{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7YzWQHjV7Ga"
      },
      "source": [
        "<details><summary><b>LICENSE</b></summary>\n",
        "\n",
        "Copyright 2018 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMDBCBXpyfjb"
      },
      "source": [
        "#  Case Study: Debugging in Regression\n",
        "\n",
        "In this Colab, you will learn how to debug a regression problem through a case study. You will:\n",
        "\n",
        "1. Set up the problem.\n",
        "2. Interpret the correlation matrix.\n",
        "3. Implement linear and nonlinear models.\n",
        "4. Compare and choose between linear and nonlinear models.\n",
        "5. Optimize your chosen model.\n",
        "6. Debug your chosen model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YO9VBlPg-gQi"
      },
      "source": [
        "## Setup\n",
        "\n",
        "This Colab uses the wine quality dataset<sup#[1]</sup#, which is hosted at [UCI](https://archive.ics.uci.edu/ml/datasets/wine+quality). This dataset contains data on the physicochemical properties of wine along with wine quality ratings. The problem is to predict wine quality (0-10) from physicochemical properties.\n",
        "\n",
        "Please **make a copy** of this Colab before running it. Click on *File*, and then click on *Save a copy in Drive*.\n",
        "\n",
        "<small#[1] Modeling wine preferences by data mining from physicochemical properties. P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Decision Support Systems, Elsevier, 47(4):547-553, 2009.</small#"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TD2JCwZopqaP"
      },
      "source": [
        "Load libraries and data by running the next cell. Display the first few rows to verify that the dataset loaded correctly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "cellView": "both",
        "id": "1tdRTwl6sN9A",
        "outputId": "d89e86a1-d768-42ee-dcba-8653547e8b27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.0               0.3          0.4            20.7        0.0   \n",
              "1            6.3               0.3          0.3             1.6        0.0   \n",
              "2            8.1               0.3          0.4             6.9        0.1   \n",
              "3            7.2               0.2          0.3             8.5        0.1   \n",
              "4            7.2               0.2          0.3             8.5        0.1   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density  pH  sulphates  alcohol  \\\n",
              "0                 45.0                 170.0      1.0 3.0        0.5      8.8   \n",
              "1                 14.0                 132.0      1.0 3.3        0.5      9.5   \n",
              "2                 30.0                  97.0      1.0 3.3        0.4     10.1   \n",
              "3                 47.0                 186.0      1.0 3.2        0.4      9.9   \n",
              "4                 47.0                 186.0      1.0 3.2        0.4      9.9   \n",
              "\n",
              "   quality  \n",
              "0        6  \n",
              "1        6  \n",
              "2        6  \n",
              "3        6  \n",
              "4        6  "
            ],
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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>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>8.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.4</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
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              "\n",
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              "        document.querySelector('#df-7254ca3b-ab43-4413-a67c-556f14b7b130 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-7254ca3b-ab43-4413-a67c-556f14b7b130');\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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Reset environment for a new run\n",
        "%reset -f\n",
        "\n",
        "# Load libraries\n",
        "from os.path import join # for joining file pathnames\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set Pandas display options\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "wineDf = pd.read_csv(\n",
        "  \"https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/winequality.csv\",\n",
        "  encoding='latin-1')\n",
        "wineDf.columns = ['fixed acidity','volatile acidity','citric acid',\n",
        "                     'residual sugar','chlorides','free sulfur dioxide',\n",
        "                     'total sulfur dioxide','density','pH',\n",
        "                     'sulphates','alcohol','quality']\n",
        "wineDf.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKNBH65pM8q9"
      },
      "source": [
        "## Check Correlation Matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mfGqkcgSHYC"
      },
      "source": [
        "Before developing your ML model, you need to select features. To find informative features, check the correlation matrix by running the following cell. Which features are informative?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "HX36srdIthHj",
        "outputId": "c44f4d67-4c81-43b0-ebf1-72fe154bb349",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "corr_wineDf = wineDf.corr()\n",
        "plt.figure(figsize=(16,10))\n",
        "sns.heatmap(corr_wineDf, annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqM4CCQJ4d7N"
      },
      "source": [
        "`alcohol` is most highly correlated with `quality`. Looking for other informative features, notice that `volatile acidity` correlates with `quality` but not with `alcohol`, making it a good second feature. Remember that a correlation matrix is not helpful if predictive signals are encoded in combinations of features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6IsKBBard07"
      },
      "source": [
        "## Validate Input Data against Data Schema"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QBbI6SBrg1k"
      },
      "source": [
        "Before processing your data, you should validate the data against a data schema as described in [Data and Feature Debugging](https://developers.google.com/machine-learning/testing-debugging/common/data-errors).\n",
        "\n",
        "First, define a function that validates data against a schema."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "NpLYlvpQsHoD"
      },
      "outputs": [],
      "source": [
        "#@title Define function to validate data\n",
        "\n",
        "def test_data_schema(input_data, schema):\n",
        "  \"\"\"Tests that the datatypes and ranges of values in the dataset\n",
        "    adhere to expectations.\n",
        "\n",
        "    Args:\n",
        "      input_function: Dataframe containing data to test\n",
        "      schema: Schema which describes the properties of the data.\n",
        "  \"\"\"\n",
        "\n",
        "  def test_dtypes():\n",
        "    for column in schema.keys():\n",
        "      assert input_data[column].map(type).eq(\n",
        "          schema[column]['dtype']).all(), (\n",
        "          \"Incorrect dtype in column '%s'.\" % column\n",
        "      )\n",
        "    print('Input dtypes are correct.')\n",
        "\n",
        "  def test_ranges():\n",
        "    for column in schema.keys():\n",
        "      schema_max = schema[column]['range']['max']\n",
        "      schema_min = schema[column]['range']['min']\n",
        "      # Assert that data falls between schema min and max.\n",
        "      assert input_data[column].max() <= schema_max, (\n",
        "          \"Maximum value of column '%s' is too low.\" % column\n",
        "      )\n",
        "      assert input_data[column].min() >= schema_min, (\n",
        "          \"Minimum value of column '%s' is too high.\" % column\n",
        "      )\n",
        "    print('Data falls within specified ranges.')\n",
        "\n",
        "  test_dtypes()\n",
        "  test_ranges()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6Frne4HTspm"
      },
      "source": [
        "To define your schema, you need to understand the statistical properties of your dataset. Generate statistics on your dataset by running the following code cell:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "WT4dm-ALr-gD",
        "outputId": "1ba6b127-b5d4-4d67-b3c2-21cee003f38c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count         6497.0            6497.0       6497.0          6497.0   \n",
              "mean             7.2               0.3          0.3             5.4   \n",
              "std              1.3               0.2          0.1             4.8   \n",
              "min              3.8               0.1          0.0             0.6   \n",
              "25%              6.4               0.2          0.2             1.8   \n",
              "50%              7.0               0.3          0.3             3.0   \n",
              "75%              7.7               0.4          0.4             8.1   \n",
              "max             15.9               1.6          1.7            65.8   \n",
              "\n",
              "       chlorides  free sulfur dioxide  total sulfur dioxide  density     pH  \\\n",
              "count     6497.0               6497.0                6497.0   6497.0 6497.0   \n",
              "mean         0.1                 30.5                 115.7      1.0    3.2   \n",
              "std          0.0                 17.7                  56.5      0.0    0.2   \n",
              "min          0.0                  1.0                   6.0      1.0    2.7   \n",
              "25%          0.0                 17.0                  77.0      1.0    3.1   \n",
              "50%          0.0                 29.0                 118.0      1.0    3.2   \n",
              "75%          0.1                 41.0                 156.0      1.0    3.3   \n",
              "max          0.6                289.0                 440.0      1.0    4.0   \n",
              "\n",
              "       sulphates  alcohol  quality  \n",
              "count     6497.0   6497.0   6497.0  \n",
              "mean         0.5     10.5      5.8  \n",
              "std          0.1      1.2      0.9  \n",
              "min          0.2      8.0      3.0  \n",
              "25%          0.4      9.5      5.0  \n",
              "50%          0.5     10.3      6.0  \n",
              "75%          0.6     11.3      6.0  \n",
              "max          2.0     14.9      9.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2ab9d8a-6ba5-4728-b41e-46e0c2adc191\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "      <td>6497.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>5.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>30.5</td>\n",
              "      <td>115.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>4.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.7</td>\n",
              "      <td>56.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.8</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>0.4</td>\n",
              "      <td>9.5</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>10.3</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.4</td>\n",
              "      <td>8.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>41.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>11.3</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.9</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1.7</td>\n",
              "      <td>65.8</td>\n",
              "      <td>0.6</td>\n",
              "      <td>289.0</td>\n",
              "      <td>440.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.9</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b2ab9d8a-6ba5-4728-b41e-46e0c2adc191 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-b2ab9d8a-6ba5-4728-b41e-46e0c2adc191');\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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      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-4eef1f73-c9c0-4bf4-a96c-f477103eee15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "wineDf.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6rSPgXENscc8"
      },
      "source": [
