[Arrangement] Simple Regression using mask images and label.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73d3f48b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56623899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_pixels_from_mask(masks_dir, masks) :\n",
    "    pixels = []\n",
    "\n",
    "    for mask in masks :\n",
    "        path = os.path.join(masks_dir, mask)\n",
    "        img = cv2.imread(path)\n",
    "\n",
    "        # non zero pixel 수 계산\n",
    "        pix = np.count_nonzero(img)\n",
    "        pixels.append(pix)\n",
    "\n",
    "    return pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1a3c809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>leaf_weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>img_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CASE01_01.png</th>\n",
       "      <td>49.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASE01_02.png</th>\n",
       "      <td>59.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASE01_03.png</th>\n",
       "      <td>72.209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASE01_04.png</th>\n",
       "      <td>85.737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CASE01_05.png</th>\n",
       "      <td>102.537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               leaf_weight\n",
       "img_name                  \n",
       "CASE01_01.png       49.193\n",
       "CASE01_02.png       59.764\n",
       "CASE01_03.png       72.209\n",
       "CASE01_04.png       85.737\n",
       "CASE01_05.png      102.537"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks_dir = './data/segmentation/testannot'\n",
    "label_data = './data/labels.csv'\n",
    "\n",
    "masks = os.listdir(masks_dir)\n",
    "masks.sort()\n",
    "\n",
    "label_df = pd.read_csv(label_data)\n",
    "label_df.set_index('img_name', inplace=True)\n",
    "label_df = label_df.sort_values(by='img_name', axis=0)\n",
    "\n",
    "label_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c15935d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pixels = calc_pixels_from_mask(masks_dir, masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adae52cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1592"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7476b43",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_data = np.array(pixels)\n",
    "x_data = x_data.reshape(-1,1)\n",
    "y_data = label_df['leaf_weight'].values\n",
    "y_data = y_data.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd3844ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x_data, valid_x_data, train_y_data, valid_y_data = \\\n",
    "train_test_split(x_data, y_data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "116f89d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler_x = MinMaxScaler()\n",
    "scaler_y = MinMaxScaler()\n",
    "\n",
    "scaler_x.fit(train_x_data)\n",
    "scaler_y.fit(train_y_data)\n",
    "\n",
    "scaled_train_x = scaler_x.transform(train_x_data)\n",
    "scaled_train_y = scaler_y.transform(train_y_data)\n",
    "\n",
    "scaled_valid_x = scaler_x.transform(valid_x_data)\n",
    "scaled_valid_y = scaler_y.transform(valid_y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "95a14e36",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1273, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9cb8a278",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(319, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_valid_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "965be674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 64)                128       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 193\n",
      "Trainable params: 193\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense \n",
    "from tensorflow.keras.optimizers import Adam,SGD\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(input_dim=1, units=64, activation='relu', use_bias=True))\n",
    "model.add(Dense(units=1, activation='linear', use_bias=True))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fdf94bde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "40/40 - 0s - loss: 0.1430 - mae: 0.1430 - val_loss: 0.1423 - val_mae: 0.1423\n",
      "Epoch 2/5000\n",
