503</td>\n",
       "      <td>1.410503</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.132557</td>\n",
       "      <td>0.970324</td>\n",
       "      <td>0.970324</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.184868</td>\n",
       "      <td>1.108500</td>\n",
       "      <td>1.108500</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.128680</td>\n",
       "      <td>1.062020</td>\n",
       "      <td>1.062020</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.066565</td>\n",
       "      <td>1.102059</td>\n",
       "      <td>1.102059</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.064576</td>\n",
       "      <td>1.038417</td>\n",
       "      <td>1.038416</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.126129</td>\n",
       "      <td>1.018886</td>\n",
       "      <td>1.018886</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.114808</td>\n",
       "      <td>0.993201</td>\n",
       "      <td>0.993200</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.049688</td>\n",
       "      <td>0.904422</td>\n",
       "      <td>0.904422</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.028367</td>\n",
       "      <td>0.947399</td>\n",
       "      <td>0.947399</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.081364</td>\n",
       "      <td>0.877820</td>\n",
       "      <td>0.877820</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.047179</td>\n",
       "      <td>1.054185</td>\n",
       "      <td>1.054185</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.045989</td>\n",
       "      <td>1.051342</td>\n",
       "      <td>1.051342</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.010033</td>\n",
       "      <td>0.890209</td>\n",
       "      <td>0.890208</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.007120</td>\n",
       "      <td>0.829773</td>\n",
       "      <td>0.829773</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.971061</td>\n",
       "      <td>0.855765</td>\n",
       "      <td>0.855765</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.043741</td>\n",
       "      <td>1.065571</td>\n",
       "      <td>1.065570</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.028475</td>\n",
       "      <td>0.900522</td>\n",
       "      <td>0.900522</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.999783</td>\n",
       "      <td>0.965913</td>\n",
       "      <td>0.965913</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.961135</td>\n",
       "      <td>0.887715</td>\n",
       "      <td>0.887715</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.988088</td>\n",
       "      <td>0.853927</td>\n",
       "      <td>0.853927</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.965361</td>\n",
       "      <td>0.783659</td>\n",
       "      <td>0.783659</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.978164</td>\n",
       "      <td>0.805522</td>\n",
       "      <td>0.805522</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.958324</td>\n",
       "      <td>0.829667</td>\n",
       "      <td>0.829667</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.927293</td>\n",
       "      <td>0.838651</td>\n",
       "      <td>0.838651</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.976686</td>\n",
       "      <td>0.891979</td>\n",
       "      <td>0.891979</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.002062</td>\n",
       "      <td>0.749168</td>\n",
       "      <td>0.749168</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.940113</td>\n",
       "      <td>0.768736</td>\n",
       "      <td>0.768736</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.922616</td>\n",
       "      <td>0.811725</td>\n",
       "      <td>0.811725</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.937721</td>\n",
       "      <td>0.767220</td>\n",
       "      <td>0.767220</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.918493</td>\n",
       "      <td>0.941856</td>\n",
       "      <td>0.941856</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.899100</td>\n",
       "      <td>0.807389</td>\n",
       "      <td>0.807389</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.892784</td>\n",
       "      <td>0.755227</td>\n",
       "      <td>0.755227</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.892376</td>\n",
       "      <td>0.746662</td>\n",
       "      <td>0.746662</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.884933</td>\n",
       "      <td>0.772348</td>\n",
       "      <td>0.772348</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.879011</td>\n",
       "      <td>0.763336</td>\n",
       "      <td>0.763336</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.892783</td>\n",
       "      <td>0.792529</td>\n",
       "      <td>0.792529</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.860771</td>\n",
       "      <td>0.750951</td>\n",
       "      <td>0.750951</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.880180</td>\n",
       "      <td>0.735915</td>\n",
       "      <td>0.735915</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.857646</td>\n",
       "      <td>0.725569</td>\n",
       "      <td>0.725569</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.860400</td>\n",
       "      <td>0.799675</td>\n",
       "      <td>0.799675</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.853873</td>\n",
       "      <td>0.737192</td>\n",
       "      <td>0.737192</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.835842</td>\n",
       "      <td>0.733929</td>\n",
       "      <td>0.733929</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.831425</td>\n",
       "      <td>0.732244</td>\n",
       "      <td>0.732244</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.825549</td>\n",
       "      <td>0.737862</td>\n",
       "      <td>0.737862</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.842592</td>\n",
       "      <td>0.741265</td>\n",
       "      <td>0.741265</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.807337</td>\n",
       "      <td>0.732682</td>\n",
       "      <td>0.732682</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.813127</td>\n",
       "      <td>0.741885</td>\n",
       "      <td>0.741885</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.823905</td>\n",
       "      <td>0.723905</td>\n",
       "      <td>0.723905</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.799172</td>\n",
       "      <td>0.722010</td>\n",
       "      <td>0.722010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.823757</td>\n",
       "      <td>0.728471</td>\n",
       "      <td>0.728471</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.823170</td>\n",
       "      <td>0.708030</td>\n",
       "      <td>0.708030</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.827335</td>\n",
       "      <td>0.740499</td>\n",
       "      <td>0.740499</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.795139</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.804040</td>\n",
       "      <td>0.715392</td>\n",
       "      <td>0.715392</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.786980</td>\n",
       "      <td>0.708767</td>\n",
       "      <td>0.708767</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.800312</td>\n",
       "      <td>0.715835</td>\n",
       "      <td>0.715835</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.813044</td>\n",
       "      <td>0.717292</td>\n",
       "      <td>0.717292</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with mae value: 6.349920749664307.\n",
      "Better model found at epoch 1 with mae value: 4.211842060089111.\n",
      "Better model found at epoch 2 with mae value: 3.7286477088928223.\n",
      "Better model found at epoch 3 with mae value: 3.278871774673462.\n",
      "Better model found at epoch 4 with mae value: 3.0248842239379883.\n",
      "Better model found at epoch 6 with mae value: 2.7453579902648926.\n",
      "Better model found at epoch 8 with mae value: 2.4791712760925293.\n",
      "Better model found at epoch 10 with mae value: 2.1995978355407715.\n",
      "Better model found at epoch 11 with mae value: 2.128817558288574.\n",
      "Better model found at epoch 12 with mae value: 1.9076170921325684.\n",
      "Better model found at epoch 14 with mae value: 1.3882628679275513.\n",
      "Better model found at epoch 15 with mae value: 1.3190016746520996.\n",
      "Better model found at epoch 16 with mae value: 0.9919068217277527.\n",
      "Better model found at epoch 24 with mae value: 0.947580873966217.\n",
      "Better model found at epoch 30 with mae value: 0.9330751895904541.\n",
      "Better model found at epoch 34 with mae value: 0.9225002527236938.\n",
      "Better model found at epoch 36 with mae value: 0.878959596157074.\n",
      "Better model found at epoch 52 with mae value: 0.8778195977210999.\n",
      "Better model found at epoch 56 with mae value: 0.8297727704048157.\n",
      "Better model found at epoch 63 with mae value: 0.7836587429046631.\n",
      "Better model found at epoch 68 with mae value: 0.7491678595542908.\n",
      "Better model found at epoch 75 with mae value: 0.7466617226600647.\n",
      "Better model found at epoch 80 with mae value: 0.73591548204422.\n",
      "Better model found at epoch 81 with mae value: 0.7255692481994629.\n",
      "Better model found at epoch 90 with mae value: 0.7239049673080444.\n",
      "Better model found at epoch 91 with mae value: 0.7220098376274109.\n",
      "Better model found at epoch 93 with mae value: 0.7080302834510803.\n"
     ]
    },
    {
     "data": {
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      "MAE for Fold-1: 0.708\n",
      "MAPE for Fold-1: 0.025\n"
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     "text": [
      "********** Fold-2 **********\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.017571</td>\n",
       "      <td>6.952042</td>\n",
       "      <td>6.952042</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.771524</td>\n",
       "      <td>4.908609</td>\n",
       "      <td>4.908609</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.242905</td>\n",
       "      <td>3.516562</td>\n",
       "      <td>3.516562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.053972</td>\n",
       "      <td>2.910455</td>\n",
       "      <td>2.910455</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.773014</td>\n",
       "      <td>2.980898</td>\n",
       "      <td>2.980898</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.616123</td>\n",
       "      <td>2.870245</td>\n",
       "      <td>2.870246</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.439008</td>\n",
       "      <td>2.528094</td>\n",
       "      <td>2.528094</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.443260</td>\n",
       "      <td>2.696085</td>\n",
       "      <td>2.696085</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.264459</td>\n",
       "      <td>2.244734</td>\n",
       "      <td>2.244734</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.027125</td>\n",
       "      <td>2.911088</td>\n",
       "      <td>2.911088</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.804218</td>\n",
       "      <td>1.890759</td>\n",
       "      <td>1.890759</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.518318</td>\n",
       "      <td>2.414806</td>\n",
       "      <td>2.414806</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.110645</td>\n",
       "      <td>1.732432</td>\n",
       "      <td>1.732433</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.976188</td>\n",
       "      <td>1.734978</td>\n",
       "      <td>1.734978</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.593390</td>\n",
       "      <td>1.306018</td>\n",
       "      <td>1.306018</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.371465</td>\n",
       "      <td>1.811246</td>\n",
       "      <td>1.811245</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.361489</td>\n",
       "      <td>1.338238</td>\n",
       "      <td>1.338238</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.318564</td>\n",
       "      <td>1.285261</td>\n",
       "      <td>1.285261</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.286480</td>\n",
       "      <td>1.504492</td>\n",
       "      <td>1.504492</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.292150</td>\n",
       "      <td>1.380863</td>\n",
       "      <td>1.380863</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.315449</td>\n",
       "      <td>1.248867</td>\n",
       "      <td>1.248868</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.248785</td>\n",
       "      <td>1.537092</td>\n",
       "      <td>1.537092</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.246672</td>\n",
       "      <td>1.434179</td>\n",
       "      <td>1.434179</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.203905</td>\n",
       "      <td>0.972793</td>\n",
       "      <td>0.972793</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.186664</td>\n",
       "      <td>1.044722</td>\n",
       "      <td>1.044722</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.135714</td>\n",
       "      <td>1.144747</td>\n",
       "      <td>1.144747</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.167497</td>\n",
       "      <td>1.015527</td>\n",
       "      <td>1.015527</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.161951</td>\n",
       "      <td>1.189978</td>\n",
       "      <td>1.189978</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.164900</td>\n",
       "      <td>1.037447</td>\n",
       "      <td>1.037447</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.188049</td>\n",
       "      <td>1.103764</td>\n",
       "      <td>1.103764</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.185891</td>\n",
       "      <td>1.127717</td>\n",
       "      <td>1.127717</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.185717</td>\n",
       "      <td>1.269932</td>\n",
       "      <td>1.269932</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.163743</td>\n",
       "      <td>1.097939</td>\n",
       "      <td>1.097939</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.189164</td>\n",
       "      <td>1.174668</td>\n",
       "      <td>1.174668</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.177821</td>\n",
       "      <td>1.075405</td>\n",
       "      <td>1.075405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.195073</td>\n",
       "      <td>1.332650</td>\n",
       "      <td>1.332650</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.183675</td>\n",
       "      <td>1.001233</td>\n",
       "      <td>1.001233</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.136073</td>\n",
       "      <td>1.109736</td>\n",
       "      <td>1.109736</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.143884</td>\n",
       "      <td>1.176099</td>\n",
       "      <td>1.176099</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.163467</td>\n",
       "      <td>1.054047</td>\n",
       "      <td>1.054047</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.257934</td>\n",
       "      <td>1.551328</td>\n",
       "      <td>1.551328</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.158926</td>\n",
       "      <td>0.947763</td>\n",
       "      <td>0.947763</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.113764</td>\n",
       "      <td>1.124026</td>\n",
       "      <td>1.124026</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.155954</td>\n",
       "      <td>1.294371</td>\n",
       "      <td>1.294371</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.105413</td>\n",
       "      <td>1.074637</td>\n",
       "      <td>1.074637</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.098604</td>\n",
       "      <td>1.230599</td>\n",
       "      <td>1.230599</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.116332</td>\n",
       "      <td>1.407747</td>\n",
       "      <td>1.407747</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.097870</td>\n",
       "      <td>1.000888</td>\n",
       "      <td>1.000888</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.015892</td>\n",
       "      <td>0.967912</td>\n",
       "      <td>0.967912</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.996472</td>\n",
       "      <td>0.952191</td>\n",
       "      <td>0.952191</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.037245</td>\n",
       "      <td>1.012524</td>\n",
       "      <td>1.012524</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.149293</td>\n",
       "      <td>1.037982</td>\n",
       "      <td>1.037982</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.093905</td>\n",
       "      <td>1.027098</td>\n",
       "      <td>1.027098</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.020546</td>\n",
       "      <td>1.141184</td>\n",
       "      <td>1.141184</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.074950</td>\n",
       "      <td>1.130630</td>\n",
       "      <td>1.130630</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.036399</td>\n",
       "      <td>1.231973</td>\n",
       "      <td>1.231973</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.099197</td>\n",
       "      <td>1.117992</td>\n",
       "      <td>1.117992</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.099370</td>\n",
       "      <td>1.016551</td>\n",
       "      <td>1.016551</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.064467</td>\n",
       "      <td>1.027723</td>\n",
       "      <td>1.027723</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.031851</td>\n",
       "      <td>1.048473</td>\n",
       "      <td>1.048473</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.019954</td>\n",
       "      <td>0.979335</td>\n",
       "      <td>0.979335</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.996039</td>\n",
       "      <td>0.991421</td>\n",
       "      <td>0.991421</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.009109</td>\n",
       "      <td>1.023935</td>\n",
       "      <td>1.023935</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.965079</td>\n",
       "      <td>0.927022</td>\n",
       "      <td>0.927022</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.924940</td>\n",
       "      <td>1.022787</td>\n",
       "      <td>1.022787</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.953970</td>\n",
       "      <td>0.988595</td>\n",
       "      <td>0.988595</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.935377</td>\n",
       "      <td>0.903810</td>\n",
       "      <td>0.903810</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.928451</td>\n",
       "      <td>0.899854</td>\n",
       "      <td>0.899854</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.958548</td>\n",
       "      <td>0.861999</td>\n",
       "      <td>0.861999</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.921636</td>\n",
       "      <td>0.854527</td>\n",
       "      <td>0.854527</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.920998</td>\n",
       "      <td>0.882173</td>\n",
       "      <td>0.882173</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.903333</td>\n",
       "      <td>0.979479</td>\n",
       "      <td>0.979479</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.906108</td>\n",
       "      <td>0.891728</td>\n",
       "      <td>0.891728</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.924002</td>\n",
       "      <td>0.864545</td>\n",
       "      <td>0.864545</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.901015</td>\n",
       "      <td>0.853657</td>\n",
       "      <td>0.853657</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.901441</td>\n",
       "      <td>0.857023</td>\n",
       "      <td>0.857023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.878650</td>\n",
       "      <td>0.802243</td>\n",
       "      <td>0.802244</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.890223</td>\n",
       "      <td>0.807818</td>\n",
       "      <td>0.807818</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.860781</td>\n",
       "      <td>0.810199</td>\n",
       "      <td>0.810199</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.871982</td>\n",
       "      <td>0.887661</td>\n",
       "      <td>0.887661</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.857358</td>\n",
       "      <td>0.821796</td>\n",
       "      <td>0.821796</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.868658</td>\n",
       "      <td>0.805215</td>\n",
       "      <td>0.805215</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.870538</td>\n",
       "      <td>0.804346</td>\n",
       "      <td>0.804346</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.846433</td>\n",
       "      <td>0.815519</td>\n",
       "      <td>0.815519</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.849875</td>\n",
       "      <td>0.810454</td>\n",
       "      <td>0.810454</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.868288</td>\n",
       "      <td>0.840225</td>\n",
       "      <td>0.840225</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.831417</td>\n",
       "      <td>0.885957</td>\n",
       "      <td>0.885957</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.821411</td>\n",
       "      <td>0.821731</td>\n",
       "      <td>0.821731</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.843726</td>\n",
       "      <td>0.814661</td>\n",
       "      <td>0.814661</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.811104</td>\n",
       "      <td>0.789014</td>\n",
       "      <td>0.789014</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.801856</td>\n",
       "      <td>0.818683</td>\n",
       "      <td>0.818683</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.807953</td>\n",
       "      <td>0.810908</td>\n",
       "      <td>0.810908</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.817117</td>\n",
       "      <td>0.809869</td>\n",
       "      <td>0.809869</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.803002</td>\n",
       "      <td>0.815727</td>\n",
       "      <td>0.815727</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.791148</td>\n",
       "      <td>0.813516</td>\n",
       "      <td>0.813516</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.819284</td>\n",
       "      <td>0.813552</td>\n",
       "      <td>0.813552</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.773844</td>\n",
       "      <td>0.781431</td>\n",
       "      <td>0.781431</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.781077</td>\n",
       "      <td>0.797512</td>\n",
       "      <td>0.797512</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.785807</td>\n",
       "      <td>0.792486</td>\n",
       "      <td>0.792486</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.792647</td>\n",
       "      <td>0.782421</td>\n",
       "      <td>0.782420</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 6.952042102813721.\n",
      "Better model found at epoch 1 with mae value: 4.908609390258789.\n",
      "Better model found at epoch 2 with mae value: 3.5165624618530273.\n",
      "Better model found at epoch 3 with mae value: 2.9104552268981934.\n",
      "Better model found at epoch 5 with mae value: 2.8702456951141357.\n",
      "Better model found at epoch 6 with mae value: 2.5280938148498535.\n",
      "Better model found at epoch 8 with mae value: 2.2447338104248047.\n",
      "Better model found at epoch 10 with mae value: 1.890758752822876.\n",
      "Better model found at epoch 12 with mae value: 1.7324326038360596.\n",
      "Better model found at epoch 14 with mae value: 1.3060178756713867.\n",
      "Better model found at epoch 17 with mae value: 1.2852613925933838.\n",
      "Better model found at epoch 20 with mae value: 1.2488675117492676.\n",
      "Better model found at epoch 23 with mae value: 0.9727933406829834.\n",
      "Better model found at epoch 41 with mae value: 0.9477627873420715.\n",
      "Better model found at epoch 63 with mae value: 0.9270219802856445.\n",
      "Better model found at epoch 66 with mae value: 0.903809666633606.\n",
      "Better model found at epoch 67 with mae value: 0.8998540043830872.\n",
      "Better model found at epoch 68 with mae value: 0.8619986772537231.\n",
      "Better model found at epoch 69 with mae value: 0.8545274138450623.\n",
      "Better model found at epoch 74 with mae value: 0.853657066822052.\n",
      "Better model found at epoch 76 with mae value: 0.8022435307502747.\n",
      "Better model found at epoch 89 with mae value: 0.7890137434005737.\n",
      "Better model found at epoch 96 with mae value: 0.7814309000968933.\n"
     ]
    },
    {
     "data": {
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       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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     "text": [
      "MAE for Fold-2: 0.781\n",
      "MAPE for Fold-2: 0.03\n"
     ]
    },
    {
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       "<style>\n",
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     "text": [
      "********** Fold-3 **********\n"
     ]
    },
    {
     "data": {
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.167727</td>\n",
       "      <td>7.602497</td>\n",
       "      <td>7.602497</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.032835</td>\n",
       "      <td>5.865996</td>\n",
       "      <td>5.865996</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.438140</td>\n",
       "      <td>4.252079</td>\n",
       "      <td>4.252079</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.508999</td>\n",
       "      <td>3.537846</td>\n",
       "      <td>3.537846</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.976124</td>\n",
       "      <td>3.134712</td>\n",
       "      <td>3.134712</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.756448</td>\n",
       "      <td>3.278461</td>\n",
       "      <td>3.278461</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.691345</td>\n",
       "      <td>3.202112</td>\n",
       "      <td>3.202112</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.651780</td>\n",
       "      <td>2.909228</td>\n",
       "      <td>2.909229</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.311339</td>\n",
       "      <td>2.910589</td>\n",
       "      <td>2.910589</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.258248</td>\n",
       "      <td>2.716894</td>\n",
       "      <td>2.716894</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.027256</td>\n",
       "      <td>2.748161</td>\n",
       "      <td>2.748161</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.575501</td>\n",
       "      <td>2.464731</td>\n",
       "      <td>2.464731</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.377586</td>\n",
       "      <td>2.776935</td>\n",
       "      <td>2.776936</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.074442</td>\n",
       "      <td>2.242920</td>\n",
       "      <td>2.242921</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.773380</td>\n",
       "      <td>2.259188</td>\n",
       "      <td>2.259188</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.497421</td>\n",
       "      <td>1.512754</td>\n",
       "      <td>1.512754</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.356848</td>\n",
       "      <td>1.270646</td>\n",
       "      <td>1.270646</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.310400</td>\n",
       "      <td>1.474934</td>\n",
       "      <td>1.474934</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.306829</td>\n",
       "      <td>1.327623</td>\n",
       "      <td>1.327623</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.303894</td>\n",
       "      <td>1.378230</td>\n",
       "      <td>1.378230</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.245933</td>\n",
       "      <td>0.993902</td>\n",
       "      <td>0.993902</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.236240</td>\n",
       "      <td>1.147870</td>\n",
       "      <td>1.147870</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.216376</td>\n",
       "      <td>1.166162</td>\n",
       "      <td>1.166162</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.234972</td>\n",
       "      <td>1.016213</td>\n",
       "      <td>1.016213</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.194422</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.058001</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.142049</td>\n",
       "      <td>0.931293</td>\n",
       "      <td>0.931293</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.124663</td>\n",
       "      <td>1.156959</td>\n",
       "      <td>1.156960</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.295590</td>\n",
       "      <td>1.016913</td>\n",
       "      <td>1.016913</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.221103</td>\n",
       "      <td>1.208967</td>\n",
       "      <td>1.208967</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.206240</td>\n",
       "      <td>1.163984</td>\n",
       "      <td>1.163984</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.165573</td>\n",
       "      <td>1.121812</td>\n",
       "      <td>1.121812</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.155949</td>\n",
       "      <td>0.949785</td>\n",
       "      <td>0.949785</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.240128</td>\n",
       "      <td>2.191222</td>\n",
       "      <td>2.191222</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.191418</td>\n",
       "      <td>0.827405</td>\n",
       "      <td>0.827406</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.183690</td>\n",
       "      <td>1.084768</td>\n",
       "      <td>1.084768</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.188725</td>\n",
       "      <td>0.972824</td>\n",
       "      <td>0.972824</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.137675</td>\n",
       "      <td>0.879373</td>\n",
       "      <td>0.879373</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.196242</td>\n",
       "      <td>1.076224</td>\n",
       "      <td>1.076224</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.204642</td>\n",
       "      <td>1.352580</td>\n",
       "      <td>1.352580</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.273369</td>\n",
       "      <td>0.959757</td>\n",
       "      <td>0.959757</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.138798</td>\n",
       "      <td>0.915928</td>\n",
       "      <td>0.915928</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.115114</td>\n",
       "      <td>1.117108</td>\n",
       "      <td>1.117108</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.260127</td>\n",
       "      <td>1.168544</td>\n",
       "      <td>1.168544</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.170995</td>\n",
       "      <td>1.117922</td>\n",
       "      <td>1.117922</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.254346</td>\n",
       "      <td>0.992350</td>\n",
       "      <td>0.992350</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.171749</td>\n",
       "      <td>1.246539</td>\n",
       "      <td>1.246540</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.109998</td>\n",
       "      <td>1.109427</td>\n",
       "      <td>1.109427</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.064756</td>\n",
       "      <td>0.903963</td>\n",
       "      <td>0.903963</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.052874</td>\n",
       "      <td>1.012071</td>\n",
       "      <td>1.012071</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.998083</td>\n",
       "      <td>1.052622</td>\n",
       "      <td>1.052621</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.989020</td>\n",
       "      <td>0.949962</td>\n",
       "      <td>0.949962</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.994108</td>\n",
       "      <td>0.847672</td>\n",
       "      <td>0.847672</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.145071</td>\n",
       "      <td>1.125400</td>\n",
       "      <td>1.125400</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.053969</td>\n",
       "      <td>0.832908</td>\n",
       "      <td>0.832908</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.006057</td>\n",
       "      <td>0.760562</td>\n",
       "      <td>0.760562</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.947647</td>\n",
       "      <td>0.777569</td>\n",
       "      <td>0.777569</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.975086</td>\n",
       "      <td>0.855824</td>\n",
       "      <td>0.855824</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.957580</td>\n",
       "      <td>1.021591</td>\n",
       "      <td>1.021591</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.951725</td>\n",
       "      <td>0.961548</td>\n",
       "      <td>0.961548</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.962035</td>\n",
       "      <td>0.796761</td>\n",
       "      <td>0.796761</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.937401</td>\n",
       "      <td>0.852884</td>\n",
       "      <td>0.852884</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.018103</td>\n",
       "      <td>0.980370</td>\n",
       "      <td>0.980370</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.972656</td>\n",
       "      <td>0.845911</td>\n",
       "      <td>0.845911</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.905957</td>\n",
       "      <td>0.751071</td>\n",
       "      <td>0.751071</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.929776</td>\n",
       "      <td>0.727692</td>\n",
       "      <td>0.727692</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.898672</td>\n",
       "      <td>0.809734</td>\n",
       "      <td>0.809734</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.880962</td>\n",
       "      <td>0.719440</td>\n",
       "      <td>0.719440</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.884599</td>\n",
       "      <td>0.735493</td>\n",
       "      <td>0.735493</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.898964</td>\n",
       "      <td>0.749722</td>\n",
       "      <td>0.749722</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.882328</td>\n",
       "      <td>0.789083</td>\n",
       "      <td>0.789083</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.859757</td>\n",
       "      <td>0.762929</td>\n",
       "      <td>0.762929</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.858904</td>\n",
       "      <td>0.814447</td>\n",
       "      <td>0.814447</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.869853</td>\n",
       "      <td>0.788988</td>\n",
       "      <td>0.788988</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.841711</td>\n",
       "      <td>0.745553</td>\n",
       "      <td>0.745553</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.818016</td>\n",
       "      <td>0.693930</td>\n",
       "      <td>0.693930</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.807966</td>\n",
       "      <td>0.694786</td>\n",
       "      <td>0.694786</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.826260</td>\n",
       "      <td>0.718145</td>\n",
       "      <td>0.718145</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.807240</td>\n",
       "      <td>0.709345</td>\n",
       "      <td>0.709345</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.791375</td>\n",
       "      <td>0.673885</td>\n",
       "      <td>0.673885</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.815078</td>\n",
