notebook-oxygen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting in Aquaponics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Libraries and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import sys\n",
    "\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "# np.set_printoptions(threshold=sys.maxsize)\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (14, 8)\n",
    "mpl.rcParams['axes.grid'] = True\n",
    "\n",
    "np.random.seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data adquisition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data adquisition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset ready\n"
     ]
    }
   ],
   "source": [
    "def read_file(textfile):\n",
    "    df = pd.read_csv(textfile, compression='zip', sep=\"\\t\")\n",
    "    df.drop(df.columns[0], axis=1, inplace=True)\n",
    "    print('Dataset ready')\n",
    "    return df\n",
    "\n",
    "textfile = \"dataset_aquaponics_03012021_05222021.txt.zip\"\n",
    "df = read_file(textfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Organize data in columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label                      1         2    3       4       5          6    \\\n",
      "DateTime                                                                   \n",
      "2021-04-01 00:00:12  20.880000  51.34556  0.0  6.6498  6.6667  44.003500   \n",
      "2021-04-01 00:00:38  20.822498  51.09667  0.0  6.6498  6.6667  44.007060   \n",
      "2021-04-01 00:01:04  20.853998  51.17500  0.0  6.6514  6.6681  43.999874   \n",
      "2021-04-01 00:05:57  20.928003  50.80000  0.0  6.6584  6.6718  43.621853   \n",
      "2021-04-01 00:06:23  21.000000  50.68667  0.0  6.6596  6.6731  42.650578   \n",
      "\n",
      "Label                      7       8       9       10   ...       107  \\\n",
      "DateTime                                                ...             \n",
      "2021-04-01 00:00:12  49.062016  99.262  95.587  11.690  ...  50.53125   \n",
      "2021-04-01 00:00:38  49.063637  99.262  95.587  11.690  ...  50.53125   \n",
      "2021-04-01 00:01:04  49.068360  99.255  95.599  11.687  ...  52.50001   \n",
      "2021-04-01 00:05:57  49.068610  99.289  95.774  11.615  ...  52.50001   \n",
      "2021-04-01 00:06:23  49.073135  99.273  95.758  11.613  ...  52.50001   \n",
      "\n",
      "Label                     108        109        110   111   112   113   114  \\\n",
      "DateTime                                                                      \n",
      "2021-04-01 00:00:12  77.43749  45.281254  97.781235  10.0  10.0  10.0  10.0   \n",
      "2021-04-01 00:00:38  77.43749  45.281254  97.781235  10.0  10.0  10.0  10.0   \n",
      "2021-04-01 00:01:04  76.78126  45.281254  97.781235  10.0  10.0  10.0  10.0   \n",
      "2021-04-01 00:05:57  76.78126  45.937490  97.125000  10.0  10.0  10.0  10.0   \n",
      "2021-04-01 00:06:23  76.78126  45.937490  97.781235  10.0  10.0  10.0  10.0   \n",
      "\n",
      "Label                115  116  \n",
      "DateTime                       \n",
      "2021-04-01 00:00:12  0.0  0.0  \n",
      "2021-04-01 00:00:38  0.0  0.0  \n",
      "2021-04-01 00:01:04  0.0  0.0  \n",
      "2021-04-01 00:05:57  0.0  0.0  \n",
      "2021-04-01 00:06:23  0.0  0.0  \n",
      "\n",
      "[5 rows x 114 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_grouped = pd.pivot_table(df, index = 'DateTime', columns = 'Label', values = 'Value')\n",
    "df_grouped.index = pd.to_datetime(df_grouped.index)\n",
    "\n",
    "start_date = pd.to_datetime('2021-04-01 00:00:00')\n",
    "end_date = pd.to_datetime('2021-04-17 23:59:59')\n",
    "\n",
    "df_grouped = df_grouped.loc[start_date:end_date]\n",
    "print(df_grouped.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_sensor = 9\n",
    "\n",
    "df_grouped[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting target "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variables:\n",
    "\n",
    "* 4     = pH sump B\n",
    "* 5     = pH sump A\n",
    "* 8     = % oxygen B\n",
    "* 9     = % oxygen A\n",
    "* 93*   = Weight cell 1\n",
    "* 99*   = Nitrate B \n",
    "* 100*  = Ammonia B\n",
    "* 103*  = Nitrate A \n",
    "* 104*  = Ammonia A\n",
    "* sensors are not online yet*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_target = {\n",
    "# 4: 'pH sump B',\n",
    "# 5: 'pH sump A',\n",
    "# 8: '% oxygen B',}\n",
    "9: '% oxygen A'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select features - Human knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label                      1         2       4       5          6    \\\n",
      "DateTime                                                              \n",
      "2021-04-01 00:00:12  20.880000  51.34556  6.6498  6.6667  44.003500   \n",
      "2021-04-01 00:00:38  20.822498  51.09667  6.6498  6.6667  44.007060   \n",
      "2021-04-01 00:01:04  20.853998  51.17500  6.6514  6.6681  43.999874   \n",
      "2021-04-01 00:05:57  20.928003  50.80000  6.6584  6.6718  43.621853   \n",
      "2021-04-01 00:06:23  21.000000  50.68667  6.6596  6.6731  42.650578   \n",
      "\n",
      "Label                      7       8       9       10      11   ...    91   \\\n",
      "DateTime                                                        ...          \n",
      "2021-04-01 00:00:12  49.062016  99.262  95.587  11.690  13.552  ...  406.0   \n",
      "2021-04-01 00:00:38  49.063637  99.262  95.587  11.690  13.552  ...  406.0   \n",
      "2021-04-01 00:01:04  49.068360  99.255  95.599  11.687  13.528  ...  406.0   \n",
      "2021-04-01 00:05:57  49.068610  99.289  95.774  11.615  13.512  ...  406.0   \n",
      "2021-04-01 00:06:23  49.073135  99.273  95.758  11.613  13.502  ...  406.0   \n",
      "\n",
      "Label                  92    102    103       107       108        109  \\\n",
      "DateTime                                                                 \n",
      "2021-04-01 00:00:12  406.0  8.98  322.2  50.53125  77.43749  45.281254   \n",
      "2021-04-01 00:00:38  406.0  8.98  322.2  50.53125  77.43749  45.281254   \n",
      "2021-04-01 00:01:04  406.0  9.06  324.3  52.50001  76.78126  45.281254   \n",
      "2021-04-01 00:05:57  406.0  9.01  322.0  52.50001  76.78126  45.937490   \n",
      "2021-04-01 00:06:23  406.0  8.95  322.0  52.50001  76.78126  45.937490   \n",
      "\n",
      "Label                      110  115  116  \n",
      "DateTime                                  \n",
      "2021-04-01 00:00:12  97.781235  0.0  0.0  \n",
      "2021-04-01 00:00:38  97.781235  0.0  0.0  \n",
      "2021-04-01 00:01:04  97.781235  0.0  0.0  \n",
      "2021-04-01 00:05:57  97.125000  0.0  0.0  \n",
      "2021-04-01 00:06:23  97.781235  0.0  0.0  \n",
      "\n",
      "[5 rows x 46 columns]\n"
     ]
    }
   ],
   "source": [
    "drop_signal = [3, 12, 13, 14, 15, 22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 56, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 93, 94, 95, 97, 98, 99, 100, 101, 104, 105, 106, 111, 112, 113, 114]\n",
    "df_grouped  = df_grouped.drop(columns = drop_signal)\n",