      "40/40 - 0s - loss: 0.1419 - mae: 0.1419 - val_loss: 0.1410 - val_mae: 0.1410\n",
      "Epoch 3/5000\n",
      "40/40 - 0s - loss: 0.1408 - mae: 0.1408 - val_loss: 0.1397 - val_mae: 0.1397\n",
      "Epoch 4/5000\n",
      "40/40 - 0s - loss: 0.1397 - mae: 0.1397 - val_loss: 0.1384 - val_mae: 0.1384\n",
      "Epoch 5/5000\n",
      "40/40 - 0s - loss: 0.1385 - mae: 0.1385 - val_loss: 0.1372 - val_mae: 0.1372\n",
      "Epoch 6/5000\n",
      "40/40 - 0s - loss: 0.1374 - mae: 0.1374 - val_loss: 0.1359 - val_mae: 0.1359\n",
      "Epoch 7/5000\n",
      "40/40 - 0s - loss: 0.1363 - mae: 0.1363 - val_loss: 0.1347 - val_mae: 0.1347\n",
      "Epoch 8/5000\n",
      "40/40 - 0s - loss: 0.1353 - mae: 0.1353 - val_loss: 0.1335 - val_mae: 0.1335\n",
      "Epoch 9/5000\n",
      "40/40 - 0s - loss: 0.1342 - mae: 0.1342 - val_loss: 0.1323 - val_mae: 0.1323\n",
      "Epoch 10/5000\n",
      "40/40 - 0s - loss: 0.1332 - mae: 0.1332 - val_loss: 0.1312 - val_mae: 0.1312\n",
      "Epoch 11/5000\n",
      "40/40 - 0s - loss: 0.1321 - mae: 0.1321 - val_loss: 0.1300 - val_mae: 0.1300\n",
      "Epoch 12/5000\n",
      "40/40 - 0s - loss: 0.1311 - mae: 0.1311 - val_loss: 0.1289 - val_mae: 0.1289\n",
      "Epoch 13/5000\n",
      "40/40 - 0s - loss: 0.1301 - mae: 0.1301 - val_loss: 0.1278 - val_mae: 0.1278\n",
      "Epoch 14/5000\n",
      "40/40 - 0s - loss: 0.1292 - mae: 0.1292 - val_loss: 0.1267 - val_mae: 0.1267\n",
      "Epoch 15/5000\n",
      "40/40 - 0s - loss: 0.1282 - mae: 0.1282 - val_loss: 0.1257 - val_mae: 0.1257\n",
      "Epoch 16/5000\n",
      "40/40 - 0s - loss: 0.1273 - mae: 0.1273 - val_loss: 0.1246 - val_mae: 0.1246\n",
      "Epoch 17/5000\n",
      "40/40 - 0s - loss: 0.1264 - mae: 0.1264 - val_loss: 0.1236 - val_mae: 0.1236\n",
      "Epoch 18/5000\n",
      "40/40 - 0s - loss: 0.1256 - mae: 0.1256 - val_loss: 0.1226 - val_mae: 0.1226\n",
      "Epoch 19/5000\n",
      "40/40 - 0s - loss: 0.1247 - mae: 0.1247 - val_loss: 0.1217 - val_mae: 0.1217\n",
      "Epoch 20/5000\n",
      "40/40 - 0s - loss: 0.1239 - mae: 0.1239 - val_loss: 0.1207 - val_mae: 0.1207\n",
      "Epoch 21/5000\n",
      "40/40 - 0s - loss: 0.1231 - mae: 0.1231 - val_loss: 0.1197 - val_mae: 0.1197\n",
      "Epoch 22/5000\n",
      "40/40 - 0s - loss: 0.1222 - mae: 0.1222 - val_loss: 0.1188 - val_mae: 0.1188\n",
      "Epoch 23/5000\n",
      "40/40 - 0s - loss: 0.1214 - mae: 0.1214 - val_loss: 0.1178 - val_mae: 0.1178\n",
      "Epoch 24/5000\n",
      "40/40 - 0s - loss: 0.1206 - mae: 0.1206 - val_loss: 0.1169 - val_mae: 0.1169\n",
      "Epoch 25/5000\n",
      "40/40 - 0s - loss: 0.1199 - mae: 0.1199 - val_loss: 0.1160 - val_mae: 0.1160\n",
      "Epoch 26/5000\n",
      "40/40 - 0s - loss: 0.1191 - mae: 0.1191 - val_loss: 0.1152 - val_mae: 0.1152\n",
      "Epoch 27/5000\n",
      "40/40 - 0s - loss: 0.1184 - mae: 0.1184 - val_loss: 0.1143 - val_mae: 0.1143\n",
      "Epoch 28/5000\n",
      "40/40 - 0s - loss: 0.1176 - mae: 0.1176 - val_loss: 0.1135 - val_mae: 0.1135\n",
      "Epoch 29/5000\n",
      "40/40 - 0s - loss: 0.1169 - mae: 0.1169 - val_loss: 0.1127 - val_mae: 0.1127\n",
      "Epoch 30/5000\n",
      "40/40 - 0s - loss: 0.1163 - mae: 0.1163 - val_loss: 0.1119 - val_mae: 0.1119\n",
      "Epoch 31/5000\n",
      "40/40 - 0s - loss: 0.1156 - mae: 0.1156 - val_loss: 0.1111 - val_mae: 0.1111\n",
      "Epoch 32/5000\n",
      "40/40 - 0s - loss: 0.1150 - mae: 0.1150 - val_loss: 0.1103 - val_mae: 0.1103\n",
      "Epoch 33/5000\n",
      "40/40 - 0s - loss: 0.1143 - mae: 0.1143 - val_loss: 0.1096 - val_mae: 0.1096\n",
      "Epoch 34/5000\n",
      "40/40 - 0s - loss: 0.1137 - mae: 0.1137 - val_loss: 0.1089 - val_mae: 0.1089\n",
      "Epoch 35/5000\n",
      "40/40 - 0s - loss: 0.1131 - mae: 0.1131 - val_loss: 0.1082 - val_mae: 0.1082\n",
      "Epoch 36/5000\n",
      "40/40 - 0s - loss: 0.1125 - mae: 0.1125 - val_loss: 0.1075 - val_mae: 0.1075\n",
      "Epoch 37/5000\n",
      "40/40 - 0s - loss: 0.1119 - mae: 0.1119 - val_loss: 0.1068 - val_mae: 0.1068\n",
      "Epoch 38/5000\n",
      "40/40 - 0s - loss: 0.1113 - mae: 0.1113 - val_loss: 0.1062 - val_mae: 0.1062\n",
      "Epoch 39/5000\n",
      "40/40 - 0s - loss: 0.1107 - mae: 0.1107 - val_loss: 0.1056 - val_mae: 0.1056\n",
      "Epoch 40/5000\n",
      "40/40 - 0s - loss: 0.1101 - mae: 0.1101 - val_loss: 0.1050 - val_mae: 0.1050\n",