       "      <td>0.697965</td>\n",
       "      <td>0.697965</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.788270</td>\n",
       "      <td>0.700873</td>\n",
       "      <td>0.700873</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.766415</td>\n",
       "      <td>0.664595</td>\n",
       "      <td>0.664595</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.777965</td>\n",
       "      <td>0.663821</td>\n",
       "      <td>0.663821</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.779403</td>\n",
       "      <td>0.691128</td>\n",
       "      <td>0.691128</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.764348</td>\n",
       "      <td>0.687609</td>\n",
       "      <td>0.687609</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.766135</td>\n",
       "      <td>0.664266</td>\n",
       "      <td>0.664266</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.759692</td>\n",
       "      <td>0.653318</td>\n",
       "      <td>0.653318</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.770337</td>\n",
       "      <td>0.657615</td>\n",
       "      <td>0.657615</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.748434</td>\n",
       "      <td>0.680275</td>\n",
       "      <td>0.680275</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.738553</td>\n",
       "      <td>0.663555</td>\n",
       "      <td>0.663555</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.745902</td>\n",
       "      <td>0.652878</td>\n",
       "      <td>0.652878</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.768123</td>\n",
       "      <td>0.657911</td>\n",
       "      <td>0.657911</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.753192</td>\n",
       "      <td>0.671249</td>\n",
       "      <td>0.671249</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.748251</td>\n",
       "      <td>0.667882</td>\n",
       "      <td>0.667882</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.785116</td>\n",
       "      <td>0.654732</td>\n",
       "      <td>0.654732</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.768459</td>\n",
       "      <td>0.650577</td>\n",
       "      <td>0.650577</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.745890</td>\n",
       "      <td>0.646206</td>\n",
       "      <td>0.646206</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.780663</td>\n",
       "      <td>0.648241</td>\n",
       "      <td>0.648241</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.738132</td>\n",
       "      <td>0.656136</td>\n",
       "      <td>0.656136</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.728293</td>\n",
       "      <td>0.647541</td>\n",
       "      <td>0.647541</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with mae value: 7.60249662399292.\n",
      "Better model found at epoch 1 with mae value: 5.865996360778809.\n",
      "Better model found at epoch 2 with mae value: 4.252079010009766.\n",
      "Better model found at epoch 3 with mae value: 3.537846326828003.\n",
      "Better model found at epoch 4 with mae value: 3.134711503982544.\n",
      "Better model found at epoch 7 with mae value: 2.909228801727295.\n",
      "Better model found at epoch 9 with mae value: 2.7168941497802734.\n",
      "Better model found at epoch 11 with mae value: 2.464730739593506.\n",
      "Better model found at epoch 13 with mae value: 2.2429206371307373.\n",
      "Better model found at epoch 15 with mae value: 1.5127538442611694.\n",
      "Better model found at epoch 16 with mae value: 1.2706464529037476.\n",
      "Better model found at epoch 20 with mae value: 0.993901789188385.\n",
      "Better model found at epoch 25 with mae value: 0.9312928318977356.\n",
      "Better model found at epoch 33 with mae value: 0.8274055123329163.\n",
      "Better model found at epoch 54 with mae value: 0.7605619430541992.\n",
      "Better model found at epoch 63 with mae value: 0.7510707974433899.\n",
      "Better model found at epoch 64 with mae value: 0.7276923060417175.\n",
      "Better model found at epoch 66 with mae value: 0.7194399237632751.\n",
      "Better model found at epoch 74 with mae value: 0.6939297318458557.\n",
      "Better model found at epoch 78 with mae value: 0.67388516664505.\n",
      "Better model found at epoch 81 with mae value: 0.664594829082489.\n",
      "Better model found at epoch 82 with mae value: 0.6638213992118835.\n",
      "Better model found at epoch 86 with mae value: 0.6533175706863403.\n",
      "Better model found at epoch 90 with mae value: 0.6528775691986084.\n",
      "Better model found at epoch 95 with mae value: 0.6505774259567261.\n",
      "Better model found at epoch 96 with mae value: 0.646205723285675.\n"
     ]
    },
    {
     "data": {
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      "MAE for Fold-3: 0.646\n",
      "MAPE for Fold-3: 0.025\n"
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     "text": [
      "********** Fold-4 **********\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.797169</td>\n",
       "      <td>9.748543</td>\n",
       "      <td>9.748542</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.044927</td>\n",
       "      <td>7.159775</td>\n",
       "      <td>7.159775</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.289560</td>\n",
       "      <td>4.956552</td>\n",
       "      <td>4.956552</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.332365</td>\n",
       "      <td>4.248137</td>\n",
       "      <td>4.248137</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.817813</td>\n",
       "      <td>3.209848</td>\n",
       "      <td>3.209848</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.610722</td>\n",
       "      <td>3.058182</td>\n",
       "      <td>3.058182</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.374979</td>\n",
       "      <td>3.062462</td>\n",
       "      <td>3.062462</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.294510</td>\n",
       "      <td>2.900050</td>\n",
       "      <td>2.900050</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.757995</td>\n",
       "      <td>1.830666</td>\n",
       "      <td>1.830666</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.615384</td>\n",
       "      <td>1.951364</td>\n",
       "      <td>1.951364</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.293976</td>\n",
       "      <td>1.635741</td>\n",
       "      <td>1.635741</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.165751</td>\n",
       "      <td>1.593106</td>\n",
       "      <td>1.593106</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.020156</td>\n",
       "      <td>1.814891</td>\n",
       "      <td>1.814891</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.842224</td>\n",
       "      <td>2.015311</td>\n",
       "      <td>2.015311</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.561508</td>\n",
       "      <td>1.532712</td>\n",
       "      <td>1.532712</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.425425</td>\n",
       "      <td>1.288899</td>\n",
       "      <td>1.288899</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.373857</td>\n",
       "      <td>1.567683</td>\n",
       "      <td>1.567683</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.344844</td>\n",
       "      <td>1.453827</td>\n",
       "      <td>1.453827</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.350533</td>\n",
       "      <td>1.316371</td>\n",
       "      <td>1.316371</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.321985</td>\n",
       "      <td>1.470567</td>\n",
       "      <td>1.470567</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.353414</td>\n",
       "      <td>1.569289</td>\n",
       "      <td>1.569289</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.374047</td>\n",
       "      <td>1.350695</td>\n",
       "      <td>1.350695</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.350888</td>\n",
       "      <td>1.705555</td>\n",
       "      <td>1.705555</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.357135</td>\n",
       "      <td>1.245324</td>\n",
       "      <td>1.245324</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.236677</td>\n",
       "      <td>1.175909</td>\n",
       "      <td>1.175909</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.296881</td>\n",
       "      <td>1.086790</td>\n",
       "      <td>1.086790</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.296030</td>\n",
       "      <td>1.667826</td>\n",
       "      <td>1.667826</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.225832</td>\n",
       "      <td>1.215910</td>\n",
       "      <td>1.215910</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.276154</td>\n",
       "      <td>1.433831</td>\n",
       "      <td>1.433831</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.303081</td>\n",
       "      <td>1.361653</td>\n",
       "      <td>1.361653</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.339992</td>\n",
       "      <td>1.302365</td>\n",
       "      <td>1.302365</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.197203</td>\n",
       "      <td>1.152487</td>\n",
       "      <td>1.152487</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.220856</td>\n",
       "      <td>1.661972</td>\n",
       "      <td>1.661972</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.171202</td>\n",
       "      <td>1.538955</td>\n",
       "      <td>1.538955</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.225137</td>\n",
       "      <td>1.104384</td>\n",
       "      <td>1.104384</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.125639</td>\n",
       "      <td>1.122875</td>\n",
       "      <td>1.122875</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.133048</td>\n",
       "      <td>2.441754</td>\n",
       "      <td>2.441754</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.262964</td>\n",
       "      <td>1.456661</td>\n",
       "      <td>1.456661</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.151523</td>\n",
       "      <td>1.308253</td>\n",
       "      <td>1.308253</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.116936</td>\n",
       "      <td>1.101541</td>\n",
       "      <td>1.101541</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.168433</td>\n",
       "      <td>1.516041</td>\n",
       "      <td>1.516041</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.102346</td>\n",
       "      <td>1.039148</td>\n",
       "      <td>1.039148</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.404926</td>\n",
       "      <td>1.349410</td>\n",
       "      <td>1.349410</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.181122</td>\n",
       "      <td>1.059329</td>\n",
       "      <td>1.059329</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.111901</td>\n",
       "      <td>1.107100</td>\n",
       "      <td>1.107100</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.073555</td>\n",
       "      <td>1.071405</td>\n",
       "      <td>1.071405</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.039759</td>\n",
       "      <td>1.200738</td>\n",
       "      <td>1.200738</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.043747</td>\n",
       "      <td>1.170797</td>\n",
       "      <td>1.170797</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.100395</td>\n",
       "      <td>0.948193</td>\n",
       "      <td>0.948192</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.036045</td>\n",
       "      <td>1.039352</td>\n",
       "      <td>1.039352</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.160024</td>\n",
       "      <td>1.067346</td>\n",
       "      <td>1.067345</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.176892</td>\n",
       "      <td>1.211254</td>\n",
       "      <td>1.211254</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.100733</td>\n",
       "      <td>0.986728</td>\n",
       "      <td>0.986728</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.039413</td>\n",
       "      <td>0.993787</td>\n",
       "      <td>0.993787</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.945334</td>\n",
       "      <td>1.068052</td>\n",
       "      <td>1.068052</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.941569</td>\n",
       "      <td>1.035299</td>\n",
       "      <td>1.035299</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.961042</td>\n",
       "      <td>1.012206</td>\n",
       "      <td>1.012206</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.967921</td>\n",
       "      <td>0.936832</td>\n",
       "      <td>0.936832</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.975665</td>\n",
       "      <td>1.239219</td>\n",
       "      <td>1.239219</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.959623</td>\n",
       "      <td>0.894009</td>\n",
       "      <td>0.894009</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.922654</td>\n",
       "      <td>1.095332</td>\n",
       "      <td>1.095332</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.928746</td>\n",
       "      <td>0.955626</td>\n",
       "      <td>0.955626</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.908730</td>\n",
       "      <td>0.896756</td>\n",
       "      <td>0.896756</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.981674</td>\n",
       "      <td>1.038683</td>\n",
       "      <td>1.038683</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.919658</td>\n",
       "      <td>0.939677</td>\n",
       "      <td>0.939677</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.903764</td>\n",
       "      <td>0.975385</td>\n",
       "      <td>0.975385</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.903106</td>\n",
       "      <td>0.913049</td>\n",
       "      <td>0.913049</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.851162</td>\n",
       "      <td>0.882108</td>\n",
       "      <td>0.882108</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.886914</td>\n",
       "      <td>0.873741</td>\n",
       "      <td>0.873741</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.872201</td>\n",
       "      <td>0.884547</td>\n",
       "      <td>0.884547</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.883814</td>\n",
       "      <td>0.867673</td>\n",
       "      <td>0.867673</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.879720</td>\n",
       "      <td>0.951548</td>\n",
       "      <td>0.951548</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.858591</td>\n",
       "      <td>0.840368</td>\n",
       "      <td>0.840368</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.865138</td>\n",
       "      <td>0.955410</td>\n",
       "      <td>0.955410</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.845646</td>\n",
       "      <td>0.838574</td>\n",
       "      <td>0.838574</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.845374</td>\n",
       "      <td>0.886920</td>\n",
       "      <td>0.886920</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.809024</td>\n",
       "      <td>0.840668</td>\n",
       "      <td>0.840668</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.797341</td>\n",
       "      <td>0.837421</td>\n",
       "      <td>0.837421</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.802774</td>\n",
       "      <td>0.929364</td>\n",
       "      <td>0.929364</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.773079</td>\n",
       "      <td>0.816484</td>\n",
       "      <td>0.816485</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.744893</td>\n",
       "      <td>0.813778</td>\n",
       "      <td>0.813778</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.773077</td>\n",
       "      <td>0.840131</td>\n",
       "      <td>0.840131</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.790086</td>\n",
       "      <td>0.831340</td>\n",
       "      <td>0.831340</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.781488</td>\n",
       "      <td>0.819264</td>\n",
       "      <td>0.819264</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.815529</td>\n",
       "      <td>0.903022</td>\n",
       "      <td>0.903022</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.768651</td>\n",
       "      <td>0.826155</td>\n",
       "      <td>0.826155</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.783114</td>\n",
       "      <td>0.812121</td>\n",
       "      <td>0.812121</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.753324</td>\n",
       "      <td>0.798581</td>\n",
       "      <td>0.798581</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.754069</td>\n",
       "      <td>0.793269</td>\n",
       "      <td>0.793269</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.777887</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>0.799051</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.734439</td>\n",
       "      <td>0.789857</td>\n",
       "      <td>0.789857</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.731523</td>\n",
       "      <td>0.822839</td>\n",
       "      <td>0.822839</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.725318</td>\n",
       "      <td>0.793213</td>\n",
       "      <td>0.793213</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.735635</td>\n",
       "      <td>0.801159</td>\n",
       "      <td>0.801159</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.734610</td>\n",
       "      <td>0.880193</td>\n",
       "      <td>0.880193</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.746419</td>\n",
       "      <td>0.840997</td>\n",
       "      <td>0.840997</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.744712</td>\n",
       "      <td>0.806399</td>\n",
       "      <td>0.806399</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.738975</td>\n",
       "      <td>0.794512</td>\n",
       "      <td>0.794512</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.735772</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.748024</td>\n",
       "      <td>0.819876</td>\n",
       "      <td>0.819876</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 9.748541831970215.\n",
      "Better model found at epoch 1 with mae value: 7.1597747802734375.\n",
      "Better model found at epoch 2 with mae value: 4.956551551818848.\n",
      "Better model found at epoch 3 with mae value: 4.248136520385742.\n",
      "Better model found at epoch 4 with mae value: 3.2098476886749268.\n",
      "Better model found at epoch 5 with mae value: 3.0581815242767334.\n",
      "Better model found at epoch 7 with mae value: 2.900050163269043.\n",
      "Better model found at epoch 8 with mae value: 1.8306658267974854.\n",
      "Better model found at epoch 10 with mae value: 1.6357413530349731.\n",
      "Better model found at epoch 11 with mae value: 1.5931061506271362.\n",
      "Better model found at epoch 14 with mae value: 1.5327121019363403.\n",
      "Better model found at epoch 15 with mae value: 1.2888987064361572.\n",
      "Better model found at epoch 23 with mae value: 1.2453237771987915.\n",
      "Better model found at epoch 24 with mae value: 1.1759085655212402.\n",
      "Better model found at epoch 25 with mae value: 1.086789608001709.\n",
      "Better model found at epoch 41 with mae value: 1.0391480922698975.\n",
      "Better model found at epoch 48 with mae value: 0.9481924176216125.\n",
      "Better model found at epoch 57 with mae value: 0.9368317127227783.\n",
      "Better model found at epoch 59 with mae value: 0.8940087556838989.\n",
      "Better model found at epoch 67 with mae value: 0.8821078538894653.\n",
      "Better model found at epoch 68 with mae value: 0.8737408518791199.\n",
      "Better model found at epoch 70 with mae value: 0.8676728010177612.\n",
      "Better model found at epoch 72 with mae value: 0.8403684496879578.\n",
      "Better model found at epoch 74 with mae value: 0.8385736346244812.\n",
      "Better model found at epoch 77 with mae value: 0.8374208807945251.\n",
      "Better model found at epoch 79 with mae value: 0.8164845108985901.\n",
      "Better model found at epoch 80 with mae value: 0.8137778043746948.\n",
      "Better model found at epoch 86 with mae value: 0.8121214509010315.\n",
      "Better model found at epoch 87 with mae value: 0.7985808253288269.\n",
      "Better model found at epoch 88 with mae value: 0.7932693362236023.\n",
      "Better model found at epoch 90 with mae value: 0.7898568511009216.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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     "text": [
      "MAE for Fold-4: 0.79\n",
      "MAPE for Fold-4: 0.027\n"
     ]
    },
    {
     "data": {
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     "text": [
      "********** Fold-5 **********\n"
     ]
    },
    {
     "data": {
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.059049</td>\n",
       "      <td>6.380902</td>\n",
       "      <td>6.380902</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.713114</td>\n",
       "      <td>4.516955</td>\n",
       "      <td>4.516955</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.126893</td>\n",
       "      <td>3.678010</td>\n",
       "      <td>3.678010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.323877</td>\n",
       "      <td>3.328683</td>\n",
       "      <td>3.328683</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.782491</td>\n",
       "      <td>2.768857</td>\n",
       "      <td>2.768857</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.492573</td>\n",
       "      <td>2.376862</td>\n",
       "      <td>2.376862</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.025244</td>\n",
       "      <td>2.168970</td>\n",
       "      <td>2.168970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.656984</td>\n",
       "      <td>1.669548</td>\n",
       "      <td>1.669547</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.509121</td>\n",
       "      <td>1.763438</td>\n",
       "      <td>1.763439</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.500651</td>\n",
       "      <td>1.998122</td>\n",
       "      <td>1.998122</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.260318</td>\n",
       "      <td>1.783232</td>\n",
       "      <td>1.783232</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.009534</td>\n",
       "      <td>1.413077</td>\n",
       "      <td>1.413077</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.782987</td>\n",
       "      <td>1.637443</td>\n",
       "      <td>1.637443</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.680125</td>\n",
       "      <td>1.496501</td>\n",
       "      <td>1.496501</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.461925</td>\n",
       "      <td>1.162580</td>\n",
       "      <td>1.162580</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.372460</td>\n",
       "      <td>1.386851</td>\n",
       "      <td>1.386851</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.413678</td>\n",
       "      <td>1.272547</td>\n",
       "      <td>1.272547</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.327471</td>\n",
       "      <td>1.255622</td>\n",
       "      <td>1.255622</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.318370</td>\n",
       "      <td>1.328597</td>\n",
       "      <td>1.328597</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.375292</td>\n",
       "      <td>1.243514</td>\n",
       "      <td>1.243514</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.353577</td>\n",
       "      <td>1.248600</td>\n",
       "      <td>1.248600</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.301758</td>\n",
       "      <td>1.197875</td>\n",
       "      <td>1.197876</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.264175</td>\n",
       "      <td>1.300903</td>\n",
       "      <td>1.300903</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.325492</td>\n",
       "      <td>0.993551</td>\n",
       "      <td>0.993551</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.292849</td>\n",
       "      <td>1.262800</td>\n",
       "      <td>1.262800</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.278568</td>\n",
       "      <td>1.337794</td>\n",
       "      <td>1.337794</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.224851</td>\n",
       "      <td>1.420859</td>\n",
       "      <td>1.420859</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.286228</td>\n",
       "      <td>1.096487</td>\n",
       "      <td>1.096487</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.163703</td>\n",
       "      <td>1.011989</td>\n",
       "      <td>1.011989</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.144626</td>\n",
       "      <td>0.940760</td>\n",
       "      <td>0.940760</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.090627</td>\n",
       "      <td>0.950879</td>\n",
       "      <td>0.950879</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.091486</td>\n",
       "      <td>0.981552</td>\n",
       "      <td>0.981552</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.112637</td>\n",
       "      <td>1.278467</td>\n",
       "      <td>1.278467</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.113940</td>\n",
       "      <td>1.104660</td>\n",
       "      <td>1.104660</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.250695</td>\n",
       "      <td>1.088030</td>\n",
       "      <td>1.088030</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.111731</td>\n",
       "      <td>0.981685</td>\n",
       "      <td>0.981685</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.116921</td>\n",
       "      <td>1.104981</td>\n",
       "      <td>1.104981</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.075200</td>\n",
       "      <td>1.159296</td>\n",
       "      <td>1.159296</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.042569</td>\n",
       "      <td>0.892832</td>\n",
       "      <td>0.892832</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.124651</td>\n",
       "      <td>0.976246</td>\n",
       "      <td>0.976246</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.093572</td>\n",
       "      <td>0.856638</td>\n",
       "      <td>0.856638</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.072791</td>\n",
       "      <td>1.173444</td>\n",
       "      <td>1.173444</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.069365</td>\n",
       "      <td>0.928985</td>\n",
       "      <td>0.928985</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.041678</td>\n",
       "      <td>1.079089</td>\n",
       "      <td>1.079089</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.148172</td>\n",
       "      <td>1.112475</td>\n",
       "      <td>1.112475</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.128334</td>\n",
       "      <td>1.113481</td>\n",
       "      <td>1.113481</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.171248</td>\n",
       "      <td>0.986503</td>\n",
       "      <td>0.986503</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.078377</td>\n",
       "      <td>1.235434</td>\n",
       "      <td>1.235434</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.084807</td>\n",
       "      <td>1.117650</td>\n",
       "      <td>1.117650</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.043610</td>\n",
       "      <td>0.984949</td>\n",
       "      <td>0.984949</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.107748</td>\n",
       "      <td>1.147648</td>\n",
       "      <td>1.147648</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.053505</td>\n",
       "      <td>0.828573</td>\n",
       "      <td>0.828573</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.992506</td>\n",
       "      <td>0.932104</td>\n",
       "      <td>0.932104</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.952620</td>\n",
       "      <td>0.810647</td>\n",
       "      <td>0.810647</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.975386</td>\n",
       "      <td>1.117097</td>\n",
       "      <td>1.117097</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.953357</td>\n",
       "      <td>0.940454</td>\n",
       "      <td>0.940454</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.964410</td>\n",
       "      <td>0.904517</td>\n",
       "      <td>0.904517</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.901530</td>\n",
       "      <td>0.814993</td>\n",
       "      <td>0.814993</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.901609</td>\n",
       "      <td>0.859838</td>\n",
       "      <td>0.859838</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.870061</td>\n",
       "      <td>0.823506</td>\n",
       "      <td>0.823506</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.865374</td>\n",
       "      <td>0.920523</td>\n",
       "      <td>0.920523</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.901816</td>\n",
       "      <td>0.767398</td>\n",
       "      <td>0.767398</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.896593</td>\n",
       "      <td>0.866489</td>\n",
       "      <td>0.866489</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.876073</td>\n",
       "      <td>0.835046</td>\n",
       "      <td>0.835046</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.848735</td>\n",
       "      <td>0.787600</td>\n",
       "      <td>0.787600</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.874070</td>\n",
       "      <td>0.763959</td>\n",
       "      <td>0.763959</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.866857</td>\n",
       "      <td>0.756948</td>\n",
       "      <td>0.756948</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.846528</td>\n",
       "      <td>0.928431</td>\n",
       "      <td>0.928431</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.819955</td>\n",
       "      <td>0.798764</td>\n",
       "      <td>0.798764</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.844155</td>\n",
       "      <td>0.723458</td>\n",
       "      <td>0.723459</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.832381</td>\n",
       "      <td>0.763307</td>\n",
       "      <td>0.763307</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.830762</td>\n",
       "      <td>0.748365</td>\n",
       "      <td>0.748365</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.845989</td>\n",
       "      <td>0.842334</td>\n",
       "      <td>0.842334</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.853280</td>\n",
       "      <td>0.777104</td>\n",
       "      <td>0.777104</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.813025</td>\n",
       "      <td>0.781566</td>\n",
       "      <td>0.781566</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.809330</td>\n",
       "      <td>0.727361</td>\n",
       "      <td>0.727361</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.833965</td>\n",
       "      <td>0.752947</td>\n",
       "      <td>0.752947</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.849317</td>\n",
       "      <td>0.759846</td>\n",
       "      <td>0.759846</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.810851</td>\n",
       "      <td>0.721159</td>\n",
       "      <td>0.721159</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.797691</td>\n",
       "      <td>0.721736</td>\n",
       "      <td>0.721736</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.788579</td>\n",
       "      <td>0.721428</td>\n",
       "      <td>0.721428</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.767553</td>\n",
       "      <td>0.705674</td>\n",
       "      <td>0.705674</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.750067</td>\n",
       "      <td>0.698240</td>\n",
       "      <td>0.698240</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.740393</td>\n",
       "      <td>0.699617</td>\n",
       "      <td>0.699617</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.748115</td>\n",
       "      <td>0.728216</td>\n",
       "      <td>0.728216</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.770910</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.760003</td>\n",
       "      <td>0.705988</td>\n",
       "      <td>0.705988</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.740250</td>\n",
       "      <td>0.696530</td>\n",
       "      <td>0.696530</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.785644</td>\n",
       "      <td>0.725733</td>\n",
       "      <td>0.725733</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.761804</td>\n",
       "      <td>0.723930</td>\n",
       "      <td>0.723930</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.748157</td>\n",
       "      <td>0.701691</td>\n",
       "      <td>0.701691</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.745641</td>\n",
       "      <td>0.710431</td>\n",
       "      <td>0.710431</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.709251</td>\n",
       "      <td>0.691273</td>\n",
       "      <td>0.691273</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.729222</td>\n",
       "      <td>0.697373</td>\n",
       "      <td>0.697373</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.700051</td>\n",
       "      <td>0.690729</td>\n",
       "      <td>0.690729</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.723854</td>\n",
       "      <td>0.697532</td>\n",
       "      <td>0.697532</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.737382</td>\n",
       "      <td>0.724822</td>\n",
       "      <td>0.724822</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.710253</td>\n",
       "      <td>0.692488</td>\n",
       "      <td>0.692488</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.707854</td>\n",
       "      <td>0.708071</td>\n",
       "      <td>0.708071</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.691197</td>\n",
       "      <td>0.710379</td>\n",
       "      <td>0.710379</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with mae value: 6.38090181350708.\n",
      "Better model found at epoch 1 with mae value: 4.516955375671387.\n",
      "Better model found at epoch 2 with mae value: 3.678009510040283.\n",
      "Better model found at epoch 3 with mae value: 3.3286828994750977.\n",
      "Better model found at epoch 4 with mae value: 2.7688567638397217.\n",
      "Better model found at epoch 5 with mae value: 2.376862049102783.\n",
      "Better model found at epoch 6 with mae value: 2.1689698696136475.\n",
      "Better model found at epoch 7 with mae value: 1.6695473194122314.\n",
      "Better model found at epoch 11 with mae value: 1.4130768775939941.\n",
      "Better model found at epoch 14 with mae value: 1.1625803709030151.\n",
      "Better model found at epoch 23 with mae value: 0.9935506582260132.\n",
      "Better model found at epoch 29 with mae value: 0.9407601356506348.\n",
      "Better model found at epoch 38 with mae value: 0.8928316235542297.\n",
      "Better model found at epoch 40 with mae value: 0.8566378355026245.\n",
      "Better model found at epoch 51 with mae value: 0.8285734057426453.\n",
      "Better model found at epoch 53 with mae value: 0.8106472492218018.\n",
      "Better model found at epoch 61 with mae value: 0.7673976421356201.\n",
      "Better model found at epoch 65 with mae value: 0.7639592289924622.\n",
      "Better model found at epoch 66 with mae value: 0.7569484114646912.\n",
      "Better model found at epoch 69 with mae value: 0.7234585881233215.\n",
      "Better model found at epoch 78 with mae value: 0.7211589217185974.\n",
      "Better model found at epoch 81 with mae value: 0.7056741714477539.\n",
      "Better model found at epoch 82 with mae value: 0.6982404589653015.\n",
      "Better model found at epoch 87 with mae value: 0.6965302228927612.\n",
