main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.2.1 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "# Use seaborn for pairplot.\n",
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from optimizer import AdaGradOptimizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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,
   "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",
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       "      <td>82</td>\n",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Horsepower</th>\n",
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       "      <td>82</td>\n",
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       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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     USA  \n",
       "394          82  Europe  \n",
       "395          82     USA  \n",
       "396          82     USA  \n",
       "397          82     USA  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <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>2625.0</td>\n",
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       "      <td>82</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
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       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\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   False  False   True  \n",
       "394          82    True  False  False  \n",
       "395          82   False  False   True  \n",
       "396          82   False  False   True  \n",
       "397          82   False  False   True  "
      ]
     },
     "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x21159d4eba0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer = tf.keras.layers.Normalization(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer.adapt(np.array(train_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "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": [
    "first = np.array(train_features[:1],dtype=float)\n",
    "\n",
    "with np.printoptions(precision=2, suppress=True):\n",
    "  print('First example:', first)\n",
    "  print()\n",
    "  print('Normalized:', normalizer(first).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ normalization (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Normalization</span>)   │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">314</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)               │            <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ normalization (\u001b[38;5;33mNormalization\u001b[0m)   │ (\u001b[38;5;34m314\u001b[0m, \u001b[38;5;34m9\u001b[0m)               │            \u001b[38;5;34m19\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> (80.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> (80.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.218],\n",
       "       [-0.872],\n",
       "       [ 0.404],\n",
       "       [-1.051],\n",
       "       [-0.003],\n",
       "       [-0.228],\n",
       "       [ 0.162],\n",
       "       [-1.065],\n",
       "       [ 0.183],\n",
       "       [ 0.861]], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=AdaGradOptimizer(learning_rate=.5),\n",
    "    loss='mean_absolute_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 22.8179 - val_loss: 21.7898\n",
      "Epoch 2/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 21.2034 - val_loss: 20.7327\n",
      "Epoch 3/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 19.8461 - val_loss: 19.9107\n",
      "Epoch 4/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 19.0007 - val_loss: 19.2204\n",
      "Epoch 5/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 17.4789 - val_loss: 18.6083\n",
      "Epoch 6/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 16.7436 - val_loss: 18.0518\n",
      "Epoch 7/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 16.5789 - val_loss: 17.5392\n",
      "Epoch 8/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 14.8406 - val_loss: 17.0605\n",
      "Epoch 9/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 15.2291 - val_loss: 16.6167\n",
      "Epoch 10/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 15.4173 - val_loss: 16.2077\n",
      "Epoch 11/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 14.6461 - val_loss: 15.8162\n",
      "Epoch 12/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 14.2893 - val_loss: 15.4253\n",
      "Epoch 13/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 14.0572 - val_loss: 15.0693\n",
      "Epoch 14/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 13.5170 - val_loss: 14.7161\n",
      "Epoch 15/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 13.2748 - val_loss: 14.3746\n",
      "Epoch 16/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 12.3427 - val_loss: 14.0373\n",
      "Epoch 17/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 12.2840 - val_loss: 13.7194\n",
      "Epoch 18/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 11.6476 - val_loss: 13.4135\n",
      "Epoch 19/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 11.2332 - val_loss: 13.1010\n",
      "Epoch 20/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 11.1589 - val_loss: 12.8218\n",
      "Epoch 21/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.5051 - val_loss: 12.4953\n",
      "Epoch 22/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 11.0486 - val_loss: 12.2315\n",
      "Epoch 23/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.3241 - val_loss: 11.9543\n",
      "Epoch 24/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 10.2569 - val_loss: 11.7071\n",
      "Epoch 25/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.5798 - val_loss: 11.4297\n",
      "Epoch 26/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.9610 - val_loss: 11.1694\n",
      "Epoch 27/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.6806 - val_loss: 10.9077\n",
      "Epoch 28/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.8496 - val_loss: 10.6763\n",
      "Epoch 29/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.7112 - val_loss: 10.4178\n",
