TF_Regression.ipynb

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   "id": "37b940e6-69e6-4b11-95fd-80c5b63fb464",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Make numpy printouts easier to read.\n",
    "np.set_printoptions(precision=3, suppress=True)\n",
    "\n",
    "#Show plots inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1dfccb2b-7317-4817-a460-7d3b93e0ceb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21aedea6-1d4f-4a12-ae42-5668d58b1890",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
    "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "\n",
    "raw_dataset = pd.read_csv(url, names=column_names,\n",
    "                          na_values='?', comment='\\t',\n",
    "                          sep=' ', skipinitialspace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a108132b-a89c-4695-8501-f26b81c65ab8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "0  18.0          8         307.0       130.0  3504.0          12.0   \n",
       "1  15.0          8         350.0       165.0  3693.0          11.5   \n",
       "2  18.0          8         318.0       150.0  3436.0          11.0   \n",
       "3  16.0          8         304.0       150.0  3433.0          12.0   \n",
       "4  17.0          8         302.0       140.0  3449.0          10.5   \n",
       "\n",
       "   Model Year  Origin  \n",
       "0          70       1  \n",
       "1          70       1  \n",
       "2          70       1  \n",
       "3          70       1  \n",
       "4          70       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1692d599-13fc-4c13-90fd-919fd9e470e3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
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       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
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       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7bde16d-caeb-47a3-adfb-34f9ba60e6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean the data \n",
    "#examine for missing values \n",
    "\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e57e5a41-7256-4d92-8379-6c93f92c1eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#since only 6 missing values, drop those rows \n",
    "\n",
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1497ea7-4601-44d0-b942-a91b29bb2e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The \"Origin\" column is really categorical, not numeric. So convert that to a one-hot with pd.get_dummies:\n",
    "\n",
    "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "142a5d0a-055e-4046-91ea-d657995369ea",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Europe</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
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       "      <td>135.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
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       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Europe  Japan  USA  \n",
       "393          82       0      0    1  \n",
       "394          82       1      0    0  \n",
       "395          82       0      0    1  \n",
       "396          82       0      0    1  \n",
       "397          82       0      0    1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd9dedaf-4379-4498-8736-822efa81d406",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split into train and test \n",
    "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d34bc42f-a896-4ea0-a023-9ea7e60e0de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fea18ac8bb0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#some EDA\n",
    "\n",
    "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "935ef436-d990-4985-bbf6-64541abb7a6c",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MPG</th>\n",
       "      <td>314.0</td>\n",
       "      <td>23.310510</td>\n",
       "      <td>7.728652</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.95</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "MPG           314.0    23.310510    7.728652    10.0    17.00    22.0   \n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "\n",
       "                  75%     max  \n",
       "MPG             28.95    46.6  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  \n",
       "USA              1.00     1.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also look at the overall statistics, note how each feature covers a very different range:\n",
    "\n",
    "train_dataset.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "13c06564-ad39-447c-aae7-3d31eeeb686c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the target value, the \"label\", from the features. This label is the value that you will train the model to predict.\n",
    "\n",
    "train_features = train_dataset.copy()\n",
    "test_features = test_dataset.copy()\n",
    "\n",
    "train_labels = train_features.pop('MPG')\n",
    "test_labels = test_features.pop('MPG')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29a579ef-0a3f-456d-9271-83bd6a9701b9",
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MPG</th>\n",
       "      <td>23.310510</td>\n",
       "      <td>7.728652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean         std\n",
       "MPG             23.310510    7.728652\n",
       "Cylinders        5.477707    1.699788\n",
       "Displacement   195.318471  104.331589\n",