      "Better model found at epoch 92 with mae value: 0.6912726759910583.\n",
      "Better model found at epoch 94 with mae value: 0.6907292604446411.\n"
     ]
    },
    {
     "data": {
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      "MAE for Fold-5: 0.691\n",
      "MAPE for Fold-5: 0.026\n"
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     "text": [
      "********** Fold-6 **********\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.321075</td>\n",
       "      <td>7.141307</td>\n",
       "      <td>7.141307</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.935143</td>\n",
       "      <td>5.777171</td>\n",
       "      <td>5.777171</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.342429</td>\n",
       "      <td>4.586880</td>\n",
       "      <td>4.586880</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.431265</td>\n",
       "      <td>4.166836</td>\n",
       "      <td>4.166836</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.830432</td>\n",
       "      <td>3.986797</td>\n",
       "      <td>3.986797</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.619912</td>\n",
       "      <td>3.826002</td>\n",
       "      <td>3.826002</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.550987</td>\n",
       "      <td>3.656565</td>\n",
       "      <td>3.656565</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.376337</td>\n",
       "      <td>3.845668</td>\n",
       "      <td>3.845668</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.310631</td>\n",
       "      <td>3.487045</td>\n",
       "      <td>3.487045</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.251078</td>\n",
       "      <td>3.648043</td>\n",
       "      <td>3.648043</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.023741</td>\n",
       "      <td>3.195679</td>\n",
       "      <td>3.195679</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.945624</td>\n",
       "      <td>3.323414</td>\n",
       "      <td>3.323413</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.636033</td>\n",
       "      <td>2.489587</td>\n",
       "      <td>2.489587</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.368190</td>\n",
       "      <td>2.038637</td>\n",
       "      <td>2.038637</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2.080675</td>\n",
       "      <td>1.811966</td>\n",
       "      <td>1.811966</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.637049</td>\n",
       "      <td>1.628159</td>\n",
       "      <td>1.628159</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.398822</td>\n",
       "      <td>1.703631</td>\n",
       "      <td>1.703631</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.318249</td>\n",
       "      <td>1.258965</td>\n",
       "      <td>1.258965</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.323698</td>\n",
       "      <td>1.733488</td>\n",
       "      <td>1.733488</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.329582</td>\n",
       "      <td>1.319339</td>\n",
       "      <td>1.319339</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.246083</td>\n",
       "      <td>1.184998</td>\n",
       "      <td>1.184998</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.278598</td>\n",
       "      <td>1.563183</td>\n",
       "      <td>1.563183</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.227644</td>\n",
       "      <td>1.373386</td>\n",
       "      <td>1.373386</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.256154</td>\n",
       "      <td>1.165691</td>\n",
       "      <td>1.165691</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.251719</td>\n",
       "      <td>1.618983</td>\n",
       "      <td>1.618984</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.238256</td>\n",
       "      <td>1.187032</td>\n",
       "      <td>1.187032</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.208054</td>\n",
       "      <td>1.636991</td>\n",
       "      <td>1.636991</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.235376</td>\n",
       "      <td>1.331555</td>\n",
       "      <td>1.331555</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.217824</td>\n",
       "      <td>1.187522</td>\n",
       "      <td>1.187522</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.174314</td>\n",
       "      <td>1.074469</td>\n",
       "      <td>1.074469</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.175715</td>\n",
       "      <td>1.123258</td>\n",
       "      <td>1.123258</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.124957</td>\n",
       "      <td>1.069908</td>\n",
       "      <td>1.069908</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.098591</td>\n",
       "      <td>1.339007</td>\n",
       "      <td>1.339007</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.087868</td>\n",
       "      <td>1.086041</td>\n",
       "      <td>1.086041</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.082038</td>\n",
       "      <td>1.091987</td>\n",
       "      <td>1.091986</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.147660</td>\n",
       "      <td>1.123446</td>\n",
       "      <td>1.123446</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.105966</td>\n",
       "      <td>1.123897</td>\n",
       "      <td>1.123897</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.047677</td>\n",
       "      <td>1.015393</td>\n",
       "      <td>1.015393</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.036701</td>\n",
       "      <td>0.974646</td>\n",
       "      <td>0.974646</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.100183</td>\n",
       "      <td>1.459881</td>\n",
       "      <td>1.459881</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.157682</td>\n",
       "      <td>1.267767</td>\n",
       "      <td>1.267767</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.089019</td>\n",
       "      <td>0.893751</td>\n",
       "      <td>0.893751</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.061707</td>\n",
       "      <td>0.985824</td>\n",
       "      <td>0.985824</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.015613</td>\n",
       "      <td>0.930625</td>\n",
       "      <td>0.930625</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.035926</td>\n",
       "      <td>0.979869</td>\n",
       "      <td>0.979869</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.003161</td>\n",
       "      <td>0.966939</td>\n",
       "      <td>0.966939</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.057011</td>\n",
       "      <td>0.923214</td>\n",
       "      <td>0.923214</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.186351</td>\n",
       "      <td>1.128902</td>\n",
       "      <td>1.128903</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.152382</td>\n",
       "      <td>1.082624</td>\n",
       "      <td>1.082624</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.064231</td>\n",
       "      <td>1.140488</td>\n",
       "      <td>1.140488</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.031465</td>\n",
       "      <td>0.943522</td>\n",
       "      <td>0.943522</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.064721</td>\n",
       "      <td>0.989143</td>\n",
       "      <td>0.989143</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.071438</td>\n",
       "      <td>0.884116</td>\n",
       "      <td>0.884116</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.124935</td>\n",
       "      <td>0.997378</td>\n",
       "      <td>0.997378</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.104099</td>\n",
       "      <td>0.959226</td>\n",
       "      <td>0.959226</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.061817</td>\n",
       "      <td>1.033540</td>\n",
       "      <td>1.033540</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.020661</td>\n",
       "      <td>1.229364</td>\n",
       "      <td>1.229364</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.104116</td>\n",
       "      <td>1.158039</td>\n",
       "      <td>1.158039</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.005810</td>\n",
       "      <td>0.901882</td>\n",
       "      <td>0.901882</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.007146</td>\n",
       "      <td>0.922766</td>\n",
       "      <td>0.922766</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.992756</td>\n",
       "      <td>0.976953</td>\n",
       "      <td>0.976953</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.957680</td>\n",
       "      <td>1.024365</td>\n",
       "      <td>1.024365</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.958207</td>\n",
       "      <td>0.989556</td>\n",
       "      <td>0.989556</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.008333</td>\n",
       "      <td>0.896891</td>\n",
       "      <td>0.896891</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.952746</td>\n",
       "      <td>0.863600</td>\n",
       "      <td>0.863600</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.924013</td>\n",
       "      <td>0.824996</td>\n",
       "      <td>0.824996</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.937021</td>\n",
       "      <td>0.842768</td>\n",
       "      <td>0.842768</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.916254</td>\n",
       "      <td>0.902018</td>\n",
       "      <td>0.902018</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.920319</td>\n",
       "      <td>0.815662</td>\n",
       "      <td>0.815662</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.937932</td>\n",
       "      <td>1.063403</td>\n",
       "      <td>1.063403</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.944691</td>\n",
       "      <td>0.838678</td>\n",
       "      <td>0.838678</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.953316</td>\n",
       "      <td>0.825324</td>\n",
       "      <td>0.825324</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.945109</td>\n",
       "      <td>0.885342</td>\n",
       "      <td>0.885342</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.929934</td>\n",
       "      <td>0.888858</td>\n",
       "      <td>0.888858</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.894280</td>\n",
       "      <td>0.905219</td>\n",
       "      <td>0.905219</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.869254</td>\n",
       "      <td>0.895131</td>\n",
       "      <td>0.895131</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.848818</td>\n",
       "      <td>0.917260</td>\n",
       "      <td>0.917260</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.824412</td>\n",
       "      <td>0.811008</td>\n",
       "      <td>0.811008</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.862246</td>\n",
       "      <td>0.850587</td>\n",
       "      <td>0.850587</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.879614</td>\n",
       "      <td>0.852205</td>\n",
       "      <td>0.852205</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.832231</td>\n",
       "      <td>0.879306</td>\n",
       "      <td>0.879306</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.847373</td>\n",
       "      <td>0.797180</td>\n",
       "      <td>0.797180</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.826266</td>\n",
       "      <td>0.838355</td>\n",
       "      <td>0.838355</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.837383</td>\n",
       "      <td>0.828382</td>\n",
       "      <td>0.828382</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.817346</td>\n",
       "      <td>0.796250</td>\n",
       "      <td>0.796250</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.821500</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>0.775967</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.813109</td>\n",
       "      <td>0.779221</td>\n",
       "      <td>0.779221</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.779921</td>\n",
       "      <td>0.786159</td>\n",
       "      <td>0.786159</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.778150</td>\n",
       "      <td>0.751146</td>\n",
       "      <td>0.751146</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.801880</td>\n",
       "      <td>0.805936</td>\n",
       "      <td>0.805936</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.787098</td>\n",
       "      <td>0.784811</td>\n",
       "      <td>0.784811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.791248</td>\n",
       "      <td>0.784222</td>\n",
       "      <td>0.784222</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.786523</td>\n",
       "      <td>0.810258</td>\n",
       "      <td>0.810258</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.775433</td>\n",
       "      <td>0.773668</td>\n",
       "      <td>0.773668</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.782958</td>\n",
       "      <td>0.792950</td>\n",
       "      <td>0.792950</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.796101</td>\n",
       "      <td>0.760717</td>\n",
       "      <td>0.760717</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.771324</td>\n",
       "      <td>0.774871</td>\n",
       "      <td>0.774871</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.784317</td>\n",
       "      <td>0.783014</td>\n",
       "      <td>0.783014</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.769496</td>\n",
       "      <td>0.784161</td>\n",
       "      <td>0.784161</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.761474</td>\n",
       "      <td>0.791023</td>\n",
       "      <td>0.791023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 7.141307353973389.\n",
      "Better model found at epoch 1 with mae value: 5.7771711349487305.\n",
      "Better model found at epoch 2 with mae value: 4.586879730224609.\n",
      "Better model found at epoch 3 with mae value: 4.166836261749268.\n",
      "Better model found at epoch 4 with mae value: 3.986797332763672.\n",
      "Better model found at epoch 5 with mae value: 3.8260021209716797.\n",
      "Better model found at epoch 6 with mae value: 3.6565654277801514.\n",
      "Better model found at epoch 8 with mae value: 3.4870448112487793.\n",
      "Better model found at epoch 10 with mae value: 3.1956794261932373.\n",
      "Better model found at epoch 12 with mae value: 2.489586591720581.\n",
      "Better model found at epoch 13 with mae value: 2.0386369228363037.\n",
      "Better model found at epoch 14 with mae value: 1.8119661808013916.\n",
      "Better model found at epoch 15 with mae value: 1.6281590461730957.\n",
      "Better model found at epoch 17 with mae value: 1.2589645385742188.\n",
      "Better model found at epoch 20 with mae value: 1.1849983930587769.\n",
      "Better model found at epoch 23 with mae value: 1.1656910181045532.\n",
      "Better model found at epoch 29 with mae value: 1.0744694471359253.\n",
      "Better model found at epoch 31 with mae value: 1.0699084997177124.\n",
      "Better model found at epoch 37 with mae value: 1.0153926610946655.\n",
      "Better model found at epoch 38 with mae value: 0.974646270275116.\n",
      "Better model found at epoch 41 with mae value: 0.893751323223114.\n",
      "Better model found at epoch 52 with mae value: 0.884115993976593.\n",
      "Better model found at epoch 64 with mae value: 0.8636001944541931.\n",
      "Better model found at epoch 65 with mae value: 0.824996292591095.\n",
      "Better model found at epoch 68 with mae value: 0.8156618475914001.\n",
      "Better model found at epoch 77 with mae value: 0.8110082149505615.\n",
      "Better model found at epoch 81 with mae value: 0.79718017578125.\n",
      "Better model found at epoch 84 with mae value: 0.7962496876716614.\n",
      "Better model found at epoch 85 with mae value: 0.7759669423103333.\n",
      "Better model found at epoch 88 with mae value: 0.7511459589004517.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE for Fold-6: 0.751\n",
      "MAPE for Fold-6: 0.035\n"
     ]
    },
    {
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     "text": [
      "********** Fold-7 **********\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.506797</td>\n",
       "      <td>9.349718</td>\n",
       "      <td>9.349719</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.016289</td>\n",
       "      <td>6.224902</td>\n",
       "      <td>6.224901</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.663968</td>\n",
       "      <td>5.357358</td>\n",
       "      <td>5.357358</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.567714</td>\n",
       "      <td>3.796411</td>\n",
       "      <td>3.796411</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.024645</td>\n",
       "      <td>3.620268</td>\n",
       "      <td>3.620268</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.716016</td>\n",
       "      <td>3.435146</td>\n",
       "      <td>3.435146</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.448395</td>\n",
       "      <td>3.166633</td>\n",
       "      <td>3.166633</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.216831</td>\n",
       "      <td>2.790354</td>\n",
       "      <td>2.790353</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.322335</td>\n",
       "      <td>2.673064</td>\n",
       "      <td>2.673064</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.941935</td>\n",
       "      <td>2.353942</td>\n",
       "      <td>2.353942</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.785805</td>\n",
       "      <td>2.674561</td>\n",
       "      <td>2.674561</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.467395</td>\n",
       "      <td>2.063298</td>\n",
       "      <td>2.063298</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.407082</td>\n",
       "      <td>2.173795</td>\n",
       "      <td>2.173795</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.983861</td>\n",
       "      <td>2.430203</td>\n",
       "      <td>2.430203</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.587336</td>\n",
       "      <td>1.341961</td>\n",
       "      <td>1.341961</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.386598</td>\n",
       "      <td>1.228041</td>\n",
       "      <td>1.228041</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.299272</td>\n",
       "      <td>1.130940</td>\n",
       "      <td>1.130940</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.296140</td>\n",
       "      <td>1.218971</td>\n",
       "      <td>1.218971</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.335106</td>\n",
       "      <td>1.362222</td>\n",
       "      <td>1.362223</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.348650</td>\n",
       "      <td>1.101897</td>\n",
       "      <td>1.101897</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.230722</td>\n",
       "      <td>1.219105</td>\n",
       "      <td>1.219105</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.236625</td>\n",
       "      <td>1.064668</td>\n",
       "      <td>1.064668</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.272536</td>\n",
       "      <td>1.146344</td>\n",
       "      <td>1.146344</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.247460</td>\n",
       "      <td>1.077027</td>\n",
       "      <td>1.077027</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.186458</td>\n",
       "      <td>1.684884</td>\n",
       "      <td>1.684884</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.186605</td>\n",
       "      <td>1.021172</td>\n",
       "      <td>1.021172</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.176087</td>\n",
       "      <td>1.205137</td>\n",
       "      <td>1.205137</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.233966</td>\n",
       "      <td>1.103529</td>\n",
       "      <td>1.103529</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.172469</td>\n",
       "      <td>1.157408</td>\n",
       "      <td>1.157408</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.138884</td>\n",
       "      <td>1.092817</td>\n",
       "      <td>1.092817</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.082121</td>\n",
       "      <td>0.909765</td>\n",
       "      <td>0.909765</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.100744</td>\n",
       "      <td>1.220084</td>\n",
       "      <td>1.220084</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.167098</td>\n",
       "      <td>1.412652</td>\n",
       "      <td>1.412652</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.078679</td>\n",
       "      <td>0.937832</td>\n",
       "      <td>0.937832</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.048705</td>\n",
       "      <td>0.879390</td>\n",
       "      <td>0.879390</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.065289</td>\n",
       "      <td>1.182733</td>\n",
       "      <td>1.182733</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.081274</td>\n",
       "      <td>1.250045</td>\n",
       "      <td>1.250045</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.083551</td>\n",
       "      <td>1.035202</td>\n",
       "      <td>1.035202</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.110685</td>\n",
       "      <td>0.905146</td>\n",
       "      <td>0.905146</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.053688</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>0.975111</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.076354</td>\n",
       "      <td>0.992438</td>\n",
       "      <td>0.992438</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.032237</td>\n",
       "      <td>0.924135</td>\n",
       "      <td>0.924135</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.047639</td>\n",
       "      <td>1.024304</td>\n",
       "      <td>1.024304</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.039844</td>\n",
       "      <td>0.874012</td>\n",
       "      <td>0.874012</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.049117</td>\n",
       "      <td>0.931080</td>\n",
       "      <td>0.931080</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.013282</td>\n",
       "      <td>1.006591</td>\n",
       "      <td>1.006591</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.092164</td>\n",
       "      <td>0.898083</td>\n",
       "      <td>0.898083</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.123521</td>\n",
       "      <td>0.944315</td>\n",
       "      <td>0.944315</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.978844</td>\n",
       "      <td>0.901668</td>\n",
       "      <td>0.901668</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.993919</td>\n",
       "      <td>0.907698</td>\n",
       "      <td>0.907698</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.004257</td>\n",
       "      <td>1.081982</td>\n",
       "      <td>1.081982</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.957806</td>\n",
       "      <td>0.944857</td>\n",
       "      <td>0.944857</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.942967</td>\n",
       "      <td>1.097959</td>\n",
       "      <td>1.097959</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.982612</td>\n",
       "      <td>1.079926</td>\n",
       "      <td>1.079926</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.047064</td>\n",
       "      <td>0.948027</td>\n",
       "      <td>0.948027</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.985112</td>\n",
       "      <td>0.928131</td>\n",
       "      <td>0.928131</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.962506</td>\n",
       "      <td>1.100473</td>\n",
       "      <td>1.100473</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.980571</td>\n",
       "      <td>0.958881</td>\n",
       "      <td>0.958881</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.037583</td>\n",
       "      <td>1.103685</td>\n",
       "      <td>1.103684</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.016568</td>\n",
       "      <td>0.945811</td>\n",
       "      <td>0.945811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.009485</td>\n",
       "      <td>0.897434</td>\n",
       "      <td>0.897434</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.001304</td>\n",
       "      <td>0.930167</td>\n",
       "      <td>0.930167</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.972883</td>\n",
       "      <td>1.035950</td>\n",
       "      <td>1.035950</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.899993</td>\n",
       "      <td>0.880197</td>\n",
       "      <td>0.880197</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.918351</td>\n",
       "      <td>0.900520</td>\n",
       "      <td>0.900520</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.924035</td>\n",
       "      <td>0.896533</td>\n",
       "      <td>0.896533</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.972053</td>\n",
       "      <td>0.862375</td>\n",
       "      <td>0.862375</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.979787</td>\n",
       "      <td>0.840572</td>\n",
       "      <td>0.840572</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.949332</td>\n",
       "      <td>0.858259</td>\n",
       "      <td>0.858259</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.950963</td>\n",
       "      <td>1.065705</td>\n",
       "      <td>1.065705</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.898223</td>\n",
       "      <td>0.820865</td>\n",
       "      <td>0.820865</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.895449</td>\n",
       "      <td>0.905361</td>\n",
       "      <td>0.905361</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.906771</td>\n",
       "      <td>0.845466</td>\n",
       "      <td>0.845466</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.899536</td>\n",
       "      <td>0.864640</td>\n",
       "      <td>0.864640</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.901238</td>\n",
       "      <td>0.850171</td>\n",
       "      <td>0.850171</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.891670</td>\n",
       "      <td>0.808724</td>\n",
       "      <td>0.808724</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.922311</td>\n",
       "      <td>0.881367</td>\n",
       "      <td>0.881367</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.873919</td>\n",
       "      <td>0.789920</td>\n",
       "      <td>0.789920</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.852358</td>\n",
       "      <td>0.827062</td>\n",
       "      <td>0.827062</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.880849</td>\n",
       "      <td>0.904750</td>\n",
       "      <td>0.904750</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.862711</td>\n",
       "      <td>0.897850</td>\n",
       "      <td>0.897850</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.872165</td>\n",
       "      <td>0.772362</td>\n",
       "      <td>0.772362</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.825144</td>\n",
       "      <td>0.802081</td>\n",
       "      <td>0.802080</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.801650</td>\n",
       "      <td>0.782580</td>\n",
       "      <td>0.782580</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.825829</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>0.780199</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.814410</td>\n",
       "      <td>0.769120</td>\n",
       "      <td>0.769120</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.829796</td>\n",
       "      <td>0.761208</td>\n",
       "      <td>0.761208</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.786751</td>\n",
       "      <td>0.765385</td>\n",
       "      <td>0.765385</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.823791</td>\n",
       "      <td>0.791150</td>\n",
       "      <td>0.791150</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.801020</td>\n",
       "      <td>0.762067</td>\n",
       "      <td>0.762067</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.798152</td>\n",
       "      <td>0.758584</td>\n",
       "      <td>0.758584</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.786212</td>\n",
       "      <td>0.747878</td>\n",
       "      <td>0.747878</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.790395</td>\n",
       "      <td>0.749349</td>\n",
       "      <td>0.749349</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.803179</td>\n",
       "      <td>0.772444</td>\n",
       "      <td>0.772444</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.751798</td>\n",
       "      <td>0.746230</td>\n",
       "      <td>0.746230</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.770283</td>\n",
       "      <td>0.749340</td>\n",
       "      <td>0.749340</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.805262</td>\n",
       "      <td>0.813592</td>\n",
       "      <td>0.813592</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.781786</td>\n",
       "      <td>0.753242</td>\n",
       "      <td>0.753242</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.762399</td>\n",
       "      <td>0.754669</td>\n",
       "      <td>0.754669</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.758485</td>\n",
       "      <td>0.786635</td>\n",
       "      <td>0.786635</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "text": [
      "Better model found at epoch 0 with mae value: 9.349719047546387.\n",
      "Better model found at epoch 1 with mae value: 6.22490119934082.\n",
      "Better model found at epoch 2 with mae value: 5.357357978820801.\n",
      "Better model found at epoch 3 with mae value: 3.79641056060791.\n",
      "Better model found at epoch 4 with mae value: 3.620267629623413.\n",
      "Better model found at epoch 5 with mae value: 3.435145616531372.\n",
      "Better model found at epoch 6 with mae value: 3.166633129119873.\n",
      "Better model found at epoch 7 with mae value: 2.790353298187256.\n",
      "Better model found at epoch 8 with mae value: 2.6730642318725586.\n",
      "Better model found at epoch 9 with mae value: 2.353942394256592.\n",
      "Better model found at epoch 11 with mae value: 2.063297748565674.\n",
      "Better model found at epoch 14 with mae value: 1.34196138381958.\n",
      "Better model found at epoch 15 with mae value: 1.2280406951904297.\n",
      "Better model found at epoch 16 with mae value: 1.1309397220611572.\n",
      "Better model found at epoch 19 with mae value: 1.1018973588943481.\n",
      "Better model found at epoch 21 with mae value: 1.0646679401397705.\n",
      "Better model found at epoch 25 with mae value: 1.0211721658706665.\n",
      "Better model found at epoch 30 with mae value: 0.9097650647163391.\n",
      "Better model found at epoch 34 with mae value: 0.8793904185295105.\n",
      "Better model found at epoch 43 with mae value: 0.8740118145942688.\n",
      "Better model found at epoch 66 with mae value: 0.8623751401901245.\n",
      "Better model found at epoch 67 with mae value: 0.8405722975730896.\n",
      "Better model found at epoch 70 with mae value: 0.8208650350570679.\n",
      "Better model found at epoch 75 with mae value: 0.8087241649627686.\n",
      "Better model found at epoch 77 with mae value: 0.7899197936058044.\n",
      "Better model found at epoch 81 with mae value: 0.7723619341850281.\n",
      "Better model found at epoch 85 with mae value: 0.7691201567649841.\n",
      "Better model found at epoch 86 with mae value: 0.7612076997756958.\n",
      "Better model found at epoch 90 with mae value: 0.758583664894104.\n",
      "Better model found at epoch 91 with mae value: 0.7478779554367065.\n",
      "Better model found at epoch 94 with mae value: 0.7462295293807983.\n"
     ]
    },
    {
     "data": {
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       "\n",
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     "text": [
      "MAE for Fold-7: 0.746\n",
      "MAPE for Fold-7: 0.025\n"
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     "text": [
      "********** Fold-8 **********\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9.615080</td>\n",
       "      <td>6.959351</td>\n",
       "      <td>6.959351</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.421461</td>\n",
       "      <td>5.209568</td>\n",
       "      <td>5.209567</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.981572</td>\n",
       "      <td>4.131376</td>\n",
       "      <td>4.131376</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.251060</td>\n",
       "      <td>4.032043</td>\n",
       "      <td>4.032043</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.742053</td>\n",
       "      <td>3.374114</td>\n",
       "      <td>3.374114</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.413600</td>\n",
       "      <td>3.176762</td>\n",
       "      <td>3.176762</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.178507</td>\n",
       "      <td>3.221071</td>\n",
       "      <td>3.221072</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.997388</td>\n",
       "      <td>2.611778</td>\n",
       "      <td>2.611778</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.644730</td>\n",
       "      <td>2.379006</td>\n",
       "      <td>2.379006</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.481716</td>\n",
       "      <td>2.007166</td>\n",
       "      <td>2.007166</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.286284</td>\n",
       "      <td>1.836639</td>\n",
       "      <td>1.836639</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.162204</td>\n",
       "      <td>2.222008</td>\n",
       "      <td>2.222008</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.086478</td>\n",
       "      <td>1.857865</td>\n",
       "      <td>1.857865</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.805261</td>\n",
       "      <td>2.067176</td>\n",
       "      <td>2.067175</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.724849</td>\n",
       "      <td>1.663762</td>\n",
       "      <td>1.663762</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.489947</td>\n",
       "      <td>1.674510</td>\n",
       "      <td>1.674510</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.342932</td>\n",
       "      <td>1.190635</td>\n",
       "      <td>1.190635</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.370850</td>\n",
       "      <td>1.235419</td>\n",
       "      <td>1.235419</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.336725</td>\n",
       "      <td>1.231277</td>\n",
       "      <td>1.231277</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.389303</td>\n",
       "      <td>2.032520</td>\n",
       "      <td>2.032520</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.316908</td>\n",
       "      <td>1.218709</td>\n",
       "      <td>1.218709</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.349532</td>\n",
       "      <td>1.212693</td>\n",
       "      <td>1.212693</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.328233</td>\n",
       "      <td>1.143135</td>\n",
       "      <td>1.143135</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.313457</td>\n",
       "      <td>1.224665</td>\n",
       "      <td>1.224665</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.485652</td>\n",
       "      <td>1.529246</td>\n",
       "      <td>1.529246</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.325662</td>\n",
       "      <td>1.194294</td>\n",
       "      <td>1.194294</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.515381</td>\n",
       "      <td>1.661752</td>\n",
       "      <td>1.661752</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.374614</td>\n",
       "      <td>1.176847</td>\n",