      "Epoch 41/5000\n",
      "40/40 - 0s - loss: 0.1096 - mae: 0.1096 - val_loss: 0.1044 - val_mae: 0.1044\n",
      "Epoch 42/5000\n",
      "40/40 - 0s - loss: 0.1090 - mae: 0.1090 - val_loss: 0.1039 - val_mae: 0.1039\n",
      "Epoch 43/5000\n",
      "40/40 - 0s - loss: 0.1085 - mae: 0.1085 - val_loss: 0.1033 - val_mae: 0.1033\n",
      "Epoch 44/5000\n",
      "40/40 - 0s - loss: 0.1080 - mae: 0.1080 - val_loss: 0.1028 - val_mae: 0.1028\n",
      "Epoch 45/5000\n",
      "40/40 - 0s - loss: 0.1075 - mae: 0.1075 - val_loss: 0.1022 - val_mae: 0.1022\n",
      "Epoch 46/5000\n",
      "40/40 - 0s - loss: 0.1070 - mae: 0.1070 - val_loss: 0.1017 - val_mae: 0.1017\n",
      "Epoch 47/5000\n",
      "40/40 - 0s - loss: 0.1065 - mae: 0.1065 - val_loss: 0.1012 - val_mae: 0.1012\n",
      "Epoch 48/5000\n",
      "40/40 - 0s - loss: 0.1061 - mae: 0.1061 - val_loss: 0.1007 - val_mae: 0.1007\n",
      "Epoch 49/5000\n",
      "40/40 - 0s - loss: 0.1056 - mae: 0.1056 - val_loss: 0.1002 - val_mae: 0.1002\n",
      "Epoch 50/5000\n",
      "40/40 - 0s - loss: 0.1052 - mae: 0.1052 - val_loss: 0.0998 - val_mae: 0.0998\n",
      "Epoch 51/5000\n",
      "40/40 - 0s - loss: 0.1048 - mae: 0.1048 - val_loss: 0.0993 - val_mae: 0.0993\n",
      "Epoch 52/5000\n",
      "40/40 - 0s - loss: 0.1044 - mae: 0.1044 - val_loss: 0.0989 - val_mae: 0.0989\n",
      "Epoch 53/5000\n",
      "40/40 - 0s - loss: 0.1040 - mae: 0.1040 - val_loss: 0.0984 - val_mae: 0.0984\n",
      "Epoch 54/5000\n",
      "40/40 - 0s - loss: 0.1036 - mae: 0.1036 - val_loss: 0.0980 - val_mae: 0.0980\n",
      "Epoch 55/5000\n",
      "40/40 - 0s - loss: 0.1032 - mae: 0.1032 - val_loss: 0.0976 - val_mae: 0.0976\n",
      "Epoch 56/5000\n",
      "40/40 - 0s - loss: 0.1028 - mae: 0.1028 - val_loss: 0.0972 - val_mae: 0.0972\n",
      "Epoch 57/5000\n",
      "40/40 - 0s - loss: 0.1024 - mae: 0.1024 - val_loss: 0.0969 - val_mae: 0.0969\n",
      "Epoch 58/5000\n",
      "40/40 - 0s - loss: 0.1021 - mae: 0.1021 - val_loss: 0.0965 - val_mae: 0.0965\n",
      "Epoch 59/5000\n",
      "40/40 - 0s - loss: 0.1017 - mae: 0.1017 - val_loss: 0.0961 - val_mae: 0.0961\n",
      "Epoch 60/5000\n",
      "40/40 - 0s - loss: 0.1014 - mae: 0.1014 - val_loss: 0.0958 - val_mae: 0.0958\n",
      "Epoch 61/5000\n",
      "40/40 - 0s - loss: 0.1010 - mae: 0.1010 - val_loss: 0.0955 - val_mae: 0.0955\n",
      "Epoch 62/5000\n",
      "40/40 - 0s - loss: 0.1007 - mae: 0.1007 - val_loss: 0.0951 - val_mae: 0.0951\n",
      "Epoch 63/5000\n",
      "40/40 - 0s - loss: 0.1004 - mae: 0.1004 - val_loss: 0.0948 - val_mae: 0.0948\n",
      "Epoch 64/5000\n",
      "40/40 - 0s - loss: 0.1001 - mae: 0.1001 - val_loss: 0.0945 - val_mae: 0.0945\n",
      "Epoch 65/5000\n",
      "40/40 - 0s - loss: 0.0998 - mae: 0.0998 - val_loss: 0.0942 - val_mae: 0.0942\n",
      "Epoch 66/5000\n",
      "40/40 - 0s - loss: 0.0995 - mae: 0.0995 - val_loss: 0.0939 - val_mae: 0.0939\n",
      "Epoch 67/5000\n",
      "40/40 - 0s - loss: 0.0992 - mae: 0.0992 - val_loss: 0.0936 - val_mae: 0.0936\n",
      "Epoch 68/5000\n",
      "40/40 - 0s - loss: 0.0989 - mae: 0.0989 - val_loss: 0.0933 - val_mae: 0.0933\n",
      "Epoch 69/5000\n",
      "40/40 - 0s - loss: 0.0986 - mae: 0.0986 - val_loss: 0.0930 - val_mae: 0.0930\n",
      "Epoch 70/5000\n",
      "40/40 - 0s - loss: 0.0983 - mae: 0.0983 - val_loss: 0.0927 - val_mae: 0.0927\n",
      "Epoch 71/5000\n",
      "40/40 - 0s - loss: 0.0981 - mae: 0.0981 - val_loss: 0.0924 - val_mae: 0.0924\n",
      "Epoch 72/5000\n",
      "40/40 - 0s - loss: 0.0978 - mae: 0.0978 - val_loss: 0.0921 - val_mae: 0.0921\n",
      "Epoch 73/5000\n",
      "40/40 - 0s - loss: 0.0975 - mae: 0.0975 - val_loss: 0.0919 - val_mae: 0.0919\n",
      "Epoch 74/5000\n",
      "40/40 - 0s - loss: 0.0973 - mae: 0.0973 - val_loss: 0.0916 - val_mae: 0.0916\n",
      "Epoch 75/5000\n",
      "40/40 - 0s - loss: 0.0970 - mae: 0.0970 - val_loss: 0.0913 - val_mae: 0.0913\n",
      "Epoch 76/5000\n",
      "40/40 - 0s - loss: 0.0967 - mae: 0.0967 - val_loss: 0.0911 - val_mae: 0.0911\n",
      "Epoch 77/5000\n",
      "40/40 - 0s - loss: 0.0965 - mae: 0.0965 - val_loss: 0.0908 - val_mae: 0.0908\n",
      "Epoch 78/5000\n",
      "40/40 - 0s - loss: 0.0962 - mae: 0.0962 - val_loss: 0.0906 - val_mae: 0.0906\n",
      "Epoch 79/5000\n",
      "40/40 - 0s - loss: 0.0960 - mae: 0.0960 - val_loss: 0.0903 - val_mae: 0.0903\n",
      "Epoch 80/5000\n",
      "40/40 - 0s - loss: 0.0957 - mae: 0.0957 - val_loss: 0.0901 - val_mae: 0.0901\n",
      "Epoch 81/5000\n",
      "40/40 - 0s - loss: 0.0955 - mae: 0.0955 - val_loss: 0.0898 - val_mae: 0.0898\n",
      "Epoch 82/5000\n",
      "40/40 - 0s - loss: 0.0952 - mae: 0.0952 - val_loss: 0.0896 - val_mae: 0.0896\n",
      "Epoch 83/5000\n",
      "40/40 - 0s - loss: 0.0950 - mae: 0.0950 - val_loss: 0.0893 - val_mae: 0.0893\n",
      "Epoch 84/5000\n",
      "40/40 - 0s - loss: 0.0947 - mae: 0.0947 - val_loss: 0.0891 - val_mae: 0.0891\n",
      "Epoch 85/5000\n",
      "40/40 - 0s - loss: 0.0945 - mae: 0.0945 - val_loss: 0.0888 - val_mae: 0.0888\n",
      "Epoch 86/5000\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 360/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 450/5000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 987/5000\n",
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     ]
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2847/5000\n",
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    },
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     "text": [
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     ]
    },
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3820/5000\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 3909/5000\n",
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     ]
    },
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     "text": [
      "Epoch 3998/5000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4941/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4942/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4943/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4944/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4945/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4946/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4947/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4948/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4949/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4950/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4951/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4952/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4953/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4954/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4955/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4956/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4957/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4958/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4959/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4960/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4961/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4962/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4963/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4964/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4965/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4966/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4967/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4968/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4969/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4970/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4971/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4972/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4973/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4974/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4975/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4976/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4977/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4978/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4979/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4980/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4981/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4982/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4983/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4984/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4985/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4986/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4987/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4988/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4989/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4990/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4991/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4992/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4993/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4994/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4995/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4996/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4997/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4998/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 4999/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n",
      "Epoch 5000/5000\n",
      "40/40 - 0s - loss: 0.0193 - mae: 0.0193 - val_loss: 0.0181 - val_mae: 0.0181\n"
     ]
    }
   ],
   "source": [
    "LEARNING_RATE = 1e-4\n",
    "model.compile(loss='mae',\n",
    "             optimizer=SGD(learning_rate=LEARNING_RATE),\n",
    "             metrics=['mae'])\n",
    "\n",
    "history = model.fit(scaled_train_x,\n",
    "                    scaled_train_y,\n",
    "                   epochs=5000,\n",
    "                    validation_data = (scaled_valid_x, scaled_valid_y),\n",
    "                   verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc2ce356",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "EPOCHS = 5000\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(EPOCHS)\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "ax1= fig.add_subplot(1,1,1)\n",
    "ax1.plot(epochs, loss, label='Training loss')\n",
    "ax1.plot(epochs, val_loss, label='Validation loss')\n",
    "ax1.set_title('Training and Validation loss')\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_ylabel('Loss Value')\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2808a1",
   "metadata": {},
   "source": [
    "model.save('./Model_Save/Simple_linear_regression_with_pixels_epoch5000.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1502bb43",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_predict = model.predict(scaled_valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "530b5110",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_predict = scaler_y.inverse_transform(valid_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "383d97b8",
   "metadata": {
    "collapsed": true
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   "outputs": [
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       "       [4.14990e+01],\n",
       "       [1.78519e+02],\n",
       "       [5.71470e+01],\n",
       "       [1.56489e+02],\n",
       "       [3.13600e+00],\n",
       "       [8.71000e-01],\n",
       "       [5.31800e+00],\n",
       "       [5.83200e+00],\n",
       "       [1.37300e+00],\n",
       "       [5.66900e+00],\n",
       "       [8.75000e-01],\n",
       "       [1.18189e+02],\n",
       "       [5.88980e+01],\n",
       "       [2.03578e+02],\n",
       "       [4.39900e+00],\n",
       "       [1.31028e+02],\n",
       "       [9.62620e+01],\n",
       "       [6.81630e+01],\n",
       "       [2.87790e+02],\n",
       "       [1.29780e+01],\n",
       "       [1.93083e+02],\n",
       "       [2.94950e+01],\n",
       "       [2.86220e+02],\n",
       "       [1.41097e+02],\n",
       "       [1.67456e+02],\n",
       "       [1.48020e+01],\n",
       "       [6.10990e+01],\n",
       "       [6.91500e+00],\n",
       "       [1.92360e+01],\n",
       "       [1.05498e+02],\n",
       "       [8.98800e+00],\n",
       "       [3.73203e+02],\n",
       "       [7.25900e+00],\n",
       "       [4.80000e-01],\n",
       "       [2.03820e+01],\n",
       "       [8.80000e-02],\n",
       "       [0.00000e+00],\n",
       "       [2.51500e+00],\n",
       "       [1.44570e+01],\n",
       "       [3.00200e+00],\n",
       "       [1.00860e+01],\n",
       "       [1.37610e+01],\n",
       "       [4.60690e+01],\n",
       "       [8.43000e-01],\n",
       "       [2.30811e+02],\n",
       "       [2.42700e+00],\n",
       "       [1.20250e+01],\n",
       "       [3.02911e+02],\n",
       "       [1.21073e+02],\n",
       "       [4.23600e+00],\n",
       "       [5.25400e+00],\n",
       "       [2.43899e+02],\n",