      "Epoch 30/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.6537 - val_loss: 10.1740\n",
      "Epoch 31/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.5851 - val_loss: 9.9452\n",
      "Epoch 32/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.7476 - val_loss: 9.7264\n",
      "Epoch 33/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.7976 - val_loss: 9.5202\n",
      "Epoch 34/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.6709 - val_loss: 9.2854\n",
      "Epoch 35/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.7433 - val_loss: 9.0612\n",
      "Epoch 36/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0930 - val_loss: 8.8396\n",
      "Epoch 37/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.8827 - val_loss: 8.6270\n",
      "Epoch 38/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.0256 - val_loss: 8.4235\n",
      "Epoch 39/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.9304 - val_loss: 8.2276\n",
      "Epoch 40/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.5139 - val_loss: 8.0299\n",
      "Epoch 41/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.7367 - val_loss: 7.8470\n",
      "Epoch 42/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.3529 - val_loss: 7.6654\n",
      "Epoch 43/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.2435 - val_loss: 7.4775\n",
      "Epoch 44/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.8039 - val_loss: 7.3252\n",
      "Epoch 45/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4823 - val_loss: 7.1583\n",
      "Epoch 46/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.7952 - val_loss: 7.0177\n",
      "Epoch 47/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.4720 - val_loss: 6.8662\n",
      "Epoch 48/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.2221 - val_loss: 6.7060\n",
      "Epoch 49/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.1943 - val_loss: 6.5023\n",
      "Epoch 50/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.3710 - val_loss: 6.3562\n",
      "Epoch 51/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.0598 - val_loss: 6.1935\n",
      "Epoch 52/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.9067 - val_loss: 6.0173\n",
      "Epoch 53/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.1809 - val_loss: 5.8342\n",
      "Epoch 54/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.4810 - val_loss: 5.6822\n",
      "Epoch 55/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.0643 - val_loss: 5.5276\n",
      "Epoch 56/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3874 - val_loss: 5.3687\n",
      "Epoch 57/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.5502 - val_loss: 5.2173\n",
      "Epoch 58/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.1522 - val_loss: 5.0846\n",
      "Epoch 59/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9485 - val_loss: 4.9339\n",
      "Epoch 60/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.8107 - val_loss: 4.7850\n",
      "Epoch 61/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.3256 - val_loss: 4.6459\n",
      "Epoch 62/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.8224 - val_loss: 4.4948\n",
      "Epoch 63/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.2473 - val_loss: 4.3210\n",
      "Epoch 64/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.5285 - val_loss: 4.1980\n",
      "Epoch 65/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.3611 - val_loss: 4.0601\n",
      "Epoch 66/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.2495 - val_loss: 3.9526\n",
      "Epoch 67/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.3185 - val_loss: 3.8639\n",
      "Epoch 68/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.1566 - val_loss: 3.7267\n",
      "Epoch 69/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.9549 - val_loss: 3.6580\n",
      "Epoch 70/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9194 - val_loss: 3.5980\n",
      "Epoch 71/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.8976 - val_loss: 3.5218\n",
      "Epoch 72/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9654 - val_loss: 3.4672\n",
      "Epoch 73/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.0109 - val_loss: 3.4031\n",
      "Epoch 74/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.0245 - val_loss: 3.3343\n",
      "Epoch 75/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6972 - val_loss: 3.2453\n",
      "Epoch 76/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.9337 - val_loss: 3.1823\n",
      "Epoch 77/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7647 - val_loss: 3.1294\n",
      "Epoch 78/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.9819 - val_loss: 3.0965\n",
      "Epoch 79/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5589 - val_loss: 3.0388\n",
      "Epoch 80/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.8176 - val_loss: 3.0144\n",
      "Epoch 81/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.8366 - val_loss: 2.9823\n",
      "Epoch 82/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5804 - val_loss: 2.9540\n",
      "Epoch 83/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9478 - val_loss: 2.9530\n",
      "Epoch 84/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.6493 - val_loss: 2.9477\n",
      "Epoch 85/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6217 - val_loss: 2.8833\n",
      "Epoch 86/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5940 - val_loss: 2.8627\n",
      "Epoch 87/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.4170 - val_loss: 2.8537\n",
      "Epoch 88/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6859 - val_loss: 2.8552\n",
      "Epoch 89/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4012 - val_loss: 2.8154\n",
      "Epoch 90/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5501 - val_loss: 2.7982\n",
      "Epoch 91/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7885 - val_loss: 2.7778\n",
      "Epoch 92/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5585 - val_loss: 2.7645\n",
      "Epoch 93/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5845 - val_loss: 2.7507\n",
      "Epoch 94/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.3175 - val_loss: 2.7418\n",
      "Epoch 95/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5992 - val_loss: 2.7483\n",
      "Epoch 96/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.6665 - val_loss: 2.7085\n",
      "Epoch 97/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5148 - val_loss: 2.7111\n",
      "Epoch 98/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4519 - val_loss: 2.7028\n",
      "Epoch 99/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4170 - val_loss: 2.6770\n",
      "Epoch 100/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4368 - val_loss: 2.6666\n",
      "CPU times: total: 1.61 s\n",
      "Wall time: 6.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    epochs=100,\n",
    "    # Suppress logging.\n",
    "    verbose=1,\n",
    "    # Calculate validation results on 20% of the training data.\n",
    "    validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ normalization (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Normalization</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ normalization (\u001b[38;5;33mNormalization\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │            \u001b[38;5;34m19\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> (80.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> (80.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelBuilt = tf.keras.Sequential([\n",
    "    normalizer,\n",
    "    layers.Dense(units=1)\n",
    "])\n",
    "modelBuilt.compile(optimizer=\"adagrad\", loss='mean_absolute_error')\n",
    "\n",
    "\n",
    "modelBuilt.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelBuilt.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.5), loss='mean_absolute_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 21.8188 - val_loss: 21.3523\n",
      "Epoch 2/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 20.2211 - val_loss: 20.3066\n",
      "Epoch 3/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 19.5926 - val_loss: 19.4911\n",
      "Epoch 4/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 18.2323 - val_loss: 18.7983\n",
      "Epoch 5/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 17.5589 - val_loss: 18.1919\n",
      "Epoch 6/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 16.5115 - val_loss: 17.6506\n",
      "Epoch 7/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 16.2322 - val_loss: 17.1411\n",
      "Epoch 8/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 15.5879 - val_loss: 16.6720\n",
      "Epoch 9/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 15.0957 - val_loss: 16.2373\n",
      "Epoch 10/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 15.0242 - val_loss: 15.8311\n",
      "Epoch 11/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 14.8698 - val_loss: 15.4760\n",
      "Epoch 12/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 13.9428 - val_loss: 15.0712\n",
      "Epoch 13/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 13.2556 - val_loss: 14.7025\n",
      "Epoch 14/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 12.9614 - val_loss: 14.3431\n",
      "Epoch 15/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 12.4253 - val_loss: 13.9910\n",
      "Epoch 16/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 12.0034 - val_loss: 13.6738\n",
      "Epoch 17/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 11.9978 - val_loss: 13.3636\n",
      "Epoch 18/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 11.9354 - val_loss: 13.0609\n",
      "Epoch 19/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 11.2603 - val_loss: 12.7569\n",
      "Epoch 20/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 10.8773 - val_loss: 12.4688\n",
      "Epoch 21/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.2939 - val_loss: 12.1562\n",
      "Epoch 22/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.2397 - val_loss: 11.8899\n",
      "Epoch 23/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 10.0493 - val_loss: 11.6081\n",
      "Epoch 24/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.5285 - val_loss: 11.3175\n",
      "Epoch 25/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.7024 - val_loss: 11.0602\n",
      "Epoch 26/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 9.8095 - val_loss: 10.8183\n",
      "Epoch 27/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.8362 - val_loss: 10.5545\n",
      "Epoch 28/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.6577 - val_loss: 10.3083\n",
      "Epoch 29/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.0779 - val_loss: 10.0463\n",
      "Epoch 30/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8.7500 - val_loss: 9.8303\n",
      "Epoch 31/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.6565 - val_loss: 9.6101\n",
      "Epoch 32/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.1026 - val_loss: 9.3872\n",
      "Epoch 33/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 7.1046 - val_loss: 9.1479\n",
      "Epoch 34/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.1118 - val_loss: 8.9281\n",
      "Epoch 35/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.9234 - val_loss: 8.7251\n",
      "Epoch 36/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.6516 - val_loss: 8.5433\n",
      "Epoch 37/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.8617 - val_loss: 8.3307\n",
      "Epoch 38/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.8981 - val_loss: 8.1222\n",
      "Epoch 39/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6.1126 - val_loss: 7.9204\n",
      "Epoch 40/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 6.0176 - val_loss: 7.7251\n",
      "Epoch 41/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.8595 - val_loss: 7.5403\n",
      "Epoch 42/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.3556 - val_loss: 7.3509\n",
      "Epoch 43/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.5909 - val_loss: 7.1718\n",