       "      <td>1.176847</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.232773</td>\n",
       "      <td>1.128127</td>\n",
       "      <td>1.128127</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.189572</td>\n",
       "      <td>1.198250</td>\n",
       "      <td>1.198250</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.230331</td>\n",
       "      <td>1.327841</td>\n",
       "      <td>1.327841</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.221141</td>\n",
       "      <td>1.272877</td>\n",
       "      <td>1.272877</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.148339</td>\n",
       "      <td>1.284268</td>\n",
       "      <td>1.284268</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.230761</td>\n",
       "      <td>1.151589</td>\n",
       "      <td>1.151589</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.141396</td>\n",
       "      <td>1.002712</td>\n",
       "      <td>1.002712</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.143659</td>\n",
       "      <td>1.221897</td>\n",
       "      <td>1.221897</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.171336</td>\n",
       "      <td>1.159390</td>\n",
       "      <td>1.159390</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.154966</td>\n",
       "      <td>1.458604</td>\n",
       "      <td>1.458604</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.090686</td>\n",
       "      <td>1.075429</td>\n",
       "      <td>1.075429</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.325037</td>\n",
       "      <td>1.244252</td>\n",
       "      <td>1.244252</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.239113</td>\n",
       "      <td>1.315518</td>\n",
       "      <td>1.315518</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.300725</td>\n",
       "      <td>1.279317</td>\n",
       "      <td>1.279317</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.151573</td>\n",
       "      <td>1.001259</td>\n",
       "      <td>1.001259</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.201371</td>\n",
       "      <td>1.196246</td>\n",
       "      <td>1.196246</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.334390</td>\n",
       "      <td>1.103227</td>\n",
       "      <td>1.103227</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.310453</td>\n",
       "      <td>1.552100</td>\n",
       "      <td>1.552100</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.160383</td>\n",
       "      <td>1.133323</td>\n",
       "      <td>1.133323</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.175739</td>\n",
       "      <td>1.233397</td>\n",
       "      <td>1.233397</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.130503</td>\n",
       "      <td>1.109692</td>\n",
       "      <td>1.109692</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.068019</td>\n",
       "      <td>1.054520</td>\n",
       "      <td>1.054520</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.044046</td>\n",
       "      <td>0.955487</td>\n",
       "      <td>0.955487</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.994593</td>\n",
       "      <td>0.974787</td>\n",
       "      <td>0.974787</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.977304</td>\n",
       "      <td>0.852172</td>\n",
       "      <td>0.852172</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.050606</td>\n",
       "      <td>1.001511</td>\n",
       "      <td>1.001511</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.979677</td>\n",
       "      <td>1.218565</td>\n",
       "      <td>1.218565</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.970999</td>\n",
       "      <td>0.933805</td>\n",
       "      <td>0.933805</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.954747</td>\n",
       "      <td>0.884873</td>\n",
       "      <td>0.884873</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.962249</td>\n",
       "      <td>0.871321</td>\n",
       "      <td>0.871321</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.949748</td>\n",
       "      <td>1.181471</td>\n",
       "      <td>1.181471</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.952387</td>\n",
       "      <td>0.976875</td>\n",
       "      <td>0.976875</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.927258</td>\n",
       "      <td>0.789913</td>\n",
       "      <td>0.789913</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.926154</td>\n",
       "      <td>0.843260</td>\n",
       "      <td>0.843260</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.933985</td>\n",
       "      <td>0.821018</td>\n",
       "      <td>0.821018</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.926529</td>\n",
       "      <td>0.805258</td>\n",
       "      <td>0.805258</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.903941</td>\n",
       "      <td>0.838147</td>\n",
       "      <td>0.838147</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.914175</td>\n",
       "      <td>0.831154</td>\n",
       "      <td>0.831154</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.861880</td>\n",
       "      <td>0.849759</td>\n",
       "      <td>0.849759</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.855605</td>\n",
       "      <td>0.861071</td>\n",
       "      <td>0.861071</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.835007</td>\n",
       "      <td>0.802208</td>\n",
       "      <td>0.802208</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.876407</td>\n",
       "      <td>0.826857</td>\n",
       "      <td>0.826857</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.852256</td>\n",
       "      <td>0.736147</td>\n",
       "      <td>0.736147</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.853084</td>\n",
       "      <td>0.813722</td>\n",
       "      <td>0.813722</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.818692</td>\n",
       "      <td>0.758690</td>\n",
       "      <td>0.758690</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.832318</td>\n",
       "      <td>0.744010</td>\n",
       "      <td>0.744010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.825120</td>\n",
       "      <td>0.773278</td>\n",
       "      <td>0.773277</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.850214</td>\n",
       "      <td>0.860601</td>\n",
       "      <td>0.860601</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.851670</td>\n",
       "      <td>0.785346</td>\n",
       "      <td>0.785347</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.803138</td>\n",
       "      <td>0.745005</td>\n",
       "      <td>0.745005</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.806189</td>\n",
       "      <td>0.723758</td>\n",
       "      <td>0.723758</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.806804</td>\n",
       "      <td>0.707263</td>\n",
       "      <td>0.707263</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.765118</td>\n",
       "      <td>0.723940</td>\n",
       "      <td>0.723940</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.772135</td>\n",
       "      <td>0.789528</td>\n",
       "      <td>0.789528</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.786775</td>\n",
       "      <td>0.714616</td>\n",
       "      <td>0.714616</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.795197</td>\n",
       "      <td>0.729648</td>\n",
       "      <td>0.729648</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.794412</td>\n",
       "      <td>0.713888</td>\n",
       "      <td>0.713888</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.773456</td>\n",
       "      <td>0.697272</td>\n",
       "      <td>0.697272</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.748440</td>\n",
       "      <td>0.713389</td>\n",
       "      <td>0.713389</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.740665</td>\n",
       "      <td>0.742604</td>\n",
       "      <td>0.742604</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.748237</td>\n",
       "      <td>0.696448</td>\n",
       "      <td>0.696448</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.754902</td>\n",
       "      <td>0.696179</td>\n",
       "      <td>0.696179</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.777381</td>\n",
       "      <td>0.703059</td>\n",
       "      <td>0.703059</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.746971</td>\n",
       "      <td>0.694704</td>\n",
       "      <td>0.694704</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.742665</td>\n",
       "      <td>0.694186</td>\n",
       "      <td>0.694186</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.725710</td>\n",
       "      <td>0.695282</td>\n",
       "      <td>0.695282</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.744262</td>\n",
       "      <td>0.689651</td>\n",
       "      <td>0.689651</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.740848</td>\n",
       "      <td>0.712442</td>\n",
       "      <td>0.712442</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.727855</td>\n",
       "      <td>0.688588</td>\n",
       "      <td>0.688588</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.736542</td>\n",
       "      <td>0.737237</td>\n",
       "      <td>0.737237</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.746703</td>\n",
       "      <td>0.700707</td>\n",
       "      <td>0.700707</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.741421</td>\n",
       "      <td>0.697040</td>\n",
       "      <td>0.697040</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 6.959351062774658.\n",
      "Better model found at epoch 1 with mae value: 5.209567070007324.\n",
      "Better model found at epoch 2 with mae value: 4.131376266479492.\n",
      "Better model found at epoch 3 with mae value: 4.032042980194092.\n",
      "Better model found at epoch 4 with mae value: 3.3741135597229004.\n",
      "Better model found at epoch 5 with mae value: 3.1767618656158447.\n",
      "Better model found at epoch 7 with mae value: 2.6117775440216064.\n",
      "Better model found at epoch 8 with mae value: 2.3790056705474854.\n",
      "Better model found at epoch 9 with mae value: 2.0071659088134766.\n",
      "Better model found at epoch 10 with mae value: 1.8366389274597168.\n",
      "Better model found at epoch 14 with mae value: 1.6637622117996216.\n",
      "Better model found at epoch 16 with mae value: 1.1906346082687378.\n",
      "Better model found at epoch 22 with mae value: 1.1431353092193604.\n",
      "Better model found at epoch 28 with mae value: 1.128127098083496.\n",
      "Better model found at epoch 34 with mae value: 1.0027124881744385.\n",
      "Better model found at epoch 42 with mae value: 1.0012590885162354.\n",
      "Better model found at epoch 50 with mae value: 0.9554868936538696.\n",
      "Better model found at epoch 52 with mae value: 0.8521720170974731.\n",
      "Better model found at epoch 60 with mae value: 0.7899132966995239.\n",
      "Better model found at epoch 70 with mae value: 0.7361471652984619.\n",
      "Better model found at epoch 78 with mae value: 0.7237581610679626.\n",
      "Better model found at epoch 79 with mae value: 0.7072634696960449.\n",
      "Better model found at epoch 85 with mae value: 0.6972719430923462.\n",
      "Better model found at epoch 88 with mae value: 0.6964476108551025.\n",
      "Better model found at epoch 89 with mae value: 0.6961793303489685.\n",
      "Better model found at epoch 91 with mae value: 0.6947036981582642.\n",
      "Better model found at epoch 92 with mae value: 0.694186270236969.\n",
      "Better model found at epoch 94 with mae value: 0.6896507740020752.\n",
      "Better model found at epoch 96 with mae value: 0.6885880827903748.\n"
     ]
    },
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      "MAE for Fold-8: 0.689\n",
      "MAPE for Fold-8: 0.025\n"
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      "********** Fold-9 **********\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9.950379</td>\n",
       "      <td>7.031493</td>\n",
       "      <td>7.031493</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.634106</td>\n",
       "      <td>5.002737</td>\n",
       "      <td>5.002737</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.247873</td>\n",
       "      <td>3.828521</td>\n",
       "      <td>3.828521</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.347271</td>\n",
       "      <td>3.536563</td>\n",
       "      <td>3.536563</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.772502</td>\n",
       "      <td>3.400855</td>\n",
       "      <td>3.400855</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.703294</td>\n",
       "      <td>3.246887</td>\n",
       "      <td>3.246887</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.697301</td>\n",
       "      <td>3.135875</td>\n",
       "      <td>3.135875</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.571564</td>\n",
       "      <td>3.491164</td>\n",
       "      <td>3.491164</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.219327</td>\n",
       "      <td>2.884135</td>\n",
       "      <td>2.884135</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.162643</td>\n",
       "      <td>2.611609</td>\n",
       "      <td>2.611609</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.939210</td>\n",
       "      <td>2.402879</td>\n",
       "      <td>2.402879</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.724921</td>\n",
       "      <td>2.495200</td>\n",
       "      <td>2.495200</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2.400144</td>\n",
       "      <td>2.032518</td>\n",
       "      <td>2.032518</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2.234763</td>\n",
       "      <td>1.826277</td>\n",
       "      <td>1.826277</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.902792</td>\n",
       "      <td>1.449996</td>\n",
       "      <td>1.449996</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.442181</td>\n",
       "      <td>1.339236</td>\n",
       "      <td>1.339236</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.367095</td>\n",
       "      <td>1.269902</td>\n",
       "      <td>1.269902</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.335628</td>\n",
       "      <td>1.219611</td>\n",
       "      <td>1.219611</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.310599</td>\n",
       "      <td>1.131138</td>\n",
       "      <td>1.131138</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.332455</td>\n",
       "      <td>1.228226</td>\n",
       "      <td>1.228226</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.266717</td>\n",
       "      <td>1.288016</td>\n",
       "      <td>1.288015</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.241030</td>\n",
       "      <td>1.196326</td>\n",
       "      <td>1.196327</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.274650</td>\n",
       "      <td>1.154124</td>\n",
       "      <td>1.154124</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.240477</td>\n",
       "      <td>1.167637</td>\n",
       "      <td>1.167637</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.297241</td>\n",
       "      <td>1.142856</td>\n",
       "      <td>1.142856</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.218875</td>\n",
       "      <td>1.041776</td>\n",
       "      <td>1.041776</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.281328</td>\n",
       "      <td>1.210508</td>\n",
       "      <td>1.210508</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.176798</td>\n",
       "      <td>1.268835</td>\n",
       "      <td>1.268835</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.137486</td>\n",
       "      <td>1.001955</td>\n",
       "      <td>1.001955</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.112575</td>\n",
       "      <td>1.052588</td>\n",
       "      <td>1.052588</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.158510</td>\n",
       "      <td>0.959680</td>\n",
       "      <td>0.959680</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.144133</td>\n",
       "      <td>1.148023</td>\n",
       "      <td>1.148023</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.136502</td>\n",
       "      <td>0.997260</td>\n",
       "      <td>0.997260</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.143296</td>\n",
       "      <td>1.166458</td>\n",
       "      <td>1.166458</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.143595</td>\n",
       "      <td>1.067306</td>\n",
       "      <td>1.067306</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.143044</td>\n",
       "      <td>1.128764</td>\n",
       "      <td>1.128764</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.067041</td>\n",
       "      <td>0.989315</td>\n",
       "      <td>0.989315</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.149463</td>\n",
       "      <td>1.037103</td>\n",
       "      <td>1.037103</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.173294</td>\n",
       "      <td>1.469260</td>\n",
       "      <td>1.469260</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.082212</td>\n",
       "      <td>1.018463</td>\n",
       "      <td>1.018463</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.183991</td>\n",
       "      <td>2.223811</td>\n",
       "      <td>2.223811</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.223212</td>\n",
       "      <td>1.096009</td>\n",
       "      <td>1.096009</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.220328</td>\n",
       "      <td>0.842914</td>\n",
       "      <td>0.842914</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.093698</td>\n",
       "      <td>1.156912</td>\n",
       "      <td>1.156912</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.088836</td>\n",
       "      <td>1.004598</td>\n",
       "      <td>1.004598</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.125880</td>\n",
       "      <td>0.927052</td>\n",
       "      <td>0.927052</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.101241</td>\n",
       "      <td>0.819809</td>\n",
       "      <td>0.819809</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.069860</td>\n",
       "      <td>0.956251</td>\n",
       "      <td>0.956251</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.999377</td>\n",
       "      <td>0.929117</td>\n",
       "      <td>0.929117</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.015285</td>\n",
       "      <td>0.957545</td>\n",
       "      <td>0.957545</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.029724</td>\n",
       "      <td>0.969495</td>\n",
       "      <td>0.969495</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.064010</td>\n",
       "      <td>1.015239</td>\n",
       "      <td>1.015239</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.053224</td>\n",
       "      <td>1.001031</td>\n",
       "      <td>1.001031</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.083634</td>\n",
       "      <td>0.935650</td>\n",
       "      <td>0.935651</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.079700</td>\n",
       "      <td>1.087816</td>\n",
       "      <td>1.087816</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.101116</td>\n",
       "      <td>0.912201</td>\n",
       "      <td>0.912201</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.059825</td>\n",
       "      <td>0.841285</td>\n",
       "      <td>0.841285</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.034007</td>\n",
       "      <td>0.886627</td>\n",
       "      <td>0.886627</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.037399</td>\n",
       "      <td>0.905132</td>\n",
       "      <td>0.905132</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.974375</td>\n",
       "      <td>0.824855</td>\n",
       "      <td>0.824855</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.995121</td>\n",
       "      <td>0.939513</td>\n",
       "      <td>0.939513</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.984123</td>\n",
       "      <td>0.796177</td>\n",
       "      <td>0.796177</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.970849</td>\n",
       "      <td>0.758866</td>\n",
       "      <td>0.758866</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.931889</td>\n",
       "      <td>0.797685</td>\n",
       "      <td>0.797685</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.948161</td>\n",
       "      <td>0.845808</td>\n",
       "      <td>0.845808</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.936860</td>\n",
       "      <td>0.774140</td>\n",
       "      <td>0.774140</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.937933</td>\n",
       "      <td>0.764251</td>\n",
       "      <td>0.764251</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.937875</td>\n",
       "      <td>0.807285</td>\n",
       "      <td>0.807285</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.931108</td>\n",
       "      <td>0.822180</td>\n",
       "      <td>0.822180</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.940467</td>\n",
       "      <td>0.809427</td>\n",
       "      <td>0.809427</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.954299</td>\n",
       "      <td>0.793677</td>\n",
       "      <td>0.793677</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.915425</td>\n",
       "      <td>0.783972</td>\n",
       "      <td>0.783973</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.925148</td>\n",
       "      <td>0.810744</td>\n",
       "      <td>0.810744</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.887353</td>\n",
       "      <td>0.720848</td>\n",
       "      <td>0.720848</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.892506</td>\n",
       "      <td>0.776932</td>\n",
       "      <td>0.776932</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.873638</td>\n",
       "      <td>0.769290</td>\n",
       "      <td>0.769290</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.843208</td>\n",
       "      <td>0.723085</td>\n",
       "      <td>0.723086</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.865317</td>\n",
       "      <td>0.802051</td>\n",
       "      <td>0.802051</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.853279</td>\n",
       "      <td>0.711380</td>\n",
       "      <td>0.711380</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.827139</td>\n",
       "      <td>0.738119</td>\n",
       "      <td>0.738119</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.846337</td>\n",
       "      <td>0.729983</td>\n",
       "      <td>0.729983</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.821131</td>\n",
       "      <td>0.704890</td>\n",
       "      <td>0.704890</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.811406</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.839475</td>\n",
       "      <td>0.741662</td>\n",
       "      <td>0.741662</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.796502</td>\n",
       "      <td>0.689289</td>\n",
       "      <td>0.689289</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.798183</td>\n",
       "      <td>0.707763</td>\n",
       "      <td>0.707763</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.779424</td>\n",
       "      <td>0.699222</td>\n",
       "      <td>0.699222</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.770342</td>\n",
       "      <td>0.682389</td>\n",
       "      <td>0.682389</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.792908</td>\n",
       "      <td>0.681886</td>\n",
       "      <td>0.681886</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.786072</td>\n",
       "      <td>0.685298</td>\n",
       "      <td>0.685298</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.787969</td>\n",
       "      <td>0.685713</td>\n",
       "      <td>0.685713</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.784227</td>\n",
       "      <td>0.675319</td>\n",
       "      <td>0.675319</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.775872</td>\n",
       "      <td>0.682695</td>\n",
       "      <td>0.682695</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.771619</td>\n",
       "      <td>0.675076</td>\n",
       "      <td>0.675076</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.773222</td>\n",
       "      <td>0.679866</td>\n",
       "      <td>0.679866</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.779410</td>\n",
       "      <td>0.683970</td>\n",
       "      <td>0.683970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.765192</td>\n",
       "      <td>0.679488</td>\n",
       "      <td>0.679488</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.762756</td>\n",
       "      <td>0.682496</td>\n",
       "      <td>0.682497</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.762699</td>\n",
       "      <td>0.676400</td>\n",
       "      <td>0.676400</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.757330</td>\n",
       "      <td>0.694112</td>\n",
       "      <td>0.694112</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 7.031492710113525.\n",
      "Better model found at epoch 1 with mae value: 5.002737045288086.\n",
      "Better model found at epoch 2 with mae value: 3.8285205364227295.\n",
      "Better model found at epoch 3 with mae value: 3.53656268119812.\n",
      "Better model found at epoch 4 with mae value: 3.40085506439209.\n",
      "Better model found at epoch 5 with mae value: 3.246886730194092.\n",
      "Better model found at epoch 6 with mae value: 3.1358747482299805.\n",
      "Better model found at epoch 8 with mae value: 2.8841350078582764.\n",
      "Better model found at epoch 9 with mae value: 2.6116089820861816.\n",
      "Better model found at epoch 10 with mae value: 2.402879476547241.\n",
      "Better model found at epoch 12 with mae value: 2.032517671585083.\n",
      "Better model found at epoch 13 with mae value: 1.8262768983840942.\n",
      "Better model found at epoch 14 with mae value: 1.449995994567871.\n",
      "Better model found at epoch 15 with mae value: 1.3392363786697388.\n",
      "Better model found at epoch 16 with mae value: 1.269901990890503.\n",
      "Better model found at epoch 17 with mae value: 1.2196110486984253.\n",
      "Better model found at epoch 18 with mae value: 1.1311382055282593.\n",
      "Better model found at epoch 25 with mae value: 1.041776180267334.\n",
      "Better model found at epoch 28 with mae value: 1.0019550323486328.\n",
      "Better model found at epoch 30 with mae value: 0.9596799612045288.\n",
      "Better model found at epoch 42 with mae value: 0.8429142236709595.\n",
      "Better model found at epoch 46 with mae value: 0.8198093771934509.\n",
      "Better model found at epoch 61 with mae value: 0.796177089214325.\n",
      "Better model found at epoch 62 with mae value: 0.7588655948638916.\n",
      "Better model found at epoch 73 with mae value: 0.7208479642868042.\n",
      "Better model found at epoch 78 with mae value: 0.7113799452781677.\n",
      "Better model found at epoch 81 with mae value: 0.7048899531364441.\n",
      "Better model found at epoch 84 with mae value: 0.6892890334129333.\n",
      "Better model found at epoch 87 with mae value: 0.6823887825012207.\n",
      "Better model found at epoch 88 with mae value: 0.6818859577178955.\n",
      "Better model found at epoch 91 with mae value: 0.6753187775611877.\n",
      "Better model found at epoch 93 with mae value: 0.6750755906105042.\n"
     ]
    },
    {
     "data": {
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       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        border: none;\n",
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       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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     "text": [
      "MAE for Fold-9: 0.675\n",
      "MAPE for Fold-9: 0.029\n"
     ]
    },
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     "text": [
      "********** Fold-10 **********\n"
     ]
    },
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     "data": {
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10.594398</td>\n",
       "      <td>5.021165</td>\n",
       "      <td>5.021165</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.116370</td>\n",
       "      <td>3.812995</td>\n",
       "      <td>3.812995</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5.552407</td>\n",
       "      <td>3.310080</td>\n",
       "      <td>3.310080</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.791626</td>\n",
       "      <td>3.117662</td>\n",
       "      <td>3.117662</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.129464</td>\n",
       "      <td>3.079677</td>\n",
       "      <td>3.079678</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.891649</td>\n",
       "      <td>2.929113</td>\n",
       "      <td>2.929113</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.440039</td>\n",
       "      <td>2.765415</td>\n",
       "      <td>2.765415</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2.982450</td>\n",
       "      <td>2.406815</td>\n",
       "      <td>2.406815</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2.694833</td>\n",
       "      <td>2.327292</td>\n",
       "      <td>2.327291</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2.452182</td>\n",
       "      <td>1.915935</td>\n",
       "      <td>1.915935</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2.287879</td>\n",
       "      <td>2.329336</td>\n",
       "      <td>2.329336</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2.068929</td>\n",
       "      <td>1.627092</td>\n",
       "      <td>1.627093</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.922473</td>\n",
       "      <td>1.634660</td>\n",
       "      <td>1.634660</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.758870</td>\n",
       "      <td>1.620103</td>\n",
       "      <td>1.620103</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.504489</td>\n",
       "      <td>1.243737</td>\n",
       "      <td>1.243737</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.435404</td>\n",
       "      <td>1.280043</td>\n",
       "      <td>1.280043</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.423173</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.427601</td>\n",
       "      <td>1.496478</td>\n",
       "      <td>1.496478</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.434675</td>\n",
       "      <td>1.379876</td>\n",
       "      <td>1.379876</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.399103</td>\n",
       "      <td>1.163598</td>\n",
       "      <td>1.163598</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.330647</td>\n",
       "      <td>1.304206</td>\n",
       "      <td>1.304206</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.324300</td>\n",
       "      <td>1.001660</td>\n",
       "      <td>1.001660</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.334898</td>\n",
       "      <td>1.174171</td>\n",
       "      <td>1.174171</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.347564</td>\n",
       "      <td>1.296198</td>\n",
       "      <td>1.296198</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.248017</td>\n",
       "      <td>0.968442</td>\n",
       "      <td>0.968442</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.208300</td>\n",
       "      <td>1.090533</td>\n",
       "      <td>1.090533</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.303123</td>\n",
       "      <td>1.353886</td>\n",
       "      <td>1.353886</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.228391</td>\n",
       "      <td>1.038174</td>\n",
       "      <td>1.038174</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.182289</td>\n",
       "      <td>1.081760</td>\n",
       "      <td>1.081760</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.161881</td>\n",
       "      <td>0.995151</td>\n",
       "      <td>0.995151</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.132342</td>\n",
       "      <td>1.093262</td>\n",
       "      <td>1.093262</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.202173</td>\n",
       "      <td>0.941358</td>\n",
       "      <td>0.941359</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.291106</td>\n",
       "      <td>1.748270</td>\n",
       "      <td>1.748270</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.194357</td>\n",
       "      <td>0.898757</td>\n",
       "      <td>0.898757</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.159614</td>\n",
       "      <td>1.428291</td>\n",
       "      <td>1.428291</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.072179</td>\n",
       "      <td>0.967244</td>\n",
       "      <td>0.967244</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.056110</td>\n",
       "      <td>0.953213</td>\n",
       "      <td>0.953213</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.101101</td>\n",
       "      <td>1.069843</td>\n",
       "      <td>1.069843</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.107558</td>\n",
       "      <td>1.085859</td>\n",
       "      <td>1.085859</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.200827</td>\n",
       "      <td>1.087197</td>\n",
       "      <td>1.087197</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.169792</td>\n",
       "      <td>0.948448</td>\n",
       "      <td>0.948448</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.168934</td>\n",
       "      <td>1.107951</td>\n",
       "      <td>1.107951</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.195725</td>\n",
       "      <td>1.137897</td>\n",
       "      <td>1.137897</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.103070</td>\n",
       "      <td>0.908777</td>\n",
       "      <td>0.908777</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.146817</td>\n",
       "      <td>0.947085</td>\n",
       "      <td>0.947085</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.146157</td>\n",
       "      <td>0.969411</td>\n",
       "      <td>0.969411</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.078792</td>\n",
       "      <td>0.846950</td>\n",
       "      <td>0.846950</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.100455</td>\n",
       "      <td>0.857442</td>\n",
       "      <td>0.857442</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.043590</td>\n",
       "      <td>0.934127</td>\n",
       "      <td>0.934127</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.058096</td>\n",
       "      <td>2.175658</td>\n",
       "      <td>2.175658</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.084740</td>\n",
       "      <td>0.909595</td>\n",
       "      <td>0.909595</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.006523</td>\n",
       "      <td>0.975479</td>\n",
       "      <td>0.975479</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.031271</td>\n",
       "      <td>0.883535</td>\n",
       "      <td>0.883535</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.975776</td>\n",
       "      <td>0.902691</td>\n",
       "      <td>0.902691</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.007123</td>\n",
       "      <td>1.023877</td>\n",
       "      <td>1.023877</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.059703</td>\n",
       "      <td>0.779919</td>\n",
       "      <td>0.779919</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.951168</td>\n",
       "      <td>0.858532</td>\n",
       "      <td>0.858532</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.934983</td>\n",
       "      <td>0.823007</td>\n",
       "      <td>0.823007</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.914568</td>\n",