       "Horsepower     104.869427   38.096214\n",
       "Weight        2990.251592  843.898596\n",
       "Acceleration    15.559236    2.789230\n",
       "Model Year      75.898089    3.675642\n",
       "Europe           0.178344    0.383413\n",
       "Japan            0.197452    0.398712\n",
       "USA              0.624204    0.485101"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize features \n",
    "\n",
    "train_dataset.describe().transpose()[['mean', 'std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3b95ee70-9a56-4697-8f4d-58b79cfb3bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize \n",
    "#The preprocessing.Normalization layer is a clean and simple way to build that preprocessing into your model.\n",
    "\n",
    "\n",
    "normalizer = preprocessing.Normalization(axis=-1)\n",
    "\n",
    "#Then .adapt() it to the data:\n",
    "normalizer.adapt(np.array(train_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1932422e-186c-4d3e-948b-70d9855b50a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   5.478  195.318  104.869 2990.252   15.559   75.898    0.178    0.197\n",
      "    0.624]\n"
     ]
    }
   ],
   "source": [
    "print(normalizer.mean.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7cf43711-2539-4ce3-add6-278041f1dcca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First example: [[   4.    90.    75.  2125.    14.5   74.     0.     0.     1. ]]\n",
      "\n",
      "Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5   0.78]]\n"
     ]
    }
   ],
   "source": [
    "# When the layer is called it returns the input data, with each feature independently normalized:\n",
    "\n",
    "first = np.array(train_features[:1])\n",
    "\n",
    "with np.printoptions(precision=2, suppress=True):\n",
    "  print('First example:', first)\n",
    "  print()\n",
    "  print('Normalized:', normalizer(first).numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d335f62-5a6e-4d79-9edf-dd8d3ae7bd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model design \n",
    "#First create the horsepower Normalization layer:\n",
    "\n",
    "horsepower = np.array(train_features['Horsepower'])\n",
    "\n",
    "horsepower_normalizer = preprocessing.Normalization(input_shape=[1,], axis=None)\n",
    "horsepower_normalizer.adapt(horsepower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ada3d87-3ed2-4bd8-8002-c14ebb8eb829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization_2 (Normalizati (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 5\n",
      "Trainable params: 2\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#build sequential model \n",
    "\n",
    "horsepower_model = tf.keras.Sequential([\n",
    "    horsepower_normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])\n",
    "\n",
    "horsepower_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa1c07cc-8824-4d93-9440-17963ed7631e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# configure the training procedure using the Model.compile() method. The most important arguments to compile are the loss and the optimizer since \n",
    "#these define what will be optimized (mean_absolute_error) and how (using the optimizers.Adam).\n",
    "\n",
    "horsepower_model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
    "    loss='mean_absolute_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa670afd-7a0c-4675-8638-be450dca14d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.66 s, sys: 569 ms, total: 4.23 s\n",
      "Wall time: 3.87 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = horsepower_model.fit(\n",
    "    train_features['Horsepower'], train_labels,\n",
    "    epochs=100,\n",
    "    # suppress logging\n",
    "    verbose=0,\n",
    "    # Calculate validation results on 20% of the training data\n",
    "    validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1da40463-ed7d-4b20-9bc1-d3abb6a9e419",
   "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>loss</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3.804432</td>\n",
       "      <td>4.195601</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.805728</td>\n",
       "      <td>4.174712</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>3.803736</td>\n",
       "      <td>4.191761</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>3.801955</td>\n",
       "      <td>4.190697</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3.803113</td>\n",
       "      <td>4.182279</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss  val_loss  epoch\n",
       "95  3.804432  4.195601     95\n",
       "96  3.805728  4.174712     96\n",
       "97  3.803736  4.191761     97\n",
       "98  3.801955  4.190697     98\n",
       "99  3.803113  4.182279     99"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualize the model's training progress using the stats stored in the history object.\n",
    "\n",
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72769c28-91f8-4a08-8cef-f97fec9985ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph form \n",
    "\n",
    "def plot_loss(history):\n",
    "  plt.plot(history.history['loss'], label='loss')\n",
    "  plt.plot(history.history['val_loss'], label='val_loss')\n",
    "  plt.ylim([0, 10])\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Error [MPG]')\n",
    "  plt.legend()\n",