        "Using the statistics generated above, define the data schema in the following code cell. For demonstration purposes, restrict your data schema to the first three data columns. For each data column, enter the:\n",
        "\n",
        " * minimum value\n",
        " * maximum value\n",
        " * data type\n",
        "\n",
        "As an example, the values for the first column are filled out. After entering the values, run the code cell to confirm that your input data matches the schema."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YQ5CUFlsshm8",
        "outputId": "f3c51b81-e3fa-4fd9-fc5a-291dcb06fee6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validating wine data against data schema...\n",
            "Input dtypes are correct.\n",
            "Data falls within specified ranges.\n"
          ]
        }
      ],
      "source": [
        "wine_schema = {\n",
        "    'fixed acidity': {\n",
        "        'range': {\n",
        "            'min': 3.8,\n",
        "            'max': 15.9\n",
        "        },\n",
        "        'dtype': float,\n",
        "    },\n",
        "    'volatile acidity': {\n",
        "        'range': {\n",
        "            'min': 0.08,  # Placeholder value, update with the actual minimum\n",
        "            'max': 1.58  # Placeholder value, update with the actual maximum\n",
        "        },\n",
        "        'dtype': float,  # Placeholder value, update with the actual dtype\n",
        "    },\n",
        "    'citric acid': {\n",
        "        'range': {\n",
        "            'min': 0.0,  # Placeholder value, update with the actual minimum\n",
        "            'max': 1.66  # Placeholder value, update with the actual maximum\n",
        "        },\n",
        "        'dtype': float,  # Placeholder value, update with the actual dtype\n",
        "    }\n",
        "}\n",
        "\n",
        "print('Validating wine data against data schema...')\n",
        "test_data_schema(wineDf, wine_schema)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fko5u9Y3hiKW"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "L_fiqb-whjWu",
        "outputId": "4f0fbec8-3495-46cb-a79e-c065c16bb9c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validating wine data against data schema...\n",
            "Input dtypes are correct.\n",
            "Data falls within specified ranges.\n"
          ]
        }
      ],
      "source": [
        "wine_schema = {\n",
        "    'fixed acidity': {\n",
        "        'range': {\n",
        "            'min': 3.7,\n",
        "            'max': 15.9\n",
        "        },\n",
        "        'dtype': float,\n",
        "    },\n",
        "    'volatile acidity': {\n",
        "        'range': {\n",
        "            'min': 0.08,  # minimum value\n",
        "            'max': 1.6   # maximum value\n",
        "        },\n",
        "        'dtype': float,    # data type\n",
        "    },\n",
        "    'citric acid': {\n",
        "        'range': {\n",
        "            'min': 0.0, # minimum value\n",
        "            'max': 1.7  # maximum value\n",
        "        },\n",
        "        'dtype': float,   # data type\n",
        "    }\n",
        "}\n",
        "\n",
        "print('Validating wine data against data schema...')\n",
        "test_data_schema(wineDf, wine_schema)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ks-c6qeRNDbN"
      },
      "source": [
        "## Split and Normalize Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqz_CWW-1nuC"
      },
      "source": [
        "Split the dataset into data and labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BstIdTwr2A8J"
      },
      "outputs": [],
      "source": [
        "wineFeatures = wineDf.copy(deep=True)\n",
        "wineFeatures.drop(columns='quality',inplace=True)\n",
        "wineLabels = wineDf['quality'].copy(deep=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zV8hRjJHNJcd"
      },
      "source": [
        "Normalize data using z-score."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "8eWEEY2N8jvM"
      },
      "outputs": [],
      "source": [
        "def normalizeData(arr):\n",
        "  stdArr = np.std(arr)\n",
        "  meanArr = np.mean(arr)\n",
        "  arr = (arr-meanArr)/stdArr\n",
        "  return arr\n",
        "\n",
        "for str1 in wineFeatures.columns:\n",
        "   wineFeatures[str1] = normalizeData(wineFeatures[str1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRYctLRsxoKQ"
      },
      "source": [
        "## Test Engineered Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9FmtMP7xyfW"
      },
      "source": [
        "After normalizing your data, you should test your engineered data for errors as described in [Data and Feature Debugging](https://developers.google.com/machine-learning/testing-debugging/common/data-errors). In this section, you will test that engineering data:\n",
        "\n",
        "* Has the expected number of rows and columns.\n",
        "* Does not have null values.\n",
        "\n",
        "First, set up the testing functions by running the following code cell:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "cellView": "both",
        "id": "OCkV2ti1ygqe"
      },
      "outputs": [],
      "source": [
        "import unittest\n",
        "\n",
        "def test_input_dim(df, n_rows, n_columns):\n",
        "  assert len(df) == n_rows, \"Unexpected number of rows.\"\n",
        "  assert len(df.columns) == n_columns, \"Unexpected number of columns.\"\n",
        "  print('Engineered data has the expected number of rows and columns.')\n",
        "\n",
        "def test_nulls(df):\n",
        "  dataNulls = df.isnull().sum().sum()\n",
        "  assert dataNulls == 0, \"Nulls in engineered data.\"\n",
        "  print('Engineered features do not contain nulls.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrCc2NAE4J5A"
      },
      "source": [
        "Your input data had 6497 examples and 11 feature columns. Test whether your engineered data has the expected number of rows and columns by running the following cell. Confirm that the test fails if you change the values below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "cellView": "form",
        "id": "97BqVwqx4JKQ",
        "outputId": "70845ffe-941a-442b-e832-ba962b5ef667",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Engineered data has the expected number of rows and columns.\n"
          ]
        }
      ],
      "source": [
        "#@title Test dimensions of engineered data\n",
        "wine_feature_rows = 6497 #@param\n",
        "wine_feature_cols = 11 #@param\n",
        "test_input_dim(wineFeatures,\n",
        "               wine_feature_rows,\n",
        "               wine_feature_cols)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gb7CxKrN5PDq"
      },
      "source": [
        "Test that your engineered data does not contain nulls by running the code below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Gg3krAmq4aBf",
        "outputId": "601e076a-d578-48df-94d2-134b4c3be196",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Engineered features do not contain nulls.\n"
          ]
        }
      ],
      "source": [
        "test_nulls(wineFeatures)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaU8hlK-rzbc"
      },
      "source": [
        "## Check Splits for Statistical Equivalence\n",
        "\n",
        "As described in the [Data Debugging](https://developers.google.com/machine-learning/testing-debugging/common/data-errors) guidelines, before developing your model, you should check that your training and validation splits are equally representative. Assuming a training:validation split of 80:20, compare the mean and the standard deviation of the splits by running the next two code cells. Note that this comparison is not a rigorous test for statistical equivalence but simply a quick and dirty comparison of the splits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "GBeiaCDUshkS",
        "outputId": "8047671d-1d75-488c-ebba-325900d0c797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count         5197.0            5197.0       5197.0          5197.0   \n",
              "mean            -0.2              -0.3          0.1             0.2   \n",
              "std              0.7               0.8          0.9             1.1   \n",
              "min             -2.6              -1.6         -2.2            -1.0   \n",
              "25%             -0.7              -0.8         -0.4            -0.8   \n",
              "50%             -0.3              -0.4         -0.1            -0.1   \n",
              "75%              0.1               0.0          0.5             0.9   \n",
              "max              6.0               6.0          9.2            12.7   \n",
              "\n",
              "       chlorides  free sulfur dioxide  total sulfur dioxide  density     pH  \\\n",
              "count     5197.0               5197.0                5197.0   5197.0 5197.0   \n",
              "mean        -0.2                  0.2                   0.3     -0.2   -0.1   \n",
              "std          0.9                  1.0                   0.8      1.0    1.0   \n",
              "min         -1.3                 -1.6                  -1.9     -2.5   -3.1   \n",
              "25%         -0.6                 -0.5                  -0.2     -1.0   -0.8   \n",
              "50%         -0.3                  0.1                   0.3     -0.2   -0.2   \n",
              "75%         -0.1                  0.8                   0.9      0.6    0.4   \n",
              "max         15.8                 14.6                   5.7     14.8    4.2   \n",
              "\n",
              "       sulphates  alcohol  \n",
              "count     5197.0   5197.0  \n",
              "mean        -0.2     -0.0  \n",
              "std          0.9      1.0  \n",
              "min         -2.1     -2.1  \n",
              "25%         -0.8     -0.9  \n",
              "50%         -0.3     -0.2  \n",
              "75%          0.2      0.7  \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",
              "      <th>chlorides</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5197.0</td>\n",
              "      <td>5197.0</td>\n",
              "      <td>5197.0</td>\n",
              "      <td>5197.0</td>\n",
              "      <td>5197.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.2</td>\n",
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              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>-0.0</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.6</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>-1.9</td>\n",
              "      <td>-2.5</td>\n",
              "      <td>-3.1</td>\n",
              "      <td>-2.1</td>\n",
              "      <td>-2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.7</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.2</td>\n",
              "      <td>12.7</td>\n",
              "      <td>15.8</td>\n",
              "      <td>14.6</td>\n",
              "      <td>5.7</td>\n",
              "      <td>14.8</td>\n",
              "      <td>4.2</td>\n",
              "      <td>9.9</td>\n",
              "      <td>3.1</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-18844894-c647-41e9-a703-5aecfa2a3961');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "splitIdx = int(wineFeatures.shape[0] * 8 / 10)\n",
        "wineFeatures.iloc[0:splitIdx, :].describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "BvG69ze-uS2g",
        "outputId": "29c25644-eed9-459e-85c3-dd3de6428adc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count         1299.0            1299.0       1299.0          1299.0   \n",
              "mean             0.9               1.1         -0.3            -0.6   \n",
              "std              1.4               1.1          1.3             0.3   \n",
              "min             -1.8              -1.3         -2.2            -1.0   \n",
              "25%             -0.1               0.3         -1.5            -0.7   \n",
              "50%              0.6               1.0         -0.3            -0.7   \n",
              "75%              1.7               1.8          0.8            -0.6   \n",