       "       [4.31040e+01],\n",
       "       [2.08630e+01],\n",
       "       [1.97830e+01],\n",
       "       [1.53971e+02],\n",
       "       [4.21914e+02],\n",
       "       [7.45840e+01],\n",
       "       [2.50275e+02],\n",
       "       [2.97000e-01],\n",
       "       [6.95220e+01],\n",
       "       [9.23880e+01],\n",
       "       [9.40900e+00],\n",
       "       [9.61840e+01],\n",
       "       [1.42744e+02],\n",
       "       [7.92220e+01],\n",
       "       [1.65480e+01],\n",
       "       [2.07332e+02],\n",
       "       [3.61912e+02],\n",
       "       [1.79800e+00],\n",
       "       [5.12650e+01],\n",
       "       [5.15940e+01],\n",
       "       [1.79448e+02],\n",
       "       [2.54067e+02],\n",
       "       [3.55700e+01],\n",
       "       [1.37349e+02],\n",
       "       [3.37108e+02],\n",
       "       [1.81300e+00],\n",
       "       [1.95970e+01],\n",
       "       [3.58350e+01],\n",
       "       [2.27082e+02],\n",
       "       [1.76400e+00],\n",
       "       [4.68600e+00],\n",
       "       [1.14000e-01],\n",
       "       [2.95089e+02],\n",
       "       [7.38500e+00],\n",
       "       [1.00100e+00],\n",
       "       [4.58420e+01],\n",
       "       [3.32087e+02],\n",
       "       [3.21320e+01],\n",
       "       [8.16000e-01],\n",
       "       [1.43913e+02],\n",
       "       [4.03000e-01],\n",
       "       [7.52220e+01],\n",
       "       [3.64090e+01],\n",
       "       [3.51260e+02],\n",
       "       [2.64121e+02],\n",
       "       [1.80559e+02],\n",
       "       [1.17433e+02],\n",
       "       [3.70000e-02],\n",
       "       [2.13000e+00],\n",
       "       [1.15960e+02],\n",
       "       [5.63000e-01],\n",
       "       [2.23540e+02],\n",
       "       [8.18440e+01],\n",
       "       [2.72065e+02],\n",
       "       [5.80080e+01],\n",
       "       [1.30520e+01],\n",
       "       [2.48300e+00],\n",
       "       [5.43050e+01],\n",
       "       [2.43500e+01],\n",
       "       [3.32100e+00],\n",
       "       [1.15390e+01],\n",
       "       [9.40000e-02],\n",
       "       [3.51460e+01],\n",
       "       [8.51870e+01],\n",
       "       [4.40000e-01],\n",
       "       [8.35030e+01],\n",
       "       [3.93460e+02],\n",
       "       [2.70800e+00],\n",
       "       [1.54809e+02],\n",
       "       [3.08673e+02],\n",
       "       [4.32690e+01],\n",
       "       [2.03000e-01],\n",
       "       [1.08026e+02],\n",
       "       [1.69100e+00],\n",
       "       [1.37100e+00],\n",
       "       [3.67230e+01],\n",
       "       [2.37170e+01],\n",
       "       [4.40000e-01],\n",
       "       [3.69830e+01],\n",
       "       [2.80068e+02],\n",
       "       [2.05350e+01],\n",
       "       [6.93980e+01]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_y_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "15c23c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.metrics import mean_absolute_error as MAE\n",
    "\n",
    "loss = MAE(valid_y_data, valid_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4e807bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.703931"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e75c87d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import normalize\n",
    "\n",
    "NMAE = normalize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "88d10ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027784456"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(NMAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf836a6f",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a64761e2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "\n",
    "lr.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc7b4245",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lr.predict(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "691a1b84",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "plt.scatter(x_data.squeeze(), y_data)\n",
    "plt.plot(x_data.squeeze(), pred, 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b137847",
   "metadata": {},
   "source": [
    "# TEST IMAGES\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:machine] *",
   "language": "python",
   "name": "conda-env-machine-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}