    "  plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec820b69-87a7-4c80-b118-37d267c4c7fe",
   "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": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b7ef188e-4a5a-4e74-b7ea-97c3a62d4584",
   "metadata": {},
   "outputs": [],
   "source": [
    "#collect results on test set for later \n",
    "test_results = {}\n",
    "\n",
    "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
    "    test_features['Horsepower'],\n",
    "    test_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bfbe30d5-030f-47bc-8d63-99f203a690be",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.linspace(0.0, 250, 251)\n",
    "y = horsepower_model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bd2fd224-af1c-4468-8781-2c43e4f8ce4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_horsepower(x, y):\n",
    "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
    "  plt.plot(x, y, color='k', label='Predictions')\n",
    "  plt.xlabel('Horsepower')\n",
    "  plt.ylabel('MPG')\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "49482590-35b0-4252-9653-632d5f46e8c5",
   "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": [
    "plot_horsepower(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5c8fb6c5-4729-4cb5-9d15-4ff4cf8411e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now build a model using multiple inputs \n",
    "#This time use the Normalization layer that was adapted to the whole dataset.\n",
    "\n",
    "\n",
    "linear_model = tf.keras.Sequential([\n",
    "    normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4aa633cb-bf03-4d70-951f-3437d717a1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.015],\n",
       "       [-1.491],\n",
       "       [ 0.412],\n",
       "       [-1.235],\n",
       "       [-1.098],\n",
       "       [-0.723],\n",
       "       [-1.121],\n",
       "       [ 0.214],\n",
       "       [-0.08 ],\n",
       "       [-0.334]], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0c9630f1-e9a3-4657-a98e-7bf79ed85955",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile model \n",
    "linear_model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.1),\n",
    "    loss='mean_absolute_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "db6c6645-8e53-4a5a-9444-aac744d4463c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.31 s, sys: 374 ms, total: 3.69 s\n",
      "Wall time: 3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = linear_model.fit(\n",
    "    train_features, train_labels, \n",
    "    epochs=100,\n",
    "    # suppress logging\n",
    "    verbose=0,\n",
    "    # Calculate validation results on 20% of the training data\n",
    "    validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cfedffb6-f5e5-4464-a689-3c3779468b40",
   "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": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1fcbfe7d-0369-4880-a024-11ae151c1681",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test results for later \n",
    "test_results['linear_model'] = linear_model.evaluate(\n",
    "    test_features, test_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "672cb86e-4149-46c5-9969-6190ca273ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DNN with hidden layers \n",
    "\n",
    "def build_and_compile_model(norm):\n",
    "  model = keras.Sequential([\n",
    "      norm,\n",
    "      layers.Dense(64, activation='relu'),\n",
    "      layers.Dense(64, activation='relu'),\n",
    "      layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  model.compile(loss='mean_absolute_error',\n",
    "                optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5b12956b-4beb-4899-af30-db2710a053e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# horsepower\n",
    "\n",
    "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3bcd9c4a-d708-4443-92e8-53d983c5e65a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization_2 (Normalizati (None, 1)                 3         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                128       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,356\n",
      "Trainable params: 4,353\n",
      "Non-trainable params: 3\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dnn_horsepower_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "461f33fc-258e-4f19-8ee9-f126f4556fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.01 s, sys: 765 ms, total: 4.78 s\n",
      "Wall time: 3.45 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = dnn_horsepower_model.fit(\n",
    "    train_features['Horsepower'], train_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "77065928-f5b2-434b-aead-9e49d7d9f31f",
   "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": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8a8eb970-2105-4b92-83eb-5c9e3e4828fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 12 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fea1f7a3ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    }
   ],
   "source": [
    "# see how it takes advantage of hte non linearities \n",
    "\n",
    "x = tf.linspace(0.0, 250, 251)\n",