       "      <td>0.907096</td>\n",
       "      <td>0.907096</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.916423</td>\n",
       "      <td>0.773242</td>\n",
       "      <td>0.773242</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.045040</td>\n",
       "      <td>0.869973</td>\n",
       "      <td>0.869973</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.074388</td>\n",
       "      <td>1.016991</td>\n",
       "      <td>1.016991</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.023287</td>\n",
       "      <td>0.920381</td>\n",
       "      <td>0.920381</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.984060</td>\n",
       "      <td>0.779048</td>\n",
       "      <td>0.779048</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.946982</td>\n",
       "      <td>0.905324</td>\n",
       "      <td>0.905324</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.926757</td>\n",
       "      <td>0.846883</td>\n",
       "      <td>0.846883</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.897302</td>\n",
       "      <td>0.772117</td>\n",
       "      <td>0.772117</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.927122</td>\n",
       "      <td>0.771047</td>\n",
       "      <td>0.771047</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.922841</td>\n",
       "      <td>0.871439</td>\n",
       "      <td>0.871439</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.976860</td>\n",
       "      <td>0.854772</td>\n",
       "      <td>0.854772</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.003985</td>\n",
       "      <td>0.770952</td>\n",
       "      <td>0.770952</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.952092</td>\n",
       "      <td>0.851139</td>\n",
       "      <td>0.851139</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.935625</td>\n",
       "      <td>0.800840</td>\n",
       "      <td>0.800840</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.885931</td>\n",
       "      <td>0.767741</td>\n",
       "      <td>0.767741</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.907698</td>\n",
       "      <td>0.742225</td>\n",
       "      <td>0.742225</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.908229</td>\n",
       "      <td>0.779497</td>\n",
       "      <td>0.779497</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.874056</td>\n",
       "      <td>0.781264</td>\n",
       "      <td>0.781264</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.854991</td>\n",
       "      <td>0.732449</td>\n",
       "      <td>0.732449</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.873034</td>\n",
       "      <td>0.808621</td>\n",
       "      <td>0.808622</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.820680</td>\n",
       "      <td>0.715588</td>\n",
       "      <td>0.715587</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.852553</td>\n",
       "      <td>0.716867</td>\n",
       "      <td>0.716867</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.864351</td>\n",
       "      <td>0.695008</td>\n",
       "      <td>0.695008</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.829665</td>\n",
       "      <td>0.688919</td>\n",
       "      <td>0.688919</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.830358</td>\n",
       "      <td>0.709895</td>\n",
       "      <td>0.709895</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.846824</td>\n",
       "      <td>0.695385</td>\n",
       "      <td>0.695385</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.857113</td>\n",
       "      <td>0.697271</td>\n",
       "      <td>0.697271</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.840990</td>\n",
       "      <td>0.698924</td>\n",
       "      <td>0.698924</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.850390</td>\n",
       "      <td>0.709470</td>\n",
       "      <td>0.709470</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.817102</td>\n",
       "      <td>0.704093</td>\n",
       "      <td>0.704093</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.834251</td>\n",
       "      <td>0.699202</td>\n",
       "      <td>0.699202</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.813409</td>\n",
       "      <td>0.688799</td>\n",
       "      <td>0.688799</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.812223</td>\n",
       "      <td>0.696631</td>\n",
       "      <td>0.696632</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.819958</td>\n",
       "      <td>0.687041</td>\n",
       "      <td>0.687041</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.810883</td>\n",
       "      <td>0.680481</td>\n",
       "      <td>0.680481</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.819839</td>\n",
       "      <td>0.721049</td>\n",
       "      <td>0.721049</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.814671</td>\n",
       "      <td>0.691986</td>\n",
       "      <td>0.691986</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.844825</td>\n",
       "      <td>0.693068</td>\n",
       "      <td>0.693068</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.821087</td>\n",
       "      <td>0.701352</td>\n",
       "      <td>0.701352</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.817099</td>\n",
       "      <td>0.686036</td>\n",
       "      <td>0.686036</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.814836</td>\n",
       "      <td>0.681718</td>\n",
       "      <td>0.681718</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with mae value: 5.021164894104004.\n",
      "Better model found at epoch 1 with mae value: 3.812995433807373.\n",
      "Better model found at epoch 2 with mae value: 3.310080051422119.\n",
      "Better model found at epoch 3 with mae value: 3.117661714553833.\n",
      "Better model found at epoch 4 with mae value: 3.0796775817871094.\n",
      "Better model found at epoch 5 with mae value: 2.9291133880615234.\n",
      "Better model found at epoch 6 with mae value: 2.7654147148132324.\n",
      "Better model found at epoch 7 with mae value: 2.40681529045105.\n",
      "Better model found at epoch 8 with mae value: 2.327291250228882.\n",
      "Better model found at epoch 9 with mae value: 1.9159350395202637.\n",
      "Better model found at epoch 11 with mae value: 1.6270925998687744.\n",
      "Better model found at epoch 13 with mae value: 1.620103120803833.\n",
      "Better model found at epoch 14 with mae value: 1.2437366247177124.\n",
      "Better model found at epoch 19 with mae value: 1.1635982990264893.\n",
      "Better model found at epoch 21 with mae value: 1.0016601085662842.\n",
      "Better model found at epoch 24 with mae value: 0.9684416055679321.\n",
      "Better model found at epoch 31 with mae value: 0.9413585066795349.\n",
      "Better model found at epoch 33 with mae value: 0.8987569808959961.\n",
      "Better model found at epoch 46 with mae value: 0.8469497561454773.\n",
      "Better model found at epoch 55 with mae value: 0.7799186706542969.\n",
      "Better model found at epoch 59 with mae value: 0.7732423543930054.\n",
      "Better model found at epoch 66 with mae value: 0.7721170783042908.\n",
      "Better model found at epoch 67 with mae value: 0.7710466384887695.\n",
      "Better model found at epoch 70 with mae value: 0.7709517478942871.\n",
      "Better model found at epoch 73 with mae value: 0.767740786075592.\n",
      "Better model found at epoch 74 with mae value: 0.7422252297401428.\n",
      "Better model found at epoch 77 with mae value: 0.7324492335319519.\n",
      "Better model found at epoch 79 with mae value: 0.7155874967575073.\n",
      "Better model found at epoch 81 with mae value: 0.6950077414512634.\n",
      "Better model found at epoch 82 with mae value: 0.6889187693595886.\n",
      "Better model found at epoch 90 with mae value: 0.6887986063957214.\n",
      "Better model found at epoch 92 with mae value: 0.687041163444519.\n",
      "Better model found at epoch 93 with mae value: 0.6804805397987366.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
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    },
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      "text/html": [],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE for Fold-10: 0.68\n",
      "MAPE for Fold-10: 0.031\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF MAE: 0.716\n",
      "Average MAE: 0.716+/-0.046\n",
      "OOF MAPE: 0.028\n",
      "Average MAPE: 0.028+/-0.003\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "if Inference:\n",
    "    model_path = '../input/ecm-output-of-final-notebook/'\n",
    "else:\n",
    "    model_path = './'\n",
    "\n",
    "oof_pred_fastai, pred_fastai = fit_fastai(Nfold, train_df, test_df, train_cols, categorical_cols, TARGET, model_path)\n",
    "\n",
    "train_df = train_df.copy()\n",
    "train_df['oof_fastai'] = oof_pred_fastai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ee6323e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:05:59.981763Z",
     "iopub.status.busy": "2023-10-12T12:05:59.980942Z",
     "iopub.status.idle": "2023-10-12T12:06:00.044820Z",
     "shell.execute_reply": "2023-10-12T12:06:00.043816Z"
    },
    "papermill": {
     "duration": 0.099767,
     "end_time": "2023-10-12T12:06:00.046655",
     "exception": false,
     "start_time": "2023-10-12T12:05:59.946888",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Time     Energy\n",
      "0  2023-01-01 06:00:00_B_0  62.765106\n",
      "1  2023-01-01 11:00:00_B_0  68.255066\n",
      "2  2023-01-01 12:00:00_B_0  69.561508\n",
      "3  2023-01-01 13:00:00_B_0  69.703896\n",
      "4  2023-01-01 23:00:00_B_0  83.915932\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "\n",
    "submission_fastai = pd.DataFrame(data = {'Time': test_df['ID'].values, 'Energy': pred_fastai})\n",
    "print(submission_fastai.head())\n",
    "submission_fastai.to_csv(f'submission_fastai.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7b9d051",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:00.115532Z",
     "iopub.status.busy": "2023-10-12T12:06:00.114546Z",
     "iopub.status.idle": "2023-10-12T12:06:00.119182Z",
     "shell.execute_reply": "2023-10-12T12:06:00.118290Z"
    },
    "papermill": {
     "duration": 0.040723,
     "end_time": "2023-10-12T12:06:00.120898",
     "exception": false,
     "start_time": "2023-10-12T12:06:00.080175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# OOF MAE: 0.686\n",
    "# Average MAE: 0.686+/-0.048\n",
    "# OOF MAPE: 0.027\n",
    "# Average MAPE: 0.027+/-0.003"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bae2d8ae",
   "metadata": {
    "papermill": {
     "duration": 0.032334,
     "end_time": "2023-10-12T12:06:00.186156",
     "exception": false,
     "start_time": "2023-10-12T12:06:00.153822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Keras Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc41f070",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:00.254784Z",
     "iopub.status.busy": "2023-10-12T12:06:00.253774Z",
     "iopub.status.idle": "2023-10-12T12:06:08.785785Z",
     "shell.execute_reply": "2023-10-12T12:06:08.784802Z"
    },
    "papermill": {
     "duration": 8.568898,
     "end_time": "2023-10-12T12:06:08.787938",
     "exception": false,
     "start_time": "2023-10-12T12:06:00.219040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from keras import backend as K\n",
    "from keras.backend import sigmoid\n",
    "from tensorflow.keras.utils import get_custom_objects\n",
    "from keras.layers import Activation\n",
    "from keras.backend import sigmoid\n",
    "from keras import backend as K\n",
    "\n",
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7917e6c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:08.856422Z",
     "iopub.status.busy": "2023-10-12T12:06:08.855561Z",
     "iopub.status.idle": "2023-10-12T12:06:08.873532Z",
     "shell.execute_reply": "2023-10-12T12:06:08.872430Z"
    },
    "papermill": {
     "duration": 0.053512,
     "end_time": "2023-10-12T12:06:08.875277",
     "exception": false,
     "start_time": "2023-10-12T12:06:08.821765",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "def mean_absolute_error(y_true, y_pred):\n",
    "         return K.mean(K.abs( (y_true - y_pred)))\n",
    "\n",
    "es = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss', patience=50, verbose=0,\n",
    "    mode='min',restore_best_weights=True)\n",
    "\n",
    "plateau = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor='val_loss', factor=0.25, patience=8, verbose=0,\n",
    "    mode='min')\n",
    "\n",
    "def swish(x, beta = 1):\n",
    "    return (x * sigmoid(beta * x))\n",
    "\n",
    "def mish(x, beta = 1):\n",
    "    return (x * K.tanh(K.softplus(x)))\n",
    "\n",
    "\n",
    "get_custom_objects().update({'swish': Activation(swish)})\n",
    "get_custom_objects().update({'mish': Activation(mish)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8b965e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:08.942571Z",
     "iopub.status.busy": "2023-10-12T12:06:08.942111Z",
     "iopub.status.idle": "2023-10-12T12:06:08.949601Z",
     "shell.execute_reply": "2023-10-12T12:06:08.948648Z"
    },
    "papermill": {
     "duration": 0.043103,
     "end_time": "2023-10-12T12:06:08.951232",
     "exception": false,
     "start_time": "2023-10-12T12:06:08.908129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "def base_model(hidden_units, embedding_size, train_cols, \n",
    "               categorical_cols, uniques):\n",
    "\n",
    "    n_cont=0\n",
    "    initial_inputs=[]\n",
    "    for col in categorical_cols:\n",
    "        temp_input = keras.Input(shape=(1,), name=col)\n",
    "        n_cont+=1\n",
    "        initial_inputs.append(temp_input)\n",
    "        \n",
    "    num_input = keras.Input(shape=(len(train_cols)-n_cont,), name='num_data')\n",
    "    initial_inputs.append(num_input)\n",
    "\n",
    "    #embedding, flatenning and concatenating\n",
    "    all_inputs=[]\n",
    "    for i, col in enumerate(categorical_cols):\n",
    "        temp_embedded = keras.layers.Embedding(int(uniques[col]), embedding_size, \n",
    "                                               input_length=1, name=f'{col}_embedding')(initial_inputs[i])\n",
    "        temp_flattened = keras.layers.Flatten()(temp_embedded)\n",
    "        all_inputs.append(temp_flattened)\n",
    "    \n",
    "    all_inputs.append(num_input)\n",
    "    out = keras.layers.Concatenate()(all_inputs)\n",
    "    \n",
    "    # Add one or more hidden layers\n",
    "    for n_hidden in hidden_units:\n",
    "        out = keras.layers.Dense(n_hidden, activation='swish')(out)\n",
    "\n",
    "    # A single output: our predicted rating\n",
    "    out = keras.layers.Dense(1, activation='linear', name='prediction')(out)\n",
    "\n",
    "    model = keras.Model(inputs = initial_inputs, outputs = out)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9db379bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:09.019305Z",
     "iopub.status.busy": "2023-10-12T12:06:09.019010Z",
     "iopub.status.idle": "2023-10-12T12:06:09.035562Z",
     "shell.execute_reply": "2023-10-12T12:06:09.034751Z"
    },
    "papermill": {
     "duration": 0.052968,
     "end_time": "2023-10-12T12:06:09.037231",
     "exception": false,
     "start_time": "2023-10-12T12:06:08.984263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "def fit_keras_nn(Nfolds, train_df, test_df, train_cols, cat_cols, TARGET, \n",
    "                   model_path):\n",
    "    \n",
    "    model_name = 'Keras_NN'\n",
    "    oof_keras = np.zeros(train_df.shape[0])\n",
    "    pred_keras = np.zeros(test_df.shape[0])\n",
    "    scores=[]\n",
    "    scores_pvt = []\n",
    "    \n",
    "    train_df = train_df.copy()\n",
    "    test_df = test_df.copy()\n",
    "    train_df = train_df.fillna(0)\n",
    "    test_df = test_df.fillna(0)\n",
    "    \n",
    "    cont_cols = train_cols.copy()\n",
    "    uniques={}\n",
    "    for col in cat_cols:\n",
    "        cont_cols.remove(col)\n",
    "        le = LabelEncoder()\n",
    "        le.fit(pd.concat([train_df[col], test_df[col]]))\n",
    "        train_df[col] = le.transform(train_df[col].values)\n",
    "        test_df[col] = le.transform(test_df[col].values)\n",
    "        uniques[col] = len(pd.concat([train_df[col], test_df[col]], axis=0).unique())\n",
    "        \n",
    "    scaler = StandardScaler().fit(pd.concat([train_df[cont_cols], test_df[cont_cols]], axis=0))       \n",
    "    train_df[cont_cols] = scaler.transform(train_df[cont_cols].values)\n",
    "    test_df[cont_cols] = scaler.transform(test_df[cont_cols].values)\n",
    "    \n",
    "    test_inputs=[]\n",
    "    for col in cat_cols:\n",
    "        test_inputs.append(test_df[col].values)\n",
    "        \n",
    "    test_inputs.append(test_df[cont_cols].values)\n",
    "    \n",
    "    for fold in range(Nfolds):\n",
    "        \n",
    "        print(\"*\"*10, f'Fold-{fold+1}', \"*\"*10,)\n",
    "      \n",
    "        X_train = train_df.loc[train_df.fold!=fold, train_cols]\n",
    "        y_train = train_df.loc[train_df.fold!=fold, TARGET]\n",
    "        X_val = train_df.loc[train_df.fold==fold, train_cols]\n",
    "        y_val = train_df.loc[train_df.fold==fold, TARGET]\n",
    "        \n",
    "        model = base_model(hidden_units=(256, 512, 1024, 512, 256), embedding_size=16, #16\n",
    "                           train_cols=train_cols, categorical_cols=cat_cols, \n",
    "                          uniques=uniques)\n",
    "    \n",
    "        model.compile(\n",
    "            keras.optimizers.Adam(learning_rate=0.002),\n",
    "            loss=mean_absolute_error\n",
    "        )\n",
    "\n",
    "\n",
    "        train_inputs=[]\n",
    "        for col in cat_cols:\n",
    "            train_inputs.append(X_train[col].values)\n",
    "    \n",
    "        train_inputs.append(X_train[cont_cols].values)\n",
    "\n",
    "        val_inputs=[]\n",
    "        for col in cat_cols:\n",
    "            val_inputs.append(X_val[col].values)\n",
    "\n",
    "        val_inputs.append(X_val[cont_cols].values)\n",
    "        \n",
    "#         if os.path.isfile(model_path + f'{model_name}_{fold}.h5'):\n",
    "#             model =  keras.models.load_model(model_path + f'{model_name}_{fold}.h5', \n",
    "#                                              custom_objects={'swish': swish, 'Activation': Activation, \n",
    "#                                                              'mean_absolute_error':mean_absolute_error})\n",
    "        \n",
    "#         else:\n",
    "\n",
    "        model.fit(train_inputs, \n",
    "                  y_train.values,               \n",
    "                  batch_size=1024,\n",
    "                  epochs=1000,\n",
    "                  validation_data=(val_inputs, y_val.values),\n",
    "                  callbacks=[es, plateau],\n",
    "                  validation_batch_size=len(y_val),\n",
    "                  shuffle=True, verbose = 1)\n",
    "\n",
    "        model.save(f\"{model_name}_{fold}.h5\")\n",
    "            \n",
    "        preds = model.predict(val_inputs).reshape(1,-1)[0]\n",
    "        \n",
    "        score = mean_absolute_error(y_val.values, preds)\n",
    "        score_pvt = mean_absolute_percentage_error(y_val.values, preds)\n",
    "        scores.append(score)\n",
    "        scores_pvt.append(score_pvt)\n",
    "        oof_keras[X_val.index] = preds\n",
    "        \n",
    "        print(f'MAE for Fold-{fold+1}:', np.round(score, 3))\n",
    "        print(f'MAPE for Fold-{fold+1}:', np.round(score_pvt, 3))\n",
    "        \n",
    "        pred_keras += model.predict(test_inputs).reshape(1,-1)[0]/Nfolds\n",
    "        \n",
    "    score = mean_absolute_error(train_df[TARGET],oof_keras)\n",
    "    score_pvt = mean_absolute_percentage_error(train_df[TARGET],oof_keras)\n",
    "    print(f'OOF MAE:', np.round(score, 3))\n",
    "    print(f'Average MAE:', f'{np.round(np.mean(scores), 3)}+/-{np.round(np.std(scores), 3)}')\n",
    "    print(f'OOF MAPE:', np.round(score_pvt, 3))\n",
    "    print(f'Average MAPE:', f'{np.round(np.mean(scores_pvt), 3)}+/-{np.round(np.std(scores_pvt), 3)}')\n",
    "    \n",
    "        \n",
    "    return oof_keras, pred_keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b7fe9117",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:06:09.113189Z",
     "iopub.status.busy": "2023-10-12T12:06:09.112262Z",
     "iopub.status.idle": "2023-10-12T12:21:16.203302Z",
     "shell.execute_reply": "2023-10-12T12:21:16.202126Z"
    },
    "papermill": {
     "duration": 907.127569,
     "end_time": "2023-10-12T12:21:16.205130",
     "exception": false,
     "start_time": "2023-10-12T12:06:09.077561",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********** Fold-1 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 8s 33ms/step - loss: 5.4211 - val_loss: 3.0744 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 2.4071 - val_loss: 2.1394 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 13ms/step - loss: 1.7897 - val_loss: 1.6051 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.3474 - val_loss: 1.2088 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.2002 - val_loss: 1.1308 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.1032 - val_loss: 1.0071 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0502 - val_loss: 0.9708 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0138 - val_loss: 1.0047 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9992 - val_loss: 1.1629 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.0707 - val_loss: 1.5457 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.0711 - val_loss: 0.8567 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8874 - val_loss: 0.9685 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9024 - val_loss: 1.0075 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8902 - val_loss: 0.8723 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8477 - val_loss: 0.8276 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8826 - val_loss: 0.8481 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8600 - val_loss: 1.1256 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8512 - val_loss: 0.8383 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0288 - val_loss: 0.8843 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8769 - val_loss: 0.8367 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8244 - val_loss: 0.9408 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8208 - val_loss: 0.9168 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8312 - val_loss: 0.8256 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8075 - val_loss: 0.8156 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7898 - val_loss: 0.7851 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7797 - val_loss: 0.8376 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8395 - val_loss: 1.0351 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8754 - val_loss: 0.8386 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7702 - val_loss: 0.8281 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7452 - val_loss: 0.7865 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7461 - val_loss: 0.7916 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7508 - val_loss: 0.7851 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7586 - val_loss: 0.8265 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6493 - val_loss: 0.6995 - lr: 5.0000e-04\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6282 - val_loss: 0.6969 - lr: 5.0000e-04\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6271 - val_loss: 0.6963 - lr: 5.0000e-04\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6198 - val_loss: 0.6888 - lr: 5.0000e-04\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6208 - val_loss: 0.6878 - lr: 5.0000e-04\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6185 - val_loss: 0.6914 - lr: 5.0000e-04\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6157 - val_loss: 0.6889 - lr: 5.0000e-04\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6148 - val_loss: 0.6797 - lr: 5.0000e-04\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6134 - val_loss: 0.6951 - lr: 5.0000e-04\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6153 - val_loss: 0.6855 - lr: 5.0000e-04\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.6117 - val_loss: 0.6874 - lr: 5.0000e-04\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6137 - val_loss: 0.6905 - lr: 5.0000e-04\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6079 - val_loss: 0.6894 - lr: 5.0000e-04\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6053 - val_loss: 0.6927 - lr: 5.0000e-04\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6001 - val_loss: 0.6890 - lr: 5.0000e-04\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6044 - val_loss: 0.6973 - lr: 5.0000e-04\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5718 - val_loss: 0.6652 - lr: 1.2500e-04\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5636 - val_loss: 0.6638 - lr: 1.2500e-04\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5624 - val_loss: 0.6635 - lr: 1.2500e-04\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5612 - val_loss: 0.6650 - lr: 1.2500e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5600 - val_loss: 0.6630 - lr: 1.2500e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5598 - val_loss: 0.6646 - lr: 1.2500e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5583 - val_loss: 0.6678 - lr: 1.2500e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5589 - val_loss: 0.6649 - lr: 1.2500e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5565 - val_loss: 0.6631 - lr: 1.2500e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5567 - val_loss: 0.6722 - lr: 1.2500e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5557 - val_loss: 0.6590 - lr: 1.2500e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5543 - val_loss: 0.6628 - lr: 1.2500e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5531 - val_loss: 0.6639 - lr: 1.2500e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5542 - val_loss: 0.6695 - lr: 1.2500e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5515 - val_loss: 0.6639 - lr: 1.2500e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5522 - val_loss: 0.6618 - lr: 1.2500e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5510 - val_loss: 0.6626 - lr: 1.2500e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5494 - val_loss: 0.6641 - lr: 1.2500e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5489 - val_loss: 0.6620 - lr: 1.2500e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5380 - val_loss: 0.6573 - lr: 3.1250e-05\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5359 - val_loss: 0.6570 - lr: 3.1250e-05\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5355 - val_loss: 0.6582 - lr: 3.1250e-05\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5350 - val_loss: 0.6593 - lr: 3.1250e-05\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5345 - val_loss: 0.6584 - lr: 3.1250e-05\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5343 - val_loss: 0.6588 - lr: 3.1250e-05\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5338 - val_loss: 0.6582 - lr: 3.1250e-05\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5335 - val_loss: 0.6584 - lr: 3.1250e-05\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5337 - val_loss: 0.6581 - lr: 3.1250e-05\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5328 - val_loss: 0.6592 - lr: 3.1250e-05\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5299 - val_loss: 0.6569 - lr: 7.8125e-06\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5292 - val_loss: 0.6571 - lr: 7.8125e-06\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5294 - val_loss: 0.6570 - lr: 7.8125e-06\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5290 - val_loss: 0.6567 - lr: 7.8125e-06\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5288 - val_loss: 0.6573 - lr: 7.8125e-06\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5289 - val_loss: 0.6576 - lr: 7.8125e-06\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5286 - val_loss: 0.6571 - lr: 7.8125e-06\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5285 - val_loss: 0.6571 - lr: 7.8125e-06\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5285 - val_loss: 0.6573 - lr: 7.8125e-06\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5283 - val_loss: 0.6570 - lr: 7.8125e-06\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5283 - val_loss: 0.6570 - lr: 7.8125e-06\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5282 - val_loss: 0.6570 - lr: 7.8125e-06\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5272 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5271 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5271 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5270 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5270 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5269 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5269 - val_loss: 0.6567 - lr: 1.9531e-06\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5269 - val_loss: 0.6568 - lr: 1.9531e-06\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5266 - val_loss: 0.6567 - lr: 4.8828e-07\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5266 - val_loss: 0.6567 - lr: 4.8828e-07\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6568 - lr: 4.8828e-07\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6567 - lr: 4.8828e-07\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6567 - lr: 4.8828e-07\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6567 - lr: 4.8828e-07\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6568 - lr: 4.8828e-07\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5265 - val_loss: 0.6568 - lr: 4.8828e-07\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.2207e-07\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 3.0518e-08\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 7.6294e-09\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.9073e-09\n",
      "Epoch 139/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 140/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 141/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 142/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 143/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 144/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 145/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 146/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 4.7684e-10\n",
      "Epoch 147/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.1921e-10\n",
      "Epoch 148/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.1921e-10\n",
      "Epoch 149/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5264 - val_loss: 0.6567 - lr: 1.1921e-10\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-1: 0.657\n",
      "MAPE for Fold-1: 0.023\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "********** Fold-2 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 24ms/step - loss: 5.3382 - val_loss: 2.9328 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 2.2813 - val_loss: 2.2119 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 16ms/step - loss: 1.6580 - val_loss: 1.6066 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 1.2334 - val_loss: 1.8032 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.1300 - val_loss: 1.1683 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.0659 - val_loss: 1.6079 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0471 - val_loss: 1.0955 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9781 - val_loss: 1.0924 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9676 - val_loss: 1.1837 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.1760 - val_loss: 1.3461 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9653 - val_loss: 0.9895 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8735 - val_loss: 1.0218 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8744 - val_loss: 1.0741 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8651 - val_loss: 1.0032 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8410 - val_loss: 0.9896 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8891 - val_loss: 1.0200 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8537 - val_loss: 0.9572 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8243 - val_loss: 1.0380 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9460 - val_loss: 1.0481 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8068 - val_loss: 0.9632 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7891 - val_loss: 0.9440 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8066 - val_loss: 0.9320 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7760 - val_loss: 0.9393 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9002 - val_loss: 0.9486 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7725 - val_loss: 1.0332 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8895 - val_loss: 1.0444 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8316 - val_loss: 0.8981 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7515 - val_loss: 0.9201 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7281 - val_loss: 0.9370 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7498 - val_loss: 0.8974 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7862 - val_loss: 0.9435 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7478 - val_loss: 0.9696 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7268 - val_loss: 0.9065 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7183 - val_loss: 0.9681 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7635 - val_loss: 1.3969 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7781 - val_loss: 0.8866 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7253 - val_loss: 0.8854 - lr: 0.0020\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7283 - val_loss: 0.9188 - lr: 0.0020\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7003 - val_loss: 0.9296 - lr: 0.0020\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7122 - val_loss: 0.8995 - lr: 0.0020\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6976 - val_loss: 0.8760 - lr: 0.0020\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7039 - val_loss: 0.9455 - lr: 0.0020\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8196 - val_loss: 0.8704 - lr: 0.0020\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7010 - val_loss: 1.0059 - lr: 0.0020\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6798 - val_loss: 0.8720 - lr: 0.0020\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6889 - val_loss: 0.8889 - lr: 0.0020\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7471 - val_loss: 0.8844 - lr: 0.0020\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6737 - val_loss: 0.8820 - lr: 0.0020\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6668 - val_loss: 0.8672 - lr: 0.0020\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6739 - val_loss: 0.8447 - lr: 0.0020\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6635 - val_loss: 0.8671 - lr: 0.0020\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6955 - val_loss: 1.0084 - lr: 0.0020\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6651 - val_loss: 0.8590 - lr: 0.0020\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6519 - val_loss: 0.8768 - lr: 0.0020\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6706 - val_loss: 0.8525 - lr: 0.0020\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6526 - val_loss: 0.8441 - lr: 0.0020\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6588 - val_loss: 0.8735 - lr: 0.0020\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6528 - val_loss: 0.8492 - lr: 0.0020\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6598 - val_loss: 0.8620 - lr: 0.0020\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6322 - val_loss: 0.8472 - lr: 0.0020\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6502 - val_loss: 0.9570 - lr: 0.0020\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6617 - val_loss: 0.8323 - lr: 0.0020\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.6386 - val_loss: 1.0224 - lr: 0.0020\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6737 - val_loss: 0.8439 - lr: 0.0020\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6282 - val_loss: 0.8791 - lr: 0.0020\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6642 - val_loss: 0.9477 - lr: 0.0020\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6596 - val_loss: 0.8808 - lr: 0.0020\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6335 - val_loss: 0.9292 - lr: 0.0020\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6536 - val_loss: 0.8591 - lr: 0.0020\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6800 - val_loss: 0.8824 - lr: 0.0020\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5407 - val_loss: 0.7981 - lr: 5.0000e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5151 - val_loss: 0.7896 - lr: 5.0000e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5086 - val_loss: 0.7943 - lr: 5.0000e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5062 - val_loss: 0.7873 - lr: 5.0000e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5032 - val_loss: 0.7923 - lr: 5.0000e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.7892 - lr: 5.0000e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4987 - val_loss: 0.8048 - lr: 5.0000e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4963 - val_loss: 0.7860 - lr: 5.0000e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4922 - val_loss: 0.7931 - lr: 5.0000e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4948 - val_loss: 0.7926 - lr: 5.0000e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4933 - val_loss: 0.7989 - lr: 5.0000e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4900 - val_loss: 0.8116 - lr: 5.0000e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4879 - val_loss: 0.7891 - lr: 5.0000e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4880 - val_loss: 0.7932 - lr: 5.0000e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4849 - val_loss: 0.7979 - lr: 5.0000e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4867 - val_loss: 0.7951 - lr: 5.0000e-04\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4527 - val_loss: 0.7807 - lr: 1.2500e-04\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4434 - val_loss: 0.7811 - lr: 1.2500e-04\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4414 - val_loss: 0.7817 - lr: 1.2500e-04\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4414 - val_loss: 0.7817 - lr: 1.2500e-04\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4377 - val_loss: 0.7803 - lr: 1.2500e-04\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4366 - val_loss: 0.7802 - lr: 1.2500e-04\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4342 - val_loss: 0.7841 - lr: 1.2500e-04\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4330 - val_loss: 0.7848 - lr: 1.2500e-04\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4321 - val_loss: 0.7882 - lr: 1.2500e-04\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4323 - val_loss: 0.7849 - lr: 1.2500e-04\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4307 - val_loss: 0.7836 - lr: 1.2500e-04\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4284 - val_loss: 0.7850 - lr: 1.2500e-04\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4275 - val_loss: 0.7847 - lr: 1.2500e-04\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4181 - val_loss: 0.7812 - lr: 3.1250e-05\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4148 - val_loss: 0.7821 - lr: 3.1250e-05\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4137 - val_loss: 0.7836 - lr: 3.1250e-05\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4132 - val_loss: 0.7821 - lr: 3.1250e-05\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4129 - val_loss: 0.7820 - lr: 3.1250e-05\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4120 - val_loss: 0.7828 - lr: 3.1250e-05\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4114 - val_loss: 0.7821 - lr: 3.1250e-05\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4112 - val_loss: 0.7823 - lr: 3.1250e-05\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4081 - val_loss: 0.7821 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4074 - val_loss: 0.7820 - lr: 7.8125e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4071 - val_loss: 0.7822 - lr: 7.8125e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4070 - val_loss: 0.7824 - lr: 7.8125e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4067 - val_loss: 0.7823 - lr: 7.8125e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4067 - val_loss: 0.7823 - lr: 7.8125e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4065 - val_loss: 0.7820 - lr: 7.8125e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4063 - val_loss: 0.7823 - lr: 7.8125e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4055 - val_loss: 0.7822 - lr: 1.9531e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4053 - val_loss: 0.7822 - lr: 1.9531e-06\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4052 - val_loss: 0.7821 - lr: 1.9531e-06\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4052 - val_loss: 0.7823 - lr: 1.9531e-06\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4051 - val_loss: 0.7821 - lr: 1.9531e-06\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4051 - val_loss: 0.7823 - lr: 1.9531e-06\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4050 - val_loss: 0.7822 - lr: 1.9531e-06\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4050 - val_loss: 0.7822 - lr: 1.9531e-06\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4047 - val_loss: 0.7823 - lr: 4.8828e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4047 - val_loss: 0.7823 - lr: 4.8828e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4047 - val_loss: 0.7823 - lr: 4.8828e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4047 - val_loss: 0.7822 - lr: 4.8828e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4046 - val_loss: 0.7822 - lr: 4.8828e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4046 - val_loss: 0.7822 - lr: 4.8828e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4046 - val_loss: 0.7823 - lr: 4.8828e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4046 - val_loss: 0.7823 - lr: 4.8828e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 1.2207e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 1.2207e-07\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4045 - val_loss: 0.7822 - lr: 1.2207e-07\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 1.2207e-07\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 1.2207e-07\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7822 - lr: 1.2207e-07\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7822 - lr: 1.2207e-07\n",
      "Epoch 139/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 1.2207e-07\n",
      "Epoch 140/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 3.0518e-08\n",
      "Epoch 141/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 3.0518e-08\n",
      "Epoch 142/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4045 - val_loss: 0.7823 - lr: 3.0518e-08\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-2: 0.78\n",
      "MAPE for Fold-2: 0.03\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "********** Fold-3 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 24ms/step - loss: 5.3070 - val_loss: 2.5224 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 2.4449 - val_loss: 1.8016 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.7180 - val_loss: 2.2280 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.4054 - val_loss: 1.0549 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.1322 - val_loss: 1.1731 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0446 - val_loss: 0.9631 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0224 - val_loss: 0.9413 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.1268 - val_loss: 1.0368 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9950 - val_loss: 0.8441 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9138 - val_loss: 0.8994 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9134 - val_loss: 0.8991 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.1054 - val_loss: 0.9464 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0279 - val_loss: 0.8347 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8561 - val_loss: 0.8169 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8678 - val_loss: 0.9268 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8763 - val_loss: 0.8158 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8280 - val_loss: 0.8302 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8437 - val_loss: 0.8964 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.8536 - val_loss: 0.8616 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.8676 - val_loss: 0.8157 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8156 - val_loss: 0.7572 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8086 - val_loss: 0.8057 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7926 - val_loss: 0.7986 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9209 - val_loss: 0.8298 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8737 - val_loss: 0.8453 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7932 - val_loss: 0.8598 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9210 - val_loss: 0.7893 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7661 - val_loss: 0.7689 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7545 - val_loss: 0.7482 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7756 - val_loss: 0.7869 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7528 - val_loss: 0.7972 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7827 - val_loss: 0.7750 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7406 - val_loss: 0.7422 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7437 - val_loss: 0.7391 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7605 - val_loss: 0.7733 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7268 - val_loss: 0.8017 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7227 - val_loss: 0.7754 - lr: 0.0020\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7227 - val_loss: 0.8101 - lr: 0.0020\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7442 - val_loss: 0.7427 - lr: 0.0020\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7128 - val_loss: 0.7263 - lr: 0.0020\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7219 - val_loss: 0.8435 - lr: 0.0020\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7332 - val_loss: 0.7622 - lr: 0.0020\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7168 - val_loss: 0.7803 - lr: 0.0020\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7100 - val_loss: 0.7131 - lr: 0.0020\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7095 - val_loss: 0.7123 - lr: 0.0020\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6944 - val_loss: 0.7158 - lr: 0.0020\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6892 - val_loss: 0.7111 - lr: 0.0020\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6962 - val_loss: 0.8063 - lr: 0.0020\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.6997 - val_loss: 0.8883 - lr: 0.0020\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7340 - val_loss: 0.7598 - lr: 0.0020\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7555 - val_loss: 0.7863 - lr: 0.0020\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6861 - val_loss: 0.6977 - lr: 0.0020\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6662 - val_loss: 0.7432 - lr: 0.0020\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6925 - val_loss: 0.7329 - lr: 0.0020\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6731 - val_loss: 0.7092 - lr: 0.0020\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6557 - val_loss: 0.7047 - lr: 0.0020\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6593 - val_loss: 0.6870 - lr: 0.0020\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6499 - val_loss: 0.7233 - lr: 0.0020\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6467 - val_loss: 0.6936 - lr: 0.0020\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6660 - val_loss: 0.7174 - lr: 0.0020\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6561 - val_loss: 0.7134 - lr: 0.0020\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7457 - val_loss: 0.9061 - lr: 0.0020\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7370 - val_loss: 0.7087 - lr: 0.0020\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6469 - val_loss: 0.7115 - lr: 0.0020\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6463 - val_loss: 0.7936 - lr: 0.0020\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5642 - val_loss: 0.6356 - lr: 5.0000e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5356 - val_loss: 0.6339 - lr: 5.0000e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5316 - val_loss: 0.6288 - lr: 5.0000e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5306 - val_loss: 0.6298 - lr: 5.0000e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6372 - lr: 5.0000e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5271 - val_loss: 0.6344 - lr: 5.0000e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5214 - val_loss: 0.6305 - lr: 5.0000e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5184 - val_loss: 0.6243 - lr: 5.0000e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5183 - val_loss: 0.6416 - lr: 5.0000e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5233 - val_loss: 0.6333 - lr: 5.0000e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5183 - val_loss: 0.6245 - lr: 5.0000e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5123 - val_loss: 0.6229 - lr: 5.0000e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5139 - val_loss: 0.6489 - lr: 5.0000e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5093 - val_loss: 0.6289 - lr: 5.0000e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5090 - val_loss: 0.6264 - lr: 5.0000e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5076 - val_loss: 0.6353 - lr: 5.0000e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5052 - val_loss: 0.6289 - lr: 5.0000e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5048 - val_loss: 0.6505 - lr: 5.0000e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5083 - val_loss: 0.6458 - lr: 5.0000e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5028 - val_loss: 0.6280 - lr: 5.0000e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4698 - val_loss: 0.6186 - lr: 1.2500e-04\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4615 - val_loss: 0.6169 - lr: 1.2500e-04\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4598 - val_loss: 0.6138 - lr: 1.2500e-04\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4569 - val_loss: 0.6142 - lr: 1.2500e-04\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4556 - val_loss: 0.6173 - lr: 1.2500e-04\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4560 - val_loss: 0.6197 - lr: 1.2500e-04\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4537 - val_loss: 0.6215 - lr: 1.2500e-04\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4528 - val_loss: 0.6215 - lr: 1.2500e-04\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4519 - val_loss: 0.6198 - lr: 1.2500e-04\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4514 - val_loss: 0.6162 - lr: 1.2500e-04\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4512 - val_loss: 0.6180 - lr: 1.2500e-04\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4408 - val_loss: 0.6146 - lr: 3.1250e-05\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4376 - val_loss: 0.6143 - lr: 3.1250e-05\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4370 - val_loss: 0.6149 - lr: 3.1250e-05\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4374 - val_loss: 0.6141 - lr: 3.1250e-05\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4361 - val_loss: 0.6159 - lr: 3.1250e-05\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4356 - val_loss: 0.6150 - lr: 3.1250e-05\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4349 - val_loss: 0.6145 - lr: 3.1250e-05\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4348 - val_loss: 0.6150 - lr: 3.1250e-05\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4320 - val_loss: 0.6146 - lr: 7.8125e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4313 - val_loss: 0.6143 - lr: 7.8125e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4312 - val_loss: 0.6143 - lr: 7.8125e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4310 - val_loss: 0.6145 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4307 - val_loss: 0.6146 - lr: 7.8125e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4307 - val_loss: 0.6144 - lr: 7.8125e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4306 - val_loss: 0.6143 - lr: 7.8125e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4304 - val_loss: 0.6148 - lr: 7.8125e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4296 - val_loss: 0.6148 - lr: 1.9531e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4294 - val_loss: 0.6147 - lr: 1.9531e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4294 - val_loss: 0.6148 - lr: 1.9531e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4293 - val_loss: 0.6147 - lr: 1.9531e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4293 - val_loss: 0.6148 - lr: 1.9531e-06\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4292 - val_loss: 0.6148 - lr: 1.9531e-06\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4293 - val_loss: 0.6147 - lr: 1.9531e-06\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4292 - val_loss: 0.6148 - lr: 1.9531e-06\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4289 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 4.8828e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4288 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4287 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4287 - val_loss: 0.6148 - lr: 1.2207e-07\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4287 - val_loss: 0.6148 - lr: 3.0518e-08\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4287 - val_loss: 0.6148 - lr: 3.0518e-08\n",
      "290/290 [==============================] - 1s 3ms/step\n",
      "MAE for Fold-3: 0.614\n",
      "MAPE for Fold-3: 0.023\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "********** Fold-4 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 24ms/step - loss: 5.2322 - val_loss: 2.7470 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 2.3610 - val_loss: 1.9594 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.8004 - val_loss: 1.3848 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 1.4400 - val_loss: 1.2922 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.2867 - val_loss: 1.2001 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0359 - val_loss: 1.0927 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0220 - val_loss: 1.0609 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0069 - val_loss: 1.0937 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9972 - val_loss: 1.1472 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9761 - val_loss: 1.0510 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9015 - val_loss: 1.0279 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9165 - val_loss: 1.0658 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8790 - val_loss: 1.1470 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9038 - val_loss: 1.1150 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9007 - val_loss: 1.1361 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8627 - val_loss: 0.9423 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8646 - val_loss: 0.9525 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8496 - val_loss: 1.0011 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8187 - val_loss: 0.9735 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8223 - val_loss: 1.0981 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8281 - val_loss: 0.9016 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.0603 - val_loss: 0.9488 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8336 - val_loss: 0.9277 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7710 - val_loss: 0.9104 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7643 - val_loss: 0.8911 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7734 - val_loss: 0.8932 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8058 - val_loss: 0.9800 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7697 - val_loss: 0.8998 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7729 - val_loss: 0.9360 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7537 - val_loss: 0.8761 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7723 - val_loss: 1.0910 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7799 - val_loss: 0.9541 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7455 - val_loss: 1.0311 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7957 - val_loss: 0.9020 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7426 - val_loss: 0.8636 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7173 - val_loss: 0.8439 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7044 - val_loss: 0.8671 - lr: 0.0020\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7096 - val_loss: 0.8736 - lr: 0.0020\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7074 - val_loss: 0.8353 - lr: 0.0020\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7364 - val_loss: 0.9549 - lr: 0.0020\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7382 - val_loss: 0.9622 - lr: 0.0020\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.7077 - val_loss: 0.8328 - lr: 0.0020\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.7020 - val_loss: 0.8417 - lr: 0.0020\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7298 - val_loss: 0.8535 - lr: 0.0020\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7169 - val_loss: 0.8951 - lr: 0.0020\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6819 - val_loss: 0.8810 - lr: 0.0020\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6807 - val_loss: 0.8909 - lr: 0.0020\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7166 - val_loss: 0.8683 - lr: 0.0020\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6820 - val_loss: 0.8443 - lr: 0.0020\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7036 - val_loss: 0.8261 - lr: 0.0020\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7155 - val_loss: 0.8819 - lr: 0.0020\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6948 - val_loss: 0.9027 - lr: 0.0020\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7408 - val_loss: 0.8404 - lr: 0.0020\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6606 - val_loss: 0.8566 - lr: 0.0020\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6546 - val_loss: 0.8371 - lr: 0.0020\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6705 - val_loss: 0.8672 - lr: 0.0020\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6791 - val_loss: 0.8488 - lr: 0.0020\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6917 - val_loss: 0.8607 - lr: 0.0020\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5642 - val_loss: 0.7643 - lr: 5.0000e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5454 - val_loss: 0.7647 - lr: 5.0000e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5384 - val_loss: 0.7669 - lr: 5.0000e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5357 - val_loss: 0.7759 - lr: 5.0000e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5357 - val_loss: 0.7635 - lr: 5.0000e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5346 - val_loss: 0.7580 - lr: 5.0000e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5319 - val_loss: 0.7723 - lr: 5.0000e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5298 - val_loss: 0.7649 - lr: 5.0000e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5247 - val_loss: 0.7653 - lr: 5.0000e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5259 - val_loss: 0.7650 - lr: 5.0000e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.7624 - lr: 5.0000e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5201 - val_loss: 0.7671 - lr: 5.0000e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5210 - val_loss: 0.7772 - lr: 5.0000e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5171 - val_loss: 0.7727 - lr: 5.0000e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4925 - val_loss: 0.7556 - lr: 1.2500e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4852 - val_loss: 0.7589 - lr: 1.2500e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4842 - val_loss: 0.7525 - lr: 1.2500e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4822 - val_loss: 0.7563 - lr: 1.2500e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4806 - val_loss: 0.7513 - lr: 1.2500e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4796 - val_loss: 0.7559 - lr: 1.2500e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4782 - val_loss: 0.7549 - lr: 1.2500e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4782 - val_loss: 0.7550 - lr: 1.2500e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4766 - val_loss: 0.7543 - lr: 1.2500e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4766 - val_loss: 0.7537 - lr: 1.2500e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4745 - val_loss: 0.7549 - lr: 1.2500e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4737 - val_loss: 0.7562 - lr: 1.2500e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4734 - val_loss: 0.7557 - lr: 1.2500e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4639 - val_loss: 0.7531 - lr: 3.1250e-05\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4617 - val_loss: 0.7532 - lr: 3.1250e-05\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4612 - val_loss: 0.7532 - lr: 3.1250e-05\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4607 - val_loss: 0.7519 - lr: 3.1250e-05\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4601 - val_loss: 0.7539 - lr: 3.1250e-05\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4600 - val_loss: 0.7547 - lr: 3.1250e-05\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4596 - val_loss: 0.7528 - lr: 3.1250e-05\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4590 - val_loss: 0.7535 - lr: 3.1250e-05\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4563 - val_loss: 0.7533 - lr: 7.8125e-06\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4558 - val_loss: 0.7538 - lr: 7.8125e-06\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4557 - val_loss: 0.7528 - lr: 7.8125e-06\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4556 - val_loss: 0.7530 - lr: 7.8125e-06\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4554 - val_loss: 0.7533 - lr: 7.8125e-06\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4553 - val_loss: 0.7530 - lr: 7.8125e-06\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4552 - val_loss: 0.7532 - lr: 7.8125e-06\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4551 - val_loss: 0.7533 - lr: 7.8125e-06\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4542 - val_loss: 0.7532 - lr: 1.9531e-06\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4541 - val_loss: 0.7531 - lr: 1.9531e-06\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4540 - val_loss: 0.7530 - lr: 1.9531e-06\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4540 - val_loss: 0.7532 - lr: 1.9531e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4540 - val_loss: 0.7531 - lr: 1.9531e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4539 - val_loss: 0.7531 - lr: 1.9531e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4539 - val_loss: 0.7533 - lr: 1.9531e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4539 - val_loss: 0.7532 - lr: 1.9531e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4536 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4536 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4536 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4536 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4536 - val_loss: 0.7533 - lr: 4.8828e-07\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 4.8828e-07\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4535 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 1.2207e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 3.0518e-08\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4534 - val_loss: 0.7532 - lr: 3.0518e-08\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-4: 0.751\n",
      "MAPE for Fold-4: 0.025\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "********** Fold-5 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 27ms/step - loss: 5.2418 - val_loss: 3.0453 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 2.3827 - val_loss: 2.3541 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.7842 - val_loss: 2.0331 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.2998 - val_loss: 1.4945 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.3206 - val_loss: 1.0440 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0431 - val_loss: 0.9916 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0491 - val_loss: 1.1931 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.9873 - val_loss: 1.0268 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9751 - val_loss: 0.9670 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9940 - val_loss: 1.0431 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9350 - val_loss: 1.0718 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9214 - val_loss: 0.8486 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8823 - val_loss: 0.8698 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8757 - val_loss: 0.9400 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8903 - val_loss: 0.9077 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8623 - val_loss: 0.8801 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9220 - val_loss: 1.0147 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.0020 - val_loss: 1.0748 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8682 - val_loss: 0.8218 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8149 - val_loss: 0.8346 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 12ms/step - loss: 0.8029 - val_loss: 0.8500 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8638 - val_loss: 0.8001 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7806 - val_loss: 0.8115 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8046 - val_loss: 0.9049 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7740 - val_loss: 0.7791 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7903 - val_loss: 0.7920 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7687 - val_loss: 0.8097 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8883 - val_loss: 0.8699 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8122 - val_loss: 0.7946 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7507 - val_loss: 0.8021 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7400 - val_loss: 0.8097 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7656 - val_loss: 0.7911 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7223 - val_loss: 0.8282 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6451 - val_loss: 0.7003 - lr: 5.0000e-04\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6225 - val_loss: 0.6944 - lr: 5.0000e-04\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6199 - val_loss: 0.6897 - lr: 5.0000e-04\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6169 - val_loss: 0.7048 - lr: 5.0000e-04\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6145 - val_loss: 0.6893 - lr: 5.0000e-04\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6129 - val_loss: 0.6943 - lr: 5.0000e-04\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6199 - val_loss: 0.6963 - lr: 5.0000e-04\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6089 - val_loss: 0.7070 - lr: 5.0000e-04\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6085 - val_loss: 0.6924 - lr: 5.0000e-04\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6060 - val_loss: 0.6973 - lr: 5.0000e-04\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6096 - val_loss: 0.7078 - lr: 5.0000e-04\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6076 - val_loss: 0.6879 - lr: 5.0000e-04\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6049 - val_loss: 0.6873 - lr: 5.0000e-04\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5983 - val_loss: 0.6835 - lr: 5.0000e-04\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5954 - val_loss: 0.6774 - lr: 5.0000e-04\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6002 - val_loss: 0.7119 - lr: 5.0000e-04\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5971 - val_loss: 0.6900 - lr: 5.0000e-04\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5891 - val_loss: 0.6828 - lr: 5.0000e-04\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5918 - val_loss: 0.6910 - lr: 5.0000e-04\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5894 - val_loss: 0.6916 - lr: 5.0000e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5917 - val_loss: 0.6897 - lr: 5.0000e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5889 - val_loss: 0.6873 - lr: 5.0000e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5916 - val_loss: 0.7069 - lr: 5.0000e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5572 - val_loss: 0.6613 - lr: 1.2500e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5482 - val_loss: 0.6587 - lr: 1.2500e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5455 - val_loss: 0.6573 - lr: 1.2500e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5455 - val_loss: 0.6621 - lr: 1.2500e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5432 - val_loss: 0.6582 - lr: 1.2500e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5416 - val_loss: 0.6590 - lr: 1.2500e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5415 - val_loss: 0.6602 - lr: 1.2500e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5407 - val_loss: 0.6562 - lr: 1.2500e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5395 - val_loss: 0.6620 - lr: 1.2500e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5397 - val_loss: 0.6589 - lr: 1.2500e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5397 - val_loss: 0.6592 - lr: 1.2500e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5381 - val_loss: 0.6591 - lr: 1.2500e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5374 - val_loss: 0.6553 - lr: 1.2500e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5364 - val_loss: 0.6575 - lr: 1.2500e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5344 - val_loss: 0.6623 - lr: 1.2500e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5349 - val_loss: 0.6618 - lr: 1.2500e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5358 - val_loss: 0.6592 - lr: 1.2500e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5327 - val_loss: 0.6574 - lr: 1.2500e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5317 - val_loss: 0.6553 - lr: 1.2500e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5343 - val_loss: 0.6568 - lr: 1.2500e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5316 - val_loss: 0.6526 - lr: 1.2500e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5311 - val_loss: 0.6618 - lr: 1.2500e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5312 - val_loss: 0.6553 - lr: 1.2500e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5285 - val_loss: 0.6536 - lr: 1.2500e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5273 - val_loss: 0.6560 - lr: 1.2500e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.5280 - val_loss: 0.6529 - lr: 1.2500e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5274 - val_loss: 0.6565 - lr: 1.2500e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5266 - val_loss: 0.6546 - lr: 1.2500e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5260 - val_loss: 0.6676 - lr: 1.2500e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5154 - val_loss: 0.6510 - lr: 3.1250e-05\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5118 - val_loss: 0.6505 - lr: 3.1250e-05\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5110 - val_loss: 0.6522 - lr: 3.1250e-05\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5108 - val_loss: 0.6504 - lr: 3.1250e-05\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5097 - val_loss: 0.6499 - lr: 3.1250e-05\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5095 - val_loss: 0.6503 - lr: 3.1250e-05\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5096 - val_loss: 0.6503 - lr: 3.1250e-05\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5092 - val_loss: 0.6511 - lr: 3.1250e-05\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5092 - val_loss: 0.6515 - lr: 3.1250e-05\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5085 - val_loss: 0.6499 - lr: 3.1250e-05\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5083 - val_loss: 0.6506 - lr: 3.1250e-05\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5079 - val_loss: 0.6502 - lr: 3.1250e-05\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5082 - val_loss: 0.6505 - lr: 3.1250e-05\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5044 - val_loss: 0.6494 - lr: 7.8125e-06\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5037 - val_loss: 0.6489 - lr: 7.8125e-06\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5037 - val_loss: 0.6494 - lr: 7.8125e-06\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5033 - val_loss: 0.6491 - lr: 7.8125e-06\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5034 - val_loss: 0.6493 - lr: 7.8125e-06\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5033 - val_loss: 0.6488 - lr: 7.8125e-06\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5033 - val_loss: 0.6493 - lr: 7.8125e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5031 - val_loss: 0.6494 - lr: 7.8125e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5029 - val_loss: 0.6495 - lr: 7.8125e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5029 - val_loss: 0.6493 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5020 - val_loss: 0.6492 - lr: 1.9531e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5018 - val_loss: 0.6491 - lr: 1.9531e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5018 - val_loss: 0.6491 - lr: 1.9531e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5018 - val_loss: 0.6492 - lr: 1.9531e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5017 - val_loss: 0.6491 - lr: 1.9531e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5017 - val_loss: 0.6492 - lr: 1.9531e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5017 - val_loss: 0.6491 - lr: 1.9531e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5017 - val_loss: 0.6493 - lr: 1.9531e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5013 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5012 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5012 - val_loss: 0.6492 - lr: 4.8828e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5012 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5012 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.2207e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 139/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 140/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 3.0518e-08\n",
      "Epoch 141/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 142/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 143/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 144/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 145/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 146/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 147/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 148/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 7.6294e-09\n",
      "Epoch 149/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "Epoch 150/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "Epoch 151/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "Epoch 152/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "Epoch 153/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "Epoch 154/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5011 - val_loss: 0.6492 - lr: 1.9073e-09\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-5: 0.649\n",
      "MAPE for Fold-5: 0.025\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "********** Fold-6 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 24ms/step - loss: 5.4040 - val_loss: 2.4896 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 2.3268 - val_loss: 2.3341 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 1.7762 - val_loss: 1.5091 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.4481 - val_loss: 1.5706 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 1.1000 - val_loss: 1.1223 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.1062 - val_loss: 1.1534 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.1439 - val_loss: 1.1316 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9831 - val_loss: 1.0501 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9395 - val_loss: 1.0416 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9878 - val_loss: 0.9888 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9189 - val_loss: 1.0398 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9510 - val_loss: 0.9397 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.8886 - val_loss: 0.9186 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8740 - val_loss: 0.9263 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8988 - val_loss: 0.9621 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8555 - val_loss: 0.9333 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8631 - val_loss: 0.9232 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8348 - val_loss: 0.9298 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9732 - val_loss: 0.9172 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8109 - val_loss: 0.8876 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8293 - val_loss: 0.9497 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8929 - val_loss: 1.2722 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9874 - val_loss: 1.0346 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7913 - val_loss: 0.8469 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7970 - val_loss: 0.9004 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7731 - val_loss: 0.9253 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7801 - val_loss: 0.9091 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7780 - val_loss: 0.8654 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7599 - val_loss: 0.8943 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.7558 - val_loss: 0.9044 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 1s 11ms/step - loss: 0.7584 - val_loss: 0.9763 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7595 - val_loss: 0.8816 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6549 - val_loss: 0.7527 - lr: 5.0000e-04\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6331 - val_loss: 0.7554 - lr: 5.0000e-04\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6300 - val_loss: 0.7419 - lr: 5.0000e-04\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6294 - val_loss: 0.7600 - lr: 5.0000e-04\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6273 - val_loss: 0.7407 - lr: 5.0000e-04\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6239 - val_loss: 0.7625 - lr: 5.0000e-04\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6231 - val_loss: 0.7401 - lr: 5.0000e-04\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6184 - val_loss: 0.7377 - lr: 5.0000e-04\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6174 - val_loss: 0.7539 - lr: 5.0000e-04\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6233 - val_loss: 0.7468 - lr: 5.0000e-04\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6138 - val_loss: 0.7420 - lr: 5.0000e-04\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6100 - val_loss: 0.7362 - lr: 5.0000e-04\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6097 - val_loss: 0.7344 - lr: 5.0000e-04\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6133 - val_loss: 0.7304 - lr: 5.0000e-04\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6099 - val_loss: 0.7600 - lr: 5.0000e-04\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6171 - val_loss: 0.7476 - lr: 5.0000e-04\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6072 - val_loss: 0.7326 - lr: 5.0000e-04\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6086 - val_loss: 0.7369 - lr: 5.0000e-04\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6050 - val_loss: 0.7275 - lr: 5.0000e-04\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6012 - val_loss: 0.7305 - lr: 5.0000e-04\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6021 - val_loss: 0.7316 - lr: 5.0000e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5957 - val_loss: 0.7305 - lr: 5.0000e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5965 - val_loss: 0.7532 - lr: 5.0000e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6010 - val_loss: 0.7330 - lr: 5.0000e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5967 - val_loss: 0.7253 - lr: 5.0000e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5911 - val_loss: 0.7457 - lr: 5.0000e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5924 - val_loss: 0.7353 - lr: 5.0000e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5947 - val_loss: 0.7197 - lr: 5.0000e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5888 - val_loss: 0.7106 - lr: 5.0000e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5858 - val_loss: 0.7450 - lr: 5.0000e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5868 - val_loss: 0.7253 - lr: 5.0000e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5841 - val_loss: 0.7368 - lr: 5.0000e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5841 - val_loss: 0.7217 - lr: 5.0000e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5789 - val_loss: 0.7387 - lr: 5.0000e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5864 - val_loss: 0.7501 - lr: 5.0000e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5791 - val_loss: 0.7251 - lr: 5.0000e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5812 - val_loss: 0.7286 - lr: 5.0000e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5402 - val_loss: 0.6981 - lr: 1.2500e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5328 - val_loss: 0.6959 - lr: 1.2500e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5303 - val_loss: 0.7001 - lr: 1.2500e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5297 - val_loss: 0.6964 - lr: 1.2500e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5283 - val_loss: 0.6992 - lr: 1.2500e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5286 - val_loss: 0.6946 - lr: 1.2500e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5279 - val_loss: 0.6958 - lr: 1.2500e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5268 - val_loss: 0.6940 - lr: 1.2500e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5262 - val_loss: 0.6969 - lr: 1.2500e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5261 - val_loss: 0.6958 - lr: 1.2500e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5236 - val_loss: 0.6925 - lr: 1.2500e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5227 - val_loss: 0.6940 - lr: 1.2500e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5263 - val_loss: 0.6984 - lr: 1.2500e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5225 - val_loss: 0.6952 - lr: 1.2500e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5224 - val_loss: 0.6916 - lr: 1.2500e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5205 - val_loss: 0.6943 - lr: 1.2500e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5200 - val_loss: 0.6923 - lr: 1.2500e-04\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5181 - val_loss: 0.6937 - lr: 1.2500e-04\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5199 - val_loss: 0.6935 - lr: 1.2500e-04\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5176 - val_loss: 0.6972 - lr: 1.2500e-04\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5186 - val_loss: 0.6950 - lr: 1.2500e-04\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5164 - val_loss: 0.7009 - lr: 1.2500e-04\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5188 - val_loss: 0.6937 - lr: 1.2500e-04\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5041 - val_loss: 0.6882 - lr: 3.1250e-05\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5019 - val_loss: 0.6888 - lr: 3.1250e-05\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5010 - val_loss: 0.6898 - lr: 3.1250e-05\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5000 - val_loss: 0.6900 - lr: 3.1250e-05\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5004 - val_loss: 0.6869 - lr: 3.1250e-05\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5002 - val_loss: 0.6893 - lr: 3.1250e-05\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4997 - val_loss: 0.6888 - lr: 3.1250e-05\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4994 - val_loss: 0.6888 - lr: 3.1250e-05\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4989 - val_loss: 0.6907 - lr: 3.1250e-05\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4984 - val_loss: 0.6889 - lr: 3.1250e-05\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4983 - val_loss: 0.6897 - lr: 3.1250e-05\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4988 - val_loss: 0.6894 - lr: 3.1250e-05\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4982 - val_loss: 0.6906 - lr: 3.1250e-05\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4948 - val_loss: 0.6872 - lr: 7.8125e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4941 - val_loss: 0.6878 - lr: 7.8125e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4938 - val_loss: 0.6877 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4937 - val_loss: 0.6874 - lr: 7.8125e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4934 - val_loss: 0.6887 - lr: 7.8125e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4934 - val_loss: 0.6876 - lr: 7.8125e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4934 - val_loss: 0.6884 - lr: 7.8125e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4932 - val_loss: 0.6879 - lr: 7.8125e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4923 - val_loss: 0.6873 - lr: 1.9531e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4921 - val_loss: 0.6874 - lr: 1.9531e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4921 - val_loss: 0.6875 - lr: 1.9531e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4920 - val_loss: 0.6876 - lr: 1.9531e-06\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4920 - val_loss: 0.6875 - lr: 1.9531e-06\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4920 - val_loss: 0.6875 - lr: 1.9531e-06\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4920 - val_loss: 0.6876 - lr: 1.9531e-06\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4919 - val_loss: 0.6875 - lr: 1.9531e-06\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4916 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4916 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4916 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4916 - val_loss: 0.6875 - lr: 4.8828e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4916 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4915 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4915 - val_loss: 0.6874 - lr: 4.8828e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4915 - val_loss: 0.6875 - lr: 4.8828e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 1.2207e-07\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 139/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 140/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 141/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 142/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 143/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 144/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 145/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 3.0518e-08\n",
      "Epoch 146/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 7.6294e-09\n",
      "Epoch 147/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4914 - val_loss: 0.6874 - lr: 7.6294e-09\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-6: 0.687\n",
      "MAPE for Fold-6: 0.032\n",
      "817/817 [==============================] - 2s 2ms/step\n",
      "********** Fold-7 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 6s 30ms/step - loss: 5.4124 - val_loss: 2.7981 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 2.3538 - val_loss: 1.9798 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.5978 - val_loss: 1.2689 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.3179 - val_loss: 1.2060 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.1298 - val_loss: 1.2258 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0499 - val_loss: 1.0799 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0597 - val_loss: 1.7026 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0937 - val_loss: 0.9760 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 13ms/step - loss: 0.9824 - val_loss: 1.0539 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9445 - val_loss: 0.9729 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9114 - val_loss: 1.2576 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8965 - val_loss: 0.9598 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8968 - val_loss: 0.9306 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8822 - val_loss: 1.0271 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8806 - val_loss: 0.9209 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.9428 - val_loss: 1.4945 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0015 - val_loss: 0.9607 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8214 - val_loss: 0.8605 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8353 - val_loss: 0.9656 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8403 - val_loss: 0.9039 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7984 - val_loss: 0.8318 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8048 - val_loss: 0.8875 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8062 - val_loss: 0.8855 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7899 - val_loss: 0.8818 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7704 - val_loss: 1.0193 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7624 - val_loss: 0.9561 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8270 - val_loss: 1.3149 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8934 - val_loss: 0.7943 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7391 - val_loss: 0.8488 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7490 - val_loss: 0.8821 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7355 - val_loss: 0.8268 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7322 - val_loss: 0.9424 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7352 - val_loss: 0.8221 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7260 - val_loss: 0.8809 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7248 - val_loss: 0.8442 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7264 - val_loss: 0.8503 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6323 - val_loss: 0.7372 - lr: 5.0000e-04\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6139 - val_loss: 0.7193 - lr: 5.0000e-04\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6107 - val_loss: 0.7321 - lr: 5.0000e-04\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6085 - val_loss: 0.7265 - lr: 5.0000e-04\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6030 - val_loss: 0.7222 - lr: 5.0000e-04\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.6015 - val_loss: 0.7175 - lr: 5.0000e-04\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.6016 - val_loss: 0.7235 - lr: 5.0000e-04\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6004 - val_loss: 0.7207 - lr: 5.0000e-04\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5986 - val_loss: 0.7162 - lr: 5.0000e-04\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5915 - val_loss: 0.7301 - lr: 5.0000e-04\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5952 - val_loss: 0.7217 - lr: 5.0000e-04\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5918 - val_loss: 0.7415 - lr: 5.0000e-04\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5950 - val_loss: 0.7210 - lr: 5.0000e-04\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5914 - val_loss: 0.7238 - lr: 5.0000e-04\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5894 - val_loss: 0.7331 - lr: 5.0000e-04\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5878 - val_loss: 0.7541 - lr: 5.0000e-04\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5880 - val_loss: 0.7216 - lr: 5.0000e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5565 - val_loss: 0.6960 - lr: 1.2500e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5493 - val_loss: 0.6989 - lr: 1.2500e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5469 - val_loss: 0.6957 - lr: 1.2500e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5467 - val_loss: 0.7024 - lr: 1.2500e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5474 - val_loss: 0.6983 - lr: 1.2500e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5455 - val_loss: 0.6994 - lr: 1.2500e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5438 - val_loss: 0.6979 - lr: 1.2500e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5439 - val_loss: 0.6946 - lr: 1.2500e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5429 - val_loss: 0.7031 - lr: 1.2500e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5428 - val_loss: 0.6932 - lr: 1.2500e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5408 - val_loss: 0.6965 - lr: 1.2500e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5406 - val_loss: 0.6985 - lr: 1.2500e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5399 - val_loss: 0.6981 - lr: 1.2500e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5388 - val_loss: 0.7035 - lr: 1.2500e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5376 - val_loss: 0.6981 - lr: 1.2500e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5378 - val_loss: 0.6955 - lr: 1.2500e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5371 - val_loss: 0.6932 - lr: 1.2500e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5380 - val_loss: 0.7003 - lr: 1.2500e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5258 - val_loss: 0.6928 - lr: 3.1250e-05\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5234 - val_loss: 0.6909 - lr: 3.1250e-05\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5231 - val_loss: 0.6923 - lr: 3.1250e-05\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5225 - val_loss: 0.6910 - lr: 3.1250e-05\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5219 - val_loss: 0.6931 - lr: 3.1250e-05\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5215 - val_loss: 0.6909 - lr: 3.1250e-05\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5211 - val_loss: 0.6911 - lr: 3.1250e-05\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5210 - val_loss: 0.6919 - lr: 3.1250e-05\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5210 - val_loss: 0.6927 - lr: 3.1250e-05\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5209 - val_loss: 0.6920 - lr: 3.1250e-05\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5178 - val_loss: 0.6899 - lr: 7.8125e-06\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5171 - val_loss: 0.6909 - lr: 7.8125e-06\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5168 - val_loss: 0.6903 - lr: 7.8125e-06\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5167 - val_loss: 0.6897 - lr: 7.8125e-06\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5167 - val_loss: 0.6904 - lr: 7.8125e-06\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5165 - val_loss: 0.6899 - lr: 7.8125e-06\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5165 - val_loss: 0.6905 - lr: 7.8125e-06\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5165 - val_loss: 0.6903 - lr: 7.8125e-06\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5163 - val_loss: 0.6904 - lr: 7.8125e-06\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5162 - val_loss: 0.6904 - lr: 7.8125e-06\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5162 - val_loss: 0.6903 - lr: 7.8125e-06\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5162 - val_loss: 0.6904 - lr: 7.8125e-06\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5152 - val_loss: 0.6903 - lr: 1.9531e-06\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5150 - val_loss: 0.6900 - lr: 1.9531e-06\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5149 - val_loss: 0.6901 - lr: 1.9531e-06\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5150 - val_loss: 0.6903 - lr: 1.9531e-06\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5149 - val_loss: 0.6902 - lr: 1.9531e-06\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5148 - val_loss: 0.6900 - lr: 1.9531e-06\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5148 - val_loss: 0.6904 - lr: 1.9531e-06\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5148 - val_loss: 0.6900 - lr: 1.9531e-06\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.5146 - val_loss: 0.6900 - lr: 4.8828e-07\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5145 - val_loss: 0.6901 - lr: 4.8828e-07\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 1.2207e-07\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5144 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 3.0518e-08\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 7.6294e-09\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 1.9073e-09\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5143 - val_loss: 0.6901 - lr: 1.9073e-09\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-7: 0.69\n",
      "MAPE for Fold-7: 0.023\n",
      "817/817 [==============================] - 2s 2ms/step\n",
      "********** Fold-8 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 27ms/step - loss: 5.5866 - val_loss: 2.8573 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 2.3552 - val_loss: 1.8431 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.7264 - val_loss: 1.3740 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.3920 - val_loss: 1.3329 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.1929 - val_loss: 1.0525 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0812 - val_loss: 1.3309 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.0427 - val_loss: 1.0120 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.2097 - val_loss: 1.2904 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0056 - val_loss: 1.0102 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.8915 - val_loss: 0.9812 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 13ms/step - loss: 0.9137 - val_loss: 0.9829 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8937 - val_loss: 0.9310 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8822 - val_loss: 0.9685 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8620 - val_loss: 0.9759 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9411 - val_loss: 0.8464 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8486 - val_loss: 0.8579 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8345 - val_loss: 0.8254 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8362 - val_loss: 0.9515 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8401 - val_loss: 0.8419 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8130 - val_loss: 0.8753 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8051 - val_loss: 0.9696 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8349 - val_loss: 1.0128 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8875 - val_loss: 0.8368 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7755 - val_loss: 0.8486 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7932 - val_loss: 0.8018 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7968 - val_loss: 0.8351 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8088 - val_loss: 0.7984 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.8031 - val_loss: 0.9569 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7807 - val_loss: 0.8254 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7796 - val_loss: 0.8161 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.7544 - val_loss: 0.9001 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7490 - val_loss: 0.8889 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7200 - val_loss: 0.8170 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7543 - val_loss: 0.7750 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7465 - val_loss: 0.8486 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7173 - val_loss: 0.8138 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7237 - val_loss: 0.7911 - lr: 0.0020\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7207 - val_loss: 0.7932 - lr: 0.0020\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7207 - val_loss: 0.7937 - lr: 0.0020\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7293 - val_loss: 0.7841 - lr: 0.0020\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7163 - val_loss: 1.0588 - lr: 0.0020\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7106 - val_loss: 0.7720 - lr: 0.0020\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7255 - val_loss: 0.8015 - lr: 0.0020\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7001 - val_loss: 0.7675 - lr: 0.0020\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7000 - val_loss: 0.8140 - lr: 0.0020\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7032 - val_loss: 0.8394 - lr: 0.0020\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6983 - val_loss: 0.8589 - lr: 0.0020\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7079 - val_loss: 0.8450 - lr: 0.0020\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6717 - val_loss: 0.7832 - lr: 0.0020\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7022 - val_loss: 0.7990 - lr: 0.0020\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6753 - val_loss: 0.8525 - lr: 0.0020\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6875 - val_loss: 0.8955 - lr: 0.0020\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5984 - val_loss: 0.6942 - lr: 5.0000e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5656 - val_loss: 0.6888 - lr: 5.0000e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5635 - val_loss: 0.6902 - lr: 5.0000e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5616 - val_loss: 0.7013 - lr: 5.0000e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5578 - val_loss: 0.6854 - lr: 5.0000e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5543 - val_loss: 0.6785 - lr: 5.0000e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5526 - val_loss: 0.6811 - lr: 5.0000e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5529 - val_loss: 0.6949 - lr: 5.0000e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5520 - val_loss: 0.6835 - lr: 5.0000e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5507 - val_loss: 0.6813 - lr: 5.0000e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5492 - val_loss: 0.7128 - lr: 5.0000e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5479 - val_loss: 0.6844 - lr: 5.0000e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5506 - val_loss: 0.6959 - lr: 5.0000e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5461 - val_loss: 0.6930 - lr: 5.0000e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5192 - val_loss: 0.6725 - lr: 1.2500e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5119 - val_loss: 0.6692 - lr: 1.2500e-04\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.5095 - val_loss: 0.6747 - lr: 1.2500e-04\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.5089 - val_loss: 0.6680 - lr: 1.2500e-04\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5068 - val_loss: 0.6688 - lr: 1.2500e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5053 - val_loss: 0.6698 - lr: 1.2500e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5047 - val_loss: 0.6777 - lr: 1.2500e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5054 - val_loss: 0.6686 - lr: 1.2500e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5036 - val_loss: 0.6706 - lr: 1.2500e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5013 - val_loss: 0.6720 - lr: 1.2500e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5021 - val_loss: 0.6744 - lr: 1.2500e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5012 - val_loss: 0.6701 - lr: 1.2500e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4919 - val_loss: 0.6665 - lr: 3.1250e-05\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4892 - val_loss: 0.6668 - lr: 3.1250e-05\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4889 - val_loss: 0.6681 - lr: 3.1250e-05\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4884 - val_loss: 0.6681 - lr: 3.1250e-05\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4881 - val_loss: 0.6671 - lr: 3.1250e-05\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4878 - val_loss: 0.6678 - lr: 3.1250e-05\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4872 - val_loss: 0.6674 - lr: 3.1250e-05\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4873 - val_loss: 0.6671 - lr: 3.1250e-05\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4871 - val_loss: 0.6654 - lr: 3.1250e-05\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4860 - val_loss: 0.6667 - lr: 3.1250e-05\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4863 - val_loss: 0.6670 - lr: 3.1250e-05\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4854 - val_loss: 0.6672 - lr: 3.1250e-05\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4851 - val_loss: 0.6675 - lr: 3.1250e-05\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4847 - val_loss: 0.6674 - lr: 3.1250e-05\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4848 - val_loss: 0.6681 - lr: 3.1250e-05\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4845 - val_loss: 0.6666 - lr: 3.1250e-05\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4837 - val_loss: 0.6691 - lr: 3.1250e-05\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4809 - val_loss: 0.6669 - lr: 7.8125e-06\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4804 - val_loss: 0.6679 - lr: 7.8125e-06\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4802 - val_loss: 0.6668 - lr: 7.8125e-06\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4801 - val_loss: 0.6677 - lr: 7.8125e-06\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4798 - val_loss: 0.6673 - lr: 7.8125e-06\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4798 - val_loss: 0.6669 - lr: 7.8125e-06\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4797 - val_loss: 0.6672 - lr: 7.8125e-06\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4795 - val_loss: 0.6669 - lr: 7.8125e-06\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4787 - val_loss: 0.6672 - lr: 1.9531e-06\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4786 - val_loss: 0.6672 - lr: 1.9531e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4786 - val_loss: 0.6673 - lr: 1.9531e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4785 - val_loss: 0.6670 - lr: 1.9531e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4784 - val_loss: 0.6671 - lr: 1.9531e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4784 - val_loss: 0.6672 - lr: 1.9531e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4784 - val_loss: 0.6669 - lr: 1.9531e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4784 - val_loss: 0.6669 - lr: 1.9531e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4781 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4781 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4781 - val_loss: 0.6671 - lr: 4.8828e-07\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4780 - val_loss: 0.6671 - lr: 4.8828e-07\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4780 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4780 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4780 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4780 - val_loss: 0.6670 - lr: 4.8828e-07\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 1.2207e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 3.0518e-08\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 7.6294e-09\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4779 - val_loss: 0.6670 - lr: 7.6294e-09\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-8: 0.665\n",
      "MAPE for Fold-8: 0.023\n",
      "817/817 [==============================] - 2s 2ms/step\n",
      "********** Fold-9 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 26ms/step - loss: 5.5874 - val_loss: 2.5306 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 13ms/step - loss: 2.5177 - val_loss: 2.1301 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.6738 - val_loss: 1.5563 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.4620 - val_loss: 1.3791 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.1892 - val_loss: 1.0820 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.0531 - val_loss: 1.0174 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 1.0026 - val_loss: 1.0113 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9765 - val_loss: 0.9612 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9569 - val_loss: 1.1089 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9655 - val_loss: 0.9183 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9809 - val_loss: 1.1962 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 1.1815 - val_loss: 1.0715 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9265 - val_loss: 0.8660 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8732 - val_loss: 0.8721 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8411 - val_loss: 0.8990 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8538 - val_loss: 0.8484 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8417 - val_loss: 0.8660 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.8361 - val_loss: 0.9966 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8340 - val_loss: 0.9098 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8176 - val_loss: 0.8599 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8000 - val_loss: 0.8009 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8214 - val_loss: 0.9262 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.9202 - val_loss: 0.8200 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8174 - val_loss: 0.8090 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7903 - val_loss: 0.7956 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.7744 - val_loss: 0.9142 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7829 - val_loss: 0.8302 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7664 - val_loss: 0.8188 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7696 - val_loss: 0.8084 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7924 - val_loss: 0.8422 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7629 - val_loss: 0.8402 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8835 - val_loss: 0.8387 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7459 - val_loss: 0.8277 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6565 - val_loss: 0.6991 - lr: 5.0000e-04\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6377 - val_loss: 0.7078 - lr: 5.0000e-04\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6326 - val_loss: 0.6990 - lr: 5.0000e-04\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6308 - val_loss: 0.6918 - lr: 5.0000e-04\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6294 - val_loss: 0.6971 - lr: 5.0000e-04\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6322 - val_loss: 0.7063 - lr: 5.0000e-04\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6270 - val_loss: 0.6904 - lr: 5.0000e-04\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6233 - val_loss: 0.6884 - lr: 5.0000e-04\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6238 - val_loss: 0.6837 - lr: 5.0000e-04\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6169 - val_loss: 0.6956 - lr: 5.0000e-04\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6150 - val_loss: 0.6905 - lr: 5.0000e-04\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6207 - val_loss: 0.6885 - lr: 5.0000e-04\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.6172 - val_loss: 0.6869 - lr: 5.0000e-04\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.6148 - val_loss: 0.6928 - lr: 5.0000e-04\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6149 - val_loss: 0.6850 - lr: 5.0000e-04\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6107 - val_loss: 0.6736 - lr: 5.0000e-04\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6066 - val_loss: 0.6809 - lr: 5.0000e-04\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6077 - val_loss: 0.6810 - lr: 5.0000e-04\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6020 - val_loss: 0.6827 - lr: 5.0000e-04\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6027 - val_loss: 0.6940 - lr: 5.0000e-04\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6098 - val_loss: 0.6761 - lr: 5.0000e-04\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5998 - val_loss: 0.6920 - lr: 5.0000e-04\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6095 - val_loss: 0.7090 - lr: 5.0000e-04\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6014 - val_loss: 0.6957 - lr: 5.0000e-04\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5688 - val_loss: 0.6579 - lr: 1.2500e-04\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5587 - val_loss: 0.6544 - lr: 1.2500e-04\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5575 - val_loss: 0.6555 - lr: 1.2500e-04\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5567 - val_loss: 0.6551 - lr: 1.2500e-04\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5559 - val_loss: 0.6581 - lr: 1.2500e-04\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5540 - val_loss: 0.6555 - lr: 1.2500e-04\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5543 - val_loss: 0.6595 - lr: 1.2500e-04\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5536 - val_loss: 0.6570 - lr: 1.2500e-04\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5534 - val_loss: 0.6556 - lr: 1.2500e-04\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5521 - val_loss: 0.6575 - lr: 1.2500e-04\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5421 - val_loss: 0.6518 - lr: 3.1250e-05\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5402 - val_loss: 0.6518 - lr: 3.1250e-05\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5395 - val_loss: 0.6510 - lr: 3.1250e-05\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5394 - val_loss: 0.6503 - lr: 3.1250e-05\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5390 - val_loss: 0.6508 - lr: 3.1250e-05\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5388 - val_loss: 0.6496 - lr: 3.1250e-05\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5383 - val_loss: 0.6507 - lr: 3.1250e-05\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5380 - val_loss: 0.6518 - lr: 3.1250e-05\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5379 - val_loss: 0.6506 - lr: 3.1250e-05\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5373 - val_loss: 0.6494 - lr: 3.1250e-05\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5365 - val_loss: 0.6520 - lr: 3.1250e-05\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5372 - val_loss: 0.6506 - lr: 3.1250e-05\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5366 - val_loss: 0.6508 - lr: 3.1250e-05\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5362 - val_loss: 0.6515 - lr: 3.1250e-05\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5358 - val_loss: 0.6492 - lr: 3.1250e-05\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5359 - val_loss: 0.6498 - lr: 3.1250e-05\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5355 - val_loss: 0.6491 - lr: 3.1250e-05\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5352 - val_loss: 0.6500 - lr: 3.1250e-05\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5349 - val_loss: 0.6490 - lr: 3.1250e-05\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5351 - val_loss: 0.6527 - lr: 3.1250e-05\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5350 - val_loss: 0.6498 - lr: 3.1250e-05\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5340 - val_loss: 0.6494 - lr: 3.1250e-05\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5344 - val_loss: 0.6500 - lr: 3.1250e-05\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5339 - val_loss: 0.6489 - lr: 3.1250e-05\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5331 - val_loss: 0.6504 - lr: 3.1250e-05\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5336 - val_loss: 0.6491 - lr: 3.1250e-05\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5328 - val_loss: 0.6504 - lr: 3.1250e-05\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5323 - val_loss: 0.6503 - lr: 3.1250e-05\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5321 - val_loss: 0.6493 - lr: 3.1250e-05\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5325 - val_loss: 0.6504 - lr: 3.1250e-05\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5319 - val_loss: 0.6509 - lr: 3.1250e-05\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5317 - val_loss: 0.6495 - lr: 3.1250e-05\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5283 - val_loss: 0.6484 - lr: 7.8125e-06\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5277 - val_loss: 0.6488 - lr: 7.8125e-06\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5275 - val_loss: 0.6488 - lr: 7.8125e-06\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5273 - val_loss: 0.6486 - lr: 7.8125e-06\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5275 - val_loss: 0.6483 - lr: 7.8125e-06\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5270 - val_loss: 0.6491 - lr: 7.8125e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5272 - val_loss: 0.6491 - lr: 7.8125e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5269 - val_loss: 0.6486 - lr: 7.8125e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5269 - val_loss: 0.6478 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5270 - val_loss: 0.6482 - lr: 7.8125e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5268 - val_loss: 0.6488 - lr: 7.8125e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5269 - val_loss: 0.6485 - lr: 7.8125e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5265 - val_loss: 0.6486 - lr: 7.8125e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5266 - val_loss: 0.6489 - lr: 7.8125e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6489 - lr: 7.8125e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5264 - val_loss: 0.6485 - lr: 7.8125e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5262 - val_loss: 0.6490 - lr: 7.8125e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5254 - val_loss: 0.6483 - lr: 1.9531e-06\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5252 - val_loss: 0.6484 - lr: 1.9531e-06\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5252 - val_loss: 0.6485 - lr: 1.9531e-06\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5251 - val_loss: 0.6484 - lr: 1.9531e-06\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5250 - val_loss: 0.6484 - lr: 1.9531e-06\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5250 - val_loss: 0.6485 - lr: 1.9531e-06\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5250 - val_loss: 0.6485 - lr: 1.9531e-06\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5251 - val_loss: 0.6486 - lr: 1.9531e-06\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5247 - val_loss: 0.6484 - lr: 4.8828e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5247 - val_loss: 0.6484 - lr: 4.8828e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6484 - lr: 4.8828e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6484 - lr: 4.8828e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6485 - lr: 4.8828e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6485 - lr: 4.8828e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6485 - lr: 4.8828e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5247 - val_loss: 0.6485 - lr: 4.8828e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6485 - lr: 1.2207e-07\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6485 - lr: 1.2207e-07\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 138/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 139/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 140/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5246 - val_loss: 0.6484 - lr: 1.2207e-07\n",
      "Epoch 141/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 142/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 143/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 144/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 145/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 146/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 147/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 148/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 3.0518e-08\n",
      "Epoch 149/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 150/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 151/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 152/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 153/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 154/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 155/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 156/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 7.6294e-09\n",
      "Epoch 157/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 1.9073e-09\n",
      "Epoch 158/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5245 - val_loss: 0.6484 - lr: 1.9073e-09\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-9: 0.648\n",
      "MAPE for Fold-9: 0.028\n",
      "817/817 [==============================] - 2s 2ms/step\n",
      "********** Fold-10 **********\n",
      "Epoch 1/1000\n",
      "82/82 [==============================] - 5s 24ms/step - loss: 5.2945 - val_loss: 3.1338 - lr: 0.0020\n",
      "Epoch 2/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 2.4169 - val_loss: 2.2003 - lr: 0.0020\n",
      "Epoch 3/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.8961 - val_loss: 1.3979 - lr: 0.0020\n",
      "Epoch 4/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.2630 - val_loss: 1.2342 - lr: 0.0020\n",
      "Epoch 5/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.3421 - val_loss: 1.3454 - lr: 0.0020\n",
      "Epoch 6/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0735 - val_loss: 0.9781 - lr: 0.0020\n",
      "Epoch 7/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 1.0139 - val_loss: 1.0794 - lr: 0.0020\n",
      "Epoch 8/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 1.0523 - val_loss: 0.9796 - lr: 0.0020\n",
      "Epoch 9/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.9703 - val_loss: 0.9111 - lr: 0.0020\n",
      "Epoch 10/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 1.0934 - val_loss: 0.9149 - lr: 0.0020\n",
      "Epoch 11/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9369 - val_loss: 0.8771 - lr: 0.0020\n",
      "Epoch 12/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9219 - val_loss: 0.8745 - lr: 0.0020\n",
      "Epoch 13/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9055 - val_loss: 0.9467 - lr: 0.0020\n",
      "Epoch 14/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8939 - val_loss: 0.8993 - lr: 0.0020\n",
      "Epoch 15/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8505 - val_loss: 0.7961 - lr: 0.0020\n",
      "Epoch 16/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.8609 - val_loss: 0.8268 - lr: 0.0020\n",
      "Epoch 17/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8758 - val_loss: 0.9355 - lr: 0.0020\n",
      "Epoch 18/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8451 - val_loss: 0.7947 - lr: 0.0020\n",
      "Epoch 19/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.8509 - val_loss: 0.7884 - lr: 0.0020\n",
      "Epoch 20/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.8419 - val_loss: 0.8459 - lr: 0.0020\n",
      "Epoch 21/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9619 - val_loss: 0.9909 - lr: 0.0020\n",
      "Epoch 22/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8949 - val_loss: 0.8379 - lr: 0.0020\n",
      "Epoch 23/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.9247 - val_loss: 0.7655 - lr: 0.0020\n",
      "Epoch 24/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7826 - val_loss: 0.8336 - lr: 0.0020\n",
      "Epoch 25/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.8080 - val_loss: 0.7540 - lr: 0.0020\n",
      "Epoch 26/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7750 - val_loss: 0.7984 - lr: 0.0020\n",
      "Epoch 27/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.7778 - val_loss: 0.7471 - lr: 0.0020\n",
      "Epoch 28/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7871 - val_loss: 0.7352 - lr: 0.0020\n",
      "Epoch 29/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7864 - val_loss: 0.8360 - lr: 0.0020\n",
      "Epoch 30/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.7526 - val_loss: 0.7223 - lr: 0.0020\n",
      "Epoch 31/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7570 - val_loss: 0.7047 - lr: 0.0020\n",
      "Epoch 32/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7993 - val_loss: 0.7238 - lr: 0.0020\n",
      "Epoch 33/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7508 - val_loss: 0.7309 - lr: 0.0020\n",
      "Epoch 34/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7472 - val_loss: 0.8748 - lr: 0.0020\n",
      "Epoch 35/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7609 - val_loss: 0.6881 - lr: 0.0020\n",
      "Epoch 36/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7262 - val_loss: 0.6884 - lr: 0.0020\n",
      "Epoch 37/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7593 - val_loss: 0.7191 - lr: 0.0020\n",
      "Epoch 38/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7105 - val_loss: 0.8023 - lr: 0.0020\n",
      "Epoch 39/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7411 - val_loss: 0.7009 - lr: 0.0020\n",
      "Epoch 40/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7247 - val_loss: 0.7145 - lr: 0.0020\n",
      "Epoch 41/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7195 - val_loss: 0.7146 - lr: 0.0020\n",
      "Epoch 42/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7126 - val_loss: 0.7368 - lr: 0.0020\n",
      "Epoch 43/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7282 - val_loss: 0.6815 - lr: 0.0020\n",
      "Epoch 44/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6972 - val_loss: 0.7099 - lr: 0.0020\n",
      "Epoch 45/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.7094 - val_loss: 0.6868 - lr: 0.0020\n",
      "Epoch 46/1000\n",
      "82/82 [==============================] - 1s 10ms/step - loss: 0.7097 - val_loss: 0.7038 - lr: 0.0020\n",
      "Epoch 47/1000\n",
      "82/82 [==============================] - 1s 12ms/step - loss: 0.7017 - val_loss: 0.7015 - lr: 0.0020\n",
      "Epoch 48/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.6891 - val_loss: 0.6662 - lr: 0.0020\n",
      "Epoch 49/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.7822 - val_loss: 0.6983 - lr: 0.0020\n",
      "Epoch 50/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7341 - val_loss: 0.6875 - lr: 0.0020\n",
      "Epoch 51/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.6810 - val_loss: 0.7278 - lr: 0.0020\n",
      "Epoch 52/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6950 - val_loss: 0.6973 - lr: 0.0020\n",
      "Epoch 53/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6864 - val_loss: 0.6641 - lr: 0.0020\n",
      "Epoch 54/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6823 - val_loss: 0.6553 - lr: 0.0020\n",
      "Epoch 55/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6812 - val_loss: 0.7054 - lr: 0.0020\n",
      "Epoch 56/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6793 - val_loss: 0.7068 - lr: 0.0020\n",
      "Epoch 57/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6955 - val_loss: 0.7486 - lr: 0.0020\n",
      "Epoch 58/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6700 - val_loss: 0.6806 - lr: 0.0020\n",
      "Epoch 59/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6735 - val_loss: 0.6931 - lr: 0.0020\n",
      "Epoch 60/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6591 - val_loss: 0.6750 - lr: 0.0020\n",
      "Epoch 61/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6576 - val_loss: 0.7441 - lr: 0.0020\n",
      "Epoch 62/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.7510 - val_loss: 0.6538 - lr: 0.0020\n",
      "Epoch 63/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6604 - val_loss: 0.6665 - lr: 0.0020\n",
      "Epoch 64/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6569 - val_loss: 0.6874 - lr: 0.0020\n",
      "Epoch 65/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.6489 - val_loss: 0.6702 - lr: 0.0020\n",
      "Epoch 66/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6563 - val_loss: 0.7178 - lr: 0.0020\n",
      "Epoch 67/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6389 - val_loss: 0.6806 - lr: 0.0020\n",
      "Epoch 68/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6508 - val_loss: 0.7464 - lr: 0.0020\n",
      "Epoch 69/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6633 - val_loss: 0.6980 - lr: 0.0020\n",
      "Epoch 70/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.6798 - val_loss: 0.6553 - lr: 0.0020\n",
      "Epoch 71/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5475 - val_loss: 0.6020 - lr: 5.0000e-04\n",
      "Epoch 72/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5273 - val_loss: 0.6076 - lr: 5.0000e-04\n",
      "Epoch 73/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5257 - val_loss: 0.5977 - lr: 5.0000e-04\n",
      "Epoch 74/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5165 - val_loss: 0.5974 - lr: 5.0000e-04\n",
      "Epoch 75/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5146 - val_loss: 0.5967 - lr: 5.0000e-04\n",
      "Epoch 76/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5084 - val_loss: 0.5912 - lr: 5.0000e-04\n",
      "Epoch 77/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.5077 - val_loss: 0.5873 - lr: 5.0000e-04\n",
      "Epoch 78/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5097 - val_loss: 0.5943 - lr: 5.0000e-04\n",
      "Epoch 79/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5079 - val_loss: 0.6069 - lr: 5.0000e-04\n",
      "Epoch 80/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.5023 - val_loss: 0.6039 - lr: 5.0000e-04\n",
      "Epoch 81/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.5059 - val_loss: 0.5962 - lr: 5.0000e-04\n",
      "Epoch 82/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4981 - val_loss: 0.6065 - lr: 5.0000e-04\n",
      "Epoch 83/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4951 - val_loss: 0.6010 - lr: 5.0000e-04\n",
      "Epoch 84/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4962 - val_loss: 0.6026 - lr: 5.0000e-04\n",
      "Epoch 85/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4929 - val_loss: 0.5977 - lr: 5.0000e-04\n",
      "Epoch 86/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4623 - val_loss: 0.5847 - lr: 1.2500e-04\n",
      "Epoch 87/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4538 - val_loss: 0.5787 - lr: 1.2500e-04\n",
      "Epoch 88/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4524 - val_loss: 0.5863 - lr: 1.2500e-04\n",
      "Epoch 89/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4502 - val_loss: 0.5853 - lr: 1.2500e-04\n",
      "Epoch 90/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4485 - val_loss: 0.5850 - lr: 1.2500e-04\n",
      "Epoch 91/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4469 - val_loss: 0.5906 - lr: 1.2500e-04\n",
      "Epoch 92/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4458 - val_loss: 0.5875 - lr: 1.2500e-04\n",
      "Epoch 93/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4444 - val_loss: 0.5892 - lr: 1.2500e-04\n",
      "Epoch 94/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4421 - val_loss: 0.5854 - lr: 1.2500e-04\n",
      "Epoch 95/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4414 - val_loss: 0.5879 - lr: 1.2500e-04\n",
      "Epoch 96/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4314 - val_loss: 0.5849 - lr: 3.1250e-05\n",
      "Epoch 97/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4290 - val_loss: 0.5852 - lr: 3.1250e-05\n",
      "Epoch 98/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4278 - val_loss: 0.5861 - lr: 3.1250e-05\n",
      "Epoch 99/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4272 - val_loss: 0.5854 - lr: 3.1250e-05\n",
      "Epoch 100/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4266 - val_loss: 0.5858 - lr: 3.1250e-05\n",
      "Epoch 101/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4261 - val_loss: 0.5865 - lr: 3.1250e-05\n",
      "Epoch 102/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4255 - val_loss: 0.5845 - lr: 3.1250e-05\n",
      "Epoch 103/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4249 - val_loss: 0.5850 - lr: 3.1250e-05\n",
      "Epoch 104/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4224 - val_loss: 0.5850 - lr: 7.8125e-06\n",
      "Epoch 105/1000\n",
      "82/82 [==============================] - 1s 8ms/step - loss: 0.4216 - val_loss: 0.5850 - lr: 7.8125e-06\n",
      "Epoch 106/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4212 - val_loss: 0.5849 - lr: 7.8125e-06\n",
      "Epoch 107/1000\n",
      "82/82 [==============================] - 1s 9ms/step - loss: 0.4211 - val_loss: 0.5854 - lr: 7.8125e-06\n",
      "Epoch 108/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4211 - val_loss: 0.5849 - lr: 7.8125e-06\n",
      "Epoch 109/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4209 - val_loss: 0.5854 - lr: 7.8125e-06\n",
      "Epoch 110/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4207 - val_loss: 0.5855 - lr: 7.8125e-06\n",
      "Epoch 111/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4206 - val_loss: 0.5855 - lr: 7.8125e-06\n",
      "Epoch 112/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4197 - val_loss: 0.5853 - lr: 1.9531e-06\n",
      "Epoch 113/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4195 - val_loss: 0.5854 - lr: 1.9531e-06\n",
      "Epoch 114/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4194 - val_loss: 0.5853 - lr: 1.9531e-06\n",
      "Epoch 115/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4194 - val_loss: 0.5853 - lr: 1.9531e-06\n",
      "Epoch 116/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4193 - val_loss: 0.5852 - lr: 1.9531e-06\n",
      "Epoch 117/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4193 - val_loss: 0.5853 - lr: 1.9531e-06\n",
      "Epoch 118/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4193 - val_loss: 0.5850 - lr: 1.9531e-06\n",
      "Epoch 119/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4192 - val_loss: 0.5852 - lr: 1.9531e-06\n",
      "Epoch 120/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4189 - val_loss: 0.5852 - lr: 4.8828e-07\n",
      "Epoch 121/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4189 - val_loss: 0.5852 - lr: 4.8828e-07\n",
      "Epoch 122/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4189 - val_loss: 0.5853 - lr: 4.8828e-07\n",
      "Epoch 123/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4189 - val_loss: 0.5853 - lr: 4.8828e-07\n",
      "Epoch 124/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4188 - val_loss: 0.5853 - lr: 4.8828e-07\n",
      "Epoch 125/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4188 - val_loss: 0.5852 - lr: 4.8828e-07\n",
      "Epoch 126/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4188 - val_loss: 0.5852 - lr: 4.8828e-07\n",
      "Epoch 127/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4188 - val_loss: 0.5853 - lr: 4.8828e-07\n",
      "Epoch 128/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4187 - val_loss: 0.5852 - lr: 1.2207e-07\n",
      "Epoch 129/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4187 - val_loss: 0.5852 - lr: 1.2207e-07\n",
      "Epoch 130/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4187 - val_loss: 0.5852 - lr: 1.2207e-07\n",
      "Epoch 131/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4187 - val_loss: 0.5852 - lr: 1.2207e-07\n",
      "Epoch 132/1000\n",
      "82/82 [==============================] - 0s 6ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 1.2207e-07\n",
      "Epoch 133/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 1.2207e-07\n",
      "Epoch 134/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 1.2207e-07\n",
      "Epoch 135/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 1.2207e-07\n",
      "Epoch 136/1000\n",
      "82/82 [==============================] - 1s 7ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 3.0518e-08\n",
      "Epoch 137/1000\n",
      "82/82 [==============================] - 1s 6ms/step - loss: 0.4187 - val_loss: 0.5853 - lr: 3.0518e-08\n",
      "290/290 [==============================] - 1s 2ms/step\n",
      "MAE for Fold-10: 0.579\n",
      "MAPE for Fold-10: 0.025\n",
      "817/817 [==============================] - 1s 2ms/step\n",
      "OOF MAE: 0.672\n",
      "Average MAE: 0.672+/-0.057\n",
      "OOF MAPE: 0.026\n",
      "Average MAPE: 0.026+/-0.003\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "if Inference:\n",
    "    model_path = '../input/ecm-output-of-final-notebook/'\n",
    "else:\n",
    "    model_path = './'\n",
    "    \n",
    "oof_pred_keras, pred_keras = fit_keras_nn(Nfold, train_df, test_df, train_cols, categorical_cols, TARGET, model_path)\n",
    "train_df = train_df.copy()\n",
    "train_df['oof_keras'] = oof_pred_keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ed759d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:21:17.850754Z",
     "iopub.status.busy": "2023-10-12T12:21:17.850239Z",
     "iopub.status.idle": "2023-10-12T12:21:17.987542Z",
     "shell.execute_reply": "2023-10-12T12:21:17.986429Z"
    },
    "papermill": {
     "duration": 0.960877,
     "end_time": "2023-10-12T12:21:17.989280",
     "exception": false,
     "start_time": "2023-10-12T12:21:17.028403",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Time     Energy\n",
      "0  2023-01-01 06:00:00_B_0  62.807578\n",
      "1  2023-01-01 11:00:00_B_0  68.676697\n",
      "2  2023-01-01 12:00:00_B_0  70.482396\n",
      "3  2023-01-01 13:00:00_B_0  69.166243\n",
      "4  2023-01-01 23:00:00_B_0  82.802210\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "submission_keras = pd.DataFrame(data = {'Time': test_df['ID'].values, 'Energy': pred_keras})\n",
    "print(submission_keras.head())\n",
    "submission_keras.to_csv('submission_keras.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e84400f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:21:19.562939Z",
     "iopub.status.busy": "2023-10-12T12:21:19.562550Z",
     "iopub.status.idle": "2023-10-12T12:21:19.566980Z",
     "shell.execute_reply": "2023-10-12T12:21:19.565919Z"
    },
    "papermill": {
     "duration": 0.831547,
     "end_time": "2023-10-12T12:21:19.568673",
     "exception": false,
     "start_time": "2023-10-12T12:21:18.737126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# OOF MAE: 0.668\n",
    "# Average MAE: 0.668+/-0.052\n",
    "# OOF MAPE: 0.025\n",
    "# Average MAPE: 0.025+/-0.003"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd72f782",
   "metadata": {
    "papermill": {
     "duration": 0.809726,
     "end_time": "2023-10-12T12:21:21.134295",
     "exception": false,
     "start_time": "2023-10-12T12:21:20.324569",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Writing output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dca7f4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-12T12:21:22.760332Z",
     "iopub.status.busy": "2023-10-12T12:21:22.760006Z",
     "iopub.status.idle": "2023-10-12T12:21:22.919862Z",
     "shell.execute_reply": "2023-10-12T12:21:22.918700Z"
    },
    "papermill": {
     "duration": 0.976645,
     "end_time": "2023-10-12T12:21:22.921664",
     "exception": false,
     "start_time": "2023-10-12T12:21:21.945019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OOF MAE ENSEMBLE: 0.6660360502206929\n",
      "OOF MAPE ENSEMBLE: 0.02566820240356784\n",
      "OOF MAE ENSEMBLE HM: 0.664281544608172\n",
      "OOF MAPE ENSEMBLE HM: 0.02546631302404832\n",
      "                      Time     Energy\n",
      "0  2023-01-01 06:00:00_B_0  62.786342\n",
      "1  2023-01-01 11:00:00_B_0  68.465882\n",
      "2  2023-01-01 12:00:00_B_0  70.021952\n",
      "3  2023-01-01 13:00:00_B_0  69.435069\n",
      "4  2023-01-01 23:00:00_B_0  83.359071\n",
      "                      Time     Energy\n",
      "0  2023-01-01 06:00:00_B_0  62.786335\n",
      "1  2023-01-01 11:00:00_B_0  68.465232\n",
      "2  2023-01-01 12:00:00_B_0  70.018924\n",
      "3  2023-01-01 13:00:00_B_0  69.434028\n",
      "4  2023-01-01 23:00:00_B_0  83.355351\n"
     ]
    }
   ],
   "source": [
    "set_seed(42)\n",
    "seed_everything(seed=42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "w1_fastai = 0.5\n",
    "w1_keras = 0.5\n",
    "new_pred_ens = train_df['oof_keras']*w1_keras + train_df['oof_fastai']*w1_fastai \n",
    "new_pred_ens_hm = (2*train_df['oof_keras']*train_df['oof_fastai'])/(train_df['oof_keras']+train_df['oof_fastai'])\n",
    "print(f'OOF MAE ENSEMBLE: {mean_absolute_error(train_df.Energy, new_pred_ens)}')\n",
    "print(f'OOF MAPE ENSEMBLE: {mean_absolute_percentage_error(train_df.Energy, new_pred_ens)}')\n",
    "\n",
    "print(f'OOF MAE ENSEMBLE HM: {mean_absolute_error(train_df.Energy, new_pred_ens_hm)}')\n",
    "print(f'OOF MAPE ENSEMBLE HM: {mean_absolute_percentage_error(train_df.Energy, new_pred_ens_hm)}')\n",
    "\n",
    "new_test_pred_ens = pred_keras*w1_keras + pred_fastai*w1_fastai\n",
    "new_test_pred_ens_hm = (2*pred_keras*pred_fastai)/(pred_keras+pred_fastai)\n",
    "\n",
    "submission_ensemble = pd.DataFrame(data = {'Time': test_df['ID'].values, 'Energy': new_test_pred_ens})\n",
    "print(submission_ensemble.head())\n",
    "submission_ensemble.to_csv('submission_ensemble.csv', index=False)\n",
    "\n",
    "submission_ensemble_hm = pd.DataFrame(data = {'Time': test_df['ID'].values, 'Energy': new_test_pred_ens_hm})\n",
    "print(submission_ensemble_hm.head())\n",
    "submission_ensemble_hm.to_csv('submission_ensemble_hm.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "552eaa4b",
   "metadata": {
    "papermill": {
     "duration": 0.817359,
     "end_time": "2023-10-12T12:21:24.504798",
     "exception": false,
     "start_time": "2023-10-12T12:21:23.687439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb90533",
   "metadata": {
    "papermill": {
     "duration": 0.753373,
     "end_time": "2023-10-12T12:21:26.144813",
     "exception": false,
     "start_time": "2023-10-12T12:21:25.391440",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47462474",
   "metadata": {
    "papermill": {
     "duration": 0.757423,
     "end_time": "2023-10-12T12:21:27.708529",
     "exception": false,
     "start_time": "2023-10-12T12:21:26.951106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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