    "print(df_grouped.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Denoising dataframe - FFT filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check sampling frecuency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 days 00:00:26\n"
     ]
    }
   ],
   "source": [
    "sampling_time = df_grouped.index.to_series().diff().median()\n",
    "print(sampling_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Design sample filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28636, 46)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f_filtering(df, cutt_fc):\n",
    "    \n",
    "    new_df = df.copy() \n",
    "    index = df.index\n",
    "    \n",
    "    dt = 25 # seconds\n",
    "    n_samples = new_df.shape[0]\n",
    "    \n",
    "    # Fast Fourier transform\n",
    "    f_hat = np.fft.fft(df.to_numpy())\n",
    "    f_mag_2 = np.real(f_hat * np.conj(f_hat)) / n_samples\n",
    "    fc_df = (1 / (dt * n_samples)) * np.arange(n_samples)\n",
    "    \n",
    "    # Cutting frecuency\n",
    "    ind_filter = f_mag_2 > cutt_fc\n",
    "    \n",
    "    # Filter signal\n",
    "    f_hat_clean = f_hat * ind_filter\n",
    "    filter_signal = np.fft.ifft(f_hat_clean)\n",
    "    \n",
    "    \n",
    "    filter_signal = np.real(filter_signal)\n",
    "    \n",
    "    return filter_signal\n",
    "\n",
    "df_grouped[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Raw data')\n",
    "plt.show()\n",
    "\n",
    "# Cutting frecuency\n",
    "cutt_fc = {\n",
    "1: 5,   \n",
    "2: 150,     \n",
    "4: 0.001,   \n",
    "5: 0.005,   \n",
    "6: 25,   \n",
    "7: 25,   \n",
    "8: 1,   \n",
    "9: 1,  \n",
    "10: 0.1,  \n",
    "11: 0.1,  \n",
    "16: 0.1,  \n",
    "17: 0.1,\n",
    "18: 500,  \n",
    "19: 5,  \n",
    "20: 20,  \n",
    "21: 20, \n",
    "102: 10, \n",
    "103: 50}\n",
    "\n",
    "df_clean = df_grouped.copy()\n",
    "\n",
    "for sensor in cutt_fc:\n",
    "    df_clean[sensor] = f_filtering(df_grouped[sensor], cutt_fc[sensor])\n",
    "    \n",
    "print(df_clean.shape)\n",
    "\n",
    "df_clean[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Filtered data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection via correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_mat = np.abs(df_clean.corr(method='pearson'))\n",
    "sns.heatmap(corr_mat)\n",
    "plt.show()\n",
    "\n",
    "corr_thl = 0.4\n",
    "corr_label = np.zeros(df_grouped.shape[1])\n",
    "\n",
    "for i in label_target:\n",
    "    corr_label = corr_label | (corr_mat[i] > corr_thl)\n",
    "\n",
    "df_clean = df_clean.loc[:, corr_label]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe  dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         count         mean        std          min          25%          50%  \\\n",
      "Label                                                                           \n",
      "7      28636.0    47.539641   2.906426    37.967173    45.538220    47.990785   \n",
      "9      28636.0    96.048075   4.577596    88.751129    92.966986    94.877775   \n",
      "17     28636.0    10.101525   0.500796     9.151747     9.751985     9.986074   \n",
      "87     28636.0  1356.293721  10.510800  1339.000000  1347.000000  1356.000000   \n",
      "88     28636.0   350.358535   6.128813   341.000000   345.000000   351.000000   \n",
      "89     28636.0   386.400894   6.437937   377.000000   381.000000   386.000000   \n",
      "90     28636.0   345.357592   5.874463   336.000000   340.000000   345.000000   \n",
      "91     28636.0   414.654945   5.023185   406.000000   410.000000   415.000000   \n",
      "92     28636.0   414.654945   5.023185   406.000000   410.000000   415.000000   \n",
      "108    28636.0    77.352089   3.954382    57.093742    74.812490    76.781260   \n",
      "110    28636.0    94.370148   3.347678    68.250000    93.187510    95.156235   \n",
      "\n",
      "               75%          max  \n",
      "Label                            \n",
      "7        49.686407    53.315345  \n",
      "9        97.098124   108.826088  \n",
      "17       10.255268    11.537642  \n",
      "87     1366.000000  1374.000000  \n",
      "88      356.000000   362.000000  \n",
      "89      392.000000   399.000000  \n",
      "90      350.000000   356.000000  \n",
      "91      419.000000   423.000000  \n",
      "92      419.000000   423.000000  \n",
      "108      77.437490    91.218740  \n",
      "110      97.125000   113.531250  \n"
     ]
    }
   ],
   "source": [
    "print(df_clean.describe().transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split dataset: train, validation and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_data = len(df_clean)\n",
    "\n",
    "train_perc, val_perc, test_perc = 0.7, 0.2, 0.1\n",
    "\n",
    "train_df    = df_clean[0:int(total_data*train_perc)]\n",
    "val_df      = df_clean[int(total_data*train_perc):int(total_data*(train_perc + val_perc))]\n",
    "test_df     = df_clean[int(total_data*(train_perc + val_perc)):total_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min-max normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_normalize_minmax(no_norm_df, df):\n",
    "    norm_df = (no_norm_df - df.min()) / (df.max() - df.min())\n",
    "    return norm_df\n",
    "\n",
    "def f_inv_normalize_minmax(norm_df, train_df):\n",
    "    df = norm_df * (train_df.max() - train_df.min()) + train_df.min()\n",
    "    return df\n",
    "\n",
    "norm_train_df   = f_normalize_minmax(train_df, df_clean)\n",
    "norm_val_df     = f_normalize_minmax(val_df, df_clean)\n",
    "norm_test_df    = f_normalize_minmax(test_df, df_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show tails with box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "melt_train_df = norm_train_df.melt(var_name = 'Label', value_name = 'Normalized value')\n",
    "plt.figure()\n",
    "ax = sns.violinplot(x = 'Label', y = 'Normalized value', data = melt_train_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot normalized data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_train_df[sample_sensor].plot(x = 'DateTime', y = 'Value', kind = 'line')\n",
    "plt.xticks(rotation=20)\n",
    "plt.title('Normalized data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create input and target dataframe - Window generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_hour_data = 65 # 65 entries ~ 1 hour - There is a variable sampling frecuency\n",
    "n_hours_in  = 3\n",
    "n_hours_out = 1\n",
    "\n",
    "input_width     = n_hour_data * n_hours_in \n",
    "label_width     = n_hour_data * n_hours_out \n",
    "offset_width    = 1 # 1 as default\n",
    "\n",
    "def f_window_gen(df, label_target, input_width, label_width, offset_width):\n",
    "\n",
    "    time_range = df.index\n",
    "    data = []\n",
    "    target = []\n",
    "\n",
    "    data_time = []\n",
    "    target_time = []\n",
    "\n",
    "    for i in range(len(time_range) - (input_width + offset_width + label_width)):\n",
    "        range_data = df.loc[time_range[i]:time_range[input_width + i]].values\n",
    "        data.append(range_data)\n",