              "max              6.7               7.5          3.2             2.1   \n",
              "\n",
              "       chlorides  free sulfur dioxide  total sulfur dioxide  density     pH  \\\n",
              "count     1299.0               1299.0                1299.0   1299.0 1299.0   \n",
              "mean         0.8                 -0.8                  -1.3      0.7    0.6   \n",
              "std          1.1                  0.6                   0.6      0.7    1.0   \n",
              "min         -1.3                 -1.7                  -1.9     -1.5   -2.2   \n",
              "25%          0.4                 -1.3                  -1.7      0.3   -0.1   \n",
              "50%          0.7                 -0.9                  -1.4      0.7    0.5   \n",
              "75%          1.0                 -0.5                  -1.0      1.1    1.1   \n",
              "max         10.4                  2.3                   3.1      3.0    4.9   \n",
              "\n",
              "       sulphates  alcohol  \n",
              "count     1299.0   1299.0  \n",
              "mean         0.8      0.0  \n",
              "std          1.0      0.9  \n",
              "min         -1.1     -1.8  \n",
              "25%          0.1     -0.7  \n",
              "50%          0.6     -0.1  \n",
              "75%          1.3      0.7  \n",
              "max          7.3      3.7  "
            ],
            "text/html": [
              "\n",
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              "        vertical-align: top;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
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              "      <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>count</th>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "      <td>1299.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>-0.8</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.4</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.8</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>-1.7</td>\n",
              "      <td>-1.9</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>-1.1</td>\n",
              "      <td>-1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-1.5</td>\n",
              "      <td>-0.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>-1.3</td>\n",
              "      <td>-1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.3</td>\n",
              "      <td>-0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>-0.9</td>\n",
              "      <td>-1.4</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>-0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.7</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>-0.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.7</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.1</td>\n",
              "      <td>10.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>7.3</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "wineFeatures.iloc[splitIdx:-1,:].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETUVOTizsgUa"
      },
      "source": [
        "The two splits are clearly not equally representative. To make the splits equally representative, you can shuffle the data.\n",
        "\n",
        "Run the following code cell to shuffle the data, and then recreate the features and labels from the shuffled data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "l6G6xnJdu3CF"
      },
      "outputs": [],
      "source": [
        "# Shuffle data\n",
        "wineDf = wineDf.sample(frac=1).reset_index(drop=True)\n",
        "# Recreate features and labels\n",
        "wineFeatures = wineDf.copy(deep=True)\n",
        "wineFeatures.drop(columns='quality',inplace=True)\n",
        "wineLabels = wineDf['quality'].copy(deep=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BbK2JuTvYZw"
      },
      "source": [
        "Now, confirm that the splits are equally representative by regenerating and comparing the statistics using the previous code cells. You may wonder why the initial splits differed so greatly. It turns out that in the wine dataset, the first 4897 rows contain data on white wines and the next 1599 rows contain data on red wines. When you split your dataset 80:20, then your training dataset contains 5197 examples, which is 94% white wine. The validation dataset is purely red wine.\n",
        "\n",
        "Ensuring your splits are statistically equivalent is a good development practice. In general, following good development practices will simplify your model debugging. To learn about testing for statistical equivalence, see [Equivalence Tests Lakens, D.](https://journals.sagepub.com/doi/10.1177/1948550617697177)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH7y19etJQyd"
      },
      "source": [
        "## Establish a Baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj7hlj7yJSU2"
      },
      "source": [
        "For a regression problem, the simplest baseline to predict the average value. Run the following code to calculate the mean-squared error (MSE) loss on the training split using the average value as a baseline. Your loss is approximately 0.75. Any model should beat this loss to justify its use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "ryhxiL7qJak8",
        "outputId": "99cfe9f4-2442-4642-9d37-6406bd041574",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7738266430037695\n"
          ]
        }
      ],
      "source": [
        "baselineMSE = np.square(wineLabels[0:splitIdx]-np.mean(wineLabels[0:splitIdx]))\n",
        "baselineMSE = np.sum(baselineMSE)/len(baselineMSE)\n",
        "print(baselineMSE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjRt-zgo5a1G"
      },
      "source": [
        "## Linear Model\n",
        "\n",
        "Following good ML dev practice, let's start with a linear model that uses the most informative feature from the correlation matrix: `alcohol`. Even if this model performs badly, we can still use it as a baseline. This model should beat our previous baseline's MSE of 0.75."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1REI7glC8GA"
      },
      "source": [
        "First, let's define a function to plot our loss and accuracy curves. The function will also print the final loss and accuracy. Instead of using `verbose=1`, you can call the function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "l-qlE8TCC8j3"
      },
      "outputs": [],
      "source": [
        "def showRegressionResults(trainHistory):\n",
        "  \"\"\"Function to:\n",
        "   * Print final loss.\n",
        "   * Plot loss curves.\n",
        "\n",
        "  Args:\n",
        "    trainHistory: object returned by model.fit\n",
        "  \"\"\"\n",
        "\n",
        "  # Print final loss\n",
        "  print(\"Final training loss: \" + str(trainHistory.history['loss'][-1]))\n",
        "  print(\"Final Validation loss: \" + str(trainHistory.history['val_loss'][-1]))\n",
        "\n",
        "  # Plot loss curves\n",
        "  plt.plot(trainHistory.history['loss'])\n",
        "  plt.plot(trainHistory.history['val_loss'])\n",
        "  plt.legend(['Training loss','Validation loss'],loc='best')\n",
        "  plt.title('Loss Curves')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSZZjpYnDLiP"
      },
      "source": [
        "For fast prototyping, let's try using a full batch per epoch to update the gradient only once per  epoch. Use the full batch by setting `batch_size = wineFeatures.shape[0]` as indicated by the code comment.\n",
        "\n",
        "What do you think of the loss curve? Can you improve it? For hints and discussion, see the following text cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "wHUSu92-0q8-",
        "outputId": "3727ba52-2508-4531-b441-20cc4dacb8a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.6339302659034729\n",
            "Final Validation loss: 0.5759974718093872\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Choose feature\n",
        "wineFeaturesSimple = wineFeatures['alcohol']\n",
        "# Define model\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(units=1, activation='linear', input_dim=1))\n",
        "# Specify the optimizer using the TF API to specify the learning rate\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01),\n",
        "              loss='mse')\n",
        "# Train the model!\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=50,\n",
        "                         batch_size=32,  # Replace with your desired batch size\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "# Plot\n",
        "showRegressionResults(trainHistory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWgwvf4qODQE"
      },
      "source": [
        "### Hint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzYID6wZ3Cff"
      },
      "source": [
        "The loss decreases but very slowly. Possible fixes are:\n",
        "\n",
        "* Increase number of epochs.\n",
        "* Increase learning rate.\n",
        "* Decrease batch size. A lower batch size can result in larger decrease in loss per epoch, under the assumption that the smaller batches stay representative of the overall data distribution.\n",
        "\n",
        "Play with these three parameters in the code above to decrease the loss."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8qvAbVIsL4w"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gx8cQD8VsZtI"
      },
      "source": [
        "Run the following code cell to train the model using a reduced batch size of 100. Reducing the batch size leads to a greater decrease in loss per epoch. The minimum achievable loss is about 0.64. This is a significant increase over our baseline of 0.75."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IDNZcb2cb_Tp",
        "outputId": "938c0cde-27a3-4325-b16d-c97c02784f67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.6591692566871643\n",
            "Final Validation loss: 0.5836994051933289\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Choose feature\n",
        "wineFeaturesSimple = wineFeatures['alcohol']\n",
        "# Define model\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(units=1, activation='linear', input_dim=1))\n",
        "# Specify the optimizer using the TF API to specify the learning rate\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01),\n",
        "              loss='mse')\n",
        "# Train the model!\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=20,\n",
        "                         batch_size=100, # set batch size here\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "# Plot\n",
        "showRegressionResults(trainHistory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkXqclbwANES"
      },
      "source": [
        "## Add Feature to Linear Model\n",
        "\n",
        "Try adding a feature to the linear model. Since you need to combine the two features into one prediction for regression, you'll also need to add a second layer. Modify the code below to implement the following changes:\n",
        "\n",
        "1. Add `'volatile acidity'` to the features in `wineFeaturesSimple`.\n",
        "1. Add a second linear layer with 1 unit.\n",
        "1. Experiment with learning rate, epochs, and batch_size to try to reduce loss.\n",
        "\n",
        "What happens to your loss?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "9l2vVIiz-06P",
        "outputId": "67ad881c-fbb4-4e4e-a8dd-15083ab7b55b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.5843960046768188\n",
            "Final Validation loss: 0.5128785371780396\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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169bhgw8+wOzZsxvcz4wZMzB16lQAwGOPPYZnn30W//3vfzFu3Lh6t3c6nXjxxRfRo0cPAMDs2bOxYMEC9fnnnnsO8+bNw7XXXgsAeP7557Fhw4aAPtuiRYswY8YM3H777QCAu+66Czt37sSiRYswduxY5OfnIz4+Hunp6TAajUhOTsbw4cMBKBORhoeH47e//S0iIyPRpUsXDBo0KKD3D5U2HUZYGSGitsxs1OPbBRmavXdrGTp0qN/j8vJyzJ8/Hx999BEKCwtRU1ODqqoq5OfnN7qfAQMGqPfDw8MRFRWFY8eONbi9xWJRgwgAJCQkqNvbbDYUFxerwQBQLio3ZMgQuN3N/8753//+d0ZH21GjRmHJkiUAgMmTJ2Px4sXo3r07xo0bh6uvvhoTJkyAwWDAlVdeiS5duqjPjRs3Tj0Nda5p031GTJ4w4qjhlSuJqO2RJAkWk0GTpTWviRMeHu73+O6778a6devw2GOP4YsvvkBubi769+8Ph8PR6H6MRuMZx6ex4FDf9qGe1DwpKQmHDh3CCy+8ALPZjNtvvx2jR4+G0+lEZGQk9u7dizfffBMJCQl48MEHkZqaGvThyS3RpsMIKyNERBeebdu2YcaMGbj22mvRv39/xMfH46effgppG6xWK+Li4rBr1y51ncvlwt69ewPaT58+fbBt2za/ddu2bfO7xpvZbMaECRPw7LPPIicnBzt27MD+/fsBAAaDAenp6XjyySfxzTff4KeffsLnn39+Fp8sOHiaBuwzQkR0IUlJScG7776LCRMmQJIkPPDAAwGdGmktd9xxBzIzM9GzZ0/07t0bzz33HEpKSgKqCv3jH//A9ddfj0GDBiE9PR0ffvgh3n33XXV0UFZWFlwuF0aMGAGLxYLXX38dZrMZXbp0wfr16/Hjjz9i9OjRiImJwYYNG+B2u9GrV69gfeQWa+NhRDlnycoIEdGF4+mnn8ZNN92EkSNHon379rj33ntRWloa8nbce++9KCoqwrRp06DX63HrrbciIyMDen3z+8tMmjQJS5YswaJFizBnzhx069YNK1euxJgxYwAA0dHRePzxx3HXXXfB5XKhf//++PDDD9GuXTtER0fj3Xffxfz581FdXY2UlBS8+eab6NevX5A+ccu16av2Prbhf3hp64+4dXR3/PPqPq22XyKicxGv2qstt9uNPn364Prrr8cjjzyidXNaTWtctbeNV0Y8fUac7MBKRESt6+jRo/j0009x2WWXwW634/nnn0deXh7+9Kc/ad20c06b7sBq0nv6jLh4moaIiFqXTqdDVlYWhg0bhlGjRmH//v347LPP0KcPK/F1te3KiNFbGWEYISKi1pWUlHTGSBiqHysjAOysjBAREWmmTYcR2TMDICsjRERE2mnbYcTAPiNERERaa9NhxMTRNERERJpr02GEk54RERFpr02HEROngyciItJcmw4jtRfK42kaIqIL2ZgxYzB37lz1cdeuXbF48eJGXyNJEt57772zfu/W2k9j5s+fj4EDBwb1PYKpTYcREzuwEhGd0yZMmIBx48bV+9wXX3wBSZLwzTffBLzfXbt24dZbbz3b5vlpKBAUFhZi/PjxrfpeF5o2HUZqp4NnGCEiOhfNnDkT2dnZ+Pnnn894buXKlRg6dCgGDBgQ8H47dOgAi8XSGk1sUnx8PGRZDsl7na8YRsDKCBG1UUIAjgptlmZeo/W3v/0tOnTogKysLL/15eXlWLt2LWbOnImTJ09i6tSp6NSpEywWC/r3748333yz0f3WPU3zww8/YPTo0QgLC0Pfvn2RnZ19xmvuvfdeXHTRRbBYLOjevTseeOABOJ1OAEBWVhYefvhhfP3115AkCZIkqW2ue5pm//79uPzyy2E2m9GuXTvceuutKC8vV5+fMWMGJk2ahEWLFiEhIQHt2rXDrFmz1PdqDrfbjQULFqBz586QZRkDBw7EJ598oj7vcDgwe/ZsJCQkICwsDF26dEFmZiYAQAiB+fPnIzk5GbIsIzExEXfeeWez37sl2vZ08AZOekZEbZizEngsUZv3/uevgCm8yc0MBgOmTZuGrKws3H///ZAkCQCwdu1auFwuTJ06FeXl5RgyZAjuvfdeREVF4aOPPsKNN96IHj16YPjw4U2+h9vtxu9//3vExcXhq6++gs1m8+tf4hUZGYmsrCwkJiZi//79uOWWWxAZGYl77rkHU6ZMwYEDB/DJJ5/gs88+AwBYrdYz9lFRUYGMjAykpaVh165dOHbsGG6++WbMnj3bL3Bt3rwZCQkJ2Lx5Mw4fPowpU6Zg4MCBuOWWW5r8PACwZMkSPPXUU1i+fDkGDRqEl19+Gb/73e9w8OBBpKSk4Nlnn8UHH3yANWvWIDk5GQUFBSgoKAAAvPPOO3jmmWewevVq9OvXD0VFRfj666+b9b4t1abDCPuMEBGd+2666SYsXLgQW7ZswZgxYwAop2iuu+46WK1WWK1W3H333er2d9xxBzZu3Ig1a9Y0K4x89tln+O6777Bx40YkJirh7LHHHjujn8e//vUv9X7Xrl1x9913Y/Xq1bjnnntgNpsREREBg8GA+Pj4Bt9r1apVqK6uxquvvorwcCWMPf/885gwYQKeeOIJxMXFAQBiYmLw/PPPQ6/Xo3fv3rjmmmuwadOmZoeRRYsW4d5778Uf//hHAMATTzyBzZs3Y/HixVi6dCny8/ORkpKCSy+9FJIkoUuXLupr8/PzER8fj/T0dBiNRiQnJzfrOJ6NNh1GvKdpXG6BGpcbBn2bPmtFRG2N0aJUKLR672bq3bs3Ro4ciZdffhljxozB4cOH8cUXX2DBggUAAJfLhcceewxr1qzBL7/8AofDAbvd3uw+If/73/+QlJSkBhEASEtLO2O7t956C88++yyOHDmC8vJy1NTUICoqqtmfw/teqampahABgFGjRsHtduPQoUNqGOnXrx/0er26TUJCAvbv39+s9ygtLcWvv/6KUaNG+a0fNWqUWuGYMWMGrrzySvTq1Qvjxo3Db3/7W1x11VUAgMmTJ2Px4sXo3r07xo0bh6uvvhoTJkyAwRC8yNCmv329lRGA1REiaoMkSTlVosXiOd3SXDNnzsQ777yDsrIyrFy5Ej169MBll10GAFi4cCGWLFmCe++9F5s3b0Zubi4yMjLgcDha7VDt2LEDN9xwA66++mqsX78e+/btw/3339+q7+HLaDT6PZYkCW53631PDR48GHl5eXjkkUdQVVWF66+/Hn/4wx8AKFcbPnToEF544QWYzWbcfvvtGD16dEB9VgLVtsOITyWE/UaIiM5d119/PXQ6HVatWoVXX30VN910k9p/ZNu2bZg4cSL+/Oc/IzU1Fd27d8f333/f7H336dMHBQUFKCwsVNft3LnTb5vt27ejS5cuuP/++zF06FCkpKTg6NGjftuYTCa4XI3PW9WnTx98/fXXqKioUNdt27YNOp0OvXr1anabGxMVFYXExERs27bNb/22bdvQt29fv+2mTJmC//znP3jrrbfwzjvv4NSpUwAAs9mMCRMm4Nlnn0VOTg527NjR7MpMS7Tp0zQGvQ4GnYQat2BlhIjoHBYREYEpU6Zg3rx5KC0txYwZM9TnUlJS8Pbbb2P79u2IiYnB008/jeLiYr8v3sakp6fjoosuwvTp07Fw4UKUlpbi/vvv99smJSUF+fn5WL16NYYNG4aPPvoI69at89uma9euyMvLQ25uLjp37ozIyMgzhvTecMMNeOihhzB9+nTMnz8fx48fxx133IEbb7xRPUXTGv7xj3/goYceQo8ePTBw4ECsXLkSubm5eOONNwAATz/9NBISEjBo0CDodDqsXbsW8fHxiI6ORlZWFlwuF0aMGAGLxYLXX38dZrPZr19Ja2vTlRHA92J5DCNEROeymTNnoqSkBBkZGX79O/71r39h8ODByMjIwJgxYxAfH49JkyY1e786nQ7r1q1DVVUVhg8fjptvvhmPPvqo3za/+93v8Le//Q2zZ8/GwIEDsX37djzwwAN+21x33XUYN24cxo4diw4dOtQ7vNhisWDjxo04deoUhg0bhj/84Q+44oor8Pzzzwd2MJpw55134q677sLf//539O/fH5988gk++OADpKSkAFBGBj355JMYOnQohg0bhp9++gkbNmyATqdDdHQ0/vOf/2DUqFEYMGAAPvvsM3z44Ydo165dq7bRlyREMwd7a6i0tBRWqxU2my3gzkJNGbTgU5RUOpH9t9FIiYts1X0TEZ1LqqurkZeXh27duiEsLEzr5tAForHfq+Z+f7Myol6fhpURIiIiLbT5MKJOfMYwQkREpIk2H0bUic8YRoiIiDTR5sOIerG8msaHYxEREVFwtPkwwsoIEbU158G4BTqPtMbvU5sPIzI7sBJRG+Gd1bOyslLjltCFxPv7VHfW2EC06UnPAMDk6cDKyggRXej0ej2io6Nx7NgxAMqcF1KA07ITeQkhUFlZiWPHjiE6OtrvWjqBavNhhJURImpLvFeU9QYSorMVHR3d6JWKm6PNh5HaPiPswEpEFz5JkpCQkICOHTsG9cJn1DYYjcazqoh4tfkwwsoIEbVFer2+Vb5EiFoDO7CyzwgREZGmGEZYGSEiItIUwwgnPSMiItJUQGFk2bJlGDBgAKKiohAVFYW0tDR8/PHHjb5m7dq16N27N8LCwtC/f39s2LDhrBrc2jjpGRERkbYCCiOdO3fG448/jj179mD37t24/PLLMXHiRBw8eLDe7bdv346pU6di5syZ2LdvHyZNmoRJkybhwIEDrdL41sDTNERERNqSxFnO4xobG4uFCxdi5syZZzw3ZcoUVFRUYP369eq6Sy65BAMHDsSLL77Y7PcoLS2F1WqFzWZDVFTU2TT3DC9tPYLHNnyH3w/qhKenDGzVfRMREbVlzf3+bnGfEZfLhdWrV6OiogJpaWn1brNjxw6kp6f7rcvIyMCOHTsa3bfdbkdpaanfEize0TSsjBAREWkj4DCyf/9+REREQJZl3HbbbVi3bh369u1b77ZFRUWIi4vzWxcXF4eioqJG3yMzMxNWq1VdkpKSAm1ms5l4moaIiEhTAYeRXr16ITc3F1999RX++te/Yvr06fj2229btVHz5s2DzWZTl4KCglbdvy+OpiEiItJWwDOwmkwm9OzZEwAwZMgQ7Nq1C0uWLMHy5cvP2DY+Ph7FxcV+64qLi5ucw16WZciyHGjTWoSjaYiIiLR11vOMuN1u2O32ep9LS0vDpk2b/NZlZ2c32MdEC+wzQkREpK2AKiPz5s3D+PHjkZycjLKyMqxatQo5OTnYuHEjAGDatGno1KkTMjMzAQBz5szBZZddhqeeegrXXHMNVq9ejd27d+Oll15q/U/SQqyMEBERaSugMHLs2DFMmzYNhYWFsFqtGDBgADZu3Igrr7wSAJCfnw+drrbYMnLkSKxatQr/+te/8M9//hMpKSl47733cPHFF7fupzgL7DNCRESkrbOeZyQUgjnPyL78Elz7wnYkxZrxxT2Xt+q+iYiI2rKgzzNyoVCH9jp5moaIiEgLbT6MsAMrERGRthhG2IGViIhIUwwj7MBKRESkqTYfRrx9RtwCqHGxOkJERBRqbT6MePuMAOw3QkREpIU2H0a8lRGA/UaIiIi00ObDiF4nwaCTALAyQkREpIU2H0YATglPRESkJYYRcEQNERGRlhhG4DMLKysjREREIccwAs7CSkREpCWGEbDPCBERkZYYRsA+I0RERFpiGIFvGGFlhIiIKNQYRsDTNERERFpiGAE7sBIREWmJYQSsjBAREWmJYQTswEpERKQlhhGwMkJERKQlhhGwzwgREZGWGEZQe5qGlREiIqLQYxgB+4wQERFpiWEE7DNCRESkJYYRcAZWIiIiLTGMgJURIiIiLTGMgKNpiIiItMQwAnZgJSIi0hLDCGpP07AyQkREFHoMI+BpGiIiIi0xjIAdWImIiLTEMAIO7SUiItISwwh8KyPswEpERBRqDCNgZYSIiEhLDCNgnxEiIiItMYyAo2mIiIi0xDCC2tM0rIwQERGFHsMIOAMrERGRlhhGUNtnxC2AGherI0RERKHEMILaPiMA+40QERGFGsMIaisjAPuNEBERhRrDCAC9ToJBJwFgZYSIiCjUGEY82ImViIhIGwwjHpz4jIiISBsMIx6c+IyIiEgbDCMeJl6fhoiISBMMIx7sM0JERKSNgMJIZmYmhg0bhsjISHTs2BGTJk3CoUOHGn1NVlYWJEnyW8LCws6q0cHAPiNERETaCCiMbNmyBbNmzcLOnTuRnZ0Np9OJq666ChUVFY2+LioqCoWFhepy9OjRs2p0MMg8TUNERKQJQyAbf/LJJ36Ps7Ky0LFjR+zZswejR49u8HWSJCE+Pr5lLQwRVkaIiIi0cVZ9Rmw2GwAgNja20e3Ky8vRpUsXJCUlYeLEiTh48GCj29vtdpSWlvotwcbRNERERNpocRhxu92YO3cuRo0ahYsvvrjB7Xr16oWXX34Z77//Pl5//XW43W6MHDkSP//8c4OvyczMhNVqVZekpKSWNrPZWBkhIiLSRovDyKxZs3DgwAGsXr260e3S0tIwbdo0DBw4EJdddhneffdddOjQAcuXL2/wNfPmzYPNZlOXgoKCljaz2TiahoiISBsB9Rnxmj17NtavX4+tW7eic+fOAb3WaDRi0KBBOHz4cIPbyLIMWZZb0rQW856mYWWEiIgotAKqjAghMHv2bKxbtw6ff/45unXrFvAbulwu7N+/HwkJCQG/Npg46RkREZE2AqqMzJo1C6tWrcL777+PyMhIFBUVAQCsVivMZjMAYNq0aejUqRMyMzMBAAsWLMAll1yCnj174vTp01i4cCGOHj2Km2++uZU/ytnhaRoiIiJtBBRGli1bBgAYM2aM3/qVK1dixowZAID8/HzodLUFl5KSEtxyyy0oKipCTEwMhgwZgu3bt6Nv375n1/JWJrMDKxERkSYCCiNCiCa3ycnJ8Xv8zDPP4JlnngmoUVrgpGdERETa4LVpPDi0l4iISBsMIx6c9IyIiEgbDCMerIwQERFpg2HEg6NpiIiItMEw4sF5RoiIiLTBMOLBPiNERETaYBjxYJ8RIiIibTCMeHCeESIiIm0wjHjUVkbYgZWIiCiUGEY8WBkhIiLSBsOIBzuwEhERaYNhxIMdWImIiLTBMOLBSc+IiIi0wTDiIftURppzdWIiIiJqHQwjHt4+I24B1LgZRoiIiEKFYcTD22cEYL8RIiKiUGIY8fANIxxRQ0REFDoMIx56nQSDTgLAyggREVEoMYz44IgaIiKi0GMY8cG5RoiIiEKPYcQHZ2ElIiIKPYYRHyZen4aIiCjkGEZ8sM8IERFR6DGM+JCNrIwQERGFGsOID5OeHViJiIhCjWHEBzuwEhERhR7DiA8O7SUiIgo9hhEf7MBKREQUegwjPlgZISIiCj2GER/sM0JERBR6DCM+WBkhIiIKPYYRH+wzQkREFHoMIz5kVkaIiIhCjmHEh8xr0xAREYUcw4gP9hkhIiIKPYYRHxxNQ0REFHoMIz5qL5THDqxEREShwjDigxfKIyIiCj2GER+1lRGGESIiolBhGPFh0rPPCBERUagxjPjg0F4iIqLQYxjxwaG9REREoccw4oPTwRMREYUew4gPVkaIiIhCj2HEByc9IyIiCj2GER+sjBAREYUew4gP9hkhIiIKvYDCSGZmJoYNG4bIyEh07NgRkyZNwqFDh5p83dq1a9G7d2+EhYWhf//+2LBhQ4sbHEyyT2VECKFxa4iIiNqGgMLIli1bMGvWLOzcuRPZ2dlwOp246qqrUFFR0eBrtm/fjqlTp2LmzJnYt28fJk2ahEmTJuHAgQNn3fjW5u0z4hZAjZthhIiIKBQkcRYlgOPHj6Njx47YsmULRo8eXe82U6ZMQUVFBdavX6+uu+SSSzBw4EC8+OKLzXqf0tJSWK1W2Gw2REVFtbS5Tap2utD7gU8AAAcfzkC4bAjaexEREV3omvv9fVZ9Rmw2GwAgNja2wW127NiB9PR0v3UZGRnYsWNHg6+x2+0oLS31W0LBe6E8gCNqiIiIQqXFYcTtdmPu3LkYNWoULr744ga3KyoqQlxcnN+6uLg4FBUVNfiazMxMWK1WdUlKSmppMwOi00kw6iUA7MRKREQUKi0OI7NmzcKBAwewevXq1mwPAGDevHmw2WzqUlBQ0Orv0RBvdYTDe4mIiEKjRZ0iZs+ejfXr12Pr1q3o3Llzo9vGx8ejuLjYb11xcTHi4+MbfI0sy5BluSVNO2uyUY8Kh4unaYiIiEIkoMqIEAKzZ8/GunXr8Pnnn6Nbt25NviYtLQ2bNm3yW5ednY20tLTAWhoirIwQERGFVkCVkVmzZmHVqlV4//33ERkZqfb7sFqtMJvNAIBp06ahU6dOyMzMBADMmTMHl112GZ566ilcc801WL16NXbv3o2XXnqplT9K65CNnPiMiIgolAKqjCxbtgw2mw1jxoxBQkKCurz11lvqNvn5+SgsLFQfjxw5EqtWrcJLL72E1NRUvP3223jvvfca7fSqJW9lhKdpiIiIQiOgykhzpiTJyck5Y93kyZMxefLkQN5KM7WVEYYRIiKiUOC1aepgnxEiIqLQYhipwzslPCsjREREocEwUofJwMoIERFRKDGM1OG9ci9H0xAREYUGw0gdrIwQERGFFsNIHewzQkREFFoMI3WoQ3udDCNEREShwDBShzq018U+I0RERKHAMFIHKyNEREShxTBSh6xWRhhGiIiIQoFhpA7Z6OnAysoIERFRSDCM1GFiZYSIiCikGEbqqL1QHjuwEhERhQLDSB28UB4REVFoMYzUUVsZYRghIiIKBYaROkx6zsBKREQUSgwjddReKI9hhIiIKBQYRurghfKIiIhCi2GkjtrKCEfTEBERhQLDSB2c9IyIiCi0GEbq4KRnREREocUwUkfthfJ4moaIiCgUGEbqYGWEiIgotBhG6vCd9EwIoXFriIiILnwMI3XInknPhABq3AwjREREwcYwUoe3MgJw4jMiIqJQYBipw9tnBODEZ0RERKHAMFKHTifBqJcAcOIzIiKiUGAYqYc6ooaVESIioqBjGKmHOgsrwwgREVHQMYzUg5URIiKi0GEYqUftXCPsM0JERBRsDCP1UK/cy4vlERERBR3DSD1M3jDCKeGJiIiCjmGkHrLB04GVlREiIqKgYxipBy+WR0REFDoMI/VQO7A62YGViIgo2BhG6sHKCBERUegwjNRDnfSMfUaIiIiCjmGkHqyMEBERhQ7DSD1q+4wwjBAREQUbw0g9aisj7MBKREQUbAwj9WBlhIiIKHQYRuohs88IERFRyDCM1IOjaYiIiEKHYaQe6oXyeNVeIiKioGMYqYf3Qnk8TUNERBR8DCP1UCsjPE1DREQUdAGHka1bt2LChAlITEyEJEl47733Gt0+JycHkiSdsRQVFbW0zUHHyggREVHoBBxGKioqkJqaiqVLlwb0ukOHDqGwsFBdOnbsGOhbh4xsYAdWIiKiUDEE+oLx48dj/PjxAb9Rx44dER0d3axt7XY77Ha7+ri0tDTg9zsb3knP7KyMEBERBV3I+owMHDgQCQkJuPLKK7Ft27ZGt83MzITValWXpKSkELVSUTvpGUfTEBERBVvQw0hCQgJefPFFvPPOO3jnnXeQlJSEMWPGYO/evQ2+Zt68ebDZbOpSUFAQ7Gb64YXyiIiIQifg0zSB6tWrF3r16qU+HjlyJI4cOYJnnnkGr732Wr2vkWUZsiwHu2kN4qRnREREoaPJ0N7hw4fj8OHDWrx1s7AyQkREFDqahJHc3FwkJCRo8dbNwj4jREREoRPwaZry8nK/qkZeXh5yc3MRGxuL5ORkzJs3D7/88gteffVVAMDixYvRrVs39OvXD9XV1VixYgU+//xzfPrpp633KVoZKyNEREShE3AY2b17N8aOHas+vuuuuwAA06dPR1ZWFgoLC5Gfn68+73A48Pe//x2//PILLBYLBgwYgM8++8xvH+catTJS44YQApIkadwiIiKiC5ckhBBaN6IppaWlsFqtsNlsiIqKCvr72aqcSH1Yqdx8/+/x6oysRERE1HzN/f7mt2w9ZJ/wwVM1REREwcUwUg9vnxGAnViJiIiCjWGkHjqdBKNe6SfCyggREVFwMYw0gBfLIyIiCg2GkQZ4O62yMkJERBRcDCMN8HZiZWWEiIgouBhGGlBbGWEHViIiomBiGGkAKyNEREShwTDSAG9lxM4+I0REREHFMNIAjqYhIiIKDYaRBvBieURERKHBMNIA9WJ5nIGViIgoqBhGGsDKCBERUWgwjDRANrLPCBERUSgwjDRAHdpbwzBCREQUTAwjDVAnPWMYISIiCiqGkQbUVkbYgZWIiCiYGEYawMoIERFRaDCMNECd9IxhhIiIKKgYRhogszJCREQUEgwjDWCfESIiotBgGGmA2meEk54REREFFcNIA9TKCCc9IyIiCiqGkQawMkJERBQaDCMNUEfTsDJCREQUVAwjDfBeKM/OyggREVFQMYw0QDZ6+4xwNA0REVEwMYw0wHuahvOMEBERBRfDSANMvGovERFRSDCMNEBmGCEiIgoJhpEG1F4oj31GiIiIgolhpAGsjBAREYUGw0gDfCc9E0Jo3BoiIqILF8NIA7yjaYQAnC6GESIiomBhGGmA9zQNwCnhiYiIgolhpAHeGVgBTnxGREQUTAwjDdDpJBj1EgBWRoiIiIKJYaQRvFgeERFR8DGMNMJ3RA0REREFB8NII9S5RlgZISIiChqGkUbUTnzGDqxERETBwjDSiNop4VkZISIiChaGkUaoHVgZRoiIiIKGYaQRJl6fhoiIKOgYRhrBPiNERETBxzDSCPYZISIiCj6GkUbIPE1DREQUdAGHka1bt2LChAlITEyEJEl47733mnxNTk4OBg8eDFmW0bNnT2RlZbWgqaFn8nRgZWWEiIgoeAIOIxUVFUhNTcXSpUubtX1eXh6uueYajB07Frm5uZg7dy5uvvlmbNy4MeDGhhorI0RERMFnCPQF48ePx/jx45u9/Ysvvohu3brhqaeeAgD06dMHX375JZ555hlkZGQE+vYhxT4jREREwRf0PiM7duxAenq637qMjAzs2LGjwdfY7XaUlpb6LVrgaBoiIqLgC3oYKSoqQlxcnN+6uLg4lJaWoqqqqt7XZGZmwmq1qktSUlKwm1kvVkaIiIiC75wcTTNv3jzYbDZ1KSgo0KQdnIGViIgo+ALuMxKo+Ph4FBcX+60rLi5GVFQUzGZzva+RZRmyLAe7aU3iaRoiIqLgC3plJC0tDZs2bfJbl52djbS0tGC/9VmTeZqGiIgo6AIOI+Xl5cjNzUVubi4AZehubm4u8vPzASinWKZNm6Zuf9ttt+HHH3/EPffcg++++w4vvPAC1qxZg7/97W+t8wmCiEN7iYiIgi/gMLJ7924MGjQIgwYNAgDcddddGDRoEB588EEAQGFhoRpMAKBbt2746KOPkJ2djdTUVDz11FNYsWLFOT+sF2AHViIiolAIuM/ImDFjIIRo8Pn6ZlcdM2YM9u3bF+hbaY4dWImIiILvnBxNc65gZYSIiCj4GEYawdE0REREwccw0ggTO7ASEREFHcNII2RetZeIiCjoGEYawcoIERFR8DGMNILzjBAREQUfw0gjakfTsAMrERFRsDCMNIKVESIiouBjGGmEb5+RxiZ6IyIiopZjGGmEdzQNADhdDCNERETBwDDSCO9pGoATnxEREQULw0gjTPraw8O5RoiIiIKDYaQROp2kBhJ2YiUiIgoOhpEm8GJ5REREwcUw0gQO7yUiIgouhpEmsDJCREQUXAwjTaitjHA0DRERUTAwjDSBlREiIqLgYhhpgnfiM/YZISIiCg6GkSaY2IGViIgoqBhGmsA+I0RERMHFMNIE9hkhIiIKLoaRJnCeESIiouBiGGkCO7ASEREFF8NIE3iahoiIKLgMWjfgXOc9TbN2dwFsVU4MSo7G4OQYdIiUNW4ZERHRhYFhpAkDOlvxxlfAjycq8OKWI+r6pFgzBifHYHByDAYlR6NPQhSMehaaiIiIAiUJIYTWjWhKaWkprFYrbDYboqKiQv7+Px4vx+6jJdiXX4K9R0/j+2NlqHvUwow6XBQXiZSOkbgoLkK5HxeBTtFmSJIU8jYTERFprbnf3wwjLWlPtRPfFNiwN78Ee/NLsC//NGxVznq3DTfp0TMuEhd1VAJKQnQYImQDIsOMiApTbiPDDLCY9AwtRER0QWEYCSG3W+CnkxX4vrgM3xeX41BxGX4oLkPeiQo4Xc07vHqd5AkpBrSPkNG9fTi6dwhH9w4R6N4hHF3bhSPMqA/yJyEiImo9DCPnAKfLjZ9OVOD74nJ8X1yGH46V4US5A2XVNSirdqq37mb8BCQJ6BRtVsJJ+3B0aWdBjMWEKLMBVrMRUWFG5dZsZGghIqJzAsPIeUIIgSqnSw0mpdU1KLJV48fj5fjxeAWOnKjAj8fLUVZd0+x9mgw6RIUZEW0xIjHajKQYM5JiLUiOtSApxoKkWDOsZiNPCxERUVA19/ubo2k0JkkSLCYDLCYD4qLC6t1GCIET5Q4loHjCyS+nq2CrcqK0qka5rXaitEqpsjhq3DhRbseJcjsOHyuvd5+RsgGdYy1IijEjNtyECNmACG8fFs/poogwg9q/JdpiRIzFBL2OAYaIiFpX266M7F4JVBwHktOAzkMBo7n19q0Bt1ugwlGjhpSSSgd+LqlEwakqFJRUouBUJQpKqnC8zN6i/UsSEGsxITbchHYRJrSLkNEu3IR24TLaRSjro81GWC1GRFuU++yYS0TUdrEy0hx7VgKFXyv3dUYgcaASTLqMBJJGAJZYTZsXKJ1O8ozOMQIxDW9X7XSpIeXnkkrYqpwos9egrLoG5Z7TReWex76nj4QATlY4cLLCgR+ONa9NRr0Eq9mEaIunT0uYAWaTHmEGPWSjHmFGHcKMymPvfbNJj1iLEnjaRyhBx2Jq27+qREQXsrZdGdn1/4CfvgTydwBlhWc+36EP0CVNCSiJg4DYHoCubU5sVuNyo6TSiZMVdpwsd+BEuR2nKhw4We7AyQo7TpQ7UFLhgK3KidNVTpyudDR7JFFzhBl1aBcuo72nIuOtwniHRkeZPbeex1bP4wjZAAMnoyMi0gQ7sAZCCKDkJyWU5O8Aju4ATv5w5namCCDuYiBhAJCQCsQPADr0Bgym1m/Tec7bMfd0pVNZqhwo9Zw+qq5xodrpQrXTjWqnC1We+3anC9U1LlQ6XCipcOCEJ/Sc7UUKTQYdImRlLpdwkwEWWe/3WKeTPO1R2lHldKHKobSl2uFCdY0bbiEQHxWGxGgzEqPDkGA1o1O0GQlWZV28NYwz8BIR1cEwcrbKj3vCyU6g4Cug+CBQU3XmdnoT0LGPEkyiOgHmGOX0jjnGfwmzAjoOuQ2UEAKVDhdOVSjBxLcSo3TarT2NVObpxFtWXYPSaieqnaG7uKEkAe0jZMgGHYx6HfQ6CQadpNzqdep9o15CuMmgnraymo2wWkzq/WjPbUSYAWajHmFGPTsNE9F5i2GktblqlGpJ4TdKP5Oib5T7dlszdyAB5mggMgGI6QrEdPPcepboZMBY/2gaahlHjRsV9hpUOGpQ6XAp9+0uz2PlfqWjBjVuAbNRr375e/utmD19WsxGPdwCKCqtxq+nqzyLcr/Qptx3uIIXfEwGHcIMOp826X363eggG3SQPX1uZINeeexz32zy+VzexaRT18kGHWpcAvYaN+w1LuXW6XO/xgW70w1JAnSSBINegk6S1MDlva/XKSPDYrwdmC3GoFWLalxulNuV4e46nQS9pw069RbsOE10DmAYCQXv6Z2ib5TKScVxoKpEWSpPAVWnlfuOsmbsTAKiEj3BpAsQnQRYOwPWJM/SqXVH+1SXKuGq5CelL0z8gDbbH+Zsud0CJyscKC6thtPlhsstUOMWqHEJ1LjPfFxWrYx4snn61tTeVyo7p6ucqHS4tP5YrSJSNiA6XBkWHm0xIcZiRIRsqK0e6b0VJKV65H0sBNT+RzbPaT7vMbJVKh2umyJJgF6S6oSV2vDiu95s0iPGM3w9xmJS2xzrCVUx4SZYTHoIAbjcAi4hIISAyw24hYBbXQcY9JL6+Yw6neexBIN6X+dZlPsmvQ66JqpfNS43qmvcyulDpwv2Gpda+VNDqKcDuGxQ9hnqMKYcD9HkJI4CQr22lxC1j4VnH8Kz3qCTWjwar8rhQkmlAyWef18SlJ+BUp3UqVVK3987vef3waDTQa+v/d04F8Otd+Rkhd0FnQTIBj1MBuU/Jk39LoUaw8i5xOVUgknlSaD0ZyUAlPwEnMoDSo4CJXmAo/75QPyEd6gNKFGJgDlWqbaYY4Awz6052nM/GpD0QOkvwInvgRM/KLcnf1Du1+2wa2kHdLsM6DEW6D5GqdSQZtxupVJRpfapcalfRN4+LVVOFxw1bk/1oraC4VvhqPb2fXG61dfU3Ye9xg2T3lthUb7QTAYdZM8Xm3cBgBq38oXjXdxCCVpuT+CqsNegpFKZ9+bc/8ty7jDoJDWgmAw6GHQ61Ljdar+qmuZM0+xDkqD+LI16HXSeYCZJEnQ6T0iTJLXa5b2vvFaC5NmHJAESap9zugQcNS7PrRtOlxuOGjccLmVp7Z+5JAERpto5j9Rbz2I06NRQf6pCuS2pdATlFK1Bp/xszD6VU4un6mgxedcZYDJIqHEJ9T8hLrfw+0+K99+OTofaqqInHOskqBU+nSSh0uFCud2pVHTtNSj3LI39Z8Wol/zCiWzQQSdJEPAEZ09oVhYlPLo9IXDljGG4uJO1VY8bw8j5RAglqHgDyumjgO1nz1IAnC4AnBWB71dnBNz1X8APABARpwSb49+dGYZie3iCyVig22+UPi9CAM4qZVt7We2tvVy5b5CB9r2A2O6AnkNx2zKXW8BW5URJpUP5gqjw3leGjdf+YXb7/MGufQzArx9NtMUEq8X/cWSY8jumVCYAl/d/5p4qhe96t/dLwKei4Q1TLp8Q5W2v7xdbSaVyv9Lhqv2i8H6ReL7EvesBeKpgbr9qmNOlrHN6ngswW/jxHQ4voIRWb+drqmXQScrvjVn5PfGvUHqqlr73m1HVOdfodZIaKFrDu7ePxODkRuaFaAGGkQuJEMrpHm84sf0MlP4KVJ+uPRVU7bmtsvn3Y9EZlGDRPsWzXKQs7Xoq1RNAqdz8vBv4cTNwZDPwyx5A+CRvSa+MJHKU+69viN4EtEsBOvZWhkd7b2O7sRMvEaD+b9nhcsNZo4QV9bHLDWeNgEEvqf18wnxOwTR0ukAIAYdLqabUVsmUypcQ3v8VK+/t+z9it+dLuO7pEkC5D5/njHodTJ5O2t7O2spjpWrQnFNOADyVF58KDHwrM8pjl1uolYDy6hqUeSoE5Xan53EN7E63Oju09zY2XLkfIRsCPrXiDbIun/DqDSwut1INqnIqfc28lcVKh8vvvtPlVk83ek/N1XZoV46VJEnqaS1vKPb+bLwB2S2gjPiTDYiUDQiXDQiX9YiUjQiXlfXe34cal1IRddT4V0YdnqDqFlBPNdVWw2qPu3ddl3aWVp/TiWGkLXO7gGob4KgAIuMBvTGw11fbgLwvlHDyYw5w8vCZ25giADnScxtRG1aOHwKclfXvVy8rVZPw9p4RR7HKraWd/305CnDZPRWXCmW/jgr/+84qJUxFJXqWTsqtHBno0SIioiBhGKHWY/tFCRje4GEMb7izq9sN2PKBY98Bx/9Xe3v8+/qHRrc2OconoCQCEfFKX5vw9p5bz2KJZZWGiCjIGEbo3OJ2K31hTv3oM9rolNJXxu9+iVKZMYYBpnDPEnHmfUOYsp/SX2uXZg+zBgBJqcJ4g4kcpfSLCYvy3Pc89t6Xo5TTTwbZ/1a9L59/o5EqTymdmk8XKCHT4qlYhbdXPu85NHqANOJ2KbNUH3hHmXMpYQDQdyLQM/28v5YXhQbDCLU99jKgtBAo84QT2y9AxTFlyHXFCc/tceVLGEH4tdcZlHCiMyqnxryLzugJLp7nJb1SlZF0tYv62HNrspw5cV7dxRTusy99/WHI7QJO53tGUx3yH1lVebKRz2L0hLX2tbcRcco8OZEJyuk/760c0brH0e0CaqqVz9dSvp3ChVsJjIYwT3gM8zz2rGOFzJ/bDfy8SwkgB9cp/4bqMoYDF12lBJOUq87uZ9XahADKipTpFooPKP8Biu0OdBqiXH/sQj+V66xWRkuWFSp/B8uKlL8niYOBuH6Bn7Y/S0ENI0uXLsXChQtRVFSE1NRUPPfccxg+fHi922ZlZeEvf/mL3zpZllFdXd3s92MYoVblqlEqMb7hxF6qVGSqSz33PY+99+2lSkdflx2ocSi3LofWn+RMOoN/QGmqndYkZV4bZyVQeQKoOBn4yC1TpCecxCuhJcxaZ4n2fyyEEhjLimr/WHr/eJYVKYtwKa+LTvYsXXzue5awKOVnd+pH4OQR4NQRn9sfm18p0xmUqlBsN2Uywrq3ltgzq0RCKOG38oTShsqTyuKoUL7svMPrvZ/fHK0En/qqTW63Er6cVcqxd1YpPw9XjXIc3C7lVrg9992164xmT3+rdspisgT2s/P9PIVf1wYQW0Htc+YYJXT0uBwo+C/w7QfKqVgvgxlISQf6TlKCSViAf6OdVf4VztJflFtnpX/49u1nZo5R7ks6ZTSgN3gUH1DuNxi0JaBDLyWYdBqs3Hbsd25e0sM7etH3b5D6N8nnb5X334/3P2JVJQ3vUy8r1a1OQ5Rw0mmIEtSCWNUNWhh56623MG3aNLz44osYMWIEFi9ejLVr1+LQoUPo2LHjGdtnZWVhzpw5OHToUO2bShLi4uJa/cMQhZQQyhe9y+ETUJzK4nZ6nvM8djk865y1XyjqF4y7zmOX8qXmnUCvvqXahmZXd/SyMnrKdzRVB8+Iqvr+R+usUv6YV5yoDSiVJ4Dy4joBoqiZE/oFicHcdD+kyETli6bGrnzh1ziU14gAhsHKUcpkhGFW/+DR2LD5+uhNSjCRI5XfB2dlbfBoLYYwTzCJrQ0ppvDa8OKu8Swu/1vvKVQvUyTQ57fAxdcp8w75/m9aCODXfcC37wPfvqdUn3w/Y2RC7alLg8nn1ue0pr28NnhUnWq9z+8l6ZQRfXH9lFB58jDwy17/kKW2WQbi+yvHTLjrLKLhdfB9DnW2qRMefQOkdx3UyT7qPPbc927fEoYw5ecQlaj8J6HyFPDrXs/fjTpkK9BpkBJMBt4AtOvRsvdsQNDCyIgRIzBs2DA8//zzAAC3242kpCTccccduO+++87YPisrC3PnzsXp06cD+wQ+GEaI6nC7lC8y3z9uvn/s3DXKOp1BmSgvWKci7GVAWXFtZcMblKptynBz9b7PAtQ51eNzP8rz2BSufFmdzvcsR33u5/v/zzcyQRm+3q6757aHchvbreF+Da4aJZy4HMpxLC/yTEKYB5z6yXObp/xPszHG8Novf+8Xv72s9rNXeW6b+6ViCFPabLQoPztvhUs9lec5Hec9pecNji0JR2e8txm4KAPo/weg55XNuzyFEEDR/tpgUt/Iu+YwWmpHxHlvTRafGa1LlNDi29/M7ZmF1xyjXMA07mIg/mIlgHToXf/PvqxY+VL+ZY8STn7Zo/yszmWSTgmwcgN92sI7eP7dJNb++zHH1F/NO/Wj/2cv+kb5d+D1l4+BLiNbtfnN/f4OaECxw+HAnj17MG/ePHWdTqdDeno6duzY0eDrysvL0aVLF7jdbgwePBiPPfYY+vXr1+D2drsddrvd78MQkQ+dvvX7arSEHKks7Xu2/r7DrMpFKOtjL1cqNZHxLeuvoDcAep/jZ+2k/M+wLmdV7SzJ9vLaDr7e4ejNOS0ihDIkveq08sVnL1OqA0aLz2JWlpaGRu97eINJZUntfWelJ9gYagOOTu9zSs+g/Ay7/Sbw/hSS5LmK+QDg8n8pX3aVpzynMz2nCGvsPqc4PeuMZiCqc+2otzBrYB2mvZ+3xq78LJr72sg4oNd4ZfHu59SPQGGu8rP27ccl6TyTntRdpwPgux5nrvftA6bTnRko/fYveV4nnblv77QJrdGZXJKUoN6uBzDgemWdywkc+7Y2nCSknv37tFBAYeTEiRNwuVxnnGKJi4vDd999V+9revXqhZdffhkDBgyAzWbDokWLMHLkSBw8eBCdO3eu9zWZmZl4+OGHA2kaEbUlckRowpjRrEza17F3y/chSbWhDUmt1rQG3yOma3Deozlt8H7ZheK91GN6lvsJVZvPRXqjEkASUoGhf2l6+yAK+ljEtLQ0TJs2DQMHDsRll12Gd999Fx06dMDy5csbfM28efNgs9nUpaCgnvN8REREdEEIqDLSvn176PV6FBcX+60vLi5GfHx8s/ZhNBoxaNAgHD7c8LlFWZYhy3IgTSMiIqLzVECVEZPJhCFDhmDTpk3qOrfbjU2bNiEtLa1Z+3C5XNi/fz8SEhICaykRERFdkAK+Is5dd92F6dOnY+jQoRg+fDgWL16MiooKdS6RadOmoVOnTsjMzAQALFiwAJdccgl69uyJ06dPY+HChTh69Chuvvnm1v0kREREdF4KOIxMmTIFx48fx4MPPoiioiIMHDgQn3zyidqpNT8/HzqfCVRKSkpwyy23oKioCDExMRgyZAi2b9+Ovn37tt6nICIiovMWp4MnIiKioGju9/d5dmUvIiIiutAwjBAREZGmGEaIiIhIUwwjREREpCmGESIiItIUwwgRERFpimGEiIiINBXwpGda8E6FUlpaqnFLiIiIqLm839tNTWl2XoSRsrIyAEBSUpAuv01ERERBU1ZWBqvV2uDz58UMrG63G7/++isiIyMhSVKr7be0tBRJSUkoKCjgzK4hwOMdWjzeocXjHVo83qHV0uMthEBZWRkSExP9LhVT13lRGdHpdOjcuXPQ9h8VFcVf5hDi8Q4tHu/Q4vEOLR7v0GrJ8W6sIuLFDqxERESkKYYRIiIi0lSbDiOyLOOhhx6CLMtaN6VN4PEOLR7v0OLxDi0e79AK9vE+LzqwEhER0YWrTVdGiIiISHsMI0RERKQphhEiIiLSFMMIERERaYphhIiIiDTVpsPI0qVL0bVrV4SFhWHEiBH473//q3WTLghbt27FhAkTkJiYCEmS8N577/k9L4TAgw8+iISEBJjNZqSnp+OHH37QprEXgMzMTAwbNgyRkZHo2LEjJk2ahEOHDvltU11djVmzZqFdu3aIiIjAddddh+LiYo1afH5btmwZBgwYoM5EmZaWho8//lh9nsc6eB5//HFIkoS5c+eq63i8W9f8+fMhSZLf0rt3b/X5YB3vNhtG3nrrLdx111146KGHsHfvXqSmpiIjIwPHjh3TumnnvYqKCqSmpmLp0qX1Pv/kk0/i2WefxYsvvoivvvoK4eHhyMjIQHV1dYhbemHYsmULZs2ahZ07dyI7OxtOpxNXXXUVKioq1G3+9re/4cMPP8TatWuxZcsW/Prrr/j973+vYavPX507d8bjjz+OPXv2YPfu3bj88ssxceJEHDx4EACPdbDs2rULy5cvx4ABA/zW83i3vn79+qGwsFBdvvzyS/W5oB1v0UYNHz5czJo1S33scrlEYmKiyMzM1LBVFx4AYt26depjt9st4uPjxcKFC9V1p0+fFrIsizfffFODFl54jh07JgCILVu2CCGU42s0GsXatWvVbf73v/8JAGLHjh1aNfOCEhMTI1asWMFjHSRlZWUiJSVFZGdni8suu0zMmTNHCMHf7WB46KGHRGpqar3PBfN4t8nKiMPhwJ49e5Cenq6u0+l0SE9Px44dOzRs2YUvLy8PRUVFfsfearVixIgRPPatxGazAQBiY2MBAHv27IHT6fQ75r1790ZycjKP+VlyuVxYvXo1KioqkJaWxmMdJLNmzcI111zjd1wB/m4Hyw8//IDExER0794dN9xwA/Lz8wEE93ifF1ftbW0nTpyAy+VCXFyc3/q4uDh89913GrWqbSgqKgKAeo+99zlqObfbjblz52LUqFG4+OKLASjH3GQyITo62m9bHvOW279/P9LS0lBdXY2IiAisW7cOffv2RW5uLo91K1u9ejX27t2LXbt2nfEcf7db34gRI5CVlYVevXqhsLAQDz/8MH7zm9/gwIEDQT3ebTKMEF2oZs2ahQMHDvid46XW16tXL+Tm5sJms+Htt9/G9OnTsWXLFq2bdcEpKCjAnDlzkJ2djbCwMK2b0yaMHz9evT9gwACMGDECXbp0wZo1a2A2m4P2vm3yNE379u2h1+vP6AFcXFyM+Ph4jVrVNniPL49965s9ezbWr1+PzZs3o3Pnzur6+Ph4OBwOnD592m97HvOWM5lM6NmzJ4YMGYLMzEykpqZiyZIlPNatbM+ePTh27BgGDx4Mg8EAg8GALVu24Nlnn4XBYEBcXByPd5BFR0fjoosuwuHDh4P6+90mw4jJZMKQIUOwadMmdZ3b7camTZuQlpamYcsufN26dUN8fLzfsS8tLcVXX33FY99CQgjMnj0b69atw+eff45u3br5PT9kyBAYjUa/Y37o0CHk5+fzmLcSt9sNu93OY93KrrjiCuzfvx+5ubnqMnToUNxwww3qfR7v4CovL8eRI0eQkJAQ3N/vs+r+eh5bvXq1kGVZZGVliW+//VbceuutIjo6WhQVFWndtPNeWVmZ2Ldvn9i3b58AIJ5++mmxb98+cfToUSGEEI8//riIjo4W77//vvjmm2/ExIkTRbdu3URVVZXGLT8//fWvfxVWq1Xk5OSIwsJCdamsrFS3ue2220RycrL4/PPPxe7du0VaWppIS0vTsNXnr/vuu09s2bJF5OXliW+++Ubcd999QpIk8emnnwoheKyDzXc0jRA83q3t73//u8jJyRF5eXli27ZtIj09XbRv314cO3ZMCBG8491mw4gQQjz33HMiOTlZmEwmMXz4cLFz506tm3RB2Lx5swBwxjJ9+nQhhDK894EHHhBxcXFClmVxxRVXiEOHDmnb6PNYfccagFi5cqW6TVVVlbj99ttFTEyMsFgs4tprrxWFhYXaNfo8dtNNN4kuXboIk8kkOnToIK644go1iAjBYx1sdcMIj3frmjJlikhISBAmk0l06tRJTJkyRRw+fFh9PljHWxJCiLOrrRARERG1XJvsM0JERETnDoYRIiIi0hTDCBEREWmKYYSIiIg0xTBCREREmmIYISIiIk0xjBAREZGmGEaIiIhIUwwjREREpCmGESIiItIUwwgRERFp6v8DX5WmyxW1LeUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Select features\n",
        "wineFeaturesSimple = wineFeatures[['alcohol', 'volatile acidity']]  # Add other features as needed\n",
        "# Define model\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSimple.shape[1],\n",
        "                             input_dim=wineFeaturesSimple.shape[1],\n",
        "                             activation='linear'))\n",
        "\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01),  # Replace with your desired learning rate\n",
        "              loss='mse')\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=50,  # Replace with your desired number of epochs\n",
        "                         batch_size=32,  # Replace with your desired batch size\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_prjsbFc71O8"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "895HsCHl7-Qk"
      },
      "source": [
        "Run the following code to add the second feature and the second layer. The training loss is about 0.59, a small decrease from the previous loss of 0.64."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "Ai06p1DEbadf",
        "outputId": "64cb12bd-dab6-447d-ecba-a9b13204b15d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.5994521975517273\n",
            "Final Validation loss: 0.5335853695869446\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Select features\n",
        "wineFeaturesSimple = wineFeatures[['alcohol', 'volatile acidity']] # add second feature\n",
        "# Define model\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSimple.shape[1],\n",
        "                             input_dim=wineFeaturesSimple.shape[1],\n",
        "                             activation='linear'))\n",
        "model.add(keras.layers.Dense(1, activation='linear')) # add second layer\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss='mse')\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=20,\n",
        "                         batch_size=100,\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvY20_w6Awz7"
      },
      "source": [
        "## Use a Nonlinear Model\n",
        "\n",
        "Let's try a nonlinear model. Modify the code below to make the following changes:\n",
        "\n",
        "1. Change the first layer to use `relu`. (Output layer stays linear since this is a regression problem.)\n",
        "1. As usual, specify the learning rate, epochs, and batch_size.\n",
        "\n",
        "Run the cell. Does the loss increase, decrease, or stay the same?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "YyH3Y_ycAv2x",
        "outputId": "a39413c1-4ba0-4682-cee7-60b3fe173bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.5843960046768188\n",
            "Final Validation loss: 0.5128785371780396\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Define\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(units=wineFeaturesSimple.shape[1],\n",
        "                             input_dim=wineFeaturesSimple.shape[1],\n",
        "                             activation='relu'))  # Replace 'relu' with your desired activation function\n",
        "model.add(keras.layers.Dense(units=1, activation='linear'))\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')\n",
        "# Fit\n",
        "model.fit(wineFeaturesSimple,\n",
        "          wineLabels,\n",
        "          epochs=50,  # Replace with your desired number of epochs\n",
        "          batch_size=32,  # Replace with your desired batch size\n",
        "          validation_split=0.2,\n",
        "          verbose=0)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDLggOvfqFbe"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sl8Ft3FrqGjl"
      },
      "source": [
        "Run the following cell to use a `relu` activation in your first hidden layer. Your loss stays about the same, perhaps declining negligibly to 0.58."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "HgsUJNaTdjDb",
        "outputId": "c03f4ba2-7b1f-45ba-9ca8-e091e6cb6438",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.5843960046768188\n",
            "Final Validation loss: 0.5128785371780396\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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169bhgw8+wOzZsxvcz4wZMzB16lQAwGOPPYZnn30W//3vfzFu3Lh6t3c6nXjxxRfRo0cPAMDs2bOxYMEC9fnnnnsO8+bNw7XXXgsAeP7557Fhw4aAPtuiRYswY8YM3H777QCAu+66Czt37sSiRYswduxY5OfnIz4+Hunp6TAajUhOTsbw4cMBKBORhoeH47e//S0iIyPRpUsXDBo0KKD3D5U2HUZYGSGitsxs1OPbBRmavXdrGTp0qN/j8vJyzJ8/Hx999BEKCwtRU1ODqqoq5OfnN7qfAQMGqPfDw8MRFRWFY8eONbi9xWJRgwgAJCQkqNvbbDYUFxerwQBQLio3ZMgQuN3N/8753//+d0ZH21GjRmHJkiUAgMmTJ2Px4sXo3r07xo0bh6uvvhoTJkyAwWDAlVdeiS5duqjPjRs3Tj0Nda5p031GTJ4w4qjhlSuJqO2RJAkWk0GTpTWviRMeHu73+O6778a6devw2GOP4YsvvkBubi769+8Ph8PR6H6MRuMZx6ex4FDf9qGe1DwpKQmHDh3CCy+8ALPZjNtvvx2jR4+G0+lEZGQk9u7dizfffBMJCQl48MEHkZqaGvThyS3RpsMIKyNERBeebdu2YcaMGbj22mvRv39/xMfH46effgppG6xWK+Li4rBr1y51ncvlwt69ewPaT58+fbBt2za/ddu2bfO7xpvZbMaECRPw7LPPIicnBzt27MD+/fsBAAaDAenp6XjyySfxzTff4KeffsLnn39+Fp8sOHiaBuwzQkR0IUlJScG7776LCRMmQJIkPPDAAwGdGmktd9xxBzIzM9GzZ0/07t0bzz33HEpKSgKqCv3jH//A9ddfj0GDBiE9PR0ffvgh3n33XXV0UFZWFlwuF0aMGAGLxYLXX38dZrMZXbp0wfr16/Hjjz9i9OjRiImJwYYNG+B2u9GrV69gfeQWa+NhRDlnycoIEdGF4+mnn8ZNN92EkSNHon379rj33ntRWloa8nbce++9KCoqwrRp06DX63HrrbciIyMDen3z+8tMmjQJS5YswaJFizBnzhx069YNK1euxJgxYwAA0dHRePzxx3HXXXfB5XKhf//++PDDD9GuXTtER0fj3Xffxfz581FdXY2UlBS8+eab6NevX5A+ccu16av2Prbhf3hp64+4dXR3/PPqPq22XyKicxGv2qstt9uNPn364Prrr8cjjzyidXNaTWtctbeNV0Y8fUac7MBKRESt6+jRo/j0009x2WWXwW634/nnn0deXh7+9Kc/ad20c06b7sBq0nv6jLh4moaIiFqXTqdDVlYWhg0bhlGjRmH//v347LPP0KcPK/F1te3KiNFbGWEYISKi1pWUlHTGSBiqHysjAOysjBAREWmmTYcR2TMDICsjRERE2mnbYcTAPiNERERaa9NhxMTRNERERJpr02GEk54RERFpr02HEROngyciItJcmw4jtRfK42kaIqIL2ZgxYzB37lz1cdeuXbF48eJGXyNJEt57772zfu/W2k9j5s+fj4EDBwb1PYKpTYcREzuwEhGd0yZMmIBx48bV+9wXX3wBSZLwzTffBLzfXbt24dZbbz3b5vlpKBAUFhZi/PjxrfpeF5o2HUZqp4NnGCEiOhfNnDkT2dnZ+Pnnn894buXKlRg6dCgGDBgQ8H47dOgAi8XSGk1sUnx8PGRZDsl7na8YRsDKCBG1UUIAjgptlmZeo/W3v/0tOnTogKysLL/15eXlWLt2LWbOnImTJ09i6tSp6NSpEywWC/r3748333yz0f3WPU3zww8/YPTo0QgLC0Pfvn2RnZ19xmvuvfdeXHTRRbBYLOjevTseeOABOJ1OAEBWVhYefvhhfP3115AkCZIkqW2ue5pm//79uPzyy2E2m9GuXTvceuutKC8vV5+fMWMGJk2ahEWLFiEhIQHt2rXDrFmz1PdqDrfbjQULFqBz586QZRkDBw7EJ598oj7vcDgwe/ZsJCQkICwsDF26dEFmZiYAQAiB+fPnIzk5GbIsIzExEXfeeWez37sl2vZ08AZOekZEbZizEngsUZv3/uevgCm8yc0MBgOmTZuGrKws3H///ZAkCQCwdu1auFwuTJ06FeXl5RgyZAjuvfdeREVF4aOPPsKNN96IHj16YPjw4U2+h9vtxu9//3vExcXhq6++gs1m8+tf4hUZGYmsrCwkJiZi//79uOWWWxAZGYl77rkHU6ZMwYEDB/DJJ5/gs88+AwBYrdYz9lFRUYGMjAykpaVh165dOHbsGG6++WbMnj3bL3Bt3rwZCQkJ2Lx5Mw4fPowpU6Zg4MCBuOWWW5r8PACwZMkSPPXUU1i+fDkGDRqEl19+Gb/73e9w8OBBpKSk4Nlnn8UHH3yANWvWIDk5GQUFBSgoKAAAvPPOO3jmmWewevVq9OvXD0VFRfj666+b9b4t1abDCPuMEBGd+2666SYsXLgQW7ZswZgxYwAop2iuu+46WK1WWK1W3H333er2d9xxBzZu3Ig1a9Y0K4x89tln+O6777Bx40YkJirh7LHHHjujn8e//vUv9X7Xrl1x9913Y/Xq1bjnnntgNpsREREBg8GA+Pj4Bt9r1apVqK6uxquvvorwcCWMPf/885gwYQKeeOIJxMXFAQBiYmLw/PPPQ6/Xo3fv3rjmmmuwadOmZoeRRYsW4d5778Uf//hHAMATTzyBzZs3Y/HixVi6dCny8/ORkpKCSy+9FJIkoUuXLupr8/PzER8fj/T0dBiNRiQnJzfrOJ6NNh1GvKdpXG6BGpcbBn2bPmtFRG2N0aJUKLR672bq3bs3Ro4ciZdffhljxozB4cOH8cUXX2DBggUAAJfLhcceewxr1qzBL7/8AofDAbvd3uw+If/73/+QlJSkBhEASEtLO2O7t956C88++yyOHDmC8vJy1NTUICoqqtmfw/teqampahABgFGjRsHtduPQoUNqGOnXrx/0er26TUJCAvbv39+s9ygtLcWvv/6KUaNG+a0fNWqUWuGYMWMGrrzySvTq1Qvjxo3Db3/7W1x11VUAgMmTJ2Px4sXo3r07xo0bh6uvvhoTJkyAwRC8yNCmv329lRGA1REiaoMkSTlVosXiOd3SXDNnzsQ777yDsrIyrFy5Ej169MBll10GAFi4cCGWLFmCe++9F5s3b0Zubi4yMjLgcDha7VDt2LEDN9xwA66++mqsX78e+/btw/3339+q7+HLaDT6PZYkCW53631PDR48GHl5eXjkkUdQVVWF66+/Hn/4wx8AKFcbPnToEF544QWYzWbcfvvtGD16dEB9VgLVtsOITyWE/UaIiM5d119/PXQ6HVatWoVXX30VN910k9p/ZNu2bZg4cSL+/Oc/IzU1Fd27d8f333/f7H336dMHBQUFKCwsVNft3LnTb5vt27ejS5cuuP/++zF06FCkpKTg6NGjftuYTCa4XI3PW9WnTx98/fXXqKioUNdt27YNOp0OvXr1anabGxMVFYXExERs27bNb/22bdvQt29fv+2mTJmC//znP3jrrbfwzjvv4NSpUwAAs9mMCRMm4Nlnn0VOTg527NjR7MpMS7Tp0zQGvQ4GnYQat2BlhIjoHBYREYEpU6Zg3rx5KC0txYwZM9TnUlJS8Pbbb2P79u2IiYnB008/jeLiYr8v3sakp6fjoosuwvTp07Fw4UKUlpbi/vvv99smJSUF+fn5WL16NYYNG4aPPvoI69at89uma9euyMvLQ25uLjp37ozIyMgzhvTecMMNeOihhzB9+nTMnz8fx48fxx133IEbb7xRPUXTGv7xj3/goYceQo8ePTBw4ECsXLkSubm5eOONNwAATz/9NBISEjBo0CDodDqsXbsW8fHxiI6ORlZWFlwuF0aMGAGLxYLXX38dZrPZr19Ja2vTlRHA92J5DCNEROeymTNnoqSkBBkZGX79O/71r39h8ODByMjIwJgxYxAfH49JkyY1e786nQ7r1q1DVVUVhg8fjptvvhmPPvqo3za/+93v8Le//Q2zZ8/GwIEDsX37djzwwAN+21x33XUYN24cxo4diw4dOtQ7vNhisWDjxo04deoUhg0bhj/84Q+44oor8Pzzzwd2MJpw55134q677sLf//539O/fH5988gk++OADpKSkAFBGBj355JMYOnQohg0bhp9++gkbNmyATqdDdHQ0/vOf/2DUqFEYMGAAPvvsM3z44Ydo165dq7bRlyREMwd7a6i0tBRWqxU2my3gzkJNGbTgU5RUOpH9t9FIiYts1X0TEZ1LqqurkZeXh27duiEsLEzr5tAForHfq+Z+f7Myol6fhpURIiIiLbT5MKJOfMYwQkREpIk2H0bUic8YRoiIiDTR5sOIerG8msaHYxEREVFwtPkwwsoIEbU158G4BTqPtMbvU5sPIzI7sBJRG+Gd1bOyslLjltCFxPv7VHfW2EC06UnPAMDk6cDKyggRXej0ej2io6Nx7NgxAMqcF1KA07ITeQkhUFlZiWPHjiE6OtrvWjqBavNhhJURImpLvFeU9QYSorMVHR3d6JWKm6PNh5HaPiPswEpEFz5JkpCQkICOHTsG9cJn1DYYjcazqoh4tfkwwsoIEbVFer2+Vb5EiFoDO7CyzwgREZGmGEZYGSEiItIUwwgnPSMiItJUQGFk2bJlGDBgAKKiohAVFYW0tDR8/PHHjb5m7dq16N27N8LCwtC/f39s2LDhrBrc2jjpGRERkbYCCiOdO3fG448/jj179mD37t24/PLLMXHiRBw8eLDe7bdv346pU6di5syZ2LdvHyZNmoRJkybhwIEDrdL41sDTNERERNqSxFnO4xobG4uFCxdi5syZZzw3ZcoUVFRUYP369eq6Sy65BAMHDsSLL77Y7PcoLS2F1WqFzWZDVFTU2TT3DC9tPYLHNnyH3w/qhKenDGzVfRMREbVlzf3+bnGfEZfLhdWrV6OiogJpaWn1brNjxw6kp6f7rcvIyMCOHTsa3bfdbkdpaanfEize0TSsjBAREWkj4DCyf/9+REREQJZl3HbbbVi3bh369u1b77ZFRUWIi4vzWxcXF4eioqJG3yMzMxNWq1VdkpKSAm1ms5l4moaIiEhTAYeRXr16ITc3F1999RX++te/Yvr06fj2229btVHz5s2DzWZTl4KCglbdvy+OpiEiItJWwDOwmkwm9OzZEwAwZMgQ7Nq1C0uWLMHy5cvP2DY+Ph7FxcV+64qLi5ucw16WZciyHGjTWoSjaYiIiLR11vOMuN1u2O32ep9LS0vDpk2b/NZlZ2c32MdEC+wzQkREpK2AKiPz5s3D+PHjkZycjLKyMqxatQo5OTnYuHEjAGDatGno1KkTMjMzAQBz5szBZZddhqeeegrXXHMNVq9ejd27d+Oll15q/U/SQqyMEBERaSugMHLs2DFMmzYNhYWFsFqtGDBgADZu3Igrr7wSAJCfnw+drrbYMnLkSKxatQr/+te/8M9//hMpKSl47733cPHFF7fupzgL7DNCRESkrbOeZyQUgjnPyL78Elz7wnYkxZrxxT2Xt+q+iYiI2rKgzzNyoVCH9jp5moaIiEgLbT6MsAMrERGRthhG2IGViIhIUwwj7MBKRESkqTYfRrx9RtwCqHGxOkJERBRqbT6MePuMAOw3QkREpIU2H0a8lRGA/UaIiIi00ObDiF4nwaCTALAyQkREpIU2H0YATglPRESkJYYRcEQNERGRlhhG4DMLKysjREREIccwAs7CSkREpCWGEbDPCBERkZYYRsA+I0RERFpiGIFvGGFlhIiIKNQYRsDTNERERFpiGAE7sBIREWmJYQSsjBAREWmJYQTswEpERKQlhhGwMkJERKQlhhGwzwgREZGWGEZQe5qGlREiIqLQYxgB+4wQERFpiWEE7DNCRESkJYYRcAZWIiIiLTGMgJURIiIiLTGMgKNpiIiItMQwAnZgJSIi0hLDCGpP07AyQkREFHoMI+BpGiIiIi0xjIAdWImIiLTEMAIO7SUiItISwwh8KyPswEpERBRqDCNgZYSIiEhLDCNgnxEiIiItMYyAo2mIiIi0xDCC2tM0rIwQERGFHsMIOAMrERGRlhhGUNtnxC2AGherI0RERKHEMILaPiMA+40QERGFGsMIaisjAPuNEBERhRrDCAC9ToJBJwFgZYSIiCjUGEY82ImViIhIGwwjHpz4jIiISBsMIx6c+IyIiEgbDCMeJl6fhoiISBMMIx7sM0JERKSNgMJIZmYmhg0bhsjISHTs2BGTJk3CoUOHGn1NVlYWJEnyW8LCws6q0cHAPiNERETaCCiMbNmyBbNmzcLOnTuRnZ0Np9OJq666ChUVFY2+LioqCoWFhepy9OjRs2p0MMg8TUNERKQJQyAbf/LJJ36Ps7Ky0LFjR+zZswejR49u8HWSJCE+Pr5lLQwRVkaIiIi0cVZ9Rmw2GwAgNja20e3Ky8vRpUsXJCUlYeLEiTh48GCj29vtdpSWlvotwcbRNERERNpocRhxu92YO3cuRo0ahYsvvrjB7Xr16oWXX34Z77//Pl5//XW43W6MHDkSP//8c4OvyczMhNVqVZekpKSWNrPZWBkhIiLSRovDyKxZs3DgwAGsXr260e3S0tIwbdo0DBw4EJdddhneffdddOjQAcuXL2/wNfPmzYPNZlOXgoKCljaz2TiahoiISBsB9Rnxmj17NtavX4+tW7eic+fOAb3WaDRi0KBBOHz4cIPbyLIMWZZb0rQW856mYWWEiIgotAKqjAghMHv2bKxbtw6ff/45unXrFvAbulwu7N+/HwkJCQG/Npg46RkREZE2AqqMzJo1C6tWrcL777+PyMhIFBUVAQCsVivMZjMAYNq0aejUqRMyMzMBAAsWLMAll1yCnj174vTp01i4cCGOHj2Km2++uZU/ytnhaRoiIiJtBBRGli1bBgAYM2aM3/qVK1dixowZAID8/HzodLUFl5KSEtxyyy0oKipCTEwMhgwZgu3bt6Nv375n1/JWJrMDKxERkSYCCiNCiCa3ycnJ8Xv8zDPP4JlnngmoUVrgpGdERETa4LVpPDi0l4iISBsMIx6c9IyIiEgbDCMerIwQERFpg2HEg6NpiIiItMEw4sF5RoiIiLTBMOLBPiNERETaYBjxYJ8RIiIibTCMeHCeESIiIm0wjHjUVkbYgZWIiCiUGEY8WBkhIiLSBsOIBzuwEhERaYNhxIMdWImIiLTBMOLBSc+IiIi0wTDiIftURppzdWIiIiJqHQwjHt4+I24B1LgZRoiIiEKFYcTD22cEYL8RIiKiUGIY8fANIxxRQ0REFDoMIx56nQSDTgLAyggREVEoMYz44IgaIiKi0GMY8cG5RoiIiEKPYcQHZ2ElIiIKPYYRHyZen4aIiCjkGEZ8sM8IERFR6DGM+JCNrIwQERGFGsOID5OeHViJiIhCjWHEBzuwEhERhR7DiA8O7SUiIgo9hhEf7MBKREQUegwjPlgZISIiCj2GER/sM0JERBR6DCM+WBkhIiIKPYYRH+wzQkREFHoMIz5kVkaIiIhCjmHEh8xr0xAREYUcw4gP9hkhIiIKPYYRHxxNQ0REFHoMIz5qL5THDqxEREShwjDigxfKIyIiCj2GER+1lRGGESIiolBhGPFh0rPPCBERUagxjPjg0F4iIqLQYxjxwaG9REREoccw4oPTwRMREYUew4gPVkaIiIhCj2HEByc9IyIiCj2GER+sjBAREYUew4gP9hkhIiIKvYDCSGZmJoYNG4bIyEh07NgRkyZNwqFDh5p83dq1a9G7d2+EhYWhf//+2LBhQ4sbHEyyT2VECKFxa4iIiNqGgMLIli1bMGvWLOzcuRPZ2dlwOp246qqrUFFR0eBrtm/fjqlTp2LmzJnYt28fJk2ahEmTJuHAgQNn3fjW5u0z4hZAjZthhIiIKBQkcRYlgOPHj6Njx47YsmULRo8eXe82U6ZMQUVFBdavX6+uu+SSSzBw4EC8+OKLzXqf0tJSWK1W2Gw2REVFtbS5Tap2utD7gU8AAAcfzkC4bAjaexEREV3omvv9fVZ9Rmw2GwAgNja2wW127NiB9PR0v3UZGRnYsWNHg6+x2+0oLS31W0LBe6E8gCNqiIiIQqXFYcTtdmPu3LkYNWoULr744ga3KyoqQlxcnN+6uLg4FBUVNfiazMxMWK1WdUlKSmppMwOi00kw6iUA7MRKREQUKi0OI7NmzcKBAwewevXq1mwPAGDevHmw2WzqUlBQ0Orv0RBvdYTDe4mIiEKjRZ0iZs+ejfXr12Pr1q3o3Llzo9vGx8ejuLjYb11xcTHi4+MbfI0sy5BluSVNO2uyUY8Kh4unaYiIiEIkoMqIEAKzZ8/GunXr8Pnnn6Nbt25NviYtLQ2bNm3yW5ednY20tLTAWhoirIwQERGFVkCVkVmzZmHVqlV4//33ERkZqfb7sFqtMJvNAIBp06ahU6dOyMzMBADMmTMHl112GZ566ilcc801WL16NXbv3o2XXnqplT9K65CNnPiMiIgolAKqjCxbtgw2mw1jxoxBQkKCurz11lvqNvn5+SgsLFQfjxw5EqtWrcJLL72E1NRUvP3223jvvfca7fSqJW9lhKdpiIiIQiOgykhzpiTJyck5Y93kyZMxefLkQN5KM7WVEYYRIiKiUOC1aepgnxEiIqLQYhipwzslPCsjREREocEwUofJwMoIERFRKDGM1OG9ci9H0xAREYUGw0gdrIwQERGFFsNIHewzQkREFFoMI3WoQ3udDCNEREShwDBShzq018U+I0RERKHAMFIHKyNEREShxTBSh6xWRhhGiIiIQoFhpA7Z6OnAysoIERFRSDCM1GFiZYSIiCikGEbqqL1QHjuwEhERhQLDSB28UB4REVFoMYzUUVsZYRghIiIKBYaROkx6zsBKREQUSgwjddReKI9hhIiIKBQYRurghfKIiIhCi2GkjtrKCEfTEBERhQLDSB2c9IyIiCi0GEbq4KRnREREocUwUkfthfJ4moaIiCgUGEbqYGWEiIgotBhG6vCd9EwIoXFriIiILnwMI3XInknPhABq3AwjREREwcYwUoe3MgJw4jMiIqJQYBipw9tnBODEZ0RERKHAMFKHTifBqJcAcOIzIiKiUGAYqYc6ooaVESIioqBjGKmHOgsrwwgREVHQMYzUg5URIiKi0GEYqUftXCPsM0JERBRsDCP1UK/cy4vlERERBR3DSD1M3jDCKeGJiIiCjmGkHrLB04GVlREiIqKgYxipBy+WR0REFDoMI/VQO7A62YGViIgo2BhG6sHKCBERUegwjNRDnfSMfUaIiIiCjmGkHqyMEBERhQ7DSD1q+4wwjBAREQUbw0g9aisj7MBKREQUbAwj9WBlhIiIKHQYRuohs88IERFRyDCM1IOjaYiIiEKHYaQe6oXyeNVeIiKioGMYqYf3Qnk8TUNERBR8DCP1UCsjPE1DREQUdAGHka1bt2LChAlITEyEJEl47733Gt0+JycHkiSdsRQVFbW0zUHHyggREVHoBBxGKioqkJqaiqVLlwb0ukOHDqGwsFBdOnbsGOhbh4xsYAdWIiKiUDEE+oLx48dj/PjxAb9Rx44dER0d3axt7XY77Ha7+ri0tDTg9zsb3knP7KyMEBERBV3I+owMHDgQCQkJuPLKK7Ft27ZGt83MzITValWXpKSkELVSUTvpGUfTEBERBVvQw0hCQgJefPFFvPPOO3jnnXeQlJSEMWPGYO/evQ2+Zt68ebDZbOpSUFAQ7Gb64YXyiIiIQifg0zSB6tWrF3r16qU+HjlyJI4cOYJnnnkGr732Wr2vkWUZsiwHu2kN4qRnREREoaPJ0N7hw4fj8OHDWrx1s7AyQkREFDqahJHc3FwkJCRo8dbNwj4jREREoRPwaZry8nK/qkZeXh5yc3MRGxuL5ORkzJs3D7/88gteffVVAMDixYvRrVs39OvXD9XV1VixYgU+//xzfPrpp633KVoZKyNEREShE3AY2b17N8aOHas+vuuuuwAA06dPR1ZWFgoLC5Gfn68+73A48Pe//x2//PILLBYLBgwYgM8++8xvH+catTJS44YQApIkadwiIiKiC5ckhBBaN6IppaWlsFqtsNlsiIqKCvr72aqcSH1Yqdx8/+/x6oysRERE1HzN/f7mt2w9ZJ/wwVM1REREwcUwUg9vnxGAnViJiIiCjWGkHjqdBKNe6SfCyggREVFwMYw0gBfLIyIiCg2GkQZ4O62yMkJERBRcDCMN8HZiZWWEiIgouBhGGlBbGWEHViIiomBiGGkAKyNEREShwTDSAG9lxM4+I0REREHFMNIAjqYhIiIKDYaRBvBieURERKHBMNIA9WJ5nIGViIgoqBhGGsDKCBERUWgwjDRANrLPCBERUSgwjDRAHdpbwzBCREQUTAwjDVAnPWMYISIiCiqGkQbUVkbYgZWIiCiYGEYawMoIERFRaDCMNECd9IxhhIiIKKgYRhogszJCREQUEgwjDWCfESIiotBgGGmA2meEk54REREFFcNIA9TKCCc9IyIiCiqGkQawMkJERBQaDCMNUEfTsDJCREQUVAwjDfBeKM/OyggREVFQMYw0QDZ6+4xwNA0REVEwMYw0wHuahvOMEBERBRfDSANMvGovERFRSDCMNEBmGCEiIgoJhpEG1F4oj31GiIiIgolhpAGsjBAREYUGw0gDfCc9E0Jo3BoiIqILF8NIA7yjaYQAnC6GESIiomBhGGmA9zQNwCnhiYiIgolhpAHeGVgBTnxGREQUTAwjDdDpJBj1EgBWRoiIiIKJYaQRvFgeERFR8DGMNMJ3RA0REREFB8NII9S5RlgZISIiChqGkUbUTnzGDqxERETBwjDSiNop4VkZISIiChaGkUaoHVgZRoiIiIKGYaQRJl6fhoiIKOgYRhrBPiNERETBxzDSCPYZISIiCj6GkUbIPE1DREQUdAGHka1bt2LChAlITEyEJEl47733mnxNTk4OBg8eDFmW0bNnT2RlZbWgqaFn8nRgZWWEiIgoeAIOIxUVFUhNTcXSpUubtX1eXh6uueYajB07Frm5uZg7dy5uvvlmbNy4MeDGhhorI0RERMFnCPQF48ePx/jx45u9/Ysvvohu3brhqaeeAgD06dMHX375JZ555hlkZGQE+vYhxT4jREREwRf0PiM7duxAenq637qMjAzs2LGjwdfY7XaUlpb6LVrgaBoiIqLgC3oYKSoqQlxcnN+6uLg4lJaWoqqqqt7XZGZmwmq1qktSUlKwm1kvVkaIiIiC75wcTTNv3jzYbDZ1KSgo0KQdnIGViIgo+ALuMxKo+Ph4FBcX+60rLi5GVFQUzGZzva+RZRmyLAe7aU3iaRoiIqLgC3plJC0tDZs2bfJbl52djbS0tGC/9VmTeZqGiIgo6AIOI+Xl5cjNzUVubi4AZehubm4u8vPzASinWKZNm6Zuf9ttt+HHH3/EPffcg++++w4vvPAC1qxZg7/97W+t8wmCiEN7iYiIgi/gMLJ7924MGjQIgwYNAgDcddddGDRoEB588EEAQGFhoRpMAKBbt2746KOPkJ2djdTUVDz11FNYsWLFOT+sF2AHViIiolAIuM/ImDFjIIRo8Pn6ZlcdM2YM9u3bF+hbaY4dWImIiILvnBxNc65gZYSIiCj4GEYawdE0REREwccw0ggTO7ASEREFHcNII2RetZeIiCjoGEYawcoIERFR8DGMNILzjBAREQUfw0gjakfTsAMrERFRsDCMNIKVESIiouBjGGmEb5+RxiZ6IyIiopZjGGmEdzQNADhdDCNERETBwDDSCO9pGoATnxEREQULw0gjTPraw8O5RoiIiIKDYaQROp2kBhJ2YiUiIgoOhpEm8GJ5REREwcUw0gQO7yUiIgouhpEmsDJCREQUXAwjTaitjHA0DRERUTAwjDSBlREiIqLgYhhpgnfiM/YZISIiCg6GkSaY2IGViIgoqBhGmsA+I0RERMHFMNIE9hkhIiIKLoaRJnCeESIiouBiGGkCO7ASEREFF8NIE3iahoiIKLgMWjfgXOc9TbN2dwFsVU4MSo7G4OQYdIiUNW4ZERHRhYFhpAkDOlvxxlfAjycq8OKWI+r6pFgzBifHYHByDAYlR6NPQhSMehaaiIiIAiUJIYTWjWhKaWkprFYrbDYboqKiQv7+Px4vx+6jJdiXX4K9R0/j+2NlqHvUwow6XBQXiZSOkbgoLkK5HxeBTtFmSJIU8jYTERFprbnf3wwjLWlPtRPfFNiwN78Ee/NLsC//NGxVznq3DTfp0TMuEhd1VAJKQnQYImQDIsOMiApTbiPDDLCY9AwtRER0QWEYCSG3W+CnkxX4vrgM3xeX41BxGX4oLkPeiQo4Xc07vHqd5AkpBrSPkNG9fTi6dwhH9w4R6N4hHF3bhSPMqA/yJyEiImo9DCPnAKfLjZ9OVOD74nJ8X1yGH46V4US5A2XVNSirdqq37mb8BCQJ6BRtVsJJ+3B0aWdBjMWEKLMBVrMRUWFG5dZsZGghIqJzAsPIeUIIgSqnSw0mpdU1KLJV48fj5fjxeAWOnKjAj8fLUVZd0+x9mgw6RIUZEW0xIjHajKQYM5JiLUiOtSApxoKkWDOsZiNPCxERUVA19/ubo2k0JkkSLCYDLCYD4qLC6t1GCIET5Q4loHjCyS+nq2CrcqK0qka5rXaitEqpsjhq3DhRbseJcjsOHyuvd5+RsgGdYy1IijEjNtyECNmACG8fFs/poogwg9q/JdpiRIzFBL2OAYaIiFpX266M7F4JVBwHktOAzkMBo7n19q0Bt1ugwlGjhpSSSgd+LqlEwakqFJRUouBUJQpKqnC8zN6i/UsSEGsxITbchHYRJrSLkNEu3IR24TLaRSjro81GWC1GRFuU++yYS0TUdrEy0hx7VgKFXyv3dUYgcaASTLqMBJJGAJZYTZsXKJ1O8ozOMQIxDW9X7XSpIeXnkkrYqpwos9egrLoG5Z7TReWex76nj4QATlY4cLLCgR+ONa9NRr0Eq9mEaIunT0uYAWaTHmEGPWSjHmFGHcKMymPvfbNJj1iLEnjaRyhBx2Jq27+qREQXsrZdGdn1/4CfvgTydwBlhWc+36EP0CVNCSiJg4DYHoCubU5sVuNyo6TSiZMVdpwsd+BEuR2nKhw4We7AyQo7TpQ7UFLhgK3KidNVTpyudDR7JFFzhBl1aBcuo72nIuOtwniHRkeZPbeex1bP4wjZAAMnoyMi0gQ7sAZCCKDkJyWU5O8Aju4ATv5w5namCCDuYiBhAJCQCsQPADr0Bgym1m/Tec7bMfd0pVNZqhwo9Zw+qq5xodrpQrXTjWqnC1We+3anC9U1LlQ6XCipcOCEJ/Sc7UUKTQYdImRlLpdwkwEWWe/3WKeTPO1R2lHldKHKobSl2uFCdY0bbiEQHxWGxGgzEqPDkGA1o1O0GQlWZV28NYwz8BIR1cEwcrbKj3vCyU6g4Cug+CBQU3XmdnoT0LGPEkyiOgHmGOX0jjnGfwmzAjoOuQ2UEAKVDhdOVSjBxLcSo3TarT2NVObpxFtWXYPSaieqnaG7uKEkAe0jZMgGHYx6HfQ6CQadpNzqdep9o15CuMmgnraymo2wWkzq/WjPbUSYAWajHmFGPTsNE9F5i2GktblqlGpJ4TdKP5Oib5T7dlszdyAB5mggMgGI6QrEdPPcepboZMBY/2gaahlHjRsV9hpUOGpQ6XAp9+0uz2PlfqWjBjVuAbNRr375e/utmD19WsxGPdwCKCqtxq+nqzyLcr/Qptx3uIIXfEwGHcIMOp826X363eggG3SQPX1uZINeeexz32zy+VzexaRT18kGHWpcAvYaN+w1LuXW6XO/xgW70w1JAnSSBINegk6S1MDlva/XKSPDYrwdmC3GoFWLalxulNuV4e46nQS9pw069RbsOE10DmAYCQXv6Z2ib5TKScVxoKpEWSpPAVWnlfuOsmbsTAKiEj3BpAsQnQRYOwPWJM/SqXVH+1SXKuGq5CelL0z8gDbbH+Zsud0CJyscKC6thtPlhsstUOMWqHEJ1LjPfFxWrYx4snn61tTeVyo7p6ucqHS4tP5YrSJSNiA6XBkWHm0xIcZiRIRsqK0e6b0VJKV65H0sBNT+RzbPaT7vMbJVKh2umyJJgF6S6oSV2vDiu95s0iPGM3w9xmJS2xzrCVUx4SZYTHoIAbjcAi4hIISAyw24hYBbXQcY9JL6+Yw6neexBIN6X+dZlPsmvQ66JqpfNS43qmvcyulDpwv2Gpda+VNDqKcDuGxQ9hnqMKYcD9HkJI4CQr22lxC1j4VnH8Kz3qCTWjwar8rhQkmlAyWef18SlJ+BUp3UqVVK3987vef3waDTQa+v/d04F8Otd+Rkhd0FnQTIBj1MBuU/Jk39LoUaw8i5xOVUgknlSaD0ZyUAlPwEnMoDSo4CJXmAo/75QPyEd6gNKFGJgDlWqbaYY4Awz6052nM/GpD0QOkvwInvgRM/KLcnf1Du1+2wa2kHdLsM6DEW6D5GqdSQZtxupVJRpfapcalfRN4+LVVOFxw1bk/1oraC4VvhqPb2fXG61dfU3Ye9xg2T3lthUb7QTAYdZM8Xm3cBgBq38oXjXdxCCVpuT+CqsNegpFKZ9+bc/8ty7jDoJDWgmAw6GHQ61Ljdar+qmuZM0+xDkqD+LI16HXSeYCZJEnQ6T0iTJLXa5b2vvFaC5NmHJAESap9zugQcNS7PrRtOlxuOGjccLmVp7Z+5JAERpto5j9Rbz2I06NRQf6pCuS2pdATlFK1Bp/xszD6VU4un6mgxedcZYDJIqHEJ9T8hLrfw+0+K99+OTofaqqInHOskqBU+nSSh0uFCud2pVHTtNSj3LI39Z8Wol/zCiWzQQSdJEPAEZ09oVhYlPLo9IXDljGG4uJO1VY8bw8j5RAglqHgDyumjgO1nz1IAnC4AnBWB71dnBNz1X8APABARpwSb49+dGYZie3iCyVig22+UPi9CAM4qZVt7We2tvVy5b5CB9r2A2O6AnkNx2zKXW8BW5URJpUP5gqjw3leGjdf+YXb7/MGufQzArx9NtMUEq8X/cWSY8jumVCYAl/d/5p4qhe96t/dLwKei4Q1TLp8Q5W2v7xdbSaVyv9Lhqv2i8H6ReL7EvesBeKpgbr9qmNOlrHN6ngswW/jxHQ4voIRWb+drqmXQScrvjVn5PfGvUHqqlr73m1HVOdfodZIaKFrDu7ePxODkRuaFaAGGkQuJEMrpHm84sf0MlP4KVJ+uPRVU7bmtsvn3Y9EZlGDRPsWzXKQs7Xoq1RNAqdz8vBv4cTNwZDPwyx5A+CRvSa+MJHKU+69viN4EtEsBOvZWhkd7b2O7sRMvEaD+b9nhcsNZo4QV9bHLDWeNgEEvqf18wnxOwTR0ukAIAYdLqabUVsmUypcQ3v8VK+/t+z9it+dLuO7pEkC5D5/njHodTJ5O2t7O2spjpWrQnFNOADyVF58KDHwrM8pjl1uolYDy6hqUeSoE5Xan53EN7E63Oju09zY2XLkfIRsCPrXiDbIun/DqDSwut1INqnIqfc28lcVKh8vvvtPlVk83ek/N1XZoV46VJEnqaS1vKPb+bLwB2S2gjPiTDYiUDQiXDQiX9YiUjQiXlfXe34cal1IRddT4V0YdnqDqFlBPNdVWw2qPu3ddl3aWVp/TiWGkLXO7gGob4KgAIuMBvTGw11fbgLwvlHDyYw5w8vCZ25giADnScxtRG1aOHwKclfXvVy8rVZPw9p4RR7HKraWd/305CnDZPRWXCmW/jgr/+84qJUxFJXqWTsqtHBno0SIioiBhGKHWY/tFCRje4GEMb7izq9sN2PKBY98Bx/9Xe3v8+/qHRrc2OconoCQCEfFKX5vw9p5bz2KJZZWGiCjIGEbo3OJ2K31hTv3oM9rolNJXxu9+iVKZMYYBpnDPEnHmfUOYsp/SX2uXZg+zBgBJqcJ4g4kcpfSLCYvy3Pc89t6Xo5TTTwbZ/1a9L59/o5EqTymdmk8XKCHT4qlYhbdXPu85NHqANOJ2KbNUH3hHmXMpYQDQdyLQM/28v5YXhQbDCLU99jKgtBAo84QT2y9AxTFlyHXFCc/tceVLGEH4tdcZlHCiMyqnxryLzugJLp7nJb1SlZF0tYv62HNrspw5cV7dxRTusy99/WHI7QJO53tGUx3yH1lVebKRz2L0hLX2tbcRcco8OZEJyuk/760c0brH0e0CaqqVz9dSvp3ChVsJjIYwT3gM8zz2rGOFzJ/bDfy8SwkgB9cp/4bqMoYDF12lBJOUq87uZ9XahADKipTpFooPKP8Biu0OdBqiXH/sQj+V66xWRkuWFSp/B8uKlL8niYOBuH6Bn7Y/S0ENI0uXLsXChQtRVFSE1NRUPPfccxg+fHi922ZlZeEvf/mL3zpZllFdXd3s92MYoVblqlEqMb7hxF6qVGSqSz33PY+99+2lSkdflx2ocSi3LofWn+RMOoN/QGmqndYkZV4bZyVQeQKoOBn4yC1TpCecxCuhJcxaZ4n2fyyEEhjLimr/WHr/eJYVKYtwKa+LTvYsXXzue5awKOVnd+pH4OQR4NQRn9sfm18p0xmUqlBsN2Uywrq3ltgzq0RCKOG38oTShsqTyuKoUL7svMPrvZ/fHK0En/qqTW63Er6cVcqxd1YpPw9XjXIc3C7lVrg9992164xmT3+rdspisgT2s/P9PIVf1wYQW0Htc+YYJXT0uBwo+C/w7QfKqVgvgxlISQf6TlKCSViAf6OdVf4VztJflFtnpX/49u1nZo5R7ks6ZTSgN3gUH1DuNxi0JaBDLyWYdBqs3Hbsd25e0sM7etH3b5D6N8nnb5X334/3P2JVJQ3vUy8r1a1OQ5Rw0mmIEtSCWNUNWhh56623MG3aNLz44osYMWIEFi9ejLVr1+LQoUPo2LHjGdtnZWVhzpw5OHToUO2bShLi4uJa/cMQhZQQyhe9y+ETUJzK4nZ6nvM8djk865y1XyjqF4y7zmOX8qXmnUCvvqXahmZXd/SyMnrKdzRVB8+Iqvr+R+usUv6YV5yoDSiVJ4Dy4joBoqiZE/oFicHcdD+kyETli6bGrnzh1ziU14gAhsHKUcpkhGFW/+DR2LD5+uhNSjCRI5XfB2dlbfBoLYYwTzCJrQ0ppvDa8OKu8Swu/1vvKVQvUyTQ57fAxdcp8w75/m9aCODXfcC37wPfvqdUn3w/Y2RC7alLg8nn1ue0pr28NnhUnWq9z+8l6ZQRfXH9lFB58jDwy17/kKW2WQbi+yvHTLjrLKLhdfB9DnW2qRMefQOkdx3UyT7qPPbc927fEoYw5ecQlaj8J6HyFPDrXs/fjTpkK9BpkBJMBt4AtOvRsvdsQNDCyIgRIzBs2DA8//zzAAC3242kpCTccccduO+++87YPisrC3PnzsXp06cD+wQ+GEaI6nC7lC8y3z9uvn/s3DXKOp1BmSgvWKci7GVAWXFtZcMblKptynBz9b7PAtQ51eNzP8rz2BSufFmdzvcsR33u5/v/zzcyQRm+3q6757aHchvbreF+Da4aJZy4HMpxLC/yTEKYB5z6yXObp/xPszHG8Novf+8Xv72s9rNXeW6b+6ViCFPabLQoPztvhUs9lec5Hec9pecNji0JR2e8txm4KAPo/weg55XNuzyFEEDR/tpgUt/Iu+YwWmpHxHlvTRafGa1LlNDi29/M7ZmF1xyjXMA07mIg/mIlgHToXf/PvqxY+VL+ZY8STn7Zo/yszmWSTgmwcgN92sI7eP7dJNb++zHH1F/NO/Wj/2cv+kb5d+D1l4+BLiNbtfnN/f4OaECxw+HAnj17MG/ePHWdTqdDeno6duzY0eDrysvL0aVLF7jdbgwePBiPPfYY+vXr1+D2drsddrvd78MQkQ+dvvX7arSEHKks7Xu2/r7DrMpFKOtjL1cqNZHxLeuvoDcAep/jZ+2k/M+wLmdV7SzJ9vLaDr7e4ejNOS0ihDIkveq08sVnL1OqA0aLz2JWlpaGRu97eINJZUntfWelJ9gYagOOTu9zSs+g/Ay7/Sbw/hSS5LmK+QDg8n8pX3aVpzynMz2nCGvsPqc4PeuMZiCqc+2otzBrYB2mvZ+3xq78LJr72sg4oNd4ZfHu59SPQGGu8rP27ccl6TyTntRdpwPgux5nrvftA6bTnRko/fYveV4nnblv77QJrdGZXJKUoN6uBzDgemWdywkc+7Y2nCSknv37tFBAYeTEiRNwuVxnnGKJi4vDd999V+9revXqhZdffhkDBgyAzWbDokWLMHLkSBw8eBCdO3eu9zWZmZl4+OGHA2kaEbUlckRowpjRrEza17F3y/chSbWhDUmt1rQG3yOma3Deozlt8H7ZheK91GN6lvsJVZvPRXqjEkASUoGhf2l6+yAK+ljEtLQ0TJs2DQMHDsRll12Gd999Fx06dMDy5csbfM28efNgs9nUpaCgnvN8REREdEEIqDLSvn176PV6FBcX+60vLi5GfHx8s/ZhNBoxaNAgHD7c8LlFWZYhy3IgTSMiIqLzVECVEZPJhCFDhmDTpk3qOrfbjU2bNiEtLa1Z+3C5XNi/fz8SEhICaykRERFdkAK+Is5dd92F6dOnY+jQoRg+fDgWL16MiooKdS6RadOmoVOnTsjMzAQALFiwAJdccgl69uyJ06dPY+HChTh69Chuvvnm1v0kREREdF4KOIxMmTIFx48fx4MPPoiioiIMHDgQn3zyidqpNT8/HzqfCVRKSkpwyy23oKioCDExMRgyZAi2b9+Ovn37tt6nICIiovMWp4MnIiKioGju9/d5dmUvIiIiutAwjBAREZGmGEaIiIhIUwwjREREpCmGESIiItIUwwgRERFpimGEiIiINBXwpGda8E6FUlpaqnFLiIiIqLm839tNTWl2XoSRsrIyAEBSUpAuv01ERERBU1ZWBqvV2uDz58UMrG63G7/++isiIyMhSVKr7be0tBRJSUkoKCjgzK4hwOMdWjzeocXjHVo83qHV0uMthEBZWRkSExP9LhVT13lRGdHpdOjcuXPQ9h8VFcVf5hDi8Q4tHu/Q4vEOLR7v0GrJ8W6sIuLFDqxERESkKYYRIiIi0lSbDiOyLOOhhx6CLMtaN6VN4PEOLR7v0OLxDi0e79AK9vE+LzqwEhER0YWrTVdGiIiISHsMI0RERKQphhEiIiLSFMMIERERaYphhIiIiDTVpsPI0qVL0bVrV4SFhWHEiBH473//q3WTLghbt27FhAkTkJiYCEmS8N577/k9L4TAgw8+iISEBJjNZqSnp+OHH37QprEXgMzMTAwbNgyRkZHo2LEjJk2ahEOHDvltU11djVmzZqFdu3aIiIjAddddh+LiYo1afH5btmwZBgwYoM5EmZaWho8//lh9nsc6eB5//HFIkoS5c+eq63i8W9f8+fMhSZLf0rt3b/X5YB3vNhtG3nrrLdx111146KGHsHfvXqSmpiIjIwPHjh3TumnnvYqKCqSmpmLp0qX1Pv/kk0/i2WefxYsvvoivvvoK4eHhyMjIQHV1dYhbemHYsmULZs2ahZ07dyI7OxtOpxNXXXUVKioq1G3+9re/4cMPP8TatWuxZcsW/Prrr/j973+vYavPX507d8bjjz+OPXv2YPfu3bj88ssxceJEHDx4EACPdbDs2rULy5cvx4ABA/zW83i3vn79+qGwsFBdvvzyS/W5oB1v0UYNHz5czJo1S33scrlEYmKiyMzM1LBVFx4AYt26depjt9st4uPjxcKFC9V1p0+fFrIsizfffFODFl54jh07JgCILVu2CCGU42s0GsXatWvVbf73v/8JAGLHjh1aNfOCEhMTI1asWMFjHSRlZWUiJSVFZGdni8suu0zMmTNHCMHf7WB46KGHRGpqar3PBfN4t8nKiMPhwJ49e5Cenq6u0+l0SE9Px44dOzRs2YUvLy8PRUVFfsfearVixIgRPPatxGazAQBiY2MBAHv27IHT6fQ75r1790ZycjKP+VlyuVxYvXo1KioqkJaWxmMdJLNmzcI111zjd1wB/m4Hyw8//IDExER0794dN9xwA/Lz8wEE93ifF1ftbW0nTpyAy+VCXFyc3/q4uDh89913GrWqbSgqKgKAeo+99zlqObfbjblz52LUqFG4+OKLASjH3GQyITo62m9bHvOW279/P9LS0lBdXY2IiAisW7cOffv2RW5uLo91K1u9ejX27t2LXbt2nfEcf7db34gRI5CVlYVevXqhsLAQDz/8MH7zm9/gwIEDQT3ebTKMEF2oZs2ahQMHDvid46XW16tXL+Tm5sJms+Htt9/G9OnTsWXLFq2bdcEpKCjAnDlzkJ2djbCwMK2b0yaMHz9evT9gwACMGDECXbp0wZo1a2A2m4P2vm3yNE379u2h1+vP6AFcXFyM+Ph4jVrVNniPL49965s9ezbWr1+PzZs3o3Pnzur6+Ph4OBwOnD592m97HvOWM5lM6NmzJ4YMGYLMzEykpqZiyZIlPNatbM+ePTh27BgGDx4Mg8EAg8GALVu24Nlnn4XBYEBcXByPd5BFR0fjoosuwuHDh4P6+90mw4jJZMKQIUOwadMmdZ3b7camTZuQlpamYcsufN26dUN8fLzfsS8tLcVXX33FY99CQgjMnj0b69atw+eff45u3br5PT9kyBAYjUa/Y37o0CHk5+fzmLcSt9sNu93OY93KrrjiCuzfvx+5ubnqMnToUNxwww3qfR7v4CovL8eRI0eQkJAQ3N/vs+r+eh5bvXq1kGVZZGVliW+//VbceuutIjo6WhQVFWndtPNeWVmZ2Ldvn9i3b58AIJ5++mmxb98+cfToUSGEEI8//riIjo4W77//vvjmm2/ExIkTRbdu3URVVZXGLT8//fWvfxVWq1Xk5OSIwsJCdamsrFS3ue2220RycrL4/PPPxe7du0VaWppIS0vTsNXnr/vuu09s2bJF5OXliW+++Ubcd999QpIk8emnnwoheKyDzXc0jRA83q3t73//u8jJyRF5eXli27ZtIj09XbRv314cO3ZMCBG8491mw4gQQjz33HMiOTlZmEwmMXz4cLFz506tm3RB2Lx5swBwxjJ9+nQhhDK894EHHhBxcXFClmVxxRVXiEOHDmnb6PNYfccagFi5cqW6TVVVlbj99ttFTEyMsFgs4tprrxWFhYXaNfo8dtNNN4kuXboIk8kkOnToIK644go1iAjBYx1sdcMIj3frmjJlikhISBAmk0l06tRJTJkyRRw+fFh9PljHWxJCiLOrrRARERG1XJvsM0JERETnDoYRIiIi0hTDCBEREWmKYYSIiIg0xTBCREREmmIYISIiIk0xjBAREZGmGEaIiIhIUwwjREREpCmGESIiItIUwwgRERFp6v8DX5WmyxW1LeUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Define\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSimple.shape[1],\n",
        "                             input_dim=wineFeaturesSimple.shape[1],\n",
        "                             activation='relu'))\n",
        "model.add(keras.layers.Dense(1, activation='linear'))\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')\n",
        "# Fit\n",
        "model.fit(wineFeaturesSimple,\n",
        "          wineLabels,\n",
        "          epochs=20,\n",
        "          batch_size=100,\n",
        "          validation_split=0.2,\n",
        "          verbose=0)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ef5AXAcjBoHb"
      },
      "source": [
        "## Optimize Your Model\n",
        "\n",
        "We have two features with one hidden layer but didn't see an improvement. At this point, it's tempting to use all your features with a high-capacity network. However, you must resist the temptation. Instead, follow the guidance in [Model Optimization](https://developers.google.com/machine-learning/testing-debugging/common/optimization) to improve model performance. For a hint and for a discussion, see the following text sections."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "y07BO1feBnT0",
        "outputId": "71fd9dea-328e-4704-b357-5bccf57052f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.7648102045059204\n",
            "Final Validation loss: 0.7018823027610779\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Choose features\n",
        "wineFeaturesSimple = wineFeatures[['alcohol', 'volatile acidity']] # add features\n",
        "\n",
        "# Define\n",
        "model = None\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSimple.shape[1],\n",
        "                             activation='relu',\n",
        "                             input_dim=wineFeaturesSimple.shape[1]))\n",
        "\n",
        "# Add more layers here\n",
        "model.add(keras.layers.Dense(1, activation='linear'))\n",
        "\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')\n",
        "\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=50,  # Replace with your desired number of epochs\n",
        "                         batch_size=32,  # Replace with your desired batch size\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syvONtOvrkBV"
      },
      "source": [
        "### Hint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cy-trUPfrk9X"
      },
      "source": [
        "You can try to reduce loss by adding features, adding layers, or playing with the hyperparameters. Before adding more features, check the correlation matrix. Don't expect your loss to decrease by much. Sadly, that is a common experience in machine learning!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nm1xp9DjCejl"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_URHTCL9QiX"
      },
      "source": [
        "Run the following code to:\n",
        "\n",
        "* Add the features chlorides and density.\n",
        "* Set training epochs to 100.\n",
        "* Set batch size to 100.\n",
        "\n",
        "Your loss reduces to about 0.56. That's a minor improvement over the previous loss of 0.58. It seems that adding more features or capacity isn't improving your model by much. Perhaps your model has a bug? In the next section, you will run a sanity check on your model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "E87kEZADeJFT",
        "outputId": "bbe0b40c-56e1-495a-bf71-4ecd7d224ca9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.5778111219406128\n",
            "Final Validation loss: 0.51249098777771\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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91rtmV4cELBUIaaKizOhkybAIIURDsXXrVsaOHcvdd99N165dCQgI4Ny5c3VaBi8vL/z9/dm9e7dpml6vZ9++fVVaTseOHdm6davFtK1bt9KpUyfTc1dXV4YNG8bHH3/Mxo0b2b59O4cOHQLAwcGByMhI3nvvPQ4ePMi5c+f466+/rmLNaodUCVUg1JhhSZYMixBCNBRt27ZlyZIlDBs2DJ1Ox8svv1ylapia8vTTTzN9+nTatGlDhw4d+OSTT7h8+XKVsksvvPACDzzwAD169CAyMpLff/+dJUuWmHo9LVy4EL1eT0REBG5ubixatAhXV1dCQkL4448/OHv2LAMHDqRx48asXLkSg8FA+/bta2uVq00Clgq0KMqwJGXmk5lXSCNn+cqEEKK+++CDD/jXv/5Fv3798PX1ZcqUKaSnp9d5OaZMmUJ8fDyjR4/G3t6exx9/nCFDhmBvX/n2O8OHD+ejjz5i5syZTJo0iZYtW7JgwQIGDx4MgLe3N++88w6TJ09Gr9fTtWtXfv/9d5o0aYK3tzdLlizhtddeIzc3l7Zt2/LDDz/QuXPnWlrj6tNpdV2ZVgvS09Px8vIiLS0NT0/PGl9++JtrSc7KZ8UzA+gcJCMYCiEartzcXKKiomjZsiUuLi7WLs41x2Aw0LFjRx544AHefPNNaxenxpT1u6rK8VvSBZXQookbyVn5RCdnS8AihBCixkRHR7NmzRoGDRpEXl4es2fPJioqioceesjaRbM50ui2EoztWM5JOxYhhBA1yM7OjoULF9K7d2/69+/PoUOHWLduHR07drR20WyOZFgqwdRTSMZiEUIIUYOCg4NL9PARpZMMSyWYAhYZi0UIIYSwCglYKsE0eJyMxSKEEEJYhQQslWBswxKXlktugd7KpRFCCCGuPRKwVEJjN0c8XFRzn5gUybIIIYQQda1aAcucOXMIDQ3FxcWFiIgIdu3aVe78qampTJgwgcDAQJydnWnXrl2ZV6N855130Ol0PPvss9UpWq3Q6XSmLMvpxEwrl0YIIYS49lQ5YFm8eDGTJ0/m1VdfZd++fXTv3p0hQ4aUebXK/Px8br75Zs6dO8cvv/zCiRMn+OKLL2jWrFmJeXfv3s28efMsroZpK8JDGgOw5dQlK5dECCGEuPZUOWD54IMPGD9+POPGjaNTp07MnTsXNzc35s+fX+r88+fPJyUlhaVLl9K/f39CQ0MZNGgQ3bt3t5gvMzOTUaNG8cUXX9C4ceNyy5CXl0d6errFrbYNbu8HwIbjl+r8SptCCCFq3+DBgy2y+6GhocyaNavc9+h0OpYuXXrVn11TyynPa6+9RlhYWK1+Rm2qUsCSn5/P3r17iYyMNC/Azo7IyEi2b99e6nuWL19O3759mTBhAv7+/nTp0oVp06ah11s2Xp0wYQK33367xbLLMn36dLy8vEy34ODgqqxGtVzXqgkujnbEp+dyPD6j1j9PCCFE5QwbNoyhQ4eW+tqWLVvQ6XQcPHiwysvdvXs3jz/++NUWz0JZQUNcXBy33nprjX5WQ1OlgCUpKQm9Xo+/v7/FdH9/f+Lj40t9z9mzZ/nll1/Q6/WsXLmSl19+mffff5+33nrLNM+PP/7Ivn37mD59eqXKMXXqVNLS0ky32NjYqqxGtbg42tO/tS8AG06UXv0lhBCi7j366KOsXbuW8+fPl3htwYIF9OrVq1pNDfz8/HBzc6uJIlYoICAAZ2fnOvms+qrWewkZDAaaNm3K559/Tnh4OCNGjOCll15i7ty5AMTGxjJp0iS+++67Sl9oy9nZGU9PT4tbXRjcoSkAG45LwCKEuEZoGuRnWedWyer3O+64Az8/PxYuXGgxPTMzk59//plHH32U5ORkRo4cSbNmzXBzc6Nr16788MMP5S73yiqhU6dOMXDgQFxcXOjUqRNr164t8Z4pU6bQrl073NzcaNWqFS+//DIFBQUALFy4kNdff51//vkHnU6HTqczlfnKKqFDhw5x44034urqSpMmTXj88cfJzDR3+hg7dizDhw9n5syZBAYG0qRJEyZMmGD6rMowGAy88cYbNG/eHGdnZ8LCwli1apXp9fz8fCZOnEhgYCAuLi6EhISYEguapvHaa6/RokULnJ2dCQoK4plnnqn0Z1dHlYbm9/X1xd7enoSEBIvpCQkJBAQElPqewMBAHB0dLS6V3bFjR+Lj401VTImJifTs2dP0ul6vZ/PmzcyePZu8vLwqXWa7Ng1up9qx7I2+TFp2AV5ujlYukRBC1LKCbJgWZJ3P/r+L4ORe4WwODg6MHj2ahQsX8tJLL6HT6QD4+eef0ev1jBw5kszMTMLDw5kyZQqenp6sWLGCRx55hNatW9OnT58KP8NgMHDPPffg7+/Pzp07SUtLK7U3q4eHBwsXLiQoKIhDhw4xfvx4PDw8+O9//8uIESM4fPgwq1atYt26dQB4eZW8oG5WVhZDhgyhb9++7N69m8TERB577DEmTpxoEZRt2LCBwMBANmzYwOnTpxkxYgRhYWGMHz++wvUB+Oijj3j//feZN28ePXr0YP78+dx5550cOXKEtm3b8vHHH7N8+XJ++uknWrRoQWxsrKlG49dff+XDDz/kxx9/pHPnzsTHx/PPP/9U6nOrq0oZFicnJ8LDw1m/fr1pmsFgYP369fTt27fU9/Tv35/Tp09jMBhM006ePElgYCBOTk7cdNNNHDp0iAMHDphuvXr1YtSoURw4cMBmghWAYB832jZthEGDzdJbSAghbMa//vUvzpw5w6ZNm0zTFixYwL333ouXlxfNmjXj+eefJywsjFatWvH0008zdOhQfvrpp0otf926dRw/fpxvvvmG7t27M3DgQKZNm1Zivv/973/069eP0NBQhg0bxvPPP2/6DFdXVxo1aoSDgwMBAQEEBATg6upaYhnff/89ubm5fPPNN3Tp0oUbb7yR2bNn8+2331okDBo3bszs2bPp0KEDd9xxB7fffrvF8bkiM2fOZMqUKTz44IO0b9+ed999l7CwMFNWKSYmhrZt2zJgwABCQkIYMGAAI0eONL0WEBBAZGQkLVq0oE+fPpUOlKqryhc/nDx5MmPGjKFXr1706dOHWbNmkZWVxbhx4wAYPXo0zZo1M6WNnnzySWbPns2kSZN4+umnOXXqFNOmTTOljjw8POjSpYvFZ7i7u9OkSZMS023BjR2acioxkw0nEhnW3UpnHUIIUVcc3VSmw1qfXUkdOnSgX79+zJ8/n8GDB3P69Gm2bNnCG2+8AajM/bRp0/jpp5+4cOEC+fn55OXlVbqNyrFjxwgODiYoyLzfL+1EffHixXz88cecOXOGzMxMCgsLq9xs4dixY3Tv3h13d3N2qX///hgMBk6cOGFqR9q5c2eLk/rAwEAOHTpUqc9IT0/n4sWL9O/f32J6//79TZmSsWPHcvPNN9O+fXuGDh3KHXfcwS233ALA/fffz6xZs2jVqhVDhw7ltttuY9iwYTg41N41lavchmXEiBHMnDmTV155hbCwMA4cOMCqVatMX2BMTAxxcXGm+YODg1m9ejW7d++mW7duPPPMM0yaNIkXX3yx5taiDg1ur9qxbDpxCYNBujcLIRo4nU5Vy1jjVlS1U1mPPvoov/76KxkZGSxYsIDWrVszaNAgAGbMmMFHH33ElClT2LBhAwcOHGDIkCHk5+fX2Fe1fft2Ro0axW233cYff/zB/v37eemll2r0M4pzdLRslqDT6SxqM65Wz549iYqK4s033yQnJ4cHHniA++67D1DH9hMnTvDpp5/i6urKU089xcCBA6vUhqaqqhUKTZw4kYkTJ5b62saNG0tM69u3Lzt27Kj08ktbhq3oFdoYD2cHkrPyOXghjbBgb2sXSQghBPDAAw8wadIkvv/+e7755huefPJJU3uWrVu3ctddd/Hwww8DqjnDyZMn6dSpU6WW3bFjR2JjY4mLiyMwMBCgxHFt27ZthISE8NJLL5mmRUdHW8zj5ORUYliP0j5r4cKFZGVlmbIsW7duxc7Ojvbt21eqvBXx9PQkKCiIrVu3moI64+cUb9Pj6enJiBEjGDFiBPfddx9Dhw4lJSUFHx8fXF1dGTZsGMOGDWPChAl06NCBQ4cOWbRJrUlyLaEqcrS34/p2Rd2bpbeQEELYjEaNGjFixAimTp1KXFwcY8eONb3Wtm1b1q5dy7Zt2zh27Bj//ve/S3QgKU9kZCTt2rVjzJgx/PPPP2zZssUiMDF+RkxMDD/++CNnzpzh448/5rfffrOYJzQ0lKioKA4cOEBSUhJ5eXklPmvUqFG4uLgwZswYDh8+zIYNG3j66ad55JFHSgwrcjVeeOEF3n33XRYvXsyJEyd48cUXOXDgAJMmTQLUQLE//PADx48f5+TJk/z8888EBATg7e3NwoUL+eqrrzh8+DBnz55l0aJFuLq6EhISUmPlu5IELNVgrBbaKOOxCCGETXn00Ue5fPkyQ4YMsWhv8r///Y+ePXsyZMgQBg8eTEBAAMOHD6/0cu3s7Pjtt9/IycmhT58+PPbYY7z99tsW89x5550899xzTJw4kbCwMLZt28bLL79sMc+9997L0KFDueGGG/Dz8yu1a7WbmxurV68mJSWF3r17c99993HTTTcxe/bsqn0ZFXjmmWeYPHky//nPf+jatSurVq1i+fLltG3bFlBtTN977z169epF7969OXfuHCtXrsTOzg5vb2+++OIL+vfvT7du3Vi3bh2///47TZo0qdEyFqfTGsA48+np6Xh5eZGWllYnY7IkZuTS523VEnv3S5H4echgP0KIhiE3N5eoqChatmxZ6bGxhKhIWb+rqhy/JcNSDU09XOjaTPWd33RSujcLIYQQtU0Clmq6wXQxRKkWEkIIIWqbBCzVdGNH1fBp/fEEUrNrp8uaEEIIIRQJWKqpe3MvOgZ6kltg4MfdtX/xRSGEEOJaJgFLNel0Osb1DwXgm23nKNTX3GA9QghhbQ2gP4awITXxe5KA5Src2T2IJu5OXEzLZc3RyvfnF0IIW2Uc6r22RmcV16bs7Gyg5Oi8VVF7g/5fA1wc7XkoogWf/HWaBVujuK1roLWLJIQQV8XBwQE3NzcuXbqEo6MjdnZyXiuqT9M0srOzSUxMxNvb+6ouaCwBy1V6+LoQPtt4ht3nLhOdnEVIk4ovhS6EELZKp9MRGBhIVFRUiWHlhagub29vAgICrmoZErBcJX9PF8KCvdkTfZmdUSkSsAgh6j0nJyfatm0r1UKiRjg6Ol5VZsVIApYa0LulD3uiL7PnXAoP9Aq2dnGEEOKq2dnZyUi3wqZI5WQN6BPqA8Duc5etXBIhhBCiYZKApQb0DGmMTgdRSVkkZuRauzhCCCFEgyMBSw3wcnWkvb8HAHskyyKEEELUOAlYakiflqpaaFdUipVLIoQQQjQ8ErDUkN6mdiwSsAghhBA1TQKWGmIMWI7FpZORW2Dl0gghhBANiwQsNSTAy4VgH1cMGuyNlnYsQgghRE2SgKUGRbRsAsD2M8lWLokQQgjRsEjAUoOub+sLwKaTl6xcEiGEEKJhkYClBg1o44tOB8fjM0hMl/FYhBBCiJoiAUsNatLImS5BXgD8fTrJyqURQgghGg4JWGqYsVpos1QLCSGEEDVGApYaNrCdH6AyLAaDZuXSCCGEEA2DBCw1rGeLxrg52ZOUmc/RuHRrF0cIIYRoECRgqWFODnb0baW6N285Je1YhBBCiJogAUstGNReVQutPhJv5ZIIIYQQDYMELLVgaJcA7HRwIDaVc0lZ1i6OEEIIUe9JwFILmnq4MKCtyrIsPXDByqURQggh6j8JWGrJ3T2CAFi6/wKaJr2FhBBCiKshAUstuaVTAK6O9pxLzuaf82nWLo4QQghRr0nAUkvcnR24pbM/oLIsQgghhKg+CVhq0fAezQD4/Z+LFOgNVi6NEEIIUX9JwFKLrm/jSxN3J5Kz8uXaQkIIIcRVkIClFjnY2zGsu7nxrRBCCCGqRwKWWmasFlpzJIGsvEIrl0YIIYSon6oVsMyZM4fQ0FBcXFyIiIhg165d5c6fmprKhAkTCAwMxNnZmXbt2rFy5UrT69OnT6d37954eHjQtGlThg8fzokTJ6pTNJvTvbkXoU3cyCnQs+aojHwrhBBCVEeVA5bFixczefJkXn31Vfbt20f37t0ZMmQIiYmJpc6fn5/PzTffzLlz5/jll184ceIEX3zxBc2aNTPNs2nTJiZMmMCOHTtYu3YtBQUF3HLLLWRl1f9RYnU6nSnLsnT/RSuXRgghhKifdFoVRzWLiIigd+/ezJ49GwCDwUBwcDBPP/00L774Yon5586dy4wZMzh+/DiOjo6V+oxLly7RtGlTNm3axMCBAyucPz09HS8vL9LS0vD09KzK6tSJc0lZDJ65ETsd7Py/SPw8nK1dJCGEEMLqqnL8rlKGJT8/n7179xIZGWlegJ0dkZGRbN++vdT3LF++nL59+zJhwgT8/f3p0qUL06ZNQ6/Xl/k5aWlqoDUfH59SX8/LyyM9Pd3iZstCfd3p3twLgwar5IKIQgghRJVVKWBJSkpCr9fj7+9vMd3f35/4+NIPxGfPnuWXX35Br9ezcuVKXn75Zd5//33eeuutUuc3GAw8++yz9O/fny5dupQ6z/Tp0/Hy8jLdgoODq7IaVnFb10AAVh6Ms3JJhBBCiPqn1nsJGQwGmjZtyueff054eDgjRozgpZdeYu7cuaXOP2HCBA4fPsyPP/5Y5jKnTp1KWlqa6RYbG1tbxa8xxoBlZ1QySZl5Vi6NEEIIUb9UKWDx9fXF3t6ehIQEi+kJCQkEBASU+p7AwEDatWuHvb29aVrHjh2Jj48nPz/fYt6JEyfyxx9/sGHDBpo3b15mOZydnfH09LS42bpgHze6FVULrZZqISGEEKJKqhSwODk5ER4ezvr1603TDAYD69evp2/fvqW+p3///pw+fRqDwTw0/cmTJwkMDMTJyQkATdOYOHEiv/32G3/99RctW7aszrrYPFO10CGpFhJCCCGqospVQpMnT+aLL77g66+/5tixYzz55JNkZWUxbtw4AEaPHs3UqVNN8z/55JOkpKQwadIkTp48yYoVK5g2bRoTJkwwzTNhwgQWLVrE999/j4eHB/Hx8cTHx5OTk1MDq2g7buuiApbtZ5I5EZ9BbkHZDY+FEEIIYeZQ1TeMGDGCS5cu8corrxAfH09YWBirVq0yNcSNiYnBzs4cBwUHB7N69Wqee+45unXrRrNmzZg0aRJTpkwxzfPZZ58BMHjwYIvPWrBgAWPHjq3GatmmFk3c6NLMk8MX0hkyazM6Hbw4tAP/HtTa2kUTQgghbFqVx2GxRbY+Dktxa48m8MYfR0hIyyNfb6C9vwern6t4rBkhhBCioanK8bvKGRZxdW7u5M/NnfxJSM8lYtp6TiVmkJOvx9XJvuI3CyGEENcoufihlTT1cMa3kTMGDY7H2/bAd0IIIYS1ScBiJTqdji7NVPrr8EUJWIQQQojySMBiRV2CvAA4ciHNyiURQgghbJsELFZkzrBIwCKEEEKURwIWK+pclGE5EZ9BfqGhgrmFEEKIa5cELFbUvLErXq6OFOg1TiZkWLs4QgghhM2SgMWKdDodnYNUtdARqRYSQgghyiQBi5V1aaaqhQ5fkJ5CQgghRFkkYLEyY4ZFGt4KIYQQZZOAxcq6mjIsaeyNTrFyaYQQQgjbJAGLlbX0deeWTv4U6DXGf7OXc0lZ1i6SEEIIYXMkYLEynU7HrAfD6Nbci5SsfMYu2EVGboG1iyWEEELYFAlYbICbkwNfjulFkJcL55Kz+XXveWsXSQghhLApErDYiKYeLvx7UGsAftgVi6ZpVi6REEIIYTskYLEhw3s0w8XRjhMJGeyLSbV2cYQQQgibIQGLDfFydeSObkEA/LArxsqlEUIIIWyHBCw2ZmSfFgD8cfAiaTnS+FYIIYQACVhsTs8W3rT39yC3wMCyAxesXRwhhBDCJkjAYmN0Oh0jegcD8Nt+CViEEEIIkIDFJt3RPRA7HeyPSSU6WQaSE0IIISRgsUFNPVzo38YXgGUHLlq5NEIIIYT1ScBio+4KawbA0gMXZEwWIYQQ1zwJWGzUkM7+ODvYcfZSFocvpFu7OEIIIYRVScBiozxcHIns6A+oLIsQQghxLZOAxYbd3UNVC/20J5a0bBmTRQghxLVLAhYbdmOHprT39yAjt5DPt5yxdnGEEEIIq5GAxYbZ2emYfEs7ABZsPUdSZp6VSySEEEJYhwQsNu6WTv50a+5Fdr6eTzdIlkUIIcS1SQIWG6fT6fjPLe0BWLQzmvOXs61cIiGEEKLuScBSDwxs68t1rXzILzTw/pqT1i6OEEIIUeckYKkHdDodL93WCVDXFzp0Ps3KJRJCCCHqlgQs9UTX5l4MDwsC4O2VR2X0WyGEENcUCVjqkeeHtMfJwY4dZ1NYdTje2sURQggh6owELPVI88ZuPDGwFQAvLzsig8kJIYS4ZkjAUs9MuLENrf3cScrM480VRwHQG6R6SAghRMMmAUs94+xgz3v3dUOng1/2nidi2jravLSSf3+7R9q1CCGEaLAkYKmHwkN8GNsvFICE9Dw0DVYfSeC7nTHWLZgQQghRSxysXQBRPS/d1pEb2jfF09WRraeTmLH6BNNWHmNgWz9aNHGzdvGEEEKIGlWtDMucOXMIDQ3FxcWFiIgIdu3aVe78qampTJgwgcDAQJydnWnXrh0rV668qmVe6xzs7RjYzo+wYG+eHNSaPi19yM7X88Iv/0jVkBBCiAanygHL4sWLmTx5Mq+++ir79u2je/fuDBkyhMTExFLnz8/P5+abb+bcuXP88ssvnDhxgi+++IJmzZpVe5nCkp2djpn3dcfV0Z6dUSlsPZ1s7SIJIYQQNUqnVfF0PCIigt69ezN79mwADAYDwcHBPP3007z44osl5p87dy4zZszg+PHjODo61sgyr5Seno6XlxdpaWl4enpWZXUalFeWHeab7dFEdvTnyzG9rF0cIYQQolxVOX5XKcOSn5/P3r17iYyMNC/Azo7IyEi2b99e6nuWL19O3759mTBhAv7+/nTp0oVp06ah1+urvcy8vDzS09MtbgJG9w0FYP3xBGJT5CKJQgghGo4qBSxJSUno9Xr8/f0tpvv7+xMfX/rIq2fPnuWXX35Br9ezcuVKXn75Zd5//33eeuutai9z+vTpeHl5mW7BwcFVWY0Gq03TRlzf1hdNg0U7oq1dHCGEEKLG1Hq3ZoPBQNOmTfn8888JDw9nxIgRvPTSS8ydO7fay5w6dSppaWmmW2xsbA2WuH4zZll+3B1LTr7euoURQgghakiVujX7+vpib29PQkKCxfSEhAQCAgJKfU9gYCCOjo7Y29ubpnXs2JH4+Hjy8/OrtUxnZ2ecnZ2rUvRrxo0dmtK8sSvnL+fww64Y/jWgpbWLJIQQQly1KmVYnJycCA8PZ/369aZpBoOB9evX07dv31Lf079/f06fPo3BYDBNO3nyJIGBgTg5OVVrmaJs9nY6nhrcBoBZ606SkpVv5RIJIYQQV6/KVUKTJ0/miy++4Ouvv+bYsWM8+eSTZGVlMW7cOABGjx7N1KlTTfM/+eSTpKSkMGnSJE6ePMmKFSuYNm0aEyZMqPQyRdWM6B1Mx0BP0nML+XDtSWsXRwghhLhqVR7pdsSIEVy6dIlXXnmF+Ph4wsLCWLVqlanRbExMDHZ25jgoODiY1atX89xzz9GtWzeaNWvGpEmTmDJlSqWXKarG3k7HK3d0YuQXO/huZzQPXxdC+wAPaxdLCCGEqLYqj8Nii2QcltI98e1eVh2J59YuAXz2cLi1iyOEEEJYqLVxWET98tzN7QBYczSBhPRcK5dGCCGEqD4JWBqw9gEe9AppjN6g8dNu6fothBCi/pKApYEbdV0LQI3LojfU+9o/IYQQ1ygJWBq4W7sE4u3myIXUHGasPsFtH20h8oNNpGZLd2chhBD1hwQsDZyLoz339WwOwNxNZzgal87pxEyWHbho5ZIJIYQQlScByzXg4etCcHW0x8XRjoiWPgAs2XfeyqUSQgghKq/K47CI+ifU152/nh+Eq6M9hQaN66at55/zaZxOzKBNUxmfRQghhO2TDMs1ItDLFW83J3wbOTO4vR8Av+67YOVSCSGEEJUjAcs16J6iNi1L91+QnkNCCCHqBQlYrkE3dWyKp4sDcWm5zFh9gstygUQhhBA2TgKWa5Czgz2jrgsBVM+hvu+s56u/o6xcKiGEEKJsErBco56/pT2zRoTROciT3AIDb/5xlFnrTtIALi0lhBCiAZKA5Rplb6djeI9m/PH0AF4Y0h6AWetO8frvR8nJ11u5dEIIIYQl6dZ8jdPpdEy4oQ0ujva8+cdRFm47x9qjCYzuG8L5yzkkZ+Xx3yEdCPV1t3ZRhRBCXMMkYBEAPDqgJc0bu/L68iNcSM1h+p/HTa/Z29nxycgeViydEEKIa50ELMJkSOcArm/ry+ebz3LofBr+Xi58vzOG1YfjuZyVT2N3J2sXUQghxDVKAhZhwc3JgWcj25meH4hJ5WhcOksPXGBc/5ZWLJkQQohrmTS6FeUa0TsYgMW7Y6UHkRBCCKuRgEWUa3hYM5wc7Dgen8GhC2nWLo4QQohrlAQsolxebo4M7RwAwGcbz1CoN1i5REIIIa5FErCICo3uq0bF/fNwPGMW7CJFhvIXQghRx3RaA2iYkJ6ejpeXF2lpaXh6elq7OA3Sn4fi+M/P/5BdNKick70dbZo24pOHetDar5GVSyeEEKI+qsrxWzIsolJu7RrIr0/2o5WfGkAuX2/gaFw6j3+zh8y8QtKyC/hk/Sm2n0m2ckmFEEI0RJJhEVViMGhk5BaSmJHLI1/tIj49l36tmxCVlEVcWi6NnB3Y+MJgfBs5W7uoQgghbJxkWEStsbPT4eXmSFt/Dz59uCeO9jq2nUkmLi0XgMy8Qj5ef8rKpRRCCNHQSMAiqq1ni8a8fXdXPJwdGBXRgvljewHw3c4Y9sVc5uWlh7n94y0cuSjdoYUQQlwdqRISV03TNHQ6HQCPLtzN+uOJFq8H+7jyx8Tr8XJztEbxhBBC2CipEhJ1yhisAEy9rQP2dup526aNaN7YldiUHJ5dvB+Dod7HxkIIIaxEriUkalSbph58OaYXl9LzGN6jGacSM7jn021sOHGJuZvP8NTgNtYuohBCiHpIMiyixt3QvikP9A7GycGOzkFevHlXFwBmrTvF2UuZV7XsmORscgv0NVFMIYQQ9YgELKLW3d+rOQPb+ZFfaGDqkkPVrhracuoSg2Zu4Jkf9tdwCYUQQtg6CVhErdPpdLw9vAuujvbsjErhsW/2cPvHW3hg3nYOna98D6LZf51G02DN0QSOx6fXYomFEELYGglYRJ0I9nHjP7e0A+Cv44kcuZjOrqgU7v50K3M2nCY9t6Dc9/8Tm8rOqBTT86+2RNVqeYUQQtgW6dYs6kyh3sCnG8+QU6CnazMvlh+4yKoj8QDY2+noEezNUze05sYO/iXeO+H7faw4GEfXZl4cupCGk70dm/47mF/3nudkQiZv3d0FTxfpNi2EEPVJVY7fErAIq9E0jZ/3nmfupjOcvZRlmv7vga3o09KHRTuiOX85h16hjVm8OxaDBquevZ7/W3KIfTGpeLo4kJ5bCMCLt3bgiUGtrbUq9V5adgEuTnY4O9hbuyjCBu08m0yorzv+ni7WLopoYCRgEfVObEo2X/0dxcJt58qcZ2A7P775Vx/+PBTHk9/tA8BOBwYNWvu5s27yIIsxYariYmoO644lMKJ3sFUO2ptOXuJSRh73hTev88+OTclm6KzNRLRqwvyxvev884Vt2x9zmbs/3Ub3YG+WTehv7eKIBqYqx28Zh0XYhGAfN167szPXtfLhhV8OYqfTMaJ3MD1bePP36STOJGbx0m0dAbilcwB3dg8it0DPf25pz11z/ubMpSz2x6bSs0Vj0zJjkrMpMBho7deo3M/OyC1g1Jc7iUrKIikjj8m3tK/Vdb1SXFoO47/eQ77eQOcgTzoG1m3QvfpIPFn5ejaeSCQjtwAPqVoTxew+p9qO/RObyvnL2TRv7GblEolrlQQswqYM7RLIoHZNsbfT4eRgZ5pWnL2djo9H9jA9v61LIEv2X+CXvefp2aIxadkFfLjuJN/uiEbTNCbc0IