GSLC_DeepLearn_Revision.ipynb

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      "cell_type": "markdown",
      "source": [
        "2602199582 - Fiona Maharani Nugraha"
      ],
      "metadata": {
        "id": "MZW97YaWKHR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
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        "id": "JTeILdeUKJ0N"
      }
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      "cell_type": "code",
      "execution_count": 4,
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        "id": "5ppCLeUJKCCW"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Sequential,Model\n",
        "from tensorflow.keras.utils import plot_model\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "SEED_VALUE=123\n",
        "random.seed(SEED_VALUE)\n",
        "np.random.seed(SEED_VALUE)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
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        },
        "id": "ayKoM1TgKTM4",
        "outputId": "7745af16-36ed-4f0e-dcca-41e55ae46397"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Data"
      ],
      "metadata": {
        "id": "2HGJ5BYLKbRt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"winequality-red.csv\")\n",
        "df.head()"
      ],
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          "height": 206
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        "outputId": "a67a9a0b-86ce-431f-93e1-79d836c317dd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
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              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1599,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7410963181276953,\n        \"min\": 4.6,\n        \"max\": 15.9,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          5.3,\n          12.7,\n          12.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17905970415353537,\n        \"min\": 0.12,\n        \"max\": 1.58,\n        \"num_unique_values\": 143,\n        \"samples\": [\n          1.025,\n          0.4,\n          0.87\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19480113740531857,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          0.37,\n          0.0,\n          0.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4099280595072798,\n        \"min\": 0.9,\n        \"max\": 15.5,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          11.0,\n          3.0,\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0470653020100901,\n        \"min\": 0.012,\n        \"max\": 0.611,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          0.096,\n          0.3429999999999999,\n          0.159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.460156969809725,\n        \"min\": 1.0,\n        \"max\": 72.0,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          11.0,\n          9.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.895324478299074,\n        \"min\": 6.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          68.0,\n          35.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0018873339538425554,\n        \"min\": 0.99007,\n        \"max\": 1.00369,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          0.99974,\n          1.0001,\n          0.99471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15438646490354277,\n        \"min\": 2.74,\n        \"max\": 4.01,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          3.07,\n          3.0,\n          3.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16950697959010996,\n        \"min\": 0.33,\n        \"max\": 2.0,\n        \"num_unique_values\": 96,\n        \"samples\": [\n          1.07,\n          1.04,\n          1.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0656675818473946,\n        \"min\": 8.4,\n        \"max\": 14.9,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          8.5,\n          9.95,\n          9.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 8,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 6
        }
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      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tL2dsNPiOQaV",
        "outputId": "4b27e7d7-12ba-48aa-d4bb-82ced1c822d8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking Missing Values and Duplicate Rows"
      ],
      "metadata": {
        "id": "0_n2vXecOVgB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2UukRMhOVFK",
        "outputId": "bae0b65f-3b12-44cb-8b63-ba572d327f70"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No missing values"
      ],
      "metadata": {
        "id": "t73mIQQSOay-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "th47-aE2OcfM",
        "outputId": "05309226-ab17-4240-e978-96d9afd73433"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "240"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXm7jOtxOeO8",
        "outputId": "9ebfb3c3-6457-4e5d-8645-286d20eb3e97"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the best correlation to decide the Y"
      ],
      "metadata": {
        "id": "elJgoTgbOfsO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "p-sOJFW9Ohw2",
        "outputId": "7dcfe9eb-eb90-4445-f961-d1300edcd382"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1359.000000       1359.000000  1359.000000     1359.000000   \n",
              "mean        8.310596          0.529478     0.272333        2.523400   \n",
              "std         1.736990          0.183031     0.195537        1.352314   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.390000     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.260000        2.200000   \n",
              "75%         9.200000          0.640000     0.430000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1359.000000          1359.000000           1359.000000  1359.000000   \n",
              "mean      0.088124            15.893304             46.825975     0.996709   \n",
              "std       0.049377            10.447270             33.408946     0.001869   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996700   \n",
              "75%       0.091000            21.000000             63.000000     0.997820   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1359.000000  1359.000000  1359.000000  1359.000000  \n",
              "mean      3.309787     0.658705    10.432315     5.623252  \n",
              "std       0.155036     0.170667     1.082065     0.823578  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
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              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.310596</td>\n",
              "      <td>0.529478</td>\n",
              "      <td>0.272333</td>\n",
              "      <td>2.523400</td>\n",
              "      <td>0.088124</td>\n",
              "      <td>15.893304</td>\n",
              "      <td>46.825975</td>\n",
              "      <td>0.996709</td>\n",
              "      <td>3.309787</td>\n",
              "      <td>0.658705</td>\n",
              "      <td>10.432315</td>\n",
              "      <td>5.623252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.736990</td>\n",
              "      <td>0.183031</td>\n",
              "      <td>0.195537</td>\n",
              "      <td>1.352314</td>\n",
              "      <td>0.049377</td>\n",
              "      <td>10.447270</td>\n",
              "      <td>33.408946</td>\n",
              "      <td>0.001869</td>\n",
              "      <td>0.155036</td>\n",
              "      <td>0.170667</td>\n",
              "      <td>1.082065</td>\n",
              "      <td>0.823578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996700</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.091000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.997820</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 477.73113083518706,\n        \"min\": 1.7369898075324683,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.310596026490067,\n          7.9,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.27913114179347,\n        \"min\": 0.12,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5294775570272259,\n          0.52,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.365620103823,\n        \"min\": 0.0,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2723325974981604,\n          0.26,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 479.1405075102164,\n        \"min\": 0.9,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.5233995584988964,\n          2.2,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.4285635802803,\n        \"min\": 0.012,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          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\"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.177024284435,\n        \"min\": 0.0018689171325591413,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9967089477557026,\n          0.9967,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 479.4635066032717,\n        \"min\": 0.15503631128729617,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.309786607799853,\n          3.31,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.2238385062737,\n        \"min\": 0.17066689057420686,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6587049300956587,\n          0.62,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 477.18075450583325,\n        \"min\": 1.0820654499402542,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.432315428010792,\n          10.2,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 478.74412202383115,\n        \"min\": 0.8235780017165553,\n        \"max\": 1359.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1359.0,\n          5.6232523914643116,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "nFPaSHnqOjn0",
        "outputId": "fd7c3e6d-d1de-41e5-ceec-145b8e80dbd3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting Columns"
      ],
      "metadata": {
        "id": "hVl1NMVJOmOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQsjAGL_On81",
        "outputId": "2e0767d5-f00f-4412-b2fc-5d5b2fbda900"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1359 entries, 0 to 1598\n",
            "Data columns (total 12 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   fixed acidity         1359 non-null   float64\n",
            " 1   volatile acidity      1359 non-null   float64\n",
            " 2   citric acid           1359 non-null   float64\n",
            " 3   residual sugar        1359 non-null   float64\n",
            " 4   chlorides             1359 non-null   float64\n",
            " 5   free sulfur dioxide   1359 non-null   float64\n",
            " 6   total sulfur dioxide  1359 non-null   float64\n",
            " 7   density               1359 non-null   float64\n",
            " 8   pH                    1359 non-null   float64\n",
            " 9   sulphates             1359 non-null   float64\n",
            " 10  alcohol               1359 non-null   float64\n",
            " 11  quality               1359 non-null   int64  \n",
            "dtypes: float64(11), int64(1)\n",
            "memory usage: 138.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No categorical columns"
      ],
      "metadata": {
        "id": "BH0QDXUWOrdL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = []\n",
        "cat_cols = []\n",
        "\n",
        "for i in df.columns:\n",
        "    if df[i].dtype == 'object':\n",
        "        cat_cols.append(i)\n",
        "    elif \"int\" in str(df[i].dtype) or \"float\" in str(df[i].dtype):\n",
        "        num_cols.append(i)\n",
        "\n",
        "print(num_cols)\n",
        "print(cat_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KkWJQLj4OtB9",
        "outputId": "bc1fa81e-f841-4dcc-a21b-eb5c0e7a261b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_cols].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "qodPiMisOuop",
        "outputId": "28479e8d-8ba6-4beb-ed91-29f215ba6990"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1359.000000       1359.000000  1359.000000     1359.000000   \n",
              "mean        8.310596          0.529478     0.272333        2.523400   \n",
              "std         1.736990          0.183031     0.195537        1.352314   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.390000     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.260000        2.200000   \n",
              "75%         9.200000          0.640000     0.430000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1359.000000          1359.000000           1359.000000  1359.000000   \n",
              "mean      0.088124            15.893304             46.825975     0.996709   \n",
              "std       0.049377            10.447270             33.408946     0.001869   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996700   \n",
              "75%       0.091000            21.000000             63.000000     0.997820   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1359.000000  1359.000000  1359.000000  1359.000000  \n",
              "mean      3.309787     0.658705    10.432315     5.623252  \n",
              "std       0.155036     0.170667     1.082065     0.823578  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "      <td>1359.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.310596</td>\n",
              "      <td>0.529478</td>\n",
              "      <td>0.272333</td>\n",
              "      <td>2.523400</td>\n",
              "      <td>0.088124</td>\n",
              "      <td>15.893304</td>\n",
              "      <td>46.825975</td>\n",
              "      <td>0.996709</td>\n",
              "      <td>3.309787</td>\n",
              "      <td>0.658705</td>\n",
              "      <td>10.432315</td>\n",
              "      <td>5.623252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.736990</td>\n",
              "      <td>0.183031</td>\n",
              "      <td>0.195537</td>\n",
              "      <td>1.352314</td>\n",
              "      <td>0.049377</td>\n",
              "      <td>10.447270</td>\n",
              "      <td>33.408946</td>\n",
              "      <td>0.001869</td>\n",
              "      <td>0.155036</td>\n",
              "      <td>0.170667</td>\n",
              "      <td>1.082065</td>\n",
              "      <td>0.823578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996700</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.091000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>0.997820</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[num_cols]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 477.73113083518706,\n        \"min\": 1.7369898075324683,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.310596026490067,\n          7.9,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.27913114179347,\n        \"min\": 0.12,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5294775570272259,\n          0.52,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.365620103823,\n        \"min\": 0.0,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.2723325974981604,\n          0.26,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 479.1405075102164,\n        \"min\": 0.9,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.5233995584988964,\n          2.2,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.4285635802803,\n        \"min\": 0.012,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.08812362030905076,\n          0.079,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 473.84988773665145,\n        \"min\": 1.0,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.893303899926417,\n          14.0,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 464.21648825247667,\n        \"min\": 6.0,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          46.82597498160412,\n          38.0,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.177024284435,\n        \"min\": 0.0018689171325591413,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9967089477557026,\n          0.9967,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 479.4635066032717,\n        \"min\": 0.15503631128729617,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.309786607799853,\n          3.31,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 480.2238385062737,\n        \"min\": 0.17066689057420686,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6587049300956587,\n          0.62,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 477.18075450583325,\n        \"min\": 1.0820654499402542,\n        \"max\": 1359.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.432315428010792,\n          10.2,\n          1359.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 478.74412202383115,\n        \"min\": 0.8235780017165553,\n        \"max\": 1359.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1359.0,\n          5.6232523914643116,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Splitting"
      ],
      "metadata": {
        "id": "KZE6tiNnOw3z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = df.drop(['quality'],axis=1),df['quality']"
      ],
      "metadata": {
        "id": "to9xVoX7OyUX"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2,random_state = 123)\n",
        "x_train,x_val,y_train,y_val = train_test_split(x_train,y_train, test_size = 0.25,random_state = 123)\n",
        "print(x_train.shape,y_train.shape)\n",
        "print(x_val.shape,y_val.shape)\n",
        "print(x_test.shape,y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLN_lP89Oz0J",
        "outputId": "893a034e-f56c-44cb-a6af-3e8af8f884e0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(815, 11) (815,)\n",
            "(272, 11) (272,)\n",
            "(272, 11) (272,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scaling"
      ],
      "metadata": {
        "id": "ink8o4PBO1bb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "6f4nlspKO20Q"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "num_cols = [col for col in x_train.columns]\n",
        "x_train[num_cols] = scaler.fit_transform(x_train[num_cols])\n",
        "x_val[num_cols] = scaler.transform(x_val[num_cols])\n",
        "x_test[num_cols] = scaler.transform(x_test[num_cols])"
      ],
      "metadata": {
        "id": "2m-_nyhFO4GD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9ignc6MO5hx",
        "outputId": "bbdbaf77-47bc-4dfc-af24-ed5665a2c420"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(815, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "pyQgAQKxO7ra",
        "outputId": "fe1cc81d-0a8c-4e83-cfe7-30da578533ae"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "862        0.236364          0.171429         0.32        0.104895   0.050699   \n",
              "785        0.454545          0.121429         0.41        0.076923   0.078671   \n",
              "501        0.500000          0.185714         0.73        0.374126   0.062937   \n",
              "119        0.190909          0.364286         0.08        0.041958   0.103147   \n",
              "21         0.245455          0.150000         0.31        0.076923   0.076923   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1471       0.163636          0.371429         0.08        0.178322   0.050699   \n",
              "1270       0.009091          0.142857         0.01        0.027972   0.017483   \n",
              "1076       0.454545          0.100000         0.56        0.055944   0.061189   \n",
              "853        0.400000          0.128571         0.39        0.020979   0.073427   \n",
              "179        0.354545          0.307143         0.14        0.083916   0.050699   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide   density        pH  \\\n",
              "862              0.071429              0.066421  0.467692  0.393701   \n",
              "785              0.128571              0.199262  0.615385  0.370079   \n",
              "501              0.514286              0.254613  0.676923  0.338583   \n",
              "119              0.285714              0.302583  0.438462  0.472441   \n",
              "21               0.300000              0.236162  0.615385  0.614173   \n",
              "...                   ...                   ...       ...       ...   \n",
              "1471             0.085714              0.033210  0.241538  0.543307   \n",
              "1270             0.342857              0.195572  0.049231  0.755906   \n",
              "1076             0.014286              0.003690  0.395385  0.322835   \n",
              "853              0.557143              0.177122  0.486154  0.574803   \n",
              "179              0.114286              0.129151  0.515385  0.354331   \n",
              "\n",
              "      sulphates   alcohol  \n",
              "862    0.042945  0.307692  \n",
              "785    0.079755  0.169231  \n",
              "501    0.294479  0.553846  \n",
              "119    0.104294  0.123077  \n",
              "21     0.171779  0.200000  \n",
              "...         ...       ...  \n",
              "1471   0.092025  0.646154  \n",
              "1270   0.233129  0.861538  \n",
              "1076   0.220859  0.461538  \n",
              "853    0.220859  0.384615  \n",
              "179    0.134969  0.169231  \n",
              "\n",
              "[815 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b8c3ea1-51aa-487b-8fe1-56decb9b00c8\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>862</th>\n",
              "      <td>0.236364</td>\n",
              "      <td>0.171429</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.104895</td>\n",
              "      <td>0.050699</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.066421</td>\n",
              "      <td>0.467692</td>\n",
              "      <td>0.393701</td>\n",
              "      <td>0.042945</td>\n",
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              "    <tr>\n",
              "      <th>785</th>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.121429</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.078671</td>\n",
              "      <td>0.128571</td>\n",
              "      <td>0.199262</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>0.370079</td>\n",
              "      <td>0.079755</td>\n",
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              "      <th>501</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.185714</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.374126</td>\n",
              "      <td>0.062937</td>\n",
              "      <td>0.514286</td>\n",
              "      <td>0.254613</td>\n",
              "      <td>0.676923</td>\n",
              "      <td>0.338583</td>\n",
              "      <td>0.294479</td>\n",
              "      <td>0.553846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
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              "      <td>0.364286</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.041958</td>\n",
              "      <td>0.103147</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.302583</td>\n",
              "      <td>0.438462</td>\n",
              "      <td>0.472441</td>\n",
              "      <td>0.104294</td>\n",
              "      <td>0.123077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.245455</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.236162</td>\n",
              "      <td>0.615385</td>\n",
              "      <td>0.614173</td>\n",
              "      <td>0.171779</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1471</th>\n",
              "      <td>0.163636</td>\n",
              "      <td>0.371429</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.178322</td>\n",
              "      <td>0.050699</td>\n",
              "      <td>0.085714</td>\n",
              "      <td>0.033210</td>\n",
              "      <td>0.241538</td>\n",
              "      <td>0.543307</td>\n",
              "      <td>0.092025</td>\n",
              "      <td>0.646154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1270</th>\n",
              "      <td>0.009091</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.027972</td>\n",
              "      <td>0.017483</td>\n",
              "      <td>0.342857</td>\n",
              "      <td>0.195572</td>\n",
              "      <td>0.049231</td>\n",
              "      <td>0.755906</td>\n",
              "      <td>0.233129</td>\n",
              "      <td>0.861538</td>\n",
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              "      <th>1076</th>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.055944</td>\n",
              "      <td>0.061189</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.003690</td>\n",
              "      <td>0.395385</td>\n",
              "      <td>0.322835</td>\n",
              "      <td>0.220859</td>\n",
              "      <td>0.461538</td>\n",
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              "      <th>853</th>\n",
              "      <td>0.400000</td>\n",
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              "      <td>0.39</td>\n",
              "      <td>0.020979</td>\n",
              "      <td>0.073427</td>\n",
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              "      <td>0.177122</td>\n",
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              "      <td>0.574803</td>\n",
              "      <td>0.220859</td>\n",
              "      <td>0.384615</td>\n",
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              "      <th>179</th>\n",
              "      <td>0.354545</td>\n",
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              "      <td>0.083916</td>\n",
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              "      <td>0.134969</td>\n",
              "      <td>0.169231</td>\n",
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              "</table>\n",
              "<p>815 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b8c3ea1-51aa-487b-8fe1-56decb9b00c8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7b8c3ea1-51aa-487b-8fe1-56decb9b00c8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-feff3b7c-9f19-4998-834a-3c5100bb6d88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 815,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1607014684562262,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          0.4090909090909091,\n          0.09999999999999992,\n          0.11818181818181822\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1292979166915944,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          0.2714285714285714,\n          0.5535714285714285,\n          0.13571428571428568\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1961212976890689,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.31,\n          0.4,\n          0.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09312068970785366,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999998,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          0.09790209790209789,\n          0.27272727272727265,\n          0.05594405594405594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08491693155587751,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          0.08041958041958044,\n          0.17132867132867136,\n          0.4003496503496504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15089734112419317,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          0.014285714285714287,\n          0.5285714285714286,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1191251187072771,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          0.2214022140221402,\n          0.2177121771217712,\n          0.09225092250922509\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14718920762909968,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 338,\n        \"samples\": [\n          0.3461538461538538,\n          0.32230769230770306,\n          0.9761538461538493\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11936598116405746,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          0.606299212598425,\n          0.393700787401575,\n          0.5118110236220472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10373334603748921,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.0,\n          0.04294478527607362,\n          0.12883435582822086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16981731805349082,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          0.3076923076923077,\n          0.2153846153846155,\n          0.4307692307692308\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Tensor Dataset"
      ],
      "metadata": {
        "id": "82Zdi57GO8sm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((x_train,y_train)).batch(32).shuffle(10)\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((x_test,y_test)).batch(32).shuffle(10)\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((x_val,y_val)).batch(32).shuffle(10)"
      ],
      "metadata": {
        "id": "UmM9BgcVO-tc"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmS8ufOaPADE",
        "outputId": "35d2d6e7-a701-4496-9cc2-a8f8251580f7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_ShuffleDataset element_spec=(TensorSpec(shape=(None, 11), dtype=tf.float64, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Model"
      ],
      "metadata": {
        "id": "FIXuqALRPCQ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sequential API"
      ],
      "metadata": {
        "id": "XqjqBGxlPEWm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "6EXekEsZPDtc"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(units=512, activation='relu', input_shape=[11]),\n",
        "    layers.Dense(units=512, activation='relu'),\n",
        "    layers.Dense(units=512, activation='relu'),\n",
        "    layers.Dense(units=1)\n",
        "])"
      ],
      "metadata": {
        "id": "_nwMh4R0PIJS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(val_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFTbIWJhPKE4",
        "outputId": "4679c5ac-91e7-40ec-9be5-bcfb499a325c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 0s 4ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [0.0324572 ],\n",
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              "       [0.02256436],\n",
              "       [0.03428393],\n",
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              "       [0.03170237],\n",
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              "       [0.03486947],\n",
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              "       [0.02711005],\n",
              "       [0.0198694 ],\n",
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              "       [0.03273062],\n",
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              "       [0.01589544],\n",
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              "       [0.0221529 ],\n",
              "       [0.02662816],\n",
              "       [0.01138607],\n",
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              "       [0.01999748],\n",
              "       [0.03730777],\n",
              "       [0.03211599],\n",
              "       [0.03701472],\n",
              "       [0.04153737],\n",
              "       [0.02742083],\n",
              "       [0.03392884],\n",
              "       [0.02756596],\n",
              "       [0.03006006],\n",
              "       [0.0364829 ],\n",
              "       [0.02791402],\n",
              "       [0.03085718],\n",
              "       [0.03670733],\n",
              "       [0.03406294],\n",
              "       [0.02731559],\n",
              "       [0.01138479],\n",
              "       [0.0159658 ],\n",
              "       [0.02289984],\n",
              "       [0.03095946],\n",
              "       [0.02236156],\n",
              "       [0.01640614],\n",
              "       [0.00550529],\n",
              "       [0.03063023],\n",
              "       [0.0620863 ],\n",
              "       [0.04975848],\n",
              "       [0.00994183],\n",
              "       [0.02327639],\n",
              "       [0.00704254],\n",
              "       [0.0254813 ],\n",
              "       [0.03551649],\n",
              "       [0.02775957],\n",
              "       [0.02776179],\n",
              "       [0.0258427 ],\n",
              "       [0.03172174],\n",
              "       [0.04525545],\n",
              "       [0.04285941],\n",
              "       [0.02911586],\n",
              "       [0.03726522],\n",
              "       [0.02047276],\n",
              "       [0.0233732 ],\n",
              "       [0.04451676],\n",
              "       [0.03658086],\n",
              "       [0.02725745],\n",
              "       [0.01272859],\n",
              "       [0.04456864],\n",
              "       [0.02266269],\n",
              "       [0.03754464],\n",
              "       [0.0326063 ],\n",
              "       [0.00804816],\n",
              "       [0.01295602],\n",
              "       [0.02416126],\n",
              "       [0.04491304],\n",
              "       [0.01636191]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = tf.constant([[i for i in range(0,11)]])"
      ],
      "metadata": {
        "id": "hbvqd2EzPMVm"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EE9FQextPNei",
        "outputId": "02b9f99c-85a1-4e38-8413-8cede53dd82a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 11), dtype=int32, numpy=array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functional API"
      ],
      "metadata": {
        "id": "x4k8UHYgPOdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tf.keras.Input(shape=(11,))\n",
        "dense1 = Dense(64, activation=\"relu\")(inputs)\n",
        "dense2 = Dense(32,activation=tf.keras.layers.LeakyReLU(alpha=0.2))(dense1)\n",
        "cat_layer = tf.keras.layers.Concatenate(axis=1)([dense1,dense2])\n",
        "out = Dense(1,activation='sigmoid')(cat_layer)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=out)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZQYw0UIPQJC",
        "outputId": "2acb902e-1e9d-4268-b698-489f2beb633d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 11)]                 0         []                            \n",
            "                                                                                                  \n",
            " dense_4 (Dense)             (None, 64)                   768       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 32)                   2080      ['dense_4[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 96)                   0         ['dense_4[0][0]',             \n",
            "                                                                     'dense_5[0][0]']             \n",
            "                                                                                                  \n",
            " dense_6 (Dense)             (None, 1)                    97        ['concatenate[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2945 (11.50 KB)\n",
            "Trainable params: 2945 (11.50 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model,show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "iKTY8UX_PV7N",
        "outputId": "ae7c2e93-a377-4ff4-bc56-434284b26838"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Model"
      ],
      "metadata": {
        "id": "ItLWUEYYRR5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),\n",
        "              loss='mean_absolute_error',\n",
        "              metrics=[tf.keras.metrics.MeanAbsoluteError(), tf.keras.metrics.MeanSquaredError()])\n"
      ],
      "metadata": {
        "id": "b92sucxARWDv"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    validation_data = (x_val, y_val),\n",
        "    batch_size=256,\n",
        "    epochs=150,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BB4dnBqhRTND",
        "outputId": "dc7846c0-b0e1-4774-a545-cb179196b52a"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/150\n",
            "4/4 [==============================] - 1s 98ms/step - loss: 0.9154 - mean_absolute_error: 0.9154 - mean_squared_error: 1.3738 - val_loss: 0.9507 - val_mean_absolute_error: 0.9507 - val_mean_squared_error: 1.2414\n",
            "Epoch 2/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.7665 - mean_absolute_error: 0.7665 - mean_squared_error: 0.8312 - val_loss: 0.7343 - val_mean_absolute_error: 0.7343 - val_mean_squared_error: 0.8141\n",
            "Epoch 3/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.6189 - mean_absolute_error: 0.6189 - mean_squared_error: 0.5866 - val_loss: 0.5693 - val_mean_absolute_error: 0.5693 - val_mean_squared_error: 0.5500\n",
            "Epoch 4/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.5570 - mean_absolute_error: 0.5570 - mean_squared_error: 0.4998 - val_loss: 0.5070 - val_mean_absolute_error: 0.5070 - val_mean_squared_error: 0.4690\n",
            "Epoch 5/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4825 - mean_absolute_error: 0.4825 - mean_squared_error: 0.4052 - val_loss: 0.5192 - val_mean_absolute_error: 0.5192 - val_mean_squared_error: 0.4765\n",
            "Epoch 6/150\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.4461 - mean_absolute_error: 0.4461 - mean_squared_error: 0.3600 - val_loss: 0.5173 - val_mean_absolute_error: 0.5173 - val_mean_squared_error: 0.4768\n",
            "Epoch 7/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4404 - mean_absolute_error: 0.4404 - mean_squared_error: 0.3519 - val_loss: 0.5205 - val_mean_absolute_error: 0.5205 - val_mean_squared_error: 0.4761\n",
            "Epoch 8/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4318 - mean_absolute_error: 0.4318 - mean_squared_error: 0.3413 - val_loss: 0.5253 - val_mean_absolute_error: 0.5253 - val_mean_squared_error: 0.4914\n",
            "Epoch 9/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4379 - mean_absolute_error: 0.4379 - mean_squared_error: 0.3510 - val_loss: 0.5226 - val_mean_absolute_error: 0.5226 - val_mean_squared_error: 0.4792\n",
            "Epoch 10/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4262 - mean_absolute_error: 0.4262 - mean_squared_error: 0.3352 - val_loss: 0.5291 - val_mean_absolute_error: 0.5291 - val_mean_squared_error: 0.4975\n",
            "Epoch 11/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.4401 - mean_absolute_error: 0.4401 - mean_squared_error: 0.3574 - val_loss: 0.5380 - val_mean_absolute_error: 0.5380 - val_mean_squared_error: 0.5024\n",
            "Epoch 12/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.4359 - mean_absolute_error: 0.4359 - mean_squared_error: 0.3437 - val_loss: 0.5212 - val_mean_absolute_error: 0.5212 - val_mean_squared_error: 0.4846\n",
            "Epoch 13/150\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.4286 - mean_absolute_error: 0.4286 - mean_squared_error: 0.3461 - val_loss: 0.5137 - val_mean_absolute_error: 0.5137 - val_mean_squared_error: 0.4698\n",
            "Epoch 14/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4223 - mean_absolute_error: 0.4223 - mean_squared_error: 0.3359 - val_loss: 0.5023 - val_mean_absolute_error: 0.5023 - val_mean_squared_error: 0.4611\n",
            "Epoch 15/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4308 - mean_absolute_error: 0.4308 - mean_squared_error: 0.3361 - val_loss: 0.5012 - val_mean_absolute_error: 0.5012 - val_mean_squared_error: 0.4597\n",
            "Epoch 16/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.4209 - mean_absolute_error: 0.4209 - mean_squared_error: 0.3396 - val_loss: 0.5174 - val_mean_absolute_error: 0.5174 - val_mean_squared_error: 0.4734\n",
            "Epoch 17/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.4242 - mean_absolute_error: 0.4242 - mean_squared_error: 0.3304 - val_loss: 0.5056 - val_mean_absolute_error: 0.5056 - val_mean_squared_error: 0.4697\n",
            "Epoch 18/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4146 - mean_absolute_error: 0.4146 - mean_squared_error: 0.3299 - val_loss: 0.5273 - val_mean_absolute_error: 0.5273 - val_mean_squared_error: 0.4844\n",
            "Epoch 19/150\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.4264 - mean_absolute_error: 0.4264 - mean_squared_error: 0.3344 - val_loss: 0.5068 - val_mean_absolute_error: 0.5068 - val_mean_squared_error: 0.4682\n",
            "Epoch 20/150\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 0.4188 - mean_absolute_error: 0.4188 - mean_squared_error: 0.3348 - val_loss: 0.5042 - val_mean_absolute_error: 0.5042 - val_mean_squared_error: 0.4666\n",
            "Epoch 21/150\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.4197 - mean_absolute_error: 0.4197 - mean_squared_error: 0.3376 - val_loss: 0.5217 - val_mean_absolute_error: 0.5217 - val_mean_squared_error: 0.4784\n",
            "Epoch 22/150\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.4203 - mean_absolute_error: 0.4203 - mean_squared_error: 0.3263 - val_loss: 0.5035 - val_mean_absolute_error: 0.5035 - val_mean_squared_error: 0.4654\n",
            "Epoch 23/150\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.4105 - mean_absolute_error: 0.4105 - mean_squared_error: 0.3202 - val_loss: 0.5063 - val_mean_absolute_error: 0.5063 - val_mean_squared_error: 0.4695\n",
            "Epoch 24/150\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.4083 - mean_absolute_error: 0.4083 - mean_squared_error: 0.3247 - val_loss: 0.5285 - val_mean_absolute_error: 0.5285 - val_mean_squared_error: 0.4914\n",
            "Epoch 25/150\n",
            "4/4 [==============================] - 0s 53ms/step - loss: 0.4234 - mean_absolute_error: 0.4234 - mean_squared_error: 0.3356 - val_loss: 0.5072 - val_mean_absolute_error: 0.5072 - val_mean_squared_error: 0.4667\n",
            "Epoch 26/150\n",
            "4/4 [==============================] - 0s 52ms/step - loss: 0.4249 - mean_absolute_error: 0.4249 - mean_squared_error: 0.3317 - val_loss: 0.5093 - val_mean_absolute_error: 0.5093 - val_mean_squared_error: 0.4736\n",
            "Epoch 27/150\n",
            "4/4 [==============================] - 0s 53ms/step - loss: 0.4165 - mean_absolute_error: 0.4165 - mean_squared_error: 0.3244 - val_loss: 0.5084 - val_mean_absolute_error: 0.5084 - val_mean_squared_error: 0.4732\n",
            "Epoch 28/150\n",
            "4/4 [==============================] - 0s 68ms/step - loss: 0.4216 - mean_absolute_error: 0.4216 - mean_squared_error: 0.3360 - val_loss: 0.5327 - val_mean_absolute_error: 0.5327 - val_mean_squared_error: 0.5026\n",
            "Epoch 29/150\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.4169 - mean_absolute_error: 0.4169 - mean_squared_error: 0.3258 - val_loss: 0.5125 - val_mean_absolute_error: 0.5125 - val_mean_squared_error: 0.4772\n",
            "Epoch 30/150\n",
            "4/4 [==============================] - 0s 59ms/step - loss: 0.4205 - mean_absolute_error: 0.4205 - mean_squared_error: 0.3244 - val_loss: 0.5058 - val_mean_absolute_error: 0.5058 - val_mean_squared_error: 0.4720\n",
            "Epoch 31/150\n",
            "4/4 [==============================] - 0s 56ms/step - loss: 0.4131 - mean_absolute_error: 0.4131 - mean_squared_error: 0.3283 - val_loss: 0.5048 - val_mean_absolute_error: 0.5048 - val_mean_squared_error: 0.4724\n",
            "Epoch 32/150\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.4053 - mean_absolute_error: 0.4053 - mean_squared_error: 0.3206 - val_loss: 0.5102 - val_mean_absolute_error: 0.5102 - val_mean_squared_error: 0.4766\n",
            "Epoch 33/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4104 - mean_absolute_error: 0.4104 - mean_squared_error: 0.3271 - val_loss: 0.5170 - val_mean_absolute_error: 0.5170 - val_mean_squared_error: 0.4769\n",
            "Epoch 34/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4053 - mean_absolute_error: 0.4053 - mean_squared_error: 0.3150 - val_loss: 0.5033 - val_mean_absolute_error: 0.5033 - val_mean_squared_error: 0.4677\n",
            "Epoch 35/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4065 - mean_absolute_error: 0.4065 - mean_squared_error: 0.3150 - val_loss: 0.5151 - val_mean_absolute_error: 0.5151 - val_mean_squared_error: 0.4816\n",
            "Epoch 36/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4104 - mean_absolute_error: 0.4104 - mean_squared_error: 0.3212 - val_loss: 0.5102 - val_mean_absolute_error: 0.5102 - val_mean_squared_error: 0.4762\n",
            "Epoch 37/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4062 - mean_absolute_error: 0.4062 - mean_squared_error: 0.3186 - val_loss: 0.5048 - val_mean_absolute_error: 0.5048 - val_mean_squared_error: 0.4752\n",
            "Epoch 38/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4071 - mean_absolute_error: 0.4071 - mean_squared_error: 0.3225 - val_loss: 0.5083 - val_mean_absolute_error: 0.5083 - val_mean_squared_error: 0.4736\n",
            "Epoch 39/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4011 - mean_absolute_error: 0.4011 - mean_squared_error: 0.3150 - val_loss: 0.5252 - val_mean_absolute_error: 0.5252 - val_mean_squared_error: 0.4948\n",
            "Epoch 40/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4091 - mean_absolute_error: 0.4091 - mean_squared_error: 0.3169 - val_loss: 0.5147 - val_mean_absolute_error: 0.5147 - val_mean_squared_error: 0.4860\n",
            "Epoch 41/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4061 - mean_absolute_error: 0.4061 - mean_squared_error: 0.3108 - val_loss: 0.5055 - val_mean_absolute_error: 0.5055 - val_mean_squared_error: 0.4788\n",
            "Epoch 42/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.4039 - mean_absolute_error: 0.4039 - mean_squared_error: 0.3191 - val_loss: 0.5128 - val_mean_absolute_error: 0.5128 - val_mean_squared_error: 0.4851\n",
            "Epoch 43/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3981 - mean_absolute_error: 0.3981 - mean_squared_error: 0.3159 - val_loss: 0.5270 - val_mean_absolute_error: 0.5270 - val_mean_squared_error: 0.4996\n",
            "Epoch 44/150\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.4015 - mean_absolute_error: 0.4015 - mean_squared_error: 0.3144 - val_loss: 0.5060 - val_mean_absolute_error: 0.5060 - val_mean_squared_error: 0.4736\n",
            "Epoch 45/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3925 - mean_absolute_error: 0.3925 - mean_squared_error: 0.3059 - val_loss: 0.5377 - val_mean_absolute_error: 0.5377 - val_mean_squared_error: 0.5173\n",
            "Epoch 46/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4337 - mean_absolute_error: 0.4337 - mean_squared_error: 0.3413 - val_loss: 0.5073 - val_mean_absolute_error: 0.5073 - val_mean_squared_error: 0.4797\n",
            "Epoch 47/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.4431 - mean_absolute_error: 0.4431 - mean_squared_error: 0.3572 - val_loss: 0.5635 - val_mean_absolute_error: 0.5635 - val_mean_squared_error: 0.5364\n",
            "Epoch 48/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4259 - mean_absolute_error: 0.4259 - mean_squared_error: 0.3350 - val_loss: 0.5211 - val_mean_absolute_error: 0.5211 - val_mean_squared_error: 0.4887\n",
            "Epoch 49/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4173 - mean_absolute_error: 0.4173 - mean_squared_error: 0.3145 - val_loss: 0.5265 - val_mean_absolute_error: 0.5265 - val_mean_squared_error: 0.5030\n",
            "Epoch 50/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4111 - mean_absolute_error: 0.4111 - mean_squared_error: 0.3182 - val_loss: 0.5152 - val_mean_absolute_error: 0.5152 - val_mean_squared_error: 0.4897\n",
            "Epoch 51/150\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.4000 - mean_absolute_error: 0.4000 - mean_squared_error: 0.3161 - val_loss: 0.5116 - val_mean_absolute_error: 0.5116 - val_mean_squared_error: 0.4852\n",
            "Epoch 52/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4069 - mean_absolute_error: 0.4069 - mean_squared_error: 0.3206 - val_loss: 0.5394 - val_mean_absolute_error: 0.5394 - val_mean_squared_error: 0.5109\n",
            "Epoch 53/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4148 - mean_absolute_error: 0.4148 - mean_squared_error: 0.3238 - val_loss: 0.5261 - val_mean_absolute_error: 0.5261 - val_mean_squared_error: 0.4982\n",
            "Epoch 54/150\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 0.4076 - mean_absolute_error: 0.4076 - mean_squared_error: 0.3088 - val_loss: 0.5227 - val_mean_absolute_error: 0.5227 - val_mean_squared_error: 0.4983\n",
            "Epoch 55/150\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 0.4097 - mean_absolute_error: 0.4097 - mean_squared_error: 0.3133 - val_loss: 0.5137 - val_mean_absolute_error: 0.5137 - val_mean_squared_error: 0.4913\n",
            "Epoch 56/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.4092 - mean_absolute_error: 0.4092 - mean_squared_error: 0.3184 - val_loss: 0.5326 - val_mean_absolute_error: 0.5326 - val_mean_squared_error: 0.4936\n",
            "Epoch 57/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4128 - mean_absolute_error: 0.4128 - mean_squared_error: 0.3178 - val_loss: 0.5365 - val_mean_absolute_error: 0.5365 - val_mean_squared_error: 0.4950\n",
            "Epoch 58/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.4183 - mean_absolute_error: 0.4183 - mean_squared_error: 0.3167 - val_loss: 0.5146 - val_mean_absolute_error: 0.5146 - val_mean_squared_error: 0.4818\n",
            "Epoch 59/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.4048 - mean_absolute_error: 0.4048 - mean_squared_error: 0.3067 - val_loss: 0.5407 - val_mean_absolute_error: 0.5407 - val_mean_squared_error: 0.5198\n",
            "Epoch 60/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4221 - mean_absolute_error: 0.4221 - mean_squared_error: 0.3261 - val_loss: 0.5554 - val_mean_absolute_error: 0.5554 - val_mean_squared_error: 0.5298\n",
            "Epoch 61/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4200 - mean_absolute_error: 0.4200 - mean_squared_error: 0.3214 - val_loss: 0.5470 - val_mean_absolute_error: 0.5470 - val_mean_squared_error: 0.5206\n",
            "Epoch 62/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4183 - mean_absolute_error: 0.4183 - mean_squared_error: 0.3218 - val_loss: 0.5235 - val_mean_absolute_error: 0.5235 - val_mean_squared_error: 0.5017\n",
            "Epoch 63/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4061 - mean_absolute_error: 0.4061 - mean_squared_error: 0.3099 - val_loss: 0.5232 - val_mean_absolute_error: 0.5232 - val_mean_squared_error: 0.4999\n",
            "Epoch 64/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.3981 - mean_absolute_error: 0.3981 - mean_squared_error: 0.3032 - val_loss: 0.5286 - val_mean_absolute_error: 0.5286 - val_mean_squared_error: 0.4989\n",
            "Epoch 65/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4015 - mean_absolute_error: 0.4015 - mean_squared_error: 0.3042 - val_loss: 0.5066 - val_mean_absolute_error: 0.5066 - val_mean_squared_error: 0.4779\n",
            "Epoch 66/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.3908 - mean_absolute_error: 0.3908 - mean_squared_error: 0.2990 - val_loss: 0.5256 - val_mean_absolute_error: 0.5256 - val_mean_squared_error: 0.5053\n",
            "Epoch 67/150\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.4031 - mean_absolute_error: 0.4031 - mean_squared_error: 0.3081 - val_loss: 0.5243 - val_mean_absolute_error: 0.5243 - val_mean_squared_error: 0.5027\n",
            "Epoch 68/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4204 - mean_absolute_error: 0.4204 - mean_squared_error: 0.3224 - val_loss: 0.5684 - val_mean_absolute_error: 0.5684 - val_mean_squared_error: 0.5454\n",
            "Epoch 69/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4240 - mean_absolute_error: 0.4240 - mean_squared_error: 0.3297 - val_loss: 0.5156 - val_mean_absolute_error: 0.5156 - val_mean_squared_error: 0.4788\n",
            "Epoch 70/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4197 - mean_absolute_error: 0.4197 - mean_squared_error: 0.3159 - val_loss: 0.5542 - val_mean_absolute_error: 0.5542 - val_mean_squared_error: 0.5443\n",
            "Epoch 71/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4258 - mean_absolute_error: 0.4258 - mean_squared_error: 0.3266 - val_loss: 0.5381 - val_mean_absolute_error: 0.5381 - val_mean_squared_error: 0.5125\n",
            "Epoch 72/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.4088 - mean_absolute_error: 0.4088 - mean_squared_error: 0.3155 - val_loss: 0.6316 - val_mean_absolute_error: 0.6316 - val_mean_squared_error: 0.6448\n",
            "Epoch 73/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4759 - mean_absolute_error: 0.4759 - mean_squared_error: 0.3862 - val_loss: 0.5304 - val_mean_absolute_error: 0.5304 - val_mean_squared_error: 0.5106\n",
            "Epoch 74/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.4113 - mean_absolute_error: 0.4113 - mean_squared_error: 0.3099 - val_loss: 0.5110 - val_mean_absolute_error: 0.5110 - val_mean_squared_error: 0.4772\n",
            "Epoch 75/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3916 - mean_absolute_error: 0.3916 - mean_squared_error: 0.2955 - val_loss: 0.5510 - val_mean_absolute_error: 0.5510 - val_mean_squared_error: 0.5235\n",
            "Epoch 76/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.4010 - mean_absolute_error: 0.4010 - mean_squared_error: 0.3015 - val_loss: 0.5202 - val_mean_absolute_error: 0.5202 - val_mean_squared_error: 0.4911\n",
            "Epoch 77/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.3950 - mean_absolute_error: 0.3950 - mean_squared_error: 0.2951 - val_loss: 0.5304 - val_mean_absolute_error: 0.5304 - val_mean_squared_error: 0.4926\n",
            "Epoch 78/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3901 - mean_absolute_error: 0.3901 - mean_squared_error: 0.2901 - val_loss: 0.5379 - val_mean_absolute_error: 0.5379 - val_mean_squared_error: 0.5056\n",
            "Epoch 79/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.4032 - mean_absolute_error: 0.4032 - mean_squared_error: 0.3052 - val_loss: 0.5151 - val_mean_absolute_error: 0.5151 - val_mean_squared_error: 0.4838\n",
            "Epoch 80/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3892 - mean_absolute_error: 0.3892 - mean_squared_error: 0.2933 - val_loss: 0.5475 - val_mean_absolute_error: 0.5475 - val_mean_squared_error: 0.5303\n",
            "Epoch 81/150\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 0.4203 - mean_absolute_error: 0.4203 - mean_squared_error: 0.3251 - val_loss: 0.5462 - val_mean_absolute_error: 0.5462 - val_mean_squared_error: 0.5178\n",
            "Epoch 82/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.4043 - mean_absolute_error: 0.4043 - mean_squared_error: 0.3080 - val_loss: 0.5313 - val_mean_absolute_error: 0.5313 - val_mean_squared_error: 0.5072\n",
            "Epoch 83/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.3900 - mean_absolute_error: 0.3900 - mean_squared_error: 0.2945 - val_loss: 0.5174 - val_mean_absolute_error: 0.5174 - val_mean_squared_error: 0.4923\n",
            "Epoch 84/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.3795 - mean_absolute_error: 0.3795 - mean_squared_error: 0.2850 - val_loss: 0.5297 - val_mean_absolute_error: 0.5297 - val_mean_squared_error: 0.5077\n",
            "Epoch 85/150\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.4047 - mean_absolute_error: 0.4047 - mean_squared_error: 0.3063 - val_loss: 0.5215 - val_mean_absolute_error: 0.5215 - val_mean_squared_error: 0.4845\n",
            "Epoch 86/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4043 - mean_absolute_error: 0.4043 - mean_squared_error: 0.3158 - val_loss: 0.5226 - val_mean_absolute_error: 0.5226 - val_mean_squared_error: 0.4920\n",
            "Epoch 87/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.3790 - mean_absolute_error: 0.3790 - mean_squared_error: 0.2868 - val_loss: 0.5241 - val_mean_absolute_error: 0.5241 - val_mean_squared_error: 0.4980\n",
            "Epoch 88/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3801 - mean_absolute_error: 0.3801 - mean_squared_error: 0.2879 - val_loss: 0.5117 - val_mean_absolute_error: 0.5117 - val_mean_squared_error: 0.4810\n",
            "Epoch 89/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3751 - mean_absolute_error: 0.3751 - mean_squared_error: 0.2799 - val_loss: 0.5246 - val_mean_absolute_error: 0.5246 - val_mean_squared_error: 0.4996\n",
            "Epoch 90/150\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.3762 - mean_absolute_error: 0.3762 - mean_squared_error: 0.2844 - val_loss: 0.5274 - val_mean_absolute_error: 0.5274 - val_mean_squared_error: 0.5041\n",
            "Epoch 91/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3756 - mean_absolute_error: 0.3756 - mean_squared_error: 0.2812 - val_loss: 0.5164 - val_mean_absolute_error: 0.5164 - val_mean_squared_error: 0.4914\n",
            "Epoch 92/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3735 - mean_absolute_error: 0.3735 - mean_squared_error: 0.2840 - val_loss: 0.5248 - val_mean_absolute_error: 0.5248 - val_mean_squared_error: 0.5033\n",
            "Epoch 93/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.3687 - mean_absolute_error: 0.3687 - mean_squared_error: 0.2815 - val_loss: 0.5239 - val_mean_absolute_error: 0.5239 - val_mean_squared_error: 0.5021\n",
            "Epoch 94/150\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 0.3773 - mean_absolute_error: 0.3773 - mean_squared_error: 0.2860 - val_loss: 0.5266 - val_mean_absolute_error: 0.5266 - val_mean_squared_error: 0.5081\n",
            "Epoch 95/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.3917 - mean_absolute_error: 0.3917 - mean_squared_error: 0.2945 - val_loss: 0.5273 - val_mean_absolute_error: 0.5273 - val_mean_squared_error: 0.5012\n",
            "Epoch 96/150\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 0.3860 - mean_absolute_error: 0.3860 - mean_squared_error: 0.2894 - val_loss: 0.5596 - val_mean_absolute_error: 0.5596 - val_mean_squared_error: 0.5495\n",
            "Epoch 97/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3907 - mean_absolute_error: 0.3907 - mean_squared_error: 0.2930 - val_loss: 0.5394 - val_mean_absolute_error: 0.5394 - val_mean_squared_error: 0.5209\n",
            "Epoch 98/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.3886 - mean_absolute_error: 0.3886 - mean_squared_error: 0.2922 - val_loss: 0.5355 - val_mean_absolute_error: 0.5355 - val_mean_squared_error: 0.5153\n",
            "Epoch 99/150\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.3913 - mean_absolute_error: 0.3913 - mean_squared_error: 0.2952 - val_loss: 0.5374 - val_mean_absolute_error: 0.5374 - val_mean_squared_error: 0.5144\n",
            "Epoch 100/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3833 - mean_absolute_error: 0.3833 - mean_squared_error: 0.2861 - val_loss: 0.5476 - val_mean_absolute_error: 0.5476 - val_mean_squared_error: 0.5354\n",
            "Epoch 101/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3960 - mean_absolute_error: 0.3960 - mean_squared_error: 0.3021 - val_loss: 0.5317 - val_mean_absolute_error: 0.5317 - val_mean_squared_error: 0.5090\n",
            "Epoch 102/150\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.3775 - mean_absolute_error: 0.3775 - mean_squared_error: 0.2861 - val_loss: 0.5302 - val_mean_absolute_error: 0.5302 - val_mean_squared_error: 0.5187\n",
            "Epoch 103/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.3754 - mean_absolute_error: 0.3754 - mean_squared_error: 0.2872 - val_loss: 0.5449 - val_mean_absolute_error: 0.5449 - val_mean_squared_error: 0.5341\n",
            "Epoch 104/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.3815 - mean_absolute_error: 0.3815 - mean_squared_error: 0.2827 - val_loss: 0.5284 - val_mean_absolute_error: 0.5284 - val_mean_squared_error: 0.5071\n",
            "Epoch 105/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3717 - mean_absolute_error: 0.3717 - mean_squared_error: 0.2768 - val_loss: 0.5239 - val_mean_absolute_error: 0.5239 - val_mean_squared_error: 0.5064\n",
            "Epoch 106/150\n",
            "4/4 [==============================] - 0s 59ms/step - loss: 0.3790 - mean_absolute_error: 0.3790 - mean_squared_error: 0.2831 - val_loss: 0.5648 - val_mean_absolute_error: 0.5648 - val_mean_squared_error: 0.5604\n",
            "Epoch 107/150\n",
            "4/4 [==============================] - 0s 68ms/step - loss: 0.4149 - mean_absolute_error: 0.4149 - mean_squared_error: 0.3163 - val_loss: 0.5244 - val_mean_absolute_error: 0.5244 - val_mean_squared_error: 0.5097\n",
            "Epoch 108/150\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.3946 - mean_absolute_error: 0.3946 - mean_squared_error: 0.2977 - val_loss: 0.5442 - val_mean_absolute_error: 0.5442 - val_mean_squared_error: 0.5316\n",
            "Epoch 109/150\n",
            "4/4 [==============================] - 0s 52ms/step - loss: 0.3813 - mean_absolute_error: 0.3813 - mean_squared_error: 0.2867 - val_loss: 0.5257 - val_mean_absolute_error: 0.5257 - val_mean_squared_error: 0.5022\n",
            "Epoch 110/150\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 0.3712 - mean_absolute_error: 0.3712 - mean_squared_error: 0.2745 - val_loss: 0.5307 - val_mean_absolute_error: 0.5307 - val_mean_squared_error: 0.5164\n",
            "Epoch 111/150\n",
            "4/4 [==============================] - 0s 62ms/step - loss: 0.3882 - mean_absolute_error: 0.3882 - mean_squared_error: 0.2900 - val_loss: 0.5230 - val_mean_absolute_error: 0.5230 - val_mean_squared_error: 0.4981\n",
            "Epoch 112/150\n",
            "4/4 [==============================] - 0s 52ms/step - loss: 0.3872 - mean_absolute_error: 0.3872 - mean_squared_error: 0.2884 - val_loss: 0.5615 - val_mean_absolute_error: 0.5615 - val_mean_squared_error: 0.5482\n",
            "Epoch 113/150\n",
            "4/4 [==============================] - 0s 53ms/step - loss: 0.4006 - mean_absolute_error: 0.4006 - mean_squared_error: 0.2986 - val_loss: 0.5655 - val_mean_absolute_error: 0.5655 - val_mean_squared_error: 0.5571\n",
            "Epoch 114/150\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 0.3991 - mean_absolute_error: 0.3991 - mean_squared_error: 0.3020 - val_loss: 0.5253 - val_mean_absolute_error: 0.5253 - val_mean_squared_error: 0.4957\n",
            "Epoch 115/150\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.3697 - mean_absolute_error: 0.3697 - mean_squared_error: 0.2749 - val_loss: 0.5464 - val_mean_absolute_error: 0.5464 - val_mean_squared_error: 0.5279\n",
            "Epoch 116/150\n",
            "4/4 [==============================] - 0s 52ms/step - loss: 0.3810 - mean_absolute_error: 0.3810 - mean_squared_error: 0.2818 - val_loss: 0.5235 - val_mean_absolute_error: 0.5235 - val_mean_squared_error: 0.5042\n",
            "Epoch 117/150\n",
            "4/4 [==============================] - 0s 47ms/step - loss: 0.3711 - mean_absolute_error: 0.3711 - mean_squared_error: 0.2751 - val_loss: 0.5182 - val_mean_absolute_error: 0.5182 - val_mean_squared_error: 0.4949\n",
            "Epoch 118/150\n",
            "4/4 [==============================] - 0s 47ms/step - loss: 0.3820 - mean_absolute_error: 0.3820 - mean_squared_error: 0.2760 - val_loss: 0.5172 - val_mean_absolute_error: 0.5172 - val_mean_squared_error: 0.4926\n",
            "Epoch 119/150\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 0.3699 - mean_absolute_error: 0.3699 - mean_squared_error: 0.2733 - val_loss: 0.5226 - val_mean_absolute_error: 0.5226 - val_mean_squared_error: 0.4985\n",
            "Epoch 120/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.3679 - mean_absolute_error: 0.3679 - mean_squared_error: 0.2717 - val_loss: 0.5383 - val_mean_absolute_error: 0.5383 - val_mean_squared_error: 0.5215\n",
            "Epoch 121/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.3998 - mean_absolute_error: 0.3998 - mean_squared_error: 0.2934 - val_loss: 0.5176 - val_mean_absolute_error: 0.5176 - val_mean_squared_error: 0.4964\n",
            "Epoch 122/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3809 - mean_absolute_error: 0.3809 - mean_squared_error: 0.2824 - val_loss: 0.5575 - val_mean_absolute_error: 0.5575 - val_mean_squared_error: 0.5491\n",
            "Epoch 123/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3975 - mean_absolute_error: 0.3975 - mean_squared_error: 0.2984 - val_loss: 0.5888 - val_mean_absolute_error: 0.5888 - val_mean_squared_error: 0.5994\n",
            "Epoch 124/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4078 - mean_absolute_error: 0.4078 - mean_squared_error: 0.3059 - val_loss: 0.5167 - val_mean_absolute_error: 0.5167 - val_mean_squared_error: 0.4949\n",
            "Epoch 125/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3848 - mean_absolute_error: 0.3848 - mean_squared_error: 0.2832 - val_loss: 0.5121 - val_mean_absolute_error: 0.5121 - val_mean_squared_error: 0.4879\n",
            "Epoch 126/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3585 - mean_absolute_error: 0.3585 - mean_squared_error: 0.2631 - val_loss: 0.5330 - val_mean_absolute_error: 0.5330 - val_mean_squared_error: 0.5215\n",
            "Epoch 127/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.3591 - mean_absolute_error: 0.3591 - mean_squared_error: 0.2659 - val_loss: 0.5152 - val_mean_absolute_error: 0.5152 - val_mean_squared_error: 0.4890\n",
            "Epoch 128/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3629 - mean_absolute_error: 0.3629 - mean_squared_error: 0.2686 - val_loss: 0.5187 - val_mean_absolute_error: 0.5187 - val_mean_squared_error: 0.4914\n",
            "Epoch 129/150\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.3779 - mean_absolute_error: 0.3779 - mean_squared_error: 0.2815 - val_loss: 0.5289 - val_mean_absolute_error: 0.5289 - val_mean_squared_error: 0.5059\n",
            "Epoch 130/150\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.3747 - mean_absolute_error: 0.3747 - mean_squared_error: 0.2750 - val_loss: 0.5288 - val_mean_absolute_error: 0.5288 - val_mean_squared_error: 0.5106\n",
            "Epoch 131/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3674 - mean_absolute_error: 0.3674 - mean_squared_error: 0.2761 - val_loss: 0.5522 - val_mean_absolute_error: 0.5522 - val_mean_squared_error: 0.5418\n",
            "Epoch 132/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.4032 - mean_absolute_error: 0.4032 - mean_squared_error: 0.3008 - val_loss: 0.6127 - val_mean_absolute_error: 0.6127 - val_mean_squared_error: 0.6161\n",
            "Epoch 133/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.4107 - mean_absolute_error: 0.4107 - mean_squared_error: 0.3045 - val_loss: 0.5425 - val_mean_absolute_error: 0.5425 - val_mean_squared_error: 0.5127\n",
            "Epoch 134/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3751 - mean_absolute_error: 0.3751 - mean_squared_error: 0.2699 - val_loss: 0.5229 - val_mean_absolute_error: 0.5229 - val_mean_squared_error: 0.4954\n",
            "Epoch 135/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.3795 - mean_absolute_error: 0.3795 - mean_squared_error: 0.2728 - val_loss: 0.5614 - val_mean_absolute_error: 0.5614 - val_mean_squared_error: 0.5416\n",
            "Epoch 136/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3941 - mean_absolute_error: 0.3941 - mean_squared_error: 0.2866 - val_loss: 0.5197 - val_mean_absolute_error: 0.5197 - val_mean_squared_error: 0.4963\n",
            "Epoch 137/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.3978 - mean_absolute_error: 0.3978 - mean_squared_error: 0.3005 - val_loss: 0.5637 - val_mean_absolute_error: 0.5637 - val_mean_squared_error: 0.5404\n",
            "Epoch 138/150\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.3928 - mean_absolute_error: 0.3928 - mean_squared_error: 0.2897 - val_loss: 0.5551 - val_mean_absolute_error: 0.5551 - val_mean_squared_error: 0.5307\n",
            "Epoch 139/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3804 - mean_absolute_error: 0.3804 - mean_squared_error: 0.2712 - val_loss: 0.5350 - val_mean_absolute_error: 0.5350 - val_mean_squared_error: 0.5099\n",
            "Epoch 140/150\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.3730 - mean_absolute_error: 0.3730 - mean_squared_error: 0.2716 - val_loss: 0.5308 - val_mean_absolute_error: 0.5308 - val_mean_squared_error: 0.5066\n",
            "Epoch 141/150\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.3562 - mean_absolute_error: 0.3562 - mean_squared_error: 0.2629 - val_loss: 0.5315 - val_mean_absolute_error: 0.5315 - val_mean_squared_error: 0.5067\n",
            "Epoch 142/150\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.3555 - mean_absolute_error: 0.3555 - mean_squared_error: 0.2627 - val_loss: 0.5284 - val_mean_absolute_error: 0.5284 - val_mean_squared_error: 0.5050\n",
            "Epoch 143/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3515 - mean_absolute_error: 0.3515 - mean_squared_error: 0.2576 - val_loss: 0.5306 - val_mean_absolute_error: 0.5306 - val_mean_squared_error: 0.5075\n",
            "Epoch 144/150\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.3737 - mean_absolute_error: 0.3737 - mean_squared_error: 0.2725 - val_loss: 0.5230 - val_mean_absolute_error: 0.5230 - val_mean_squared_error: 0.5009\n",
            "Epoch 145/150\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.3544 - mean_absolute_error: 0.3544 - mean_squared_error: 0.2595 - val_loss: 0.5356 - val_mean_absolute_error: 0.5356 - val_mean_squared_error: 0.5180\n",
            "Epoch 146/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3567 - mean_absolute_error: 0.3567 - mean_squared_error: 0.2616 - val_loss: 0.5496 - val_mean_absolute_error: 0.5496 - val_mean_squared_error: 0.5358\n",
            "Epoch 147/150\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.3570 - mean_absolute_error: 0.3570 - mean_squared_error: 0.2635 - val_loss: 0.5259 - val_mean_absolute_error: 0.5259 - val_mean_squared_error: 0.5128\n",
            "Epoch 148/150\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 0.3472 - mean_absolute_error: 0.3472 - mean_squared_error: 0.2533 - val_loss: 0.5277 - val_mean_absolute_error: 0.5277 - val_mean_squared_error: 0.5137\n",
            "Epoch 149/150\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 0.3453 - mean_absolute_error: 0.3453 - mean_squared_error: 0.2486 - val_loss: 0.5290 - val_mean_absolute_error: 0.5290 - val_mean_squared_error: 0.5107\n",
            "Epoch 150/150\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 0.3434 - mean_absolute_error: 0.3434 - mean_squared_error: 0.2497 - val_loss: 0.5306 - val_mean_absolute_error: 0.5306 - val_mean_squared_error: 0.5142\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZ2YquDuRctw",
        "outputId": "2d88437c-395c-46a8-c811-4502e78d9d96"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  0.5306223034858704],\n",
              " 'val_mean_squared_error': [1.2413536310195923,\n",
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              "  0.5571394562721252,\n",
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              "  0.5491236448287964,\n",
              "  0.5993930697441101,\n",
              "  0.4948563873767853,\n",
              "  0.4878820478916168,\n",
              "  0.521453857421875,\n",
              "  0.488978773355484,\n",
              "  0.4913628101348877,\n",
              "  0.505913257598877,\n",
              "  0.510585367679596,\n",
              "  0.5417740345001221,\n",
              "  0.6161119341850281,\n",
              "  0.5127350687980652,\n",
              "  0.4953911602497101,\n",
              "  0.5415787696838379,\n",
              "  0.49626293778419495,\n",
              "  0.5403662919998169,\n",
              "  0.5307131409645081,\n",
              "  0.5099254846572876,\n",
              "  0.50660640001297,\n",
              "  0.5066609382629395,\n",
              "  0.5049766898155212,\n",
              "  0.5075383186340332,\n",
              "  0.5009406208992004,\n",
              "  0.5179649591445923,\n",
              "  0.535811722278595,\n",
              "  0.5128235816955566,\n",
              "  0.5136989951133728,\n",
              "  0.5107436776161194,\n",
              "  0.5141773223876953]}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "plt.plot(train_loss,label=\"Train Loss\")\n",
        "plt.plot(val_loss,label='Val Loss')\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "WEyQN_WnR6eg",
        "outputId": "6dbc6edf-8453-410e-c0c3-7284b6812e6a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Both the training loss and validation loss appear to be decreasing over the course of training. This suggests that the model is learning from the data and improving its performance.\n",
        "\n",
        "Train loss is consistently higher than validation loss, indicating that data training model is likely overfitted.  If the training loss continues to decrease while the validation loss starts to increase, it would be a strong suggest that the model is overfitted.\n"
      ],
      "metadata": {
        "id": "Ok1u3t7MbllZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('functional_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sv52OlFHSwR5",
        "outputId": "8e04b97f-3cf4-4b6b-a117-1231f239c7a5"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = tf.keras.models.load_model('functional_model.h5')\n",
        "x.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Hv0Dn_PSydA",
        "outputId": "aea07ce7-0db0-4ba7-f801-689dd2732a71"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_7 (Dense)             (None, 512)               6144      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 512)               262656    \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 512)               262656    \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 1)                 513       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 531969 (2.03 MB)\n",
            "Trainable params: 531969 (2.03 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights('function_weights')"
      ],
      "metadata": {
        "id": "uQ9hNamAS2w7"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights('function_weights')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64b1fPZdS4Ux",
        "outputId": "8ad7614f-dd9a-4904-a2a8-ec8336bfdfc5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7badb7c28130>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltoWl3JHS5wh",
        "outputId": "87b8dbe3-9ca5-4129-f40c-7bea56f8f8ae"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "          0.07569339,  0.0389408 ],\n",
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              "         -0.07585432, -0.00442683]], dtype=float32)>,\n",
              " <tf.Variable 'dense_7/bias:0' shape=(512,) dtype=float32, numpy=\n",
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              "       dtype=float32)>,\n",
              " <tf.Variable 'dense_8/kernel:0' shape=(512, 512) dtype=float32, numpy=\n",
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              "         -0.02118126, -0.06420077],\n",
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              "        -0.01537192, -0.00817101, -0.00793598, -0.00852939, -0.0072049 ,\n",
              "         0.        ,  0.02264099,  0.03211968, -0.00873557,  0.02506306,\n",
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              "        -0.00693415, -0.00546042,  0.02743457, -0.01992644,  0.0302938 ,\n",
              "        -0.00935104, -0.01546398,  0.03218972,  0.03165796, -0.00601545,\n",
              "         0.0303645 , -0.00785414,  0.02799017, -0.00846847, -0.00588508,\n",
              "         0.02187919, -0.00501045, -0.00606315,  0.02424876, -0.0078348 ,\n",
              "        -0.00724349, -0.00706863,  0.02231771,  0.02025012, -0.00869825,\n",
              "        -0.00592971, -0.00361599, -0.00728601, -0.01230426,  0.02869581,\n",
              "        -0.00892963, -0.00980225,  0.0323614 , -0.0134236 , -0.00575499,\n",
              "        -0.0050187 ,  0.        ,  0.02045126,  0.01470943,  0.02571726,\n",
              "        -0.01151094, -0.00592079,  0.02872487,  0.02134148, -0.0101953 ,\n",
              "         0.01835392,  0.02792861, -0.00678527, -0.01121911,  0.02741139,\n",
              "         0.02105852, -0.00834494, -0.00502971, -0.00693465,  0.03018831,\n",
              "        -0.00598851,  0.        , -0.00550364,  0.        , -0.01010505,\n",
              "         0.03110076,  0.0315595 , -0.00807024, -0.00565065,  0.0280323 ,\n",
              "         0.02795445, -0.00712399,  0.03300354,  0.02328389,  0.02542514,\n",
              "         0.02908071, -0.00644638,  0.03062226,  0.02855031,  0.0315505 ,\n",
              "         0.03158411, -0.0080762 , -0.00723066,  0.02793468, -0.00530495,\n",
              "         0.        , -0.0260713 ,  0.03240058,  0.03318265, -0.00632555,\n",
              "        -0.00824215,  0.03113177,  0.02242737, -0.0076909 , -0.02235633,\n",
              "        -0.00844418, -0.00717066,  0.03234856,  0.02347161,  0.01982178,\n",
              "        -0.00695956, -0.01739804, -0.00512308,  0.0273148 ,  0.02855764,\n",
              "         0.        , -0.00505288, -0.00679145, -0.00584397,  0.02812311,\n",
              "         0.02170624, -0.00607742, -0.00444557, -0.01178478,  0.0263489 ,\n",
              "        -0.00596898, -0.01022218,  0.03287988, -0.00550198, -0.00583422,\n",
              "         0.01778992,  0.02669822,  0.03020437,  0.02778567, -0.0066578 ,\n",
              "         0.02526851,  0.        ,  0.02876399,  0.01760818,  0.01094602,\n",
              "         0.02819869, -0.00572278,  0.03186888,  0.00447712, -0.00732091,\n",
              "         0.01724805,  0.02103684,  0.02659211, -0.00387882,  0.02183193,\n",
              "         0.03245133,  0.        , -0.00174357, -0.00663053, -0.00838524,\n",
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              "        -0.00519301,  0.02560829, -0.00927751, -0.01026731, -0.00431002,\n",
              "         0.03136285, -0.00680521, -0.00597045,  0.03059855, -0.00197302,\n",
              "         0.03034968,  0.02690561, -0.00592147,  0.        , -0.00633288,\n",
              "         0.02620702, -0.00655699,  0.03178142,  0.01175312, -0.00746454,\n",
              "         0.02692509, -0.01681083,  0.02649432, -0.006529  ,  0.0299182 ,\n",
              "        -0.00969295,  0.        ,  0.02620391,  0.0323376 ,  0.02564169,\n",
              "         0.0116035 , -0.00571634,  0.        , -0.00776429,  0.03157946,\n",
              "        -0.00519938,  0.        , -0.01136613, -0.01168384,  0.03095668,\n",
              "        -0.00541183,  0.02323963, -0.00819912, -0.00590562, -0.00871666,\n",
              "         0.02792512, -0.00614387,  0.03001256,  0.02073787,  0.02957896,\n",
              "         0.03146003,  0.        , -0.00522318, -0.01798106,  0.03063564,\n",
              "         0.02949759,  0.03261347, -0.00610659, -0.01675151, -0.0062862 ,\n",
              "        -0.00224109, -0.00295378,  0.        , -0.00865398,  0.02777822,\n",
              "         0.02885759,  0.02733225,  0.        ,  0.        , -0.01098655,\n",
              "        -0.00598554, -0.00827306,  0.03048709,  0.02837951, -0.00693968,\n",
              "        -0.00671103, -0.00673956, -0.00631197, -0.00274005, -0.007362  ,\n",
              "        -0.01380124,  0.0229901 , -0.00698194, -0.00606754, -0.0087067 ,\n",
              "        -0.00055966, -0.00290998, -0.0034921 , -0.02094933,  0.02809572,\n",
              "        -0.00982845,  0.02910753,  0.02748668,  0.03202727,  0.0282907 ,\n",
              "        -0.00813538,  0.02363664, -0.00741167, -0.00751852,  0.02773018,\n",
              "         0.03162364, -0.00758302, -0.00497546,  0.02983746,  0.02728317,\n",
              "         0.02927814,  0.02436564,  0.03135628, -0.0060679 ,  0.03316826,\n",
              "        -0.00756577,  0.02497093,  0.02825605,  0.02023609,  0.03110435,\n",
              "         0.0204458 ,  0.03052798, -0.00774286, -0.00586258, -0.00887934,\n",
              "         0.01741372,  0.02999411,  0.02253643,  0.026676  , -0.00544185,\n",
              "         0.005688  ,  0.03232775,  0.        ,  0.02442561,  0.03018469,\n",
              "        -0.01080647, -0.00596533,  0.03213955,  0.02996625,  0.03172533,\n",
              "        -0.00660115,  0.00444012, -0.00887112, -0.01026681, -0.01016495,\n",
              "        -0.00788303,  0.03162435,  0.03176777,  0.00490386,  0.00424434,\n",
              "        -0.00501812, -0.00848021], dtype=float32)>,\n",
              " <tf.Variable 'dense_9/kernel:0' shape=(512, 512) dtype=float32, numpy=\n",
              " array([[ 0.02595603, -0.01166918,  0.04684962, ..., -0.01235181,\n",
              "          0.07883723, -0.00177474],\n",
              "        [ 0.01866576, -0.06892901,  0.01224532, ...,  0.03728894,\n",
              "         -0.01640881,  0.08063296],\n",
              "        [ 0.01104674, -0.06751963,  0.07409365, ..., -0.03511645,\n",
              "          0.11070807,  0.09839316],\n",
              "        ...,\n",
              "        [-0.01084378,  0.04569336, -0.02784735, ..., -0.05008058,\n",
              "         -0.03990222, -0.02457869],\n",
              "        [-0.06005447, -0.0115491 , -0.0246554 , ..., -0.03386721,\n",
              "         -0.01560363,  0.06643883],\n",
              "        [ 0.04719819, -0.0272484 ,  0.06724932, ...,  0.00053716,\n",
              "          0.0277166 ,  0.06570448]], dtype=float32)>,\n",
              " <tf.Variable 'dense_9/bias:0' shape=(512,) dtype=float32, numpy=\n",
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              "         0.03658634,  0.03659282, -0.00814523,  0.03623139, -0.00970006,\n",
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              "         0.        , -0.00599615, -0.00520628, -0.00758139,  0.0365993 ,\n",
              "         0.00601182, -0.01000718,  0.        ,  0.03342514,  0.        ,\n",
              "         0.03657792,  0.03566088], dtype=float32)>,\n",
              " <tf.Variable 'dense_10/kernel:0' shape=(512, 1) dtype=float32, numpy=\n",
              " array([[-6.16434515e-02],\n",
              "        [-5.31236082e-03],\n",
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              "        [-1.06787682e-02],\n",
              "        [-2.87878569e-02],\n",
              "        [ 7.87701085e-02],\n",
              "        [-1.15551902e-02],\n",
              "        [ 9.09400824e-03],\n",
              "        [ 9.03325751e-02],\n",
              "        [-2.05983762e-02],\n",
              "        [ 5.40177384e-03],\n",
              "        [ 1.08454883e-01],\n",
              "        [ 1.12855107e-01],\n",
              "        [ 7.54645094e-02],\n",
              "        [ 4.74477299e-02],\n",
              "        [-5.26729077e-02],\n",
              "        [-7.90116638e-02],\n",
              "        [-5.06796241e-02],\n",
              "        [-6.63659498e-02],\n",
              "        [ 3.14527676e-02],\n",
              "        [ 2.00681686e-02],\n",
              "        [ 4.39133011e-02],\n",
              "        [ 1.60361617e-03],\n",
              "        [ 3.98238823e-02],\n",
              "        [ 7.74638802e-02],\n",
              "        [ 9.26685184e-02],\n",
              "        [-3.86202149e-02],\n",
              "        [ 1.13299638e-01],\n",
              "        [-1.28165167e-02],\n",
              "        [ 3.65923569e-02],\n",
              "        [ 9.79961362e-04],\n",
              "        [ 1.04119167e-01],\n",
              "        [-7.21317306e-02],\n",
              "        [ 3.91911194e-02],\n",
              "        [-2.18458921e-02],\n",
              "        [-4.70673516e-02],\n",
              "        [ 4.31014523e-02],\n",
              "        [ 7.33967274e-02],\n",
              "        [-6.55609593e-02],\n",
              "        [ 7.33682066e-02],\n",
              "        [ 9.77693424e-02],\n",
              "        [-6.45130649e-02],\n",
              "        [ 5.31299226e-02],\n",
              "        [-4.36943769e-02],\n",
              "        [ 7.10269511e-02],\n",
              "        [-9.69129205e-02],\n",
              "        [ 5.68657145e-02],\n",
              "        [-1.58666205e-02],\n",
              "        [-6.56786200e-04],\n",
              "        [ 4.31613717e-03],\n",
              "        [ 1.03198558e-01],\n",
              "        [ 4.34376393e-03],\n",
              "        [-3.88021246e-02],\n",
              "        [ 8.56797323e-02],\n",
              "        [ 2.92386785e-02],\n",
              "        [-3.30087990e-02],\n",
              "        [ 9.55959409e-02],\n",
              "        [ 3.90688553e-02]], dtype=float32)>,\n",
              " <tf.Variable 'dense_10/bias:0' shape=(1,) dtype=float32, numpy=array([0.03731521], dtype=float32)>]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate Model"
      ],
      "metadata": {
        "id": "Ysdih_gdS77p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCpRGWmuS9Wt",
        "outputId": "d3ccc345-f15b-43a5-d318-7de3c8517e1d"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 1s 6ms/step - loss: 0.4690 - mean_absolute_error: 0.4674 - mean_squared_error: 0.3834\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.46904173493385315, 0.4673837125301361, 0.3833606541156769]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Callback Function - Early Stopping\n",
        "Callback function is calling back a code or a function everytime a user/computer do a certain thing."
      ],
      "metadata": {
        "id": "zzmYDqTxX_8g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import callbacks\n"
      ],
      "metadata": {
        "id": "CRc9TpP_YEOs"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = callbacks.EarlyStopping(\n",
        "    min_delta=0.001,\n",
        "    patience=30,\n",
        "    restore_best_weights=True,\n",
        ")"
      ],
      "metadata": {
        "id": "2AwtqfNjYHW7"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(units=512, activation='relu', input_shape=[11]),\n",
        "    layers.Dense(units=512, activation='relu'),\n",
        "    layers.Dense(units=512, activation='relu'),\n",
        "    layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='mae')"
      ],
      "metadata": {
        "id": "kdVRSAEJYKjR"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    validation_data = (x_val, y_val),\n",
        "    batch_size=256,\n",
        "    epochs=150,\n",
        "    callbacks=[early_stopping],\n",
        "    verbose=0,\n",
        ")"
      ],
      "metadata": {
        "id": "b2nD92qdYNek"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history.history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-J2kW6drYjn_",
        "outputId": "50b97c80-e8da-41a4-9dd3-40c16fd26ff6"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 'val_loss': [4.210285186767578,\n",
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              "  0.6035330891609192]}"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "plt.plot(train_loss,label=\"Train Loss\")\n",
        "plt.plot(val_loss,label='Val Loss')\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1zEGKGelYoOz",
        "outputId": "8a93593e-321a-41e0-fbc7-e13f78a29d46"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The gap between the curves appears to be increasing as training progresses, which suggests the model is generalizing well.\n",
        "\n",
        "Model is likely not overfitting after implemented callback function, the model is performed well."
      ],
      "metadata": {
        "id": "IakDI--up_wE"
      }
    }
  ]
}