    "        data_time.append(time_range[i:input_width + i])\n",
    "\n",
    "        range_target = df[label_target].loc[time_range[input_width + offset_width + i: input_width + offset_width + label_width + i]].values\n",
    "        target.append(range_target.flatten('F'))\n",
    "        target_time.append(time_range[input_width + offset_width + i: input_width + offset_width + label_width + i])\n",
    "\n",
    "    data = np.array(data)\n",
    "    target = np.array(target)\n",
    "\n",
    "    return data, target, data_time, target_time\n",
    "\n",
    "train_data, train_target, train_data_time, train_target_time = f_window_gen(norm_train_df, label_target, input_width, label_width, offset_width)\n",
    "val_data, val_target, val_data_time, val_target_time = f_window_gen(norm_val_df, label_target, input_width, label_width, offset_width)\n",
    "test_data, test_target, test_data_time, test_target_time = f_window_gen(norm_test_df, label_target, input_width, label_width, offset_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting - Normalized values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(label_target, label_time, label, results, label_width):\n",
    "    n_label = np.int(len(label_target))\n",
    "\n",
    "    sensor_tag = list(label_target.values())\n",
    "\n",
    "    print(label_time.shape)\n",
    "    \n",
    "    fig, ax = plt.subplots(n_label, sharex = 'all')\n",
    "    \n",
    "    for i in range(n_label):\n",
    "#         label_plot = label[((i) * label_width): ((i + 1)* label_width)]\n",
    "#         ax[i].plot(label_time, label_plot, color = 'blue', label = 'Real')\n",
    "        \n",
    "#         results_plot = results[((i) * label_width): ((i + 1)* label_width)]\n",
    "#         ax[i].plot(label_time, results_plot, color = 'orange', label = 'Prediction')\n",
    "\n",
    "#         ax[i].set_ylabel(sensor_tag[i])\n",
    "\n",
    "        label_plot = label[((i) * label_width): ((i + 1)* label_width)]\n",
    "        ax.plot(label_time, label_plot, color = 'blue', label = 'Real')\n",
    "        \n",
    "        results_plot = results[((i) * label_width): ((i + 1)* label_width)]\n",
    "        ax.plot(label_time, results_plot, color = 'orange', label = 'Prediction')\n",
    "\n",
    "        ax.set_ylabel(sensor_tag[i])\n",
    "\n",
    "    lines, labels = fig.axes[-1].get_legend_handles_labels()\n",
    "    fig.legend(lines, labels, loc = 'center right')\n",
    "    plt.xticks(rotation=20)\n",
    "    plt.xlabel('Day - Time')\n",
    "    plt.show()\n",
    "\n",
    "    pass"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### Plotting - History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "\n",
    "    acc = history.history['mean_absolute_error']\n",
    "    val_acc = history.history['val_mean_absolute_error']\n",
    "\n",
    "    loss = history.history['loss']\n",
    "    val_loss = history.history['val_loss']\n",
    "\n",
    "    # Get number of epochs\n",
    "    epochs = range(len(acc))\n",
    "\n",
    "    fig, ax = plt.subplots(2, sharex = 'all')\n",
    "\n",
    "    # Plot training and validation accuracy per epoch\n",
    "    ax[0].plot(epochs, acc, label = 'Training')\n",
    "    ax[0].plot(epochs, val_acc, label = 'Validation')\n",
    "    ax[0].title.set_text('Training and validation MAE')\n",
    "\n",
    "    # Plot training and validation loss per epoch\n",
    "    ax[1].plot(epochs, loss, label = 'Training')\n",
    "    ax[1].plot(epochs, val_loss, label = 'Validation')\n",
    "    ax[1].title.set_text('Training and validation loss')\n",
    "\n",
    "    lines, labels = fig.axes[-1].get_legend_handles_labels()\n",
    "    fig.legend(lines, labels, loc = 'center right')\n",
    "    plt.xticks(rotation=20)\n",
    "    plt.xlabel('epochs')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architecture and Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scheduler(epoch, lr):\n",
    "    if epoch < 10:\n",
    "        return lr\n",
    "    else:\n",
    "        return lr * 0.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_and_fit(model, train_data, train_target, val_data, val_target, \n",
    "                    test_data, test_target, max_epochs, batch_size, test_entry, label_width):\n",
    "\n",
    "    # Add callbacks\n",
    "    early_stop = tf.keras.callbacks.EarlyStopping(monitor = \"val_loss\", \n",
    "                                                  patience = 5)\n",
    "    lr_scheduler = tf.keras.callbacks.LearningRateScheduler(scheduler, verbose = 0)\n",
    "    callbacks = [early_stop, lr_scheduler]\n",
    "\n",
    "    # Model compile\n",
    "    model.compile(loss      = tf.losses.MeanSquaredError(),\n",
    "                  optimizer = tf.keras.optimizers.Nadam(),\n",
    "                  metrics   = [tf.metrics.MeanAbsoluteError()])\n",
    "\n",
    "    # Model fit\n",
    "    history = model.fit(train_data,\n",
    "                        train_target,\n",
    "                        batch_size = batch_size,\n",
    "                        epochs  = max_epochs,\n",
    "                        verbose = 1,\n",
    "                        shuffle = False, \n",
    "                        validation_data = (val_data, val_target),\n",
    "                        callbacks = callbacks)\n",
    "\n",
    "    model.summary()\n",
    "    \n",
    "    # Model evaluate\n",
    "    model.evaluate(test_data, test_target)\n",
    "\n",
    "    # Test model\n",
    "    test_data = np.reshape(test_data[test_entry], (1, test_data[0].shape[0], test_data[0].shape[1]))\n",
    "    test_prediction = model.predict(test_data)\n",
    "    \n",
    "    test_prediction = np.squeeze(test_prediction)\n",
    "    print(test_prediction.shape)\n",
    "    \n",
    "    # Plot results\n",
    "    plot_results(label_target, test_target_time[test_entry], test_target[test_entry], test_prediction, label_width)\n",
    "\n",
    "    plot_history(history)\n",
    "\n",
    "    return history, test_prediction\n",
    "\n",
    "test_entry = np.random.randint(0, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_linear_model(label_target, label_width):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.Flatten(),\n",
    "            \n",
    "            tf.keras.layers.Dense(units = 100, kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 300, kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = len(label_target) * label_width, kernel_initializer='random_normal', bias_initializer='zeros')])\n",
    "    return model\n",
    "\n",
    "linear_model = f_linear_model(label_target, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 19784 samples, validate on 5466 samples\n",
      "Epoch 1/50\n",
      "19784/19784 [==============================] - 3s 164us/sample - loss: 0.0958 - mean_absolute_error: 0.1250 - val_loss: 0.2852 - val_mean_absolute_error: 0.4672\n",
      "Epoch 2/50\n",
      "19784/19784 [==============================] - 2s 93us/sample - loss: 0.0054 - mean_absolute_error: 0.0529 - val_loss: 0.2907 - val_mean_absolute_error: 0.4721\n",
      "Epoch 3/50\n",
      "19784/19784 [==============================] - 2s 95us/sample - loss: 0.0032 - mean_absolute_error: 0.0419 - val_loss: 0.4353 - val_mean_absolute_error: 0.5823\n",
      "Epoch 4/50\n",
      "19784/19784 [==============================] - 2s 93us/sample - loss: 0.0026 - mean_absolute_error: 0.0378 - val_loss: 0.4409 - val_mean_absolute_error: 0.5859\n",
      "Epoch 5/50\n",
      "19784/19784 [==============================] - 2s 94us/sample - loss: 0.0021 - mean_absolute_error: 0.0347 - val_loss: 0.4514 - val_mean_absolute_error: 0.5908\n",
      "Epoch 6/50\n",
      "19784/19784 [==============================] - 2s 92us/sample - loss: 21.8748 - mean_absolute_error: 0.4299 - val_loss: 10.7572 - val_mean_absolute_error: 2.8394\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                multiple                  215700    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              multiple                  30300     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              multiple                  19565     \n",
      "=================================================================\n",
      "Total params: 265,565\n",
      "Trainable params: 265,565\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "2603/1 [=======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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- 0s 61us/sample - loss: 15.0061 - mean_absolute_error: 3.3985\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65,)\n",
      "(65,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_epochs = 50\n",
    "batch_size = 50\n",
    "\n",
    "linear_history, linear_prediction = compile_and_fit(linear_model, train_data, train_target, val_data, val_target, \n",
    "                                                    test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_mlp_model(label_target, label_width):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.Flatten(),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = 200, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "#             tf.keras.layers.LayerNormalization(),\n",
    "            tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "#             tf.keras.layers.Dense(units = 25, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "#             tf.keras.layers.LayerNormalization(),\n",
    "#             tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "#             tf.keras.layers.Dense(units = 65*7, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "#             tf.keras.layers.LayerNormalization(),\n",
    "#             tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "#             tf.keras.layers.Dense(units = 65*4, activation = 'tanh', kernel_initializer='random_normal', bias_initializer='zeros'),\n",
    "#             tf.keras.layers.LayerNormalization(),\n",
    "#             tf.keras.layers.Dropout(0.2),\n",
    "        \n",
    "            tf.keras.layers.Dense(units = len(label_target) * label_width)])\n",
    "    return model\n",
    "    \n",
    "mlp_model = f_mlp_model(label_target, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 19784 samples, validate on 5466 samples\n",
      "Epoch 1/30\n",
      "19784/19784 [==============================] - 3s 127us/sample - loss: 0.2162 - mean_absolute_error: 0.3686 - val_loss: 0.3299 - val_mean_absolute_error: 0.4919\n",
      "Epoch 2/30\n",
      "19784/19784 [==============================] - 2s 85us/sample - loss: 0.0678 - mean_absolute_error: 0.1997 - val_loss: 0.3531 - val_mean_absolute_error: 0.5019\n",
      "Epoch 3/30\n",
      "19784/19784 [==============================] - 2s 86us/sample - loss: 0.0262 - mean_absolute_error: 0.1202 - val_loss: 0.4214 - val_mean_absolute_error: 0.5634\n",
      "Epoch 4/30\n",
      "19784/19784 [==============================] - 2s 89us/sample - loss: 0.0111 - mean_absolute_error: 0.0772 - val_loss: 0.4547 - val_mean_absolute_error: 0.5851\n",
      "Epoch 5/30\n",
      "19784/19784 [==============================] - 2s 84us/sample - loss: 0.0054 - mean_absolute_error: 0.0540 - val_loss: 0.4122 - val_mean_absolute_error: 0.5609\n",
      "Epoch 6/30\n",
      "19784/19784 [==============================] - 2s 84us/sample - loss: 0.0035 - mean_absolute_error: 0.0441 - val_loss: 0.3963 - val_mean_absolute_error: 0.5431\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              multiple                  431400    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              multiple                  13065     \n",
      "=================================================================\n",
      "Total params: 444,465\n",
      "Trainable params: 444,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "2603/1 [=======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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- 0s 59us/sample - loss: 0.4532 - mean_absolute_error: 0.7191\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65,)\n",
      "(65,)\n"
     ]
    },
    {
     "data": {
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7ovHAEJFsYJo37QLeAa4VkfsGcnEROUNE1ohItYhcH2H7tSLyvois8IZ0mTyQ6xmGYRiDh2jGA/sFsBr4JPBDVZ2jqj9R1bOBo/p7YU8UbwXOBKYDl4jI9LDd3gLmqupM4EGsz5lhGIbhEY0HtgKYrapfijCsyrwBXHseUK2q61S1DbgPODd0B1VdoqpN3uLruE7VhmEYhhFVP7AsVd3nL3ie07dV9XuqWj+Aa48HNocs1wDH9LD/lcA/BnA9wzCMhNDe3k5NTQ0tLS3JLgoApaWlrFq1qsd9nnnmmSPfeeedDYkpUZ/pBFYGAoGr5syZs9NfGY2AnSIi5+MEZASwGHghLkXsBhH5PC4n40ndbL8auBqgvLycqqqqhJWtsbExoddLNplkbybZCpllb7xtLS4upry8nPHjx/sJ0JNKR0cH2dnZve0TmDFjxq4EFalPdHZ2Sm1t7fTt27f/HjjHXx9NR+bPichngXeBfcDnVPWVGJRpCzAxZHmCt24/RORU4L+Bk1Q1YuoqVb0dN2YZc+fO1crKyhgULzqqqqpI5PWSTSbZm0m2QmbZG29bV61axYQJE1JCvAAaGhooKSlJdjH6TVZWlo4aNap++/btM/Zb39uBIjIV+DrwV2AjcJmIFMWgTMuAqSIyRUTygIuBR8OufRTwW+AcVd0Z4RyGYRgpSaqI12AhKytLCdOsaII4HgNuUNUv4arwPsSJz4BQ1QCwCHgK11n6AVV9T0RuFBHfRfwZUAz8RUTeFpFHuzmdYcSWwD6O2XEpbHsm2SUxjH6RnZ3N7NmzmTFjBhdddBF1dXX9Os/NN99ctnDhwkmxLV1siEbA5qnqswDq+DlwXiwurqpPqOqhqnqwqv7AW3eDqj7qzZ+qquWqOtubzun5jIbRDZ0B6OjD4AmN6yns2ApbLW7ISE8KCwt5++23WblyJcOHD+fWW29NdpFiTjQCNlREHhKRWhHZKSJ/BZp6PcowUom3vglP9RTkGkbLdvdb9058ymMYCWTevHls2eJCDNauXcsZZ5zBnDlzOOGEE1i9ejUAzz//fPbMmTOnHX744dMXLFhw6ObNm6MJ8ksq0RTwTuAe4EJv+fPeutPiVSjDiCmdAdjwJwg0Rn9M8zb3W/cOqIK1Zxj95Jpr4O23Y3vO2bPhl7+Mbt+Ojg5eeOEFvvSlLwFw9dVXc9tttzF16lSWLl3KV7/6VZ5//nnmzJnTsWjRotVZWVncdNNNI2+88cYxv/vd72piW/LYEo2AjVLVO0OWF4vINXEqj2HEntqXoNWLDg40QU4UMUjNngfWutuJWdG4+JUv2agG/z7GoKG5uZnZs2ezZcsWDj30UE477TQaGxt59dVXufDCC7v2a211Vevbt2+XE044YWptbW1uW1tb1sSJE1N+wOJoBGy31w/rXm/5EmB3/IpkGDFm01+D8627oxSwbcH5uncGt4BtfxaqPkne6HuSXZJBSbSeUqzx28Campo49dRTufXWW7niiisYNmwYb0dwCX/0ox/lf+Mb39hy6aWX1j/++OMlN954Y8o/9NG0gf0LcBGwHdgGXAB8IZ6FMoyYoZ1Q8zfIGeKW2z6K7riW7bRlDXPzewZ5O1jjWtAABYHtyS6JEQeKior46U9/ys9//nOKioqYMmUKf/nLXwBQVd55xz3fDQ0NTJo0qR1g8eLFZckrcfREM5zKRlU9R1VHqepoVf20qm5KROEMY8Dset15U5MvccutUVYeNG+jKWciFE2CuhXxK18q0L4XgNzOgWSGM1KZWbNmMXPmTO69917uvvtu7rjjDmbNmsURRxzBI488AsBXvvKV9ksuueTgI4444vCysrJAkoscFSkfZWIYA2LzXyErF6ZcDmt/30cPrByGVwz+SMR2J1xpI2B7VsDb34ITH4LsgmSXJmVpbNw/aOmxxx7rmn/yyScP2P/UU0/tuOaaaw5ImPi1r31tNynabBTVeGCGkVa01cFHy+Gjt2Dz32DMaVA8xW3rgwfWlj0Chs2EvWugIzWSssaFNl/A9ia5IFGy8wXY9iQ0rE12SYwkYx6YMfh48TzYWRVcPvI7kDfCzbdFIWCBZmivp61gBAyfBdoB9e/DiKPjUtykk24eWFud+22tTWoxjOQTtYCJyLHAd4EC4Jeq+nCcymQY/SfQBLUvuzavyZ8FyYWxn4CsHMguhNYoqhC9Tsxt2WXOAwMXyGEClhp45TUBM7oVMBEZo6qhYUnX4lJICbAUeDi+RTOMfrDrNdAATLkMxp25/7b8sug8MK8PWFvWCCg+xAlfqgZy7H4TcoqhdFr/z5FuQRztde63xfJ7Zzo9eWC3icg/gZ+qagtQhwuh7wTSpLLcyDh2vgiSBaOOO3BbXll0bWChHlhWNgw7MnUDOd74IuSPgo8/3f9zpJsH5lchtpgHlul0G8Shqp8G3gIeF5GFwDVAPlAGfDoBZTOSSdsel6Eh3dj5Agw/CnKHHrgtf0R0UYheJ+a2LK/dbNjM1O0L1rZn4N5hW5oJmFUhGh49RiGq6mPA6UAp8BDwgarerKr25AxmmrbAQ+Nh0wPJLknf6Gh1/b5GnRh5e188MMmiLavULQ+pcMLXl2z2iSLQCC07BuaNpKsHZgLWI6HDqSxcuJCmpv7nYD///PMr7rzzzuEAn/3sZycvX7682/4Ljz/+eMkzzzwzxF/+6U9/OuqWW26JS8fobgVMRM4RkSXAk8BK4LPAuSJyn4gcHI/CpA0drS5B7GCl5hHoaIaP/pnskvSN3W9AZyuUnxR5e188sPzRIN4Q7HnD3W/bntiUM5a0e3196lf273hVJ2CSRa42Qmd77MoWL6wKMSpCh1PJzc3ltttu2297INC/d9j999+/cc6cOd32K3n++edLXnrppWJ/+Zvf/GbtokWL4tKPrCcP7P8BZ+LSSP1EVetU9d+B/wF+EI/CpAwb7oEn57k0RJF49iR47bLElikWaGd0/ZlqXM98GtOsn83OF93vqOMjb88rc1GIvVWNNm+HwjEhxw1zv6kmYJ3tTrAB6t7t3zk6ml03gaKJbjmaKM1kY1WIfWbBggVUV1dTVVXFCSecwDnnnMP06dPp6Ojguuuu42Mf+xjnnXde4c9+9rORAJ2dnSxcuHBSRUXFjAULFhy6a9eurniJefPmHfbiiy8WATz44INDp0+ffvhhhx02ff78+YeuWbMm76677hp12223lU+bNm36k08+WXzttdeOu+GGG8oBXn311cJZs2ZNO/TQQ6efdtppB9fW1mb75/zKV74y/sgjjzy8oqJixpNPPlkcyY5wegriqAc+AxQBXeE+qvohcHEf/37pxcb74aNlXhby8Qdur38fdi+Fw76e+LINhDU3w6qfwrmbXFh5JNrqYecSN9+4LnFliwU7X3ABF/nd1Fbkl7kIxfa9kFfa/XlatkHB2OByqnpggX3B+bp+emC+GBQfBPs2uqz0heUDL1u8UA1GIaaLgC2/Bva8HdtzDp8Nc37paoIaq101dzdZSQKBAM888wyf+tSnAPjnP//JypUrmTJlCrfffjulpaUsW7aM5cuXN19++eWjzj777L1Lly4tqq6uzq+url5ZU1OTe+SRRx5xxRVX7OdFbd26NWfRokUVVVVVq6dNm9a2Y8eO7PLy8o6FCxfWFhcXd9x44407AJ5++umuBukrrrhiyi9+8YtNZ511VuM111wz7lvf+ta4P/zhD5u9csq777676v777y+98cYbx51xxhkf9PZn6MkDOw8XsJEDfK63Ew0aVGHXK26+ofrA7YFmCDS4+be+mV6BDjtfdKLcU6P/tqfcl/3w2V6S1zSxr7Mddr3affsXhHRm7sXLOMAD8wWsbkBFjDmh45v11wNr8wXMaxVI9WFVOprdvc7Kc2XtrpYkU+hodtXIEdp2/eFU5s6dy4QJE7jyyisBN7jllCkuM83TTz/NXXfdxezZs/nc5z5XsGfPnpz333+/4IUXXii56KKLPsrJyaGioqJ9/vz5DeHnr6qqGjJv3ryGadOmtQGUl5d39FTU3bt3Zzc0NGSfddZZjQBf/OIXd7/++utdntaFF164B2DBggX7ampq8qIxv1sPTFV3Ab+K5iSDir1rgg9D49oD21NaPWe0bB7UvsSIEa+DngSb/gK5w2Dc6Qktbp/w20l2vd59p9yaRyB/JFRcCm9d51723Xk0ySDQDM1boXmLCzZp3uo8o30bnUfSXfsXBO1o3R1MLeXjD1qpnS4oonAs+LWtqeqB+e1f+SPdve3PwJvtaSZgoeXdu8pVeRaMTG6ZemPOL+N3bvXasdrrgP1ri/w2MHCZ5vPynCYMGTIkeLgqv/rVrzj99NNZuXJly4wZM1YBPP744z1UUcSHgoICBcjJyaGjoyOqBzmpuRBF5AwRWSMi1SJyfYTt+SJyv7d9qYhUxL1QvvcFkduAmne43+nXQ/EhHFL/G3hqHrxyMSz9Qup6LIGmoEe567XI+3S2w9YnYPynoGSqW5eofHPa6TqmfvQWbHkcPvwtrLgBXr8SlpwJT8yEB8vggSJ47BDXDvnq5+Ct/4D3fwzb/uHC58tP6f4avgcW/rXaUgt/Gw3bnnHbNAAFkTywFBMwvyZg5Hznje3b2Pdz+IJQ4gtYilfL+V6w/3ymennjTafn9ASa+xUle/rpp/Ob3/yG9nYXvLNixYr8vXv3Zp100kkNDz744IhAIMDGjRtzX3/99ZLwYysrK/e98cYbJatXr84D2LFjRzZASUlJR0NDQ3b4/mVlZR1Dhw7t8Nu37rjjjrL58+f3YZj0A0laLkQRyQZuBU4DaoBlIvKoqr4fstuVwB5VPURELgZ+gouGjB+1L7uOoTnFkV/eLZ6AFU2A2T+k6OWLoEVh0kUu7LyhGoZOjWsR+8XeVYC6cbG6E7CdL7ovufHnBr/IG9fByHkDu/YBXlOE3+atESLgxFXlFY53bTSjTnBtkoXjg7+F41yfr2g8D98DC69CbPjQeR6rfgZH/a9bV5gObWDe//7IY2HLY64asbiib+cIbQOD1PfAIgrY4ckqTfLRkEjC9nrIHt2nw6+66io2bNjA0UcfTVNTU+HIkSMnP/HEE2svu+yyuueee27oIYccMmPcuHGtRx111AFCM27cuMDNN9+84bzzzjuks7OTsrKy9ldfffXD888/v+6CCy44+B//+MewX/7yl/sNvXXnnXeu/8pXvjL5a1/7WtakSZNa77333g39M9yRzGS+84BqVV0HICL3AecCoQJ2Li7/IsCDwC0iIqpxdHNqX4FRC9xLtzFCG5gvYAXlMGIub5XdzFGnXAn7NjkB2/lCaghY43onSAdd7pb9Rv7JF8PaO5y3UxD2sG95zDUEjz0t5Dy9eGDtjU4AfBGKJFCR2pxyhgSFKJIwFY13XlB3wSb9IbQKMRT/nm5/BmpfcvMFYwDv5ZCV68qbagLmVyGWHet+61fChLP7eA4vqU7+KAJSRE6qC1iXx+j9j2V6KL16Hlh2vhP3kP/p8OFUACorK6msrOxazsrK4oc//CE//OEPWblyZfOMGTO6AifuuuuuiOM+vvHGG2v8+YsuumjvRRddFPrOZubMma0ffPBB17ozzjijqyALFixofuedd1b3dM6xY8cGtmzZElWjbjIFbDywOWS5Bjimu31UNSAi9bjAkv3+y0TkauBqgPLycqqqqvpVoNyOjziu4UPWyscpCGxjdPMaXgk716SG1zkIePGN1XTKOhrbp1D/8hugyoKs4Xz07gOsrjmkX9ePFdmdzRy968sMCWzi9XV5tOSM5eD6JxhPLivqZjAbeLfq9+wuWLDfcbN3LUGyD+Gtl5cBMD9rBB9Vv8KaXVVd+zQ2Nu739z12+0UUdAZfIorQljWctuyRtGaPpDXneFrzR7nlLG9d9kg6ZEjQa2rzpjr/LM1AtTfFDtEAJwHrP1jOxm1BG8bue4nDvPmWf36PAmDpio00tpR22XpsZxF7Nq1iTUMVqcLopjeYDixdWcOs7HLqP3iWVbXz+3SOCY3LOQR4aekKjqaE3ZveY1VDVRxKGxtGN7/KdODtdY3MBj549xW2rut7G234cxxrSktLaWg4IO4h5uQHmskhiwBF5LbX0bjX9ekLp6OjIyHlSTSDYjgVVb0duB1g7ty5GvqF0Sc2PwQ74OBjLncRbW89SuX8ma4DrM+bD0FLKSee/AkAqqqqgl80L5/KmF2vM+akk/remB4rVOHVSyHgvg2OnVwHUy+BJT+GvBnMPuVL8JfrOHJMI8yu3P/Yh/fA6EoqF3jrn57G2Kwmxob8Pfezt6MV7q+FKQth6legcDxSOIb8rFzygQMqzVOBvwxlytihTJlbGVz37kvwLjD+bAq2uEH/jjnpXKpeeTNo6xNjGFucz9gTK0kZqj+EN+CYBafAG3MpaNpEeV+f/RVLYKVwwsmfZO+Dwykflt33cySSD1fDMph9/AXwyH9w6KThHHpk5f77dLS6l3hWbren2e85jgOrVq2ipCQB/wGNtRDIJW/IaNi7h5KCzohdRBoaGhJTngSTzCCOLcDEkOUJ3rqI+4hIDi6lVfxGBq19BbLyXYReiedFhVehtexw1YeRGH0SNG2GfevjVsReqb4NNt4LM78PQ6bA1n+49XUroXQG5BS6EPnwdrCOVmiq2T86r/jgnqsQ/eqcsnmuHWbIxB5fGilBXoSM9K07XTuX368vpxhyw/pR5g5L3TD6nGLX/23v6r5n0mivh9wSkCzas0pTvw3Mf+byR0FuaeQgjuc+Dsv+NbHlShadAZcxJmeI+/W7RWQIyfTAlgFTRWQKTqgu5sD+Zo8ClwOv4TLhPx/f9q+Xoexjrj7ZD2JoqHbrfFp2HNh25DO60v3ueCHYKB5LOjtcJ9t9m51Qhk7+upbtMO6TcMR/ujao9Xe5fk3NW2DYDHeekcfCujvdw++3Me3bBOj+5S4+CDb82WXvyC5wQS0a0tXD/2fJTXjEbf/JH3Fgtgm/PbD841ByaOTj8obDvg1xL16f8NvAcoa4CMzOdhe9ediiPpyjvuv+OQFb08sBSaatzn0kZRc6EQsXsM52l1KscS3ob5NXE4ILUZd4X187QHKcx5lXCpoGqcD6SWdnp+BGQ+kiaQLmtWktAp4CsoE/qOp7InIj8KaqPgrcAfxJRKqBj4hnBpBAM+z5J0y71i37L/JIHljp9MjnKJ3u+uTsfAEO/kLfrq/q/hlDxWg/gdrkgiRCBQTc13fRRDcNn+mEd+pX3QM99kz48DfwoZcDbdiR7nfkfPjgFtfoP3y2W+d7jaEeWMnBgELjBtfY//SxjBz+XcALVfe/hiNlfk9VInlgLTu93IcCC+4O2rXfccNhz1uJKWO0BBrdh0VWDkw8D8afDcv/zXlUfvBOb7Tv7bp/aeGBtdU5b1gECkYdOCZY4zoXmdeyw3mkpcmJUCwoKGD37t2UlZXFV8S0w3XqBlfjkkTBjiednZ1SW1tbisvL20VS28BU9QngibB1N4TMtwAXJqQw7Xth8udgrNcROafIhVJHErDykyOfQwRGn+gELBQ//U24MO23XBPMa+eTle/C9YsmOu9uyMSgWBVNhCGT3Ndzdw9t+cnu4f7wVrdc6ntgXkP/rteCAtboCdiQ0CpEX8TXwfrFgJLfEfLF256mHlj4PW3dCUO9F13Z3MjH5Q1PvSjEQIP7gAHnlRz/ALxwNiz9F9fnLZqIxLYwDyywz33M5RTGseADIMRjJH/UgdX19auC8zuWRCdggSZ49zsw439i9jE2YcIEampqqK2Nc5Rk01bILoL8th53a2lpoaCg2wTyAGzfvj2no6MjVXuFdwIrA4HAVaErB0UQR0woLIf5i/dfV3zI/n3BOttdSHh3bWDg2sE2/w1eXeiq83yBCs1bB66+unCcE6Gyj8HEz4QIk/ebP2pgX1S5xS5Efcdz7h+zaIJbP6TCnXv3Mhd8Ae5FkJXrytRlv1eNuv0Z2PxXAHI0JDTXF7Ce8gqmGnllB4b1t+xw963H44Y7j6ezPXXa+dobgwIGzhs78WF4YhZ88KvoBKy93j0LeAIGzkPNmRD78saCtrpgcuWC0a66MJS9noDlj4KdVXDoV3s/Z+3LsOp/3bhvU2KTpDs3N7crXVO3BJqh5mFYt9h12Tnzbec9R4sq3DcTDv8POPxHPe5aVVXFUUcd1eM+06dPf1dVu/mCS01MwHqi5GCXG9DHr67oScDGfRJW/I976RdNdNWKY08P85wmumSxWQd0Vo894850AlY6IyiGIjB81v758xrXQ9Hk/ctUUO6+7j64xQmu5JDTGSJgadkGVuZegp0dztbOgOsXlt9LB9DQfIgFo+JdyugIhAkYuPawUce55y8a2uu7Apa6BKx1V/BjJ9VorwsKWP4oLx9iSAqtvavdR1j5KbDtyejSa/l9yXYtjZmAdYuqS+W2fjFsvM/V/OQUu3vZUA0jehaZ/Qjsc9Wl/rOZgZiA9UTxwS75baDJVSl2CVgPL7uSQ+CCutSpix53pku35Ld/+QybCR/+Ovgib1x3YH5AEVeNWL8SplwB25/bX8DSsQoxbwSgrjqwYGSwzaenewr7Z+NIJQGL9MVeOsMF77Tu7j2PZXgQB6R2O1h7fbCWoGCUN7pAXfD+1K9y1cHlJ8OGP0H9e8Hgpe7w7Q335mJJUw2s/5Pztho+cB+GE8+Hg65wQWPPHO+NBN4HAfOrtDNYwJKaCzHlKfZD6b1hRUKzcPREqogXuH/mw/8DDgoLKhk200UX+tlG9q2PHDnp58ib9u+QNyxyFWI6BXGEp5OK5qMEUjMjfSQPDIIfK/Xv9X6Otvr9gzgAWlJYwEKrEL2qzy4PSjUYuOG3U++o6v2cvoDVvR3deHnREmhyYws+/wl4eBK8818uNdoxf4DPbIcFd8GYj7vMM+AijH1UYc0tPbe7moCZB9Yj/su7odp9xUUrYKmECBz1swPXD5vpfutWuC/aSBnaAQ75MpQd4+zPG05uU0iEXlu9q7KKZbqneBOe0Lc1imphSM1BLQON+ycd9vE9jrp3XVBRd3S0usChdPLA/ChECApYay1wqIvSDTS4j7biChgy2Y1t11u3Aj8Uv7Md9rwDI8MTAvUBVRcctW4xbLrfVREOmewCRKYsDL5TQvHzbjZtDa7bu9pFlKJw2L9FvpYJmAlYjww93PWx2P0GTPx0egpYd5Qe7tq19qyAEi+R0pAIAjbuDDcB5A4jpzOkr3loRFi6MGAPLIUELDyIw6dwvHvJhw5yWfMI5JS4L/6u4708iF0CVgJI6mZ472yHjqbgM1cQ5oH5ARxDp7nf8pOh5lE30kGE9EpdtO4KRpnuXto/Adu3yVURrv+jyw2aXQSTLnBVhKNP6vn62fnuwyrUA2v2xKynQWVNwEzAeiS32HX63f4M8EMnYNlFB2ZpSEeyC1yn3boVwdDxSB5YKHnDD6xCTFcB81/SfRWw9hQSsEBD5GdRxHlh9V6QjnbC0qtcl4n9BCwsilSyvb5V2+Na7H7jBw2FVyH699IPofdD58tPdZ6QP0RQd7TWumrXhuq+tYMFmlz6ufWLYftzgDqxOuK/XPtWXyIKC8d6bWAe/nxPmXBMwEzAemXMqfDu91z2hp7SSKUjw2a6L07/Ky+SBxZK3rADgzjSTcCKJrkw+L1eQuyWHc7L9quluiMVPbDu2sDAvZA33OOqtPa85byM9sb994kUhFM4bv+qrFSivc79doXR+x6Y9xGyd7Wzxa9WnXQhrPyeG5h17Ondd39o3eW8trwRLhKxJ1RdntR1i2Hj/e4jYkgFzLgBDlrY/ww8BwiYeWDRYEEcvTHmNEBhx/ORhyBJZ4bPdOmR9rzjqpd6i1jLHUaO7gsOotdWn159wACy82DodFd1CsF72lvgTXaBm1JFwDoDLuCgOwErneEEqqkGtj7p1vkDYPpE6gZROC748kw1wgU3u8A9t60hVYhDpwXvZXaea//duxqqf9f9eVt3OW+u7BgX1BSeagxcFeHKH8Bjh7qIwY33ur6bpyyBc9bCzO8OLH1cQXce2LruB8lt2wNIegVRxRgTsN4o+5j7J9n+zOD0wAC2/t1VH/b2Eve/9AJe20k6emDg7K57x8335aMklbJx+B3je/LAwHWB2OYJWHuYgHW1gYW8AAvHu7yZqYgfAep7YOC8sKYaNx8pddT4c1wWm3e/EzmCVDu97gYjXVJqCFYjBppg/Z/huVPhkQpY8W3XP+7YxXDedpf4oLyy5/ataCkc66pufbHyPyI6mruv0m3b4/4Wsbh+mpK5lkdLVq5rDN7+7OAVsNZdvbd/wYGReOkqYMNnuhdEyy4XhdhbJ2aflBIwrzqwu3YWPxKx9uXgyAOBbqoQ88I8sJadfc9qnwh8AQqt7h11vMsSs/wbzmsZGiZgInD0TU6kVv8i8jm1w/PA5gLiElgvvQr+NgZeu8x5QUd+B85ZB6cucXkmY90OXjgWOtuCwUXN24LC1F01YtuejK4+BGsDi44xp8KWR938YBKwoolOgNrre2//ghABq3O/7XvTrwoRYNgs91v/rntZd5eBPpy84anTDyx0KJVI5A133lT17e4FPeq4AwMUIrWBFY0D1I1gMGQiKUUkwZ33O5fvc80v3bIfgRjKiKNc5pmPlh+4za9+zB/pPNHSw2HD3a57yKSLXBThqOPj7+X4ofTN21xVfvM2GDbbJRhvWOvuXzgmYOaBRcWY04Lzg0nARIJeWFQeWEhn3s52V72Rk4b1777Ne1b0rQoxd1jqeWDdCRi4asTWXe7FPOYT3j0LSRjd1QYWVoUIqdkOFskDy86DebfD0b904jXy2MjHFk10OUnD8fu85Xs5bOf9Hub/2VURHvsH148uEVV0oQKm6v7+I+cDYh5YD5iARcPQw4K54QoHkYBB8GUejQfmvzja9oSENKehB1ZY7kRr16uuX1G0HyWpVIXoRxT2VJXlVyOOOTWk/TIsijS7aP/oPD9NUyoKWHsdLmghrNpUBKZ9HT61qvuPkd4EzI9oHDUfplya+K4yBSECFmhwz2VxhSt3dwLWbgJmAhYNIu4lANG3l6QLfvLQkqm97+tXIbbXpWcexFCGzXJJjiFNgzi8gIyePLBSL5Bj7OnB/UIDOULGAuvCF7CmFAzk8NNe9ccjKpro7l14V4KWkCrEZOJ7YC3bgt0YCsa6yMbu+oKZB2YCFjUTPu36C0VKBZPOVFwGlU9CaYS2g3BCqxDTXsBmBtNJ9SWIo70+2I0gUai6Trof/ga2Pe3WtUdRhTjuTJe+aOIFQa8lNJS+PUI3iIJR7jlPVQ8sNAKxLwyZ5H7DvbDwKsRkkVvs7mXztmBGjqJxnoBF8MBUTcCwII7omXCuS8DZW1+pdCM7D8adHt2+OcUoWUjbnvQcCyyU4bOC833xwMAbQ2tE7Mvk4yel3VnlktHurAp21h1SAeeuj64NrGAUzP+jt58nYO1hVYjhHyCS5XWqTUUPrK73DufdUeQFpDRt3j/UvrXWVaPmFA20dAPH78zs9wErGOs+mFu2u24TOUOC+3Y0uTZNEzAjagabePUVEQJZxeS21aXnWGCh+G1/0HcBa9sTWwHrSbCKJsCY011/o91vwNrfuUCM3sLow/HbdEI9sLYIAgZeX7AU9MBatvffAwsVsFBadyXf+/LpEjDvb180Dob4o6Kv339YGMvCASRJwERkBHA/UAFsAC5S1T1h+8wGfgMMBTqAH6jq/QktqHEAASkmdzC0gQ2d5qrKNBDMqdcbXR5Y3cCuHa1glVe64Bq/g3lWHlT/1r3MujywIQecPiJdHlhYFWLR+AP3LRoXTLWVKtStdAI+6wf9O75oPCAuo0YorbtSZ3y3grEu1L95m+cVlgSbLBrXOQHzB+jsErA41gSkAcnywK4HnlPVH4vI9d7yt8L2aQIWquqHIjIOWC4iT6lqXYLLaoQQyCr2GsPTXMCy811VUuMGyCmM7pj+5kPsr2CF442cTMOHTsCy8rvP7xdOlwcWVoUYyaMpHOclp00hVt/kXuqHfLl/x2flOg8n3ANrqU0xD2yrmwrHBQeUBRfI0bgenp4PR/082EfPPLCkcC5Q6c3/EagiTMBU9YOQ+a0ishMYBdQlpIRGRNqlxLVFpHMYvc+o450IREu0AtaTYBWOd32yyitdiqPig6IfANWPFG340LVl9SXUuzsPrLsqxPb6A9tdkkXzdte5+OAvDqzqNlIofesu100mFSgc69q29n4QjErMG+HuUUM1vP4Flw3og1vgiOu97SZgyaBcVf3MlduBHjviiMg8IA/oYWwBIxEEsoqhvdbrQ1QYvQeQihx9U99SJnU3qGW8BCuc/DL3wmqs9sSlDwIWHoXY2e7OEVHA/FD6rTA0iu4V8eaDW115p10zsPMUTQwOMePTmmIeGLgyTviMm/e9sHWLnbiVHQu7Xw9mzTcBiw8i8iwQYbhY/jt0QVVVRLpJtwwiMhb4E3C5qnZ2s8/VwNUA5eXlVFVV9bfYfaaxsTGh10s2B3Xk09qwg93tqynTQl4bxLaH39uszmZOBNauWs7u9XcxrO1thrW+zbC2t8nrdKLWmjWSPflHUVc6i7r82bRkj4M2gc3A5s24mf5ztJYT2PgGHVlFFAWEZdH+/VU5iSw2rX2P9bVV5HTWczzw4cZatuyu2s/e4a21zALeeu3v1OfPHlB5B0pWZwvzd95MXcFxvLe8Bqjp97kOrodxTRt4ackSGvft48UlT3NioJF1WxvY1FAVqyL3m2GtO5kN0NnO5t0drPXu7fTmoYzuaGJ3/jGsyfl35vNZAqtvJRd4+Y33CGRt7PXcg/Y9paoJn4A1wFhvfiywppv9hgL/BC6I9txz5szRRLJkyZKEXi/ZbHz4s6r3Faq+dKHqY4cluzhx5YB729mpem+u6j3Zqnfjpr+NV33l86rVv1fdW+32iScvf0714cmqz31C9clj+nbsA8NUl/2bm29Y68q/dnHX5i57695329bfHZMiD4iav7uybH164Oda9Qt3rpZdztZ9NW75w98O/NyxoO694HP13k+C61f+UPXBUar7trjl58/w9hPVzo6oTh3Newp4U5OgBwOZklWF+ChwOfBj7/eR8B1EJA94CLhLVR9MbPGM7ghkFXtDPOxM3wCO/iICUxe5aqdYVAn2h5KpbiyqvLK+Vx/llgSrEHvqBpFK6aTq33O/ZR8b+LnCQ+lbUyQLh49fhQjBewAw/Xo47JpgsNGUhW6InAwfSgWS1wb2Y+ABEbkS2AhcBCAic4Evq+pV3roTgTIRucI77gpVfTvxxTV8Alleu8u+jcGouExizk3JvX7JVEBh7/sw9oy+HZtTHOzIHD66cSi5Q13EXyqMzFz/nnuZ97f/Vyi+gO3bDJSEZOFIkTD63GEuqKizdX8xE9k/UnbCp909yvD2L0iSgKnqbuCUCOvfBK7y5v8M/DnBRTN6ISBeMEBTDYyYk9zCZCL+R0NPozF3R7QemIjrN5UK2Tjq34PSI2JzLj/0vGkTcETq5EH0EXHCtW/D/h5YODmFcPh1wUChDCaz/U+jz3R5YBrIvCrEVCA06XJfBSynJBhG31sqsMJxya9C1E6ofx9Kp8fmfAXlLmq2qwoxRfIghuJ7XqEeWCRmfBvm3hz/8qQ4JmBGnwhkhaQuMgFLPPkjgtkX+jrkR25xsCNzbx3RC8cnPyP9vo0udDxWHphkQeEErwoRT8AktbJZFI513VPsfysqTMCMPhGQkJdmOndiTmf8asSBeGBdg0N2cw+LxkFzDaz6X/jon4nPwA/BAI5YCRi4asTQII78EZCVHbvzD5Rxn4LJFyc2MCiNsWS+Rp/oqkIE+0pMFiVTXV7AgbSBtde7LBtZ3bwCxp8LWx6Dt67zjh3mpbs62U2lR8Q/Aq5LwGJUhQgukKP2ZSjFS+SbIgEcPgd/wU1GVJiAGX3CBCwF8NvB+uyBhVUh9nT/Rh8Pn1rtIhF3LIGdS2D781DzsNuePypE0D4OJYfG3muoe89VZcYiAtGnaCI0baG04B1oXpta7V9GnzEBM/pEp+RBdoGLgrMqxORQPIAqxI4W6Ay4KMRohKFoHEy51E3g2qV2eGK2cwls+otbXzgWRp8MYz7uRK2npMTREssIRJ+SqaABjtp9jVuuuDS25zcSigmY0Xdyh0HHdvPAksVwbyyz3iLVwgnNh9he17/7N2QyHHSFm1RdlvQdzztR2/EcbLzH7Vc0yYnZaK/K0Q9hjxbthL2r+p99vjsqPg/FU3j77X8y+6iPwfDZsT2/kVBMwIy+kzfcDS5oApYchh0JZ70HQw/vfd9QfI+tvdF5YAMdoFXEBZSUHAKHXO0lNV7lidkSqHnUJaEF5zX67WflJ0NhpDSpITSudxlfhsXYA8vOg/KTqcsXGH1ibM9tJBwTMKPv+FVPJmDJoz+BDft5YPXBsaZihYgrV+l0OPRfnRdV964naM/DpvvdiNLgxNdvPxt9EhSEtUXFIwLRGHSYgBl9J3eY+7U2sPSiywPzqhBjGRwRCcmC4bPcNO0a1/a2562gh7b+j/Dhr92+w2Y6MSs/2XlG9e+79X31Mo2MwgTM6Dt+DjbzwNKLLg/Mq0JM9P3LynFJecs+BtO/6cb42r0s6KFV3wZrfumEL3uIG63aPpKMHjABM/pO3jCXdDS7D6MZG8nHH5W5dZdLGJtsccjKhVEL3DTjv12E5K6lTsx2VrmqRcPoARMwo+8c8kVX5WOkF34VYpM3KKRfFZwqZBdA+UluMowoMAEz+s7w2RZ+nI7khowkAFYFbKQ9lgvRMDIFX8D8YVKSXYVoGAPEBMwwMoXsIkDMAzMGDSZghpEpiLh2MF/A4h1GbxhxxgTMMDKJ3JLgQJXmgRlpjgmYYWQSOcWg3theJmBGmpMUARORESLyjIh86P0O72HfoSJSIyK3JLKMhjEo8QM5kJB5w0hPkuWBXQ88p6pTgee85e74PvBiQkplGIMdvzNz7tD4D0hpGHEmWU/wucAfvfk/Ap+OtJOIzAHKgacTUyzDGOT4nZmt+tAYBIiqJv6iInWqOsybF2CPvxyyTxbwPPB54FRgrqou6uZ8VwNXA5SXl8+577774lf4MBobGyku7uPAgmlMJtk7GG09fM/3KW9+nsacg3hz9B37bRuM9nZHJtkK0dl78sknL1fVuQkqUkyIWyYOEXkWiDToz3+HLqiqikgkFf0q8ISq1kgvI7uq6u3A7QBz587VysrKfpW5P1RVVZHI6yWbTLJ3UNr6xr1Q/TzFw8cfYNugtLcbMslWGLz2xk3AVPXU7raJyA4RGauq20RkLLAzwm7zgRNE5KtAMZAnIo2q2lN7mWEYPWFViMYgIlm5EB8FLgd+7P0+Er6Dql7qz4vIFbgqRBMvwxgIfhCHpZEyBgHJCuL4MXCaiHyIa9/6MYCIzBWR3yepTIYx+Mk1D8wYPCTFA1PV3cApEda/CVwVYf1iYHHcC2YYg50uD2xYUothGLHAOoIYRibhd142D8wYBJiAGUYmYUEcxiDCBMwwMolcq0I0Bg8mYIaRSRQfDFn5MHRasktiGAMmWWH0hmEkgyGT4OKWZJfCMGKCeWCGYRhGWmICZhiGYaQlJmCGYRhGWmICZhiGYaQlJmCGYRhGWmICZhiGYaQlJmCGYRhGWmICZhiGYaQlohppMOT0RURqgY0JvORIYFcCr5dsMsneTLIVMsveTLIVorN3sqqOSkRhYsWgE7BEIyJvqurcZJcjUWSSvZlkK2SWvZlkKwxee60K0TAMw0hLTMAMwzCMtMQEbODcnuwCJJhMsjeTbIXMsjeTbIVBaq+1gRmGYRhpiXlghmEYRlpiAmYYhmGkJSZgSUJEykTk5yLysIhcLiKS7DLFExEZJSL/JyJ3isg5yS5PvMkke+1ZHryk+r01AUseFwFjgFuAC4BFqfZwxAoRyQa+DAwB/gZ8R0Q+mdxSxR7//olIDoPcXs9GH3uWBxkh9y+l721O77sYA8X7SjsBeEBVl3mrrwD+RVXfE5Em3D/FKcCzySll7BCRs4HTgYdU9TlV7RCRhUClqm4RkWLgTBHZoarLk1vagSMiQ4G7gb8Ci1U1MFjtFZGzgEuAzcB/eqsvB64cpM/yRcCJwN2q+tpgfpZF5FNAjqo+rMHovpS+t+aBxRERmS4iLwNfArYBd4rIZ7zN64GPe/PvAiuBsxNfytghIlNF5DXgK8AK4P9E5GJv82vA8d78q8Ae4KTElzIuHAQcBkwTkYneulcYRPZ6VUk1wL8DTwLfC9m8lkH2LAOIyNW4/93VwC0i8llv0ws4UYPBcW+LReQR4E5gYcgzDCl+b03A4ksncL2qnqWqNwF/xn3RADwNHOPNNwJLgTHeF126shM4X1U/qaq3A48DR4pIFk7QjvT2qwGqgbEiUpCcog6ckKqUE4C3gVbgcM/elQwie1V1N/AGcKuq/llVW0I2P8MgepZD7uuJwB2qegvwf8CpInIM7l4PpnvbCHwf+BSwCjgOuv4OKf2eMgEbIN7Xy0UiMj1knV81uxF4zXuhgXuJf+TNv4Z76A/z3PUcYDeubj1l6cbebG+2QVW3ishQEfkJcDGwTFU7cbZPEpEpqtoB5AICtCXahr7Qi72ISB7Olj8AecAhnr3vkmb29mYrcB/wnyJyrYjcLyIXes/6q8A4EZmWZs9ytoj8i4h8wlvOERFRVRWRUtz/b6e3+1+93ynAJpy96XRvI9rqzWep6pu4Z3Y3cDSAdy+XkcLvKROwfiIi40Xkd7h/3s8D3xCRywC8NpA8VW32HnC/PnkR7msVVV2Fe2C+6W0rAwpUdUci7YiWXuzt8Oz1/9nH4L7WvgpcKSJfxtn6EfB1b59soCjkmJQiSntVVduAs1T1SVy14SwRuRL3T76DNLA3Glu9XZ8HCoEK4LfAfwDfUNUPcF5JWjzLIRwO3Azc4C13eOKVo6r1uHs2QkSKVXUfsA5n+/tAHWlwb0Poztau/1tVbcJ5YMNE5Ehv3fuk8HvKBKwPiMjIkMUW4DlgrqqeA2wFDvO+bKbjopMOAvcl460rBB4KOcfPgDYReRPXpvD3RNgRLf2w92AAVf1AVb+vqk/g/mkuUdVtwK+AKSKyDPgG8EAi7emNfthbISKzgDdF5IvAfwELgbNwL/TfkqL29tHW74rIwaq6CzhWVb+mqs8D38ZVOwH8AmhN9Wc5pDYEnMf8M+AIERnr/Z/OAP6fiAwH3sG1bx7k7b8c+ISqVgO3kuL3Nkpbu/5vPdYC9cCxIetS9z2lqjb1MOFE/hrgQ1zkTXaEfY7EeVYXe8sFQJ4376frWgh83Zv/NFARcv6ZybYzVvZG2HcS8BIw1FsuAeYk284Y2Fvgzc8A1gCPAJcC3wGuDbnvKWNvHO7tFFxAQ0nI+VP+WQ65N78HZgF/BH7krSsD8kOe3R8BP/WWK3BtQv72lL+3UdiaE+Fc5+M+PD8PzPbPA8xKtp3hk3lgvaDOvR6B8x624kJIu75uRGQ28FNcvfiZIvIlVW1RV7WEencf9+VyqbioxGuBUv/8qroicRb1zEDt9fYZKyInisivgSdwDeF7vfM3aAqFGw/A3hbv+JXAUap6rqrejauCaffOmVL2xujeDhWRBSLyG1yQzh2q2uCfPx2eZSDba9vbjqvm/QVwoYiMVtXdqtrqHb8J52nNEpEHcQEMfw7ZnvL3lt5tDfjnEMd04DpcsNl1uFoj1PFOwgyKlmQraCpNwDxgeMiy//UyBCjG9Xv5hbcuK8LxR+Pc6yO85XJgIu5L7j6ccI1Otp1xtnc8MAq4yTt+TLLtjKO9o3FBGxD8Kj/AqxkktvrP8lDgJ7jq0rS8t9764bjq/DHA13DVqLVAubd9ZMi9nYQLHR+fbDvjaOtUb/5zwI/9bak+ZbwHJiJ5InKdiKzH1WWP97epd0dVdZ+6UNNXgakiMlwjN9juw/UJaRPXufU84DBV3aSqF6vqTaq6M+5G9UCc7f007oVXq6rXquqPVHV7vG3qiQTY67f7+V/lHfG0pycSYOvhqrpXVb+lqj9M13vr7ZILTMW1c03HeZMPqeoOERkNfAY4xDvHJlV9TFW3JMq2cBJo6z2qer2mSJBGryRbQZM14X11Aofi2i9GRnHMeGAxcJm3LLgIpInAacDDwM3Jts3szSx7M8nWGNh7ubdcimvv89uKKoAdybYtk23tz5QxHpiIFIrIV0Xku94qv23qEKBaVXeJyEQRmRRyTHjOr49wjdZHe8sl3nkW4xpQH8JVEyYds3fw2ptJtkLM7Z3tr1DVd9V1E8hV1Q3AhHjZEC2ZZGssyIgBLb1GzNtx6X4qgJNUda237du4euMmvI63QK2q/oeI69QYdq7PeOfqBG5U1VtEpES9huxUwOwdvPZmkq0QV3u/A/xeVdsTZUtvZJKtMSPZLmCiJtyXZg4uCuc7IeuPx4VB+6GlE3FRWJMjnGMRLonp/5Ei4bNmb+bZm0m2Zpq9mWRrTP5eyS5AEh6QM4HXw9a9gAsZ9SPJngW+7M0LwXro0mSX3+w1ezPR1kyzN5NsHciUMW1gIbwEqIgcF7LuXlyI6TEicjguxPQ16Or/4KdaqU90YWOA2Tt47c0kWyGz7M0kW/tNxo0HpqqNIlKFG6jtFW/1H4EFwPW4iJ0Hcfm/0h6zFxik9maSrZBZ9maSrQMhI4I4whE33s2TuFx1dcD9qtoqIqNUtTaphYsDZu/gtTeTbIXMsjeTbO0vGVeFKCJHA7/GRfmcCaz2HgoZjA+F2Tt47c0kWyGz7M0kWwdCxnlgIlIJTMZ9zbT0vHf6Y/YOXjLJVsgsezPJ1oGQcQJmGIZhDA4yrgrRMAzDGByYgBmGYRhpiQmYYRiGkZaYgBmGYRhpiQmYYRiGkZZkXCYOw4iEiHTgshrkAgHgLtyotpEGe4zF9b4AfN1bnI5L1NqB67jaBryoqs/G49qGMViwMHrDAESkUVWLvfnRwD3AK6r6nQRcewMwV1V3xftahjGYsCpEwwhDVXcCVwOLxFEhIi+JyD+9aQGAiNwlIp/2jxORu0Xk3IFeX0QWi8gF3vwGEfmRiLwtIm+KyNEi8pSIrBWRL4ccc52ILBORFSLyvYGWwTDSARMww4iAqq4DsoHRwE7gNFU9GvgscLO32x3AFQAiUopLtPr3OBRnk6rOxmUoXwxcABwLfM+79ieAqcA83Ci8c0TkxDiUwzBSCmsDM4zeyQVuEZHZuHaqQwFU9QUR+bWIjALOB/6qqoE4XP9R7/ddoFjdiMkNItIqIsOAT3jTW95+xThBezEOZTGMlMEEzDAiICIH4cRqJ25I9h3ALFytRWhuuruAz+OGef9ChPP8ADgLwPOi+kOr99sZMu8v5+AGM/yRqv62n+c3jLTEqhANIwzPo7oNuEVdlFMpsM2LSLwMV7Xosxi4BkBV3w8/l6r+t6rOHoB4RcNTwL+IiB+EMt4LRDGMQY15YIbhKBSRtwmG0f8JuMnb9mvgryKyEBfmvs8/SFV3iMgq4OGEljYEVX3aG6H3NREBaMR5hTuTVSbDSAQWRm8YA0BEinBtU0dn0lDuhpEKWBWiYfQTETkVWAX8ysTLMBKPeWCGYRhGWmIemGEYhpGWmIAZhmEYaYkJmGEYhpGWmIAZhmEYaYkJmGEYhpGWmIAZhmEYacn/B1MdsS/TiSuAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_epochs = 30\n",
    "batch_size = 50\n",
    "mlp_history, mlp_prediction = compile_and_fit(mlp_model, train_data, train_target, val_data, val_target, \n",
    "                                              test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_lstm_model(input_shape, output_shape):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.LSTM(units = 200, input_shape = input_shape, kernel_initializer='random_normal', bias_initializer='zeros', dropout=0.2, return_sequences=False),\n",
    "\n",
    "            tf.keras.layers.Dense(output_shape)])\n",
    "    return model\n",
    "\n",
    "# recurrent_dropout = dropout ?\n",
    "\n",
    "input_shape = train_data[0].shape\n",
    "output_shape = train_target.shape[1]\n",
    "lstm_model = f_lstm_model(input_shape, output_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 19784 samples, validate on 5466 samples\n",
      "Epoch 1/20\n",
      "19784/19784 [==============================] - 10s 517us/sample - loss: 0.0027 - mean_absolute_error: 0.0285 - val_loss: 0.2027 - val_mean_absolute_error: 0.3994\n",
      "Epoch 2/20\n",
      "19784/19784 [==============================] - 7s 339us/sample - loss: 0.0015 - mean_absolute_error: 0.0279 - val_loss: 0.1990 - val_mean_absolute_error: 0.3940\n",
      "Epoch 3/20\n",
      "19784/19784 [==============================] - 7s 335us/sample - loss: 0.0018 - mean_absolute_error: 0.0314 - val_loss: 0.1884 - val_mean_absolute_error: 0.3817\n",
      "Epoch 4/20\n",
      "19784/19784 [==============================] - 7s 339us/sample - loss: 0.0018 - mean_absolute_error: 0.0322 - val_loss: 0.1757 - val_mean_absolute_error: 0.3675\n",
      "Epoch 5/20\n",
      "19784/19784 [==============================] - 7s 345us/sample - loss: 0.0018 - mean_absolute_error: 0.0325 - val_loss: 0.1691 - val_mean_absolute_error: 0.3594\n",
      "Epoch 6/20\n",
      "19784/19784 [==============================] - 7s 338us/sample - loss: 0.0019 - mean_absolute_error: 0.0335 - val_loss: 0.1615 - val_mean_absolute_error: 0.3500\n",
      "Epoch 7/20\n",
      "19784/19784 [==============================] - 7s 348us/sample - loss: 0.0018 - mean_absolute_error: 0.0334 - val_loss: 0.1544 - val_mean_absolute_error: 0.3410\n",
      "Epoch 8/20\n",
      "19784/19784 [==============================] - 7s 369us/sample - loss: 0.0018 - mean_absolute_error: 0.0330 - val_loss: 0.1482 - val_mean_absolute_error: 0.3331\n",
      "Epoch 9/20\n",
      "19784/19784 [==============================] - 7s 335us/sample - loss: 0.0017 - mean_absolute_error: 0.0322 - val_loss: 0.1422 - val_mean_absolute_error: 0.3254\n",
      "Epoch 10/20\n",
      "19784/19784 [==============================] - 7s 329us/sample - loss: 0.0016 - mean_absolute_error: 0.0316 - val_loss: 0.1389 - val_mean_absolute_error: 0.3207\n",
      "Epoch 11/20\n",
      "19784/19784 [==============================] - 7s 329us/sample - loss: 0.0016 - mean_absolute_error: 0.0317 - val_loss: 0.1298 - val_mean_absolute_error: 0.3084\n",
      "Epoch 12/20\n",
      "19784/19784 [==============================] - 7s 332us/sample - loss: 0.0016 - mean_absolute_error: 0.0309 - val_loss: 0.1211 - val_mean_absolute_error: 0.2962\n",
      "Epoch 13/20\n",
      "19784/19784 [==============================] - 7s 330us/sample - loss: 0.0015 - mean_absolute_error: 0.0302 - val_loss: 0.1132 - val_mean_absolute_error: 0.2845\n",
      "Epoch 14/20\n",
      "19784/19784 [==============================] - 7s 335us/sample - loss: 0.0014 - mean_absolute_error: 0.0297 - val_loss: 0.1074 - val_mean_absolute_error: 0.2753\n",
      "Epoch 15/20\n",
      "19784/19784 [==============================] - 7s 333us/sample - loss: 0.0014 - mean_absolute_error: 0.0290 - val_loss: 0.1039 - val_mean_absolute_error: 0.2694\n",
      "Epoch 16/20\n",
      "19784/19784 [==============================] - 7s 332us/sample - loss: 0.0013 - mean_absolute_error: 0.0284 - val_loss: 0.1003 - val_mean_absolute_error: 0.2631\n",
      "Epoch 17/20\n",
      "19784/19784 [==============================] - 7s 329us/sample - loss: 0.0013 - mean_absolute_error: 0.0280 - val_loss: 0.0982 - val_mean_absolute_error: 0.2593\n",
      "Epoch 18/20\n",
      "19784/19784 [==============================] - 7s 329us/sample - loss: 0.0012 - mean_absolute_error: 0.0276 - val_loss: 0.0960 - val_mean_absolute_error: 0.2554\n",
      "Epoch 19/20\n",
      "19784/19784 [==============================] - 7s 333us/sample - loss: 0.0012 - mean_absolute_error: 0.0272 - val_loss: 0.0954 - val_mean_absolute_error: 0.2539\n",
      "Epoch 20/20\n",
      "19784/19784 [==============================] - 7s 338us/sample - loss: 0.0012 - mean_absolute_error: 0.0269 - val_loss: 0.0943 - val_mean_absolute_error: 0.2519\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 200)               169600    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 65)                13065     \n",
      "=================================================================\n",
      "Total params: 182,665\n",
      "Trainable params: 182,665\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "2603/1 [=======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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- 0s 177us/sample - loss: 0.1017 - mean_absolute_error: 0.3462\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65,)\n",
      "(65,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_epochs = 20\n",
    "batch_size = 50\n",
    "lstm_history, lstm_prediction = compile_and_fit(lstm_model, train_data, train_target, val_data, val_target, \n",
    "                                                test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRU RNN model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_gru_model(input_shape, output_shape):\n",
    "    model = tf.keras.Sequential([\n",
    "            tf.keras.layers.GRU(units = 300, input_shape = input_shape, kernel_initializer='random_normal', bias_initializer='zeros', dropout=0.2, return_sequences=False),\n",
    "        \n",
    "            tf.keras.layers.Dense(output_shape)])\n",
    "    return model\n",
    "\n",
    "input_shape = train_data[0].shape\n",
    "output_shape = train_target.shape[1]\n",
    "gru_model = f_gru_model(input_shape, output_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 19784 samples, validate on 5466 samples\n",
      "Epoch 1/20\n",
      "19784/19784 [==============================] - 11s 567us/sample - loss: 0.0026 - mean_absolute_error: 0.0294 - val_loss: 0.1885 - val_mean_absolute_error: 0.3834\n",
      "Epoch 2/20\n",
      "19784/19784 [==============================] - 8s 410us/sample - loss: 0.0013 - mean_absolute_error: 0.0247 - val_loss: 0.2111 - val_mean_absolute_error: 0.4038\n",
      "Epoch 3/20\n",
      "19784/19784 [==============================] - 8s 415us/sample - loss: 0.0011 - mean_absolute_error: 0.0239 - val_loss: 0.1792 - val_mean_absolute_error: 0.3739\n",
      "Epoch 4/20\n",
      "19784/19784 [==============================] - 8s 415us/sample - loss: 0.0012 - mean_absolute_error: 0.0253 - val_loss: 0.1729 - val_mean_absolute_error: 0.3640\n",
      "Epoch 5/20\n",
      "19784/19784 [==============================] - 8s 411us/sample - loss: 0.0011 - mean_absolute_error: 0.0237 - val_loss: 0.1632 - val_mean_absolute_error: 0.3482\n",
      "Epoch 6/20\n",
      "19784/19784 [==============================] - 8s 413us/sample - loss: 9.9204e-04 - mean_absolute_error: 0.0231 - val_loss: 0.1568 - val_mean_absolute_error: 0.3444\n",
      "Epoch 7/20\n",
      "19784/19784 [==============================] - 8s 411us/sample - loss: 9.6145e-04 - mean_absolute_error: 0.0228 - val_loss: 0.1609 - val_mean_absolute_error: 0.3506\n",
      "Epoch 8/20\n",
      "19784/19784 [==============================] - 8s 415us/sample - loss: 9.6229e-04 - mean_absolute_error: 0.0230 - val_loss: 0.1506 - val_mean_absolute_error: 0.3393\n",
      "Epoch 9/20\n",
      "19784/19784 [==============================] - 8s 414us/sample - loss: 9.5437e-04 - mean_absolute_error: 0.0228 - val_loss: 0.1483 - val_mean_absolute_error: 0.3364\n",
      "Epoch 10/20\n",
      "19784/19784 [==============================] - 8s 414us/sample - loss: 9.3145e-04 - mean_absolute_error: 0.0226 - val_loss: 0.1453 - val_mean_absolute_error: 0.3335\n",
      "Epoch 11/20\n",
      "19784/19784 [==============================] - 8s 414us/sample - loss: 9.5934e-04 - mean_absolute_error: 0.0231 - val_loss: 0.1436 - val_mean_absolute_error: 0.3293\n",
      "Epoch 12/20\n",
      "19784/19784 [==============================] - 8s 413us/sample - loss: 9.7444e-04 - mean_absolute_error: 0.0234 - val_loss: 0.1334 - val_mean_absolute_error: 0.3207\n",
      "Epoch 13/20\n",
      "19784/19784 [==============================] - 8s 409us/sample - loss: 9.6758e-04 - mean_absolute_error: 0.0235 - val_loss: 0.1294 - val_mean_absolute_error: 0.3151\n",
      "Epoch 14/20\n",
      "19784/19784 [==============================] - 8s 406us/sample - loss: 9.9929e-04 - mean_absolute_error: 0.0241 - val_loss: 0.1208 - val_mean_absolute_error: 0.3049\n",
      "Epoch 15/20\n",
      "19784/19784 [==============================] - 8s 404us/sample - loss: 9.7800e-04 - mean_absolute_error: 0.0239 - val_loss: 0.1217 - val_mean_absolute_error: 0.3057\n",
      "Epoch 16/20\n",
      "19784/19784 [==============================] - 8s 404us/sample - loss: 9.9128e-04 - mean_absolute_error: 0.0241 - val_loss: 0.1184 - val_mean_absolute_error: 0.3012\n",
      "Epoch 17/20\n",
      "19784/19784 [==============================] - 8s 409us/sample - loss: 9.8195e-04 - mean_absolute_error: 0.0240 - val_loss: 0.1145 - val_mean_absolute_error: 0.2952\n",
      "Epoch 18/20\n",
      "19784/19784 [==============================] - 8s 415us/sample - loss: 9.8927e-04 - mean_absolute_error: 0.0241 - val_loss: 0.1072 - val_mean_absolute_error: 0.2853\n",
      "Epoch 19/20\n",
      "19784/19784 [==============================] - 8s 408us/sample - loss: 9.8095e-04 - mean_absolute_error: 0.0242 - val_loss: 0.1038 - val_mean_absolute_error: 0.2804\n",
      "Epoch 20/20\n",
      "19784/19784 [==============================] - 8s 404us/sample - loss: 9.7078e-04 - mean_absolute_error: 0.0241 - val_loss: 0.0975 - val_mean_absolute_error: 0.2706\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru (GRU)                    (None, 300)               281700    \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 65)                19565     \n",
      "=================================================================\n",
      "Total params: 301,265\n",
      "Trainable params: 301,265\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "2603/1 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- 0s 190us/sample - loss: 0.1369 - mean_absolute_error: 0.3765\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65,)\n",
      "(65,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_epochs = 20\n",
    "batch_size = 50\n",
    "gru_history, gru_prediction = compile_and_fit(gru_model, train_data, train_target, val_data, val_target, \n",
    "                                              test_data, test_target, max_epochs, batch_size, test_entry, label_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}