      "Epoch 44/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.1940 - val_loss: 6.9919\n",
      "Epoch 45/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.0189 - val_loss: 6.8381\n",
      "Epoch 46/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 5.1912 - val_loss: 6.6646\n",
      "Epoch 47/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.9925 - val_loss: 6.5151\n",
      "Epoch 48/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5.2109 - val_loss: 6.3581\n",
      "Epoch 49/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.5905 - val_loss: 6.1601\n",
      "Epoch 50/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.7912 - val_loss: 5.9707\n",
      "Epoch 51/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.0452 - val_loss: 5.8176\n",
      "Epoch 52/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3615 - val_loss: 5.6357\n",
      "Epoch 53/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.3156 - val_loss: 5.4613\n",
      "Epoch 54/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.4848 - val_loss: 5.3190\n",
      "Epoch 55/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1262 - val_loss: 5.1442\n",
      "Epoch 56/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4.2559 - val_loss: 4.9862\n",
      "Epoch 57/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9697 - val_loss: 4.8182\n",
      "Epoch 58/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7290 - val_loss: 4.6591\n",
      "Epoch 59/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.7617 - val_loss: 4.5155\n",
      "Epoch 60/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.9117 - val_loss: 4.3586\n",
      "Epoch 61/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.4512 - val_loss: 4.2423\n",
      "Epoch 62/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.6084 - val_loss: 4.1412\n",
      "Epoch 63/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.3290 - val_loss: 4.0129\n",
      "Epoch 64/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.5055 - val_loss: 3.9270\n",
      "Epoch 65/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.3104 - val_loss: 3.7927\n",
      "Epoch 66/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3.0282 - val_loss: 3.7490\n",
      "Epoch 67/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.0860 - val_loss: 3.6570\n",
      "Epoch 68/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.9241 - val_loss: 3.5569\n",
      "Epoch 69/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.9835 - val_loss: 3.4973\n",
      "Epoch 70/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.0878 - val_loss: 3.4015\n",
      "Epoch 71/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3.0313 - val_loss: 3.3593\n",
      "Epoch 72/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7402 - val_loss: 3.2711\n",
      "Epoch 73/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7772 - val_loss: 3.2386\n",
      "Epoch 74/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.6458 - val_loss: 3.1767\n",
      "Epoch 75/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5775 - val_loss: 3.1026\n",
      "Epoch 76/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7862 - val_loss: 3.1199\n",
      "Epoch 77/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7579 - val_loss: 3.0466\n",
      "Epoch 78/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5129 - val_loss: 3.0180\n",
      "Epoch 79/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.7933 - val_loss: 2.9984\n",
      "Epoch 80/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7895 - val_loss: 3.0049\n",
      "Epoch 81/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.5755 - val_loss: 2.9653\n",
      "Epoch 82/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5116 - val_loss: 2.9626\n",
      "Epoch 83/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7783 - val_loss: 2.9169\n",
      "Epoch 84/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.6228 - val_loss: 2.9003\n",
      "Epoch 85/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7355 - val_loss: 2.9062\n",
      "Epoch 86/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.7172 - val_loss: 2.8661\n",
      "Epoch 87/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5285 - val_loss: 2.8379\n",
      "Epoch 88/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5572 - val_loss: 2.8275\n",
      "Epoch 89/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5822 - val_loss: 2.8310\n",
      "Epoch 90/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.6565 - val_loss: 2.8193\n",
      "Epoch 91/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.6579 - val_loss: 2.7813\n",
      "Epoch 92/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.4876 - val_loss: 2.7687\n",
      "Epoch 93/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3469 - val_loss: 2.7502\n",
      "Epoch 94/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.4597 - val_loss: 2.7609\n",
      "Epoch 95/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 2.5585 - val_loss: 2.7293\n",
      "Epoch 96/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2.6656 - val_loss: 2.7147\n",
      "Epoch 97/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7398 - val_loss: 2.7138\n",
      "Epoch 98/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.3657 - val_loss: 2.7093\n",
      "Epoch 99/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.7029 - val_loss: 2.6834\n",
      "Epoch 100/100\n",
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2.5628 - val_loss: 2.7051\n",
      "CPU times: total: 1.75 s\n",
      "Wall time: 6.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = modelBuilt.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    epochs=100,\n",
    "    # Suppress logging.\n",
    "    verbose=1,\n",
    "    # Calculate validation results on 20% of the training data.\n",
    "    validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 2.4859 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.618682622909546"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_features, test_labels)"
   ]
  }
 ],
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