    "y = dnn_horsepower_model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5f3e908a-c6c6-4bcd-91c1-398dce940b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OBa0svjbZyEQyIUKHBP4Gon379rz22mt8+eWX/O53vwtaZ6+vTTYykUyI0BHwNn6llAXYAuRorQcppZKAJUAGcBC4VWt9MtDlCAWDBw/mscceY/r06WRlZfHwww/XexnMcv940mQzpFu6BHohQkB91PgfBnY7/T0JWKu1vgRYa/9b2E2bNo2hQ4fy+9//ng8//LDery9NNkKEv4AGfqVUK+Bm4F9OmwcD8+2P5wNDAlmGUBMVFcWCBQvo0KEDI0eOZP/+/fV6fWmyESL8BTRXj1JqGfAs0BQYb2/qKdRaJzjtc1JrnWhw7L3AvQBt2rTpcejQoYCVsyE6cOAAl19+OSkpKWzatIlmzZoFu0g+DfMUQgRPvefqUUoNAvK01lt9OV5rPVdr3VNr3TM5OdnPpWv4MjMzefPNN9m7dy+jR4+mvLzc/UF+YJZ2QWbmChE+AtnU0wf4lVLqILAYuFYptRDIVUqlAdh/5wWwDCGtX79+/PWvf2X16tX88Y9/DPj1XAV3mZkrRPgIWODXWk/WWrfSWmcAtwEfaa3vAN4Gxth3GwO8FagyhIP77ruPe++9l2effZZFixYF9FqugrvMzBUifARjHP8M4Hql1D7gevvfwoRSitmzZ9O3b1/uuusutm71qeXMI66Cu8zMFSJ81Evg11qv11oPsj8+obXur7W+xP67oD7KEMpiYmJYtmwZKSkpDBkyhOPHjwfkOq6CuwzzFCJ8yApcIWT79u306dOHrKwsPvroozov2F5zlE5GcxuffVeA87+IKAAFFRqiFMRGR3GutKLOo3pkhJAQgScrcIWBrKws5s2bx2effcZ9991Xp7QORh25n9YI+gAVVAZ97L+LSysY3bsNn066tk5BX0YICRE8EvhDzIgRI5g6dSqvvPIKs2fP9vk8Rh25nlq0+Qefr2t2bRkhJET9kcAfgqZPn87gwYP5/e9/z9q1a306R11G45RrXafauYwQEiK4JPCHoKioKF577TXatWvH8OHD2b17t/uDaqjraJy6NM3ICCEhgksCf4hq2rQpq1evJjY2lptuusnrkT5Go3S8UZemGU9GCMnC7UIEjgT+EJaRkcHq1avJy8tj0KBBFBUVeXysczI2AOXD9X1tmnGXCE46f4UILBnOGQZWr17N4MGDuemmm1i5ciXR0d4vs+AYXmmUi99MeoKNTydd6/W13Okz4yPDcgTqekKEKxnOGcYGDRrE3//+d1avXs1DDz3k0zDPId3SvQ6qgZq8JZ2/QgRWwFfgEvVj7NixHDx4kOeee46MjAweffRR032da/cWpSjXmnT7JKrEOCsnz5a6vZ5z05A3k7GM9gWqbYu3WSksrl0G6fwVwj+kqSeMVFRUMHr0aBYvXsyiRYu47bbbau3jaD83GsNvs1oY1iOdJV/+QGm5+38Xjv2Xb82pdj6b1WK4eIvRta1RChTVrme1KNBQWqHdnlMIYc6sqUcCf5gpKSlhwIABbNq0iQ8//JCrr7662vNm7ecOjpq/p+39jm8MRuf5dNK11Wr4USb7GkmMsxIXEy0pHYSoA7PAL009YSY2NpaVK1fSp08fBg8ezGeffUb79u2rnnfXTn60sLhq0XRX3w4czAL50cLiWsd7GvQBCs+Wsm3aAI/3F0J4Tjp3w1BSUhJr1qwxHOPvrp3c+fmaQz6NWJTxQNCWCbY6pYWQ9nwhAkcCf5jKzMw0HOPvbuJWv3bVl7l0jPb5y8gsw0lXo3q1Np2M5ckoHGuUIkrV3ibpnoUIHAn8Yaxnz54sWbKEbdu2cdttt1FWVlZVizerqS/a/IPhbFmzSVdPDelsOhnLrNZuUapq35FXtMZSM/L7MptMCOExCfxhzmiM/5Bu6VSYtLeXa+31bFnHt4IDM26ulq7ZLDXDn2/tWrXvum/za40gKi3XkqlTiACSzt0IYDTGv2WCze2oHUc+HqOOXscHA2A4bNMxkifeZqWRNYrCs6WGo3NkspYQ9U8Cf4R45plnOHToEBMnTqRNmzZMGNjX7Ygd+DkAu8qh7xzIa35AFBaXYrNamDUyy3A4ptkHkHTuChE40tQTIaKiopg3bx5XX301Y8aMIenMd9Xa5s3a/BPirC7H/tfc7u0iK7KWrxD1T2r8EcRojL8jP4/hrFqL4qdzZS5TOFiUqta0YzZS36zpxvEtQNbfFaL+yMzdCHTgwAGuvPJKbDYbn3/+ORdccAFQO49OUUmZYc6cmmxWi9smI+cZwa4CvCzCLoT/SMoGUc2WLVu45ppraN++PRs2bKBx48a19smc9K5pDd5BKXD3T8jTnD5G3zokR48QvpO0zBFo6qodXDR5DRmT3uWiyWuYumpH1XNGY/xr8qSD1VXQdx7Xv+7bfLdt/74swi4rdQnhPQn8YWrqqh0s3HS4Kj9OudYs3HS4WvB3HuP/4IMP1srjX5flGdMTbNXG9XsybNPboZ0NfaUu+VASDZXLwK+UaqWU+oXT379XSk2z/1wc+OIJXy3a/INH28eOHcvEiROZM2cOzz33XLXnPMnVkxhn9WhUjicLrHu7CLsv3xDqS0P/UBKRzV2NfyaQ4PT3/wOKAA1MD1CZhB+YZcI02v7MM88watQoJk+ezMKFC03PWXPAp81q4bFbOjKsR3rVcFCLUgzrkV6rTd6TYZveDu1syJO/GvKHkhDuhnNeprVe7fT3Wa31nwGUUhsDVyxRV2Z58o3G60dFRfHqq69y/Phx7rrrLtLS0ujfv3+tzlZd4zzDelQG9yVf/lCtSWnJlz/Q88KkasHfk2Gb3g7tbMiTvxryh5IQ7gJ/oxp/93d63NzPZRF+NKpXaxZuOmy43Znz8MmUvuNI++EovxoylEvu+jOFtpam5y/XmuVbc1ix9Yhhrp3p7+yqFbBrBnZH7bdm8Pd02cZ+7ZINRwo1hMlfDflDSQh3TT1nlFKXOv7QWhcAKKXaAT8FsmCibp4a0pk7erep1gRzR+82PDWkc9U+Nduhc0ssqBsmU6Ji2PnKZMpO57u8RnFpOWdLKwyfM5r0VZd2b6Njl2/NYViPdMPMoMEmM5JFQ+ZyHL9S6gbgb8DTwH/tm3sAfwAe1lq/F/ASIuP4A8UsFcP5/IMcX/go0c1acMHoPxHVqIlP5z8442aPrudYptGXsnpybLDIZDQRbD4tvai1/rdSKht4FHjIvnknkK213un/Yor6ZNbeHJOcQXL2FPKWPkbeyqdJHfEEKtrq1bkTbLX3r0u7dyi2mbtqthIimDwZx59LZa2/v9a6h9Z6jAT9hs3T8eOu2pttF3al+U0PU3J4Bz+umYXWxk06ZgZ1TatVpiiTRHBR9nw/rng71FMIYc7dOP57gF3AbOBbpdSv6qVUwmfetKO7m6DVpGM/Eq4Zw9ndH1O4Yb5X5Vj37c/9A44yuRpi6q6tX9rMhfAfdzX+cUBHrfWVwFXA5ICXSNSJN+PHjZZTtFmr/5No1ms4TbrdxOnNyzm99R2Py5FTWFwVyD1ZdN3dGHezpR+lKUUI77kbznlea50PoLX+XikVWw9lEnXgbVu4czv0qm05jFuyvdrzSimSrvt/lP9UwMn/zCW6aXPiLr3Ko7KMW7Kd6e/scpnW2ZMyGpVVCOE7dzX+Vkqpvzl+DP4WDYyvbeGO5hgjKspCi1vGE5N2KT++8zwlOburFkxPjLNirblYupOTZ0s9Xjtd2uuFqB/uAv8EYKvTT82/TSmlGimlvlBKfaWU2qWUmm7fnqSU+lAptc/+O7HutyEcfG0Ld9ccE2VtRMrwaViaNidv+ZPEFB1n1sgstk0bwMwRXV2e25PE39JeL0T9cTec07sevepKgGu11j8ppazAJ0qp94BsYK3WeoZSahIwCZhYh+sIJ76uaOVu4XUAS1w8KSOe4PjC8exf8AcmWGzA1Qzpls7jb+/yaNEWqBzqOahrGuu+za9VRhn7LkTguQz8Sqm3XT2vtTYd5aMrZ4Y5Zvda7T8aGAz80r59PrAeCfx+5W1b+KptOSg8q5lbE9NIGTaN3EV/4PDiaTwSNQPojclITUONY6OrzSB2LodzbiDHiCRAgr8QfuSuc/dK4AdgEbCZ2gkaXVJKWahsEroY+LvWerNSKlVrfQxAa31MKZVicuy9wL0Abdq08eaywksz39/jUdB3iG15GS0GP0r+iqfJXTWDSY0e55zrQTvVmHXiuhqRJIFfCP9x18Z/AZXpGToBfwWuB37UWm/QWm9wd3KtdbnWOgtoBVyhlOrkacG01nO11j211j2Tk5M9PSxi1WXRD19mv8Zd3IukAb+j+PstHHnnr16t6KOBjEnvkjX9g2rlDMXZuUKEIpf/X+2B+99a6zFAb2A/sF4p9aA3F9FaF1LZpHMDkKuUSgOw/87zodzCSV0X/fB1NE3TrBuJv+o2inZ8yI//mUujaO8WdCssLmXCm19VlTOYs3NltSwRSdz+T1VKxdrz9SwE7qcyfcMKD45LVkol2B/bgOuAb4G3gTH23cYAb/lUclGlrot+GI0EcrTpNY5xvfRi/C9G07THrziz9W06HXvPMN+/K6UVuqqcwZqdK6tliUjjrnN3PpXNPO8B073M0ZMGzLe380cBS7XWq5VSnwNLlVJ3A4eBEb4VXTjUtYnEk5FAmZPeNewHUErR8oaxtGnThOWvzCbh6lPEX3mrT+Uf0i2dLYcKWLS5cmEXs9W8/E36FkSkcde5+z9ULrV4KfCQ+rk2p6gcuNPM7ECt9ddAN4PtJ6i+oIuoI38s+uFuJJDZNSxKMWNYF26ZvoAxYxSvv74AFR1Ls8sH19rPLFePo5yrtuWwfGtOtdW8lm/NqbWal79J34KINO7a+KO01k3tP82cfpq6CvqiftVHE4nZNf58a+XkratnbuCTlrcS374PJz/6P85s/3e1/Ub1ao3VYtwMlFNYTJ8ZH/H427sMa97/u/SrgDa7SOZPEWm8640TDVJ9JDAzuwZQ1T5OlIX4m8cT17YnBR/8naJd66r2e2pIZ2YO70pinHFe/5zCYtMJYJ5k76wLyfwpIo3LFbgaClmBq+EyWhmrorSEU6uepOjQDpYuXUp2drZHx7kTyNW2ZMawCEc+rcAlhDtG7eBR1lgSBk+h46YXGDnyNi4e/TjnLuhaLaD60n4eyDZ3yfwpIonU+IXXnGvHUSadtukJNu676gLuuW0I5/IOkjLsj9gyu2ONUjRpFO1xquaa55ww8DKpmQvhIbMav7TxRzBfJi3VHPNuFPQd7eP/+Ow4LUY8gbV5K/JXPMW5Q19TWqF9Cvo2q4V+7ZJlvL0QfiCBP0L5OmnJLH2zIz+/c8fy0cJiLLampI58iuiEC8hbPp1zR3Z5XdbGMRaeze7Mum/z6zRRzRWZuSsiiQT+COXrbF+zdvZyrTkw42Y+nXRtVdOLYzikJS6e1JFPY2nagrw3H6fkqHeB+lxphct+gbq2/cvMXRFpJPBHKF+DqNnYdgW1AqXzMElLk0RSb3saS1wCuUunUXJ8v8dldTQnBWq8fV1TXggRamRUT4iaumpHtdQGo3q1Nsxxb8bT2b6OjtycwmKXOfs1lWvs/u/Sr+jdNpGDJ4o5WlhMvM1KI2sUJ8+WEt20Bamjnub465PIW/JHUkc9TUxKW4/K2+2JD7i5SxrLt+ZUC9J1HW+/aluO6bBSmbkrwpXU+EPQ1FU7WLjpcLXUBgs3HWbqKuM1c414MmnJuQkEPFuopVxrPv2uoKrZpLC4lHOlFdzRuw02q4XoZimkjnoGZY0ld/FUzv942KPynjxbypIvf2BYj3S/TVRztc4wyMxdEb4k8IegRZt/8Gq7EU9m+7pbh9dTxaXlrPs2v+p6MQkX0OXeP2NrFEPe4imUFnjWll5arln3bT6fTrq2Vn+CL1zdn8zcFeFMmnpCkFmyM7PtZtxNWvJnU8fRwuJa19t99+X07tOXvMV/IGXUDKyJaW7Pk1NYTOakd/0yht/V/cV6ubaAEKFE/nWHILOc997mwnfHn00dRudq3749n2xYh5Vychf/gbJTuR6dy18jb1zdX2FxqYzsEWFLAn8IGtWrtVfbfWXUD1BTjEnGzZrOni8zHCO/5DtIGDYdfb6Y429MpuyU5wuy+TLyxnm8flFJmWnGUF/PL0QokMAfgp4a0pk7erepquFblOKO3m28GtXjCed+APh5VS5n58s9a146eba0Vk191bYcXt90mJjUi0gZ+RS6pIjjiyZTdtrz4O9Nc1TN8fqFxaWgIc5q/t9ARvaIcCS5eiKYLxkpfcmqacTxYeJ8rpJj+8hdMhVLoyak3j6D6GbJHp3H04ydZmV3tUhMIDOCChFokqtHVOPrbFV/1YCPFhbXOlds2iWkjnyS8uIz5C76A2Wnf3R7Hm9G3riadeyP8wsRKiTwRyhfZ6t60+GbnmAjwWa88ErLBJvhuWLTLq0M/mcLyV08mbIz7oO/p8zKbtYp7lg0RnL4iHAjgT9C+ZqywZMOX4A7erfh00nXMqir8RDNjOY2zp4vM3wutuVlpN76BOVFheQunkLZmROm1/n9ku0eB2OzSWujerU23H5zlzTJ4SPCkgT+COVr3puaE7/Masvrvs2v9rumz74rcJmeOTa9PSkjplP+U0Fl8P+pwHC/CuCRpds9qpGbTVp7akhnw+2BzAYqRDBJ526EcrTx18x7420KhMxJ7xqmclDAgRk3mz7vqXNHdpG39DEsTVtwwahnsTRJdLm/L/dgxt29CdHQSeeuqMZfC7S7++ZQ10lgjVp1JGXE45SfySd38RTKi0663N9RI/dHfv1AZQMVItikxh/mAr2IuNE3B8fyioVnS0mIs/LTuTJKK+r27+zc4R3kLXuc6PhUUm97BkvjBJf726yWOn+bcfWtCJAlIEWDJzX+CFQfC4zU/OaQYLOC+nnC1smzpaDA5mKSlDux0VE0atOZlGGPUVaYS+6SqZSfPWW6v0Upv7TNm30rAqTTV4Q0qfGHMbMJS4GclOTLJCkzjsXV/3fpV1XHFh/cTv7yJ4hObGlf2CW+2jE1a/rO/NU2H4zXVQhfSI0/AgVqqUJfrult0FdQlXbZ+VhbRhbJw6ZRdvJoZc2/+HTV/o4aeXqA2+aD8boK4U8S+MNYMDonvZ0kZSYh7ueJXzWPtWVkkZw9ldITR8hb8kdSYkqr5ef3ZJGZupBOXxHqJPCHsUAGQKNRM6u25VBUUntSltkkKVecvyD0blt7CKctszsp2VM5/+Mhdv5rAm3GLakqx5Bu6QzrkV4tid2wHrXXHvB15E+gP1iECDRZiCWMOQKdv0ef1BztklNYzIRlX4Gm1uidxDgrj93SkSHd0ul5YVJVWeJtVk6fK8VssM+p4tKqa31x0HgIp61tD1KGTiFv5dPkLvkjjHySySvOs+VQAcu35lRbmnL51hx6XphUde9G9+BYhtHd6xOo11WI+iKdu8Jr3mToNOvwdHcOx3GeXOvs/i/IX/kMMamZpI58CmujJoZ9CukJNvq1S65apN6ML4vXC9EQSeeu8BtvOjF96Qh1bjbx5FpxF19B8tDJnM89QO6SaZSeKzLcL6ewuNoi9WZ8WbxeiFAigV94zZtOTLN9nTtva2pkjeKRJdvpM+Mjl/s5i7u4F8lDJnE+dz95b06jouSsx2U0483i9UKEEgn8wmv92hkvkFLzH5NZh+eqbTlVbfhGnFfr+ulcGVEeDgiKu6Q36dmTKT2+nx+XPV7n4O/tEFQhQoUEfuE1s4yb8XFWj3L/zHx/j2mnbk2lFRqz+OtYctL5mi/+YSxLlyzm3NE95C17nIrzvo+t9/fi9UI0FDKqR3jNrN298Gwp26YN8Pl4M2afERVa0/PCpFofRJa2vUkdPIHjq/5E3puPkzLicaJivB9j7+/F64VoKKTGL7xW1wlM3k50Mqt5J8RZa+XMmfDmVzyyZDuxl/6CFreMpyRnN3nLplNx/pzL89fH4vU1+SODqBC+CFiNXynVGlgAXEDlehlztdZ/VUolAUuADOAgcKvW2nWuXdGgTBh4mWHWSk8nMPVrl8zCTYc9vl7b5DgOnjhLafnPdX+rRaE1tfLyOM8jaNz+atCaH1f/mbzlT5AybBpRMY1qnb9ca54a0tltoPdnptO6zCMQoq4CWeMvA/5Xa90e6A3cr5TqAEwC1mqtLwHW2v8WIaSuufzN+gjM7Msrory8RoOPhkIXHcQOjTtcQ/ObH6Hkh5320T61h3omejByyN+ZTn1d81gIfwhYjV9rfQw4Zn98Rim1G0gHBgO/tO82H1gPTAxUOURgDOlWOwWCpzyd/OWsosbf3uT3b9KxH8pi5cd3ZpK7eCopt07HYmtW9bwng3dcBWpfXgdJ9CaCqV7a+JVSGUA3YDOQav9QcHw4pJgcc69SaotSakt+vnc1RNGwBWO0TEKHviRnT+V8/kFy35hM+U8/ty66Glrq4O9AbdbPEaWUtPWLgAt44FdKNQGWA+O01qc9PU5rPVdr3VNr3TM52XjcuAhN/hof7+kHiAKuyEyk6cVXkDLiccpO5XL89UcpPXkU8Kyz2WwfDT51zBoleoPK18bRhCSdvyJQAhr4lVJWKoP+61rrFfbNuUqpNPvzaUBeIMsgGh5P2tQ90TjGs3++Gvj0uwLKtcZ2YVdSRz5FRUkRx18bD3n7POqUNgvU4Ft7v6OfxOjDq7i0nOnv7JJVvkTABCzwK6UU8DKwW2v9gtNTbwNj7I/HAG8FqgyiYfLXhNjTJcYrbbkTm96OC+6YSVRsHDmvTybqh61uj3Hu0Dbi69KOFSYvxsmzpdL5KwImkDX+PsD/ANcqpbbbf24CZgDXK6X2Adfb/xYRxJM29UCzJqVzwR3PY2nehqFDh/LSSy+5PWZIt3Q+nXQtZg1MvrT3ezunQTp/hT8EclTPJ2D6f6R/oK4rGr6WCTaPR/bYrBYaWaMqF233M0vjBFJue4ZT787kvvvu4/Dhwzz99NNERbmuD5mV310QN5oHYDYnIjY6ynC4qqzyJfxB8vELv3M30anm5CUzjrz4PS9MYsKbX3k8hPOSlMZ8l1/kcT4gXVFOycf/InfzO9x+++288sorxMbGmt4LYBisn82unABmdO9G9+zqGF+uIURNZvn4JfALv3IV4GoGf0fwslmjKC6rMGz7t1ktDOuRzpIvf6g2c9eV1KYx5J4571W5tdYUf7mC/HWv0q9fP1asWMH6A0V+C9Yz399j+C3BbKEa8P5Dx9vg78+ZyKJhMgv8kqRN+JUvE50SG8fyzMDLDINjcWm52xWzavI26AMopYi7YhiXJKfyyco/07dvX2JvnkIxTQ3vxbGwu7NuT3xgeu+u5gF4E4D9NZFMUkZENknSJvzKk4lOZukPzNr96zMvfmnmL3jvvfc4fPgw2//+AOfzDtTax+geV23LMe2HcAR0I/G22onmJq/YwdRVO7x6jbzt9JWUEZFNAr/wK08yd5oFnYaQ/75lgo3+/fuzceNGLFGK468/SvHB7bX2qclVwHTU4mvOA7BZLShVO9Gc41uON6+Rv0YHyaihyCCBX/iVWYBzniRlFlzKtTY81lupTWO8PsZxLUc5u3Tpwt+XrCEmPpW8Nx/jpx1ra+3jzFXAdDTdGCW2KzT5lmD2Ladca6yW6sHfalFMGHiZVzN965paW4Q2CfzCrzzJ3GkWXBz71jzWbNKU2ReEyTd1MMyv3zjG/EPEqJz33HA5Ly97l2YZnTmxZhZn3nmG31+ZZNgG7knAdMwDODDj5qo+gnib8Sxms+8+iXHW2ivTaNhyqMCrmb6efECL8CWjekS983Tkj7v9zcb3m42UyZz0ruFqXgo4MONm07I+t2YXe/6zmFOfLsJqUTz5xHTGjRuH1Wqttt8jS7Ybnt/VyJ1uT3xgeA+NYyxU1FhvwNX4fotSht8SvB015EvHrowOarjMRvVIjV/UO2/z+XvbTGLW7GJWxTHb7vjAOXamlGa9hpF29z+wtsni0UcfpWvXrrz//vvVymh2npzCYtPmF7N7OHu+3PCezWY9mzUNuWqCMvoG4i1/r1Mg6ocM5xRB4W0+f6P9zcbGt0ywMXXVjqphoI6JYGa1YrMO05qd0NHxKTQfOoVWx7ZR+tl8brjhBm666SZeeOEFLrvsMtJdzEh2DoqO+3GU1ewejO758bd3eVXj96XNPhjDS0X9khq/CFn92hmn646LiWLhpsNVgbBcaxZuOkyLJsbt6b3bJhpuN6stn0vrxs6dO5k5cyaffPIJnTp1YtiwYfSLO0IjN33RNYdMmt2D0fZV23JMVx1r0cTqlzZ7b2vwZh90viy2I+qPBH4RssyWcNyXV3t5RTCf2HXwhHGQcjXyJTY2lvHjx7N3714efvhhNm7cyNMPj+HYS2Mo/ugfFH+/FV3uvinK7B6MtrsaMpp75nydlsN0voY34/vNvi01hKG5wpw09YiQ5a8x52bnmTDwslo5gqxRqlotOjU1leeff55nnnmGNWvWsHTpUlavXs2ZL9dgadSYRm0vx3bpldgye1Qt9O78geLNjF53tei6LIfpSXmMuBp2akY6g4NPAr8IWd5k+XR3HlM1K64mFdmYmBiGDBnCkCFDOHfuHGvXrmXW/73G+g/eo+ib9ajoGBpldCO+fR/+3yO/cXsPjhm9zikVFOYd0f6SEGc1HGWUYLJ4jlm/htkQXEkV0TBIU4/wydRVO7ho8hoyJr3LRZPXMHXVjnovw4SBlxlOZupzUZLh/n0uSsIaVWP/GjV4ZzPf31MrMVxpuXbZ5LJqWw79//IZD2yE4t738uhr6+l4z/M06TqQ8vwDHH/nBe66PosBAwYwf/58HvhFusczel0FfbN7dsVowpdZRd1su7fzAbxpSpKlJwNHAr/w2tRVOww7T4MR/I0mM43o2cZwAteInm08rsGD980eRh2jK7fn8tR9t3F6y9ucO3mcLVu2MHHiRPbv38+dd97JXddnkbT570TvX8/5Y/tIbVQ5jNPb9QfyzpR4tb9ZJ65Z57HZdm+H5nr6msow0cCSCVzCaxdNXmM6LPK7Z2+qt3L0mfGRV6mOG9L+Wms+//xzXn/9dZYvX05ubm7VcykpKZyKaUF0YjrWpHSsiS2JTmpJdEIaUdbYWud3+MvILI+bS8zKasZf762nr5G3r324OHHiBDt27ODrr7+u+v3SSy/RvXt3n84naZmF3/jSoRcI3tbIvd1utjqWWTOGN+dXSnHVVVdx1VVX8eKLL7Jnzx6+/fZb9u7dy759+3jt/U2c+34LRTs+dD4KS7MWWBPTibZ/IFiTWhKdlE50fKpXbeXedoz767319DUN9yRyJSUl7N69u1aQP3bsWNU+zZs3p3PnzpSUePdtzhMS+IXXvJ0IFSjeLoHo7f6OAOrpCBRfl2RUStGuXTvatWtXte0j+7eqipKzlJ48SllBTuXvk0cpLcjh7DfrqShxGrYaZSE64QLuWtaatdf1pHXr1rRq1arqd3p6OjExPyevMyurq9QP/uDpa+rra9nQlJWVsX//fnbu3MmuXbvYuXMnO3fuZN++fZSXV374xcTE0KFDB6677jq6dOlC586d6dy5M2lpaagA/Z+Sph7hNUcbf0139G7DU0M611s5/JXzx5fx7v4oj/NxNQPhlkMFhq+xg9aaiuLTlBbYPwxO5lR+OBTk0Li0kMLCwlrHRDVOwJaQSqdLM2iR2pJNuaCatMDSJJGouAQaNUngtr7tWbn9eNCXfAzUe2U2lNRsu9EMcKN/4wUFBezfv7/qZ8+ePezatYvdu3dz/nzl/BGlFBdddBEdO3akY8eOVUH+kksuqZb3yZ9k6UXhV57+hwg0b8eEB3oMuS/lMQtwWw4VeL36mAJmjcyi/8XNyMnJYfG6bfxzzRcUF+ZTfjqfsjMnqPjpR1RRASVnz9Q6PioqiibNEiiNaUpFbFOaxCdxeftM4hOTWH+4hPKYplgaxxNli6dxfBIzbr+KYT3bePUaecrf75XZaz2sRzrLt+bU2t69TTyfflcA2D9oz56i9OQxrmheQlZ8SbVAf/LkyWrXat26NZ06daJTp0507NiRTp060b59e+Li4nwuvy8k8AvRAHnSieltR6wnx1qUovRcEWVnfqS86CQVZ09RfvYUjSuKGHRpE/Lz88nLyyM/P5/8/HxOnDhhfDGlaNG8OcnJySQnJ5OSklL1uHnz5sTHxxMfH09CQkK1382aNSM6un5bml29FuVao7WmvOgkZSePUnbyGKWFxyg7ecz+jeoY+vzZqmOioqK48MILufjii2v9ZGZmYrM1jCYp6dwVogHypBPT2w5NT44t15qo2DhiYttAi59r7Ap4ySBFdcajb1NefJpy+wdERdXv0wzvEl/1AbFz507y8/MpKCjAXaWySZMm1T4MzB6bbWvcuLFXbeBHC4vRuoLyMycqA/vJo5TZg7vjsS516khVUUQnpBKd0JIm6e2JTkwjOrEl1sSWHHjxzmp9JqFGAr8QfrJqW0617JmJcVYeu6Wjy+YJV52YjqYOb7+TayprtxMGXuZ1J67zdZ2bWNKTmpBTaMHSuHpCu/QEGy8ZDK8sKyvj1KlTnDp1isLCwmqPN+46xIfbDnDqVCGllNC0mcKmzpOXl8e+ffuq9istdT2XwWKx0KxZM5cfEOfPn6egoIDc3FxyN31F8Ymj4JxDyRKNNeECohPSaHRhV6yJaUQnVAb46GbJKEvtEGlRKqSDPkjgF8IvVm3LqZXX5+TZUiYs+wowH2LZr12yYSduRnNbrfZobzgmPHnSfu3quo7ztEpsZHidjObGTRrR0dE0b96c5s2bV9u+alsOn323g6iePXF8hBRaLUys0WmrtebcuXO1PjRqPq657cCBA1XbTp8+jdVqJTExkeTkZFpnXsTxjB6VNfeENKyJLbE0bc4vLknmv4dPefwajerV2vhFDyES+IXwg5nv76kW9B0cKR7MAr9Zds5N35+s89j54tJy1n2bz7PZnWvV4M3SThhdt7i03DTj6abvTxpuN+Np/n6lFDabDZvNRlpamlfXcKioqEApVdUc1GfGR5QYfPs5eKLY8DXyZlRPqJHAL4QfuGqH9+U5f02YOlpYbJi185El2/1yXW/3r8+JWVFR1TPSuLq2WWbTp4Z0DotAX5Pk6hHCD1xNLPLlOX9NhnM1mc0f1/V2f2/L40/BvHZDI4FfCD+YMPCyWpk/oTJbqKtVsMyyW47q1dpwe2pT407F1KYxplkyjbJcGl0XICZa1cp4arNaTLN/jurV2qssmt5k8/R3dk5vM4kGogwNhQR+IfxgSLd0Zo7oSoLt5xmYiXFWZg7v6nJUj1l2y6eGdDbcvnnK9VyS0rjaOS5JaczmKdcb7g8YZrkEeDa7M4k18uwXl1aAriy783le/+2VhhlPe16Y5FUWTU+zeQYiO6e3mUTDOUOoTOASIoy5myBW1yyYgcqi2RCyczaEMtSV2QQuqfELEcbcdabWtbM1UJ21DSE7Z0MoQ6DIqB4hQow3OWzcZbmsaxbMQGXR9OW8zq9LvM2KUlB4ttTnPD/hkiHUiNT4hQgh3rY7u+vQ9KXD01m/dslebfeUt+Wq+boUFpdy8mxpndrm6/raNGQS+IUIId6sWQvuOzS97fCsyWwCmtl2T3lbLqPXxZmr18hfZQgl0tQjRAjxpd3ZbHKSp8/7uzye8qZcnlzPlzLV5bVpyKTGL0QIaWiTkBpKeTy5Xji0zftLwAK/UuoVpVSeUmqn07YkpdSHSql99t+Jrs4hhKiuobU711d53E2kMpuQFsgyhbJA1vjnATfU2DYJWKu1vgRYa/9bCOGhhtbuXB/l8aRDu2Y5EmzWWpPQwrHJxlcBncCllMoAVmutO9n/3gP8Umt9TCmVBqzXWrv9GJYJXEJErnCYSBUsDWUCV6rW+hiA/XeK2Y5KqXuVUluUUlvy8+s2QkAIEbrCeSJVsDTYzl2t9VytdU+tdc/k5LqNCRZChK6G0oEcTuo78Ofam3iw/86r5+sLIUJMQ+vQDgf1HfjfBsbYH48B3qrn6wshQkxD69AOBwGbwKWUWgT8EmihlDoCPAbMAJYqpe4GDgMjAnV9IUT4CNeJVMESsMCvtR5l8lT/QF1TCCGEew22c1cIIURgSOAXQogII4FfCCEijAR+IYSIMCGx5q5SKh845OPhLYAf/VicUCD3HBnkniNDXe75Qq11rRmwIRH460IptcUoV0U4k3uODHLPkSEQ9yxNPUIIEWEk8AshRISJhMA/N9gFCAK558gg9xwZ/H7PYd/GL4QQorpIqPELIYRwIoFfCCEiTFgHfqXUDUqpPUqp/UqpsFzfVyl1UCm1Qym1XSm1xb4trBa1V0q9opTKU0rtdNpmeo9Kqcn293yPUmpgcEpdNyb3/LhSKsf+Xm9XSt3k9Fw43HNrpdQ6pdRupdQupdTD9u1h+167uOfAvtda67D8ASzAd0BbIAb4CugQ7HIF4D4PAi1qbPsTMMn+eBLwXLDLWcd7vBroDux0d49AB/t7HQtk2v8NWIJ9D36658eB8Qb7hss9pwHd7Y+bAnvt9xa277WLew7oex3ONf4rgP1a6++11ueBxcDgIJepvgwG5tsfzweGBK8odae1/hgoqLHZ7B4HA4u11iVa6wPAfir/LYQUk3s2Ey73fExr/V/74zPAbiCdMH6vXdyzGb/cczgH/nTgB6e/j+D6BQ1VGvhAKbVVKXWvfZvHi9qHMLN7DPf3/QGl1Nf2piBHk0fY3bNSKgPoBmwmQt7rGvcMAXyvwznwK4Nt4Th2tY/WujtwI3C/UurqYBcoyML5fX8JuAjIAo4Bf7ZvD6t7Vko1AZYD47TWp13tarAtJO/b4J4D+l6Hc+A/ArR2+rsVcDRIZQkYrfVR++88YCWVX/siYVF7s3sM2/dda52rtS7XWlcA/8fPX/HD5p6VUlYqA+DrWusV9s1h/V4b3XOg3+twDvxfApcopTKVUjHAbVQu9h42lFKNlVJNHY+BAcBOImNRe7N7fBu4TSkVq5TKBC4BvghC+fzOEfzshlL5XkOY3LNSSgEvA7u11i84PRW277XZPQf8vQ52r3aAe8xvorKX/DtgSrDLE4D7a0tlD/9XwC7HPQLNgbXAPvvvpGCXtY73uYjKr7ulVNZ47nZ1j8AU+3u+B7gx2OX34z2/BuwAvrYHgLQwu+dfUNls8TWw3f5zUzi/1y7uOaDvtaRsEEKICBPOTT1CCCEMSOAXQogII4FfCCEijAR+IYSIMBL4hRAiwkjgF2FFKfVTjb/vVEq9GKzyCNEQSeAXwolSKjrYZfBEqJRTNEwS+EXEUEpdqJRaa098tVYp1ca+fZ5S6gWl1DrgOaXUNU550Lc5zY6eoJT60n78dPu2DKXUt0qp+fbty5RScfbn+tuP32FPtBWrlLpCKbXC/vxgpVSxUipGKdVIKfW9fftFSql/2xPvbVRKtTMqZ/2/giJcSK1BhBubUmq7099J/Jyq40VggdZ6vlLqLuBv/Jzi91LgOq11uVLqHeB+rfWn9uRZ55RSA6icHn8FlYmy3rYnxDsMXAbcbd//FeA+e/PSPKC/1nqvUmoB8Dt7GbrZr9mXyqn4l1P5f9GRlXEuMFZrvU8p1Qv4B3BtzXLW+ZUSEUtq/CLcFGutsxw/wDSn564E3rA/fo3K6fIObzoF00+BF5RSDwEJWusyKvMgDQC2Af8F2lH5QQDwg9b6U/vjhfbzXgYc0FrvtW+fD1xtP9d+pVR7Kj9EXqBy0ZW+wEb7B81VwJv2D7B/UrlYh1E5hfCJ1PhFJHPOV1JUtVHrGUqpd6nMmbJJKXUdlbX8Z7XW/3Q+gT2Hes28Jxrj9LkOG6lMo10K/IfKbwYWYDyVlbFC+4eWkSKT7UJ4TGr8IpJ8RmWWVoDRwCdGOymlLtJa79BaPwdsobJ2/z5wl71GjlIqXSnlWBCkjVLqSvvjUfbzfgtkKKUutm//H2CD/fHHwDjgc611PpVJyNoBu3RlLvYDSqkR9usopVTXut+6ED+TwC8iyUPAb5RSX1MZiB822W+cUmqnUuoroBh4T2v9AZXNRJ8rpXYAy6hcIxUql8sbYz9vEvCS1voc8Bsqm2x2ABXAHPv+m4FUKj8AoDID49f654yJo4G77dffReQsGSrqiWTnFKIO7E09q7XWnYJdFiE8JTV+IYSIMFLjF0KICCM1fiGEiDAS+IUQIsJI4BdCiAgjgV8IISKMBH4hhIgw/x85VRrwg1MXBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_horsepower(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1c0e6da9-8e69-4001-84c6-6a8c8491d877",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "normalization_1 (Normalizati (None, 9)                 19        \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,884\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 19\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#build full model \n",
    "\n",
    "dnn_model = build_and_compile_model(normalizer)\n",
    "dnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ac1b7a38-a012-4445-85d8-1ead3d08d082",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.06 s, sys: 888 ms, total: 4.94 s\n",
      "Wall time: 3.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = dnn_model.fit(\n",
    "    train_features, train_labels,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f7c66155-9dab-4887-9936-ae8245e4a66a",
   "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": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "63770728-c17f-4fac-ac1c-fdbffe04597f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#collect test results \n",
    "\n",
    "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4c60a199-ba59-4366-b58a-f105cdbbed9b",
   "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>Mean absolute error [MPG]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>horsepower_model</th>\n",
       "      <td>3.639006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_model</th>\n",
       "      <td>2.509754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dnn_model</th>\n",
       "      <td>1.761485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Mean absolute error [MPG]\n",
       "horsepower_model                   3.639006\n",
       "linear_model                       2.509754\n",
       "dnn_model                          1.761485"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now check test set performance on test sets \n",
    "\n",
    "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "10bd8f2c-7dc8-44d9-bfbe-a2152f29c0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DsB8LAGb2WCKlcplQPFhsUomNlUrpKrFnizeWZkPc8PhQ+FhYrTHgrKEtjsuqfQd6aW0RuYjbkMI9Vgr5RkzZFre3ZVbSBXHZlV+Pw8xobYFcwQb2+d3tT1iwckBDbRK9QK5+4va2jAauBT4WnnoQ+Fszey2pgrlsKFzI58bLp9OT6+vv1Rnd0cabBRtMF48e9Y2Ysi3ubcvNwGaCLScB/hT4vwT7zrph6vZHtrNw+RYO5voYO6qdnlzfIaEwc9HaAY2lxcPife2O7IobHiea2cUFx9+UtCmB8riMuP2R7Xz93ifJN3G8uv/ggDkpSQyLd+kRt6u2W9If5Q/CQWP+L2CY2negl4XLt1DcNlo8PD2JYfEuPeKGx18AN0l6TtLzwN8D/y25Yrm0yrdxHCxsFS1QWKvwnembW9zelk0EK6m/Mzx+PclCuaFX65oZha87ZvRIRra1sv3V/Ywd1c6r+w8OeH5hrcIbRJtbpU2frjCz2yT9j6LzAJjZdxMsmxsitS4GVPy6wrkqU4/rjNXN6g2izavSbcth4eMREV+HJ1guN4Rq3Wul1LT51U+9GDk8/fqLTvGgGEYq7dvyo/Db/2dmvyn8Wdho6jKgUq9HqVuaSq/zWsXwFrfB9MaY51wKlev1KLci2DGjR1Z1PTe8VGrz+DDwEWBcUbvHOwHfTS4jyg0DL3VLc8P9WxnZNvCveDC9Jb7QcXOp1NvSTtC2MYKgnSPvdeCSpArlBmfZxp1ct2JL/+jOMaPauPj0Cazb+tKAD26pFb12v/YWLYJR7a3sP5jrv86157+vpg98o1Zwd8mp1ObxIPCgpFvM7Pk6lckNQtQiO3v397D0kRc44h0D/7rLreg1oqWlPzgA3uqJHtsRR1IruLvGidvm8WNJnfkDSWMkrUqmSG4wFq96JnJ1rlyf0dXdM6BdY9bkcSjiOqPaWwcMBBvMquY+VL35xJ3bcmTh7nBmtlfSUckUyQ1G3A9jd0+O61Zs4UBv34CtID960pE8tO3litevpg3D1+5oPnFrHn2SJuYPJB0PJbcfdQ1UzYexq7snchzH7196s+K8lDj7thTyoerNJ254/A3wkKSfSvop8GvgmnIvkHScpHWSnpa0RdJXwvNjJa2WtC18HDO4X8EVmn/uybS1RN2IxLerq7vih73agWc+qKz5xJ3bcr+k6cAZBNsuXGVm0fXat/UCV5vZY5KOADZIWg3MBdaY2SJJC4AFwNdq/g3cIfIfxsLelsPaWznY23dIW0hHWyvvGCG6unsHXOPYzo6K81JqacPwQWXNpdI4j8lmtjUMDoBd4eNESRPLLYBsZruB3eH3b0h6GpgAXAicGT7tVuABPDyGVNSH9BvLnmTpwy+QM6NV4oKp43nk2b0DwqOwdlHuw+5tGK5SzeNq4AvA30X8LPYCyJImAdOAh4Gjw2DBzHaXaniVNA+YBzBx4sSop7gSihsyZ00ex90bdpIL9+jJmXHnozv6G61aJXJmh+zoVomvP+pibfo0qDeQDidY8/TbZnaPpC4z6yz4+V4zK9vu4Zs+xRe1GZOo3Lrd0dZadRuEjxhtHrVs+lTptqXsGqVmdk+F17cBdwP/VPDcFyWND2sd44E91RTYlRfVkBnnv4daBmx5G8bwVum25fzw8SiCOS5rw+NZBG0VJcNDwaIfPwGeLlr3YwVwJcFG2VcCy6sutStpMIOuSr3WaxguSqXh6X8OIOk+YEq+rSKsMdxU4dozCVZZf7JgseSvE4TGnZI+D2wHPlNz6d0A5Yabx3ltMZ+T4kqJO8J0Uj44Qi8C7y33AjN7CCJHPgOcHfN9XRlRNYL5555ccgPpcko1dvqcFFdK3PB4IJzLspTgFvoyYF1ipXIVRdUIvnrHJka1tfT3qlTSKtFnVvZWxOekuFLiDhL7S0mf5u0d45aY2b3JFctVUmqJwP1VzHzNmZWsGub5eA5XStzh6QCPASvN7CpgVThq1DXIUP3PX2leis9JcaXE3av2CwQDtsYCJxKMFP0h3nZRN8XtG6M72gZs5TgYpdoxfPsEV0rcNo8vAR8kGCGKmW3zKfn1E9W+0dYq2lpUdcNoOaVqMz6ew0WJe9tywMz6d/iRNAKfkl83Ue0bPTnj8JEjGDOqraprTejsYIJvA+mGQNzweFDS14EOSbOBnwO/SK5YrlCpGsHe/T2Mah/BpR94N+2tlf8q820V3o7hhkLc8Pga8BLwJPBF4J+BbyRVKHeocjWCnV3d/Hz9Dnr7+rjpc9NL1ipapf65K762hhsKFds8JLUAT5jZ+4F/SL5IrljUDNZCfQZjR7Vz3qnj6cn1Rc52LQ4Hb8dwg1UxPMysT9Lj4fod2+tRqOEi7pyRwh6PUkPP9+4/2H+97p5cTdPsnatG3N6W8cAWSY8Ab+ZPmtkFiZRqGIg7Z6Q4YMaMamPv/oFdtKM72g65Xs6svx3Dg8MlIW54fDPRUgxDceaMRAXMCA1cn6OjrRUJn4Pi6qpsg6mkkZK+SjDzdTLwGzN7MP9VjwI2qzhzRqICpteC4Bg7qv2Qxs6uiNpIufdxbrAq1TxuBXqAfwH+GJgCfCXpQg0HceaMlJta/9jC2Yccl2oP8bEbLimVumqnmNkVZvYjgr1pP1qHMg0LsyaPizw/6V0dzFy0lhMWrCz52lYNnM7mYzdcvVWqefTXhc2sVxH/aF1t1m19KfL8b3/3asWhuzkzZi5aG9lL43NQXL1UCo/TJL0efi+CEaavh9+bmb0z0dI1sVJtEXHG/Iu3b2mKe2k8LFy9lL1tMbNWM3tn+HWEmY0o+N6DYxBqbYuIWgl9MBtQO1eratbzcEMoqo2i1E1hq9Tfs1KqZuK9Kq7ePDwaJGp+yZ+cMZGRIw79K+loa+XvPnsazy46j98sOMtnxLrU8PBIkfcfO5rxBSEQNWHNe1VcWsQdYeqGQOFQ885RbbzW3UN+LZ/Chs+xo9rZu/9g5DW8V8WlhYdHnRQPNY+an5Jvz3g1DI5S8128V8Wlgd+21Emp1c4r8Z4Ul1YeHnUymN6QWneAcy5JHh51MpjekKjh6M41modHnUT1ksQVdwc45+rJG0zrJN/AecP9W9n92ltVvbbU2A7nGslrHnV0zpSjmdDZQWtL/NsQH8Ph0srDo072Hehl7s2PsPGFLn5w2bSStYkxo9p8VXOXCX7bUqO4ixfDwOAot8r5tee/z8PCZYKHRw3iLl4M0cFR+DwfKeqyysOjBnEWL4bSwZHnI0Vdlnl41KDUgK+dXd2csGAlx3Z28OWz3sNdG3aUDA7nsi6xBlNJN0vaI2lzwbmxklZL2hY+jknq/ZNUbsCXEYTI1+99kse27/XgcE0ryd6WW4BPFJ1bAKwxs5OANeFx5sQZ8NVn0NnR7sHhmlZi4WFmvwZeLTp9IcF2DoSPc5J6/yQVL+RTSqlp9c41g3q3eRxtZrsBzGy3pKNKPVHSPGAewMSJE+tUvPgKGztnLlrre6a4YSe1g8TMbImZzTCzGePGRe9xkhZfPus9FA8a9ZGhrtnVOzxelDQeIHzcU+f3H3L7DvRy14YdwMAtIL0b1jWzet+2rACuBBaFj8vr/P5DqnAcx42XT/fGUTesJNlVuxT4V+BkSTskfZ4gNGZL2gbMDo8zqdIAMOeaXWI1DzO7vMSPzk7qPevFg8O5FDeYppUHh3MBD48qeHA49zYPj5g8OJw7lIdHDB4czg3k4VGBB4dz0Tw8yvDgcK40D48SPDicK8/DI4IHh3OVeXgU8eBwLh4PjwIeHM7F5+ER8uBwrjoeHnhwOFeLYR8eHhzO1WZYh4cHh3O1G7bh4cHh3OAMy/Dw4HBu8IZdeHhwODc0hlV4eHA4N3SGTXh4cDg3tIZFeHhwODf0mj48PDicS0ZTh4cHh3PJadrw8OBwLllNGR4eHM4lr+nCw4PDufpoqvDw4HCufpomPDw4nKuvpggPDw7n6i/z4eHB4VxjZDo8PDica5zMhocHh3ONlcnw8OBwrvEyFx4eHM6lQ6bCw4PDufTITHh4cDiXLg0JD0mfkPSMpP+QtKDS8/vMPDicS5kR9X5DSa3ATcBsYAfwqKQVZvZUqdc8+/KbvObB4VyqNKLm8UHgP8zs92Z2ELgduLDcC/YfzHlwOJcyda95ABOAFwqOdwAfKn6SpHnAvPDwwKdOO3ZzHco2FI4EXm50IaqQpfJmqayQrfKeXO0LGhEeijhnA06YLQGWAEhab2Yzki7YUMhSWSFb5c1SWSFb5ZW0vtrXNOK2ZQdwXMHxu4FdDSiHc24QGhEejwInSTpBUjtwGbCiAeVwzg1C3W9bzKxX0l8Cq4BW4GYz21LhZUuSL9mQyVJZIVvlzVJZIVvlrbqsMhvQ3OCccxVlZoSpcy5dPDycczVJdXhUO4y93iTdLGmPpM0F58ZKWi1pW/g4ppFlzJN0nKR1kp6WtEXSV8LzaS3vSEmPSHo8LO83w/OpLC8Eo6clbZR0X3ic5rI+J+lJSZvy3bTVlje14VEwjP2PgSnA5ZKmNLZUA9wCfKLo3AJgjZmdBKwJj9OgF7jazP4QOAP4UvjnmdbyHgDOMrPTgKnAJySdQXrLC/AV4OmC4zSXFWCWmU0tGItSXXnNLJVfwIeBVQXH1wDXNLpcEeWcBGwuOH4GGB9+Px54ptFlLFHu5QTzi1JfXmAU8BjBSORUlpdgvNIa4CzgvrT/WwCeA44sOldVeVNb8yB6GPuEBpWlGkeb2W6A8PGoBpdnAEmTgGnAw6S4vOFtwCZgD7DazNJc3u8Dfw30FZxLa1khGNX9K0kbwqkgUGV5GzE8Pa5Yw9hddSQdDtwNfNXMXpei/pjTwcxywFRJncC9kt7f4CJFkvQpYI+ZbZB0ZoOLE9dMM9sl6ShgtaSt1V4gzTWPrA5jf1HSeIDwcU+Dy9NPUhtBcPyTmd0Tnk5tefPMrAt4gKB9KY3lnQlcIOk5glniZ0m6jXSWFQAz2xU+7gHuJZjtXlV50xweWR3GvgK4Mvz+SoK2hYZTUMX4CfC0mX234EdpLe+4sMaBpA7gHGArKSyvmV1jZu82s0kE/07XmtkVpLCsAJIOk3RE/nvg48Bmqi1voxtuKjTqfBL4d+B3wN80ujwR5VsK7AZ6CGpKnwfeRdBwti18HNvocoZl/SOC274ngE3h1ydTXN5TgY1heTcDC8PzqSxvQbnP5O0G01SWFfgD4PHwa0v+s1VteX14unOuJmm+bXHOpZiHh3OuJh4ezrmaeHg452ri4eGcq4mHh3OuJh4eKSfpXeG06U2S/lPSzoLj9iG4/nWSri86N1XS0xVe81eDfe8y189PF58RHj8gabsKxtJLWiZpX/j9JEnd4Z/JU5J+KKkl/NlJku6T9LtwHsc6SR8Lf3ZpuNzDfUn9Ls3MwyPlzOwVC6ZNTwV+CHwvf2xmByUNdn7SUuDSonOXAT8b5HUHa5aZFW4H0EUwDJxw5GnxDmC/C/+MTiVYwmGOpJHASmCJmZ1oZqcDXyYYJIWZ3QH81wR/h6bm4ZFBkm6R9F1J64AbimsCkjaHM2eRdEW4qM4mST8K10npZ2bPAF2SCjfe+ixwu6QvSHo0XJDnbkmjIsryQEEN4chwfkd+Ruzi8PVPSPpieH68pF+H5dks6aMxf+3bCUIN4CLgnqgnmVkv8FvgPcCfAP9qZisKfr7ZzG6J+Z6uDA+P7HovcI6ZXV3qCZL+kKBWMTP8XzlH8IEqtpTwgxkuuPOKmW0D7jGzD1iwIM/TBMPv4/o88JqZfQD4APAFSScAnyNYp2UqcBrBMPk41gAfC8PvMuCOqCeFAXc28CTwPoJ1QFwC0jwl35X3cwumrJdzNnA6wWbiAB1Ez5S8HfitpKsJPphLw/Pvl/QtoBM4nGC7jLg+Dpwq6ZLweDRwEsGEx5vDGb7LzGxTzOvlgIcIwrDDzJ4rWk7gxHDtDwOWm9kvJc0ufIKke8My/LuZXVTF7+IieHhk15sF3/dyaC1yZPgo4FYzu6bchczshfB2478AFxOs4gbBMotzzOxxSXMJJn0VK3zvkQXnBXzZzAYETthgeR7wU0mLzewfy5WvwO0E08evi/hZvs2j0BbgY/kDM/t0eIv1nZjv58rw25bm8BwwHUDSdOCE8Pwa4JJwwZf8ArfHl7jGUuB7BB/CHeG5I4DdYS0h6nYn/96nh99fUnB+FfAX4WuR9N5wKvjxBAvn/APBEgHTq/g9/wW4nrdrRpX8DJgp6YKCcwPabVxtvObRHO4G/iystj9KsIwBZvaUpG8QLDfXQrB0wJeA5yOu8XPgfxP0RuT9T4KlCp8naEM4IuJ13wHulPSnwNqC8z8mWN/1sbCL9SVgDkHtZb6kHmAf8Gdxf0kLpoDHrjWYWbeCVb6+K+n7wIvAG8C34l7DleZT8l3qhLdQM8zs5Tq815nAX5nZp5J+r2bjty0ujV4C1uS7gJMi6VLg/wB7k3yfZuU1D+dcTbzm4ZyriYeHc64mHh7OuZp4eDjnavL/Af6XvtTB8sFxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make predictions \n",
    "\n",
    "test_predictions = dnn_model.predict(test_features).flatten()\n",
    "\n",
    "a = plt.axes(aspect='equal')\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "lims = [0, 50]\n",
    "plt.xlim(lims)\n",
    "plt.ylim(lims)\n",
    "_ = plt.plot(lims, lims)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "35437bce-f842-479b-8c0c-68e89e489025",
   "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": [
    "#error distribtuion\n",
    "\n",
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins=25)\n",
    "plt.xlabel('Prediction Error [MPG]')\n",
    "_ = plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3d1908fa-9ae5-4ad3-aa3e-5ce31e8e81c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: dnn_model/assets\n"
     ]
    }
   ],
   "source": [
    "#save the model \n",
    "\n",
    "dnn_model.save('dnn_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "763955db-71d5-4bb4-9ece-f9885fb515db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the model \n",
    "\n",
    "reloaded = tf.keras.models.load_model('dnn_model')\n",
    "\n",
    "test_results['reloaded'] = reloaded.evaluate(\n",
    "    test_features, test_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5cba0805-3bfc-4393-9224-1a2a3076f1f4",
   "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>Mean absolute error [MPG]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>horsepower_model</th>\n",
       "      <td>3.639006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linear_model</th>\n",
       "      <td>2.509754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dnn_model</th>\n",
       "      <td>1.761485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reloaded</th>\n",
       "      <td>1.761485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Mean absolute error [MPG]\n",
       "horsepower_model                   3.639006\n",
       "linear_model                       2.509754\n",
       "dnn_model                          1.761485\n",
       "reloaded                           1.761485"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ead505-13f1-4b24-8965-76e18f9731e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
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  "language_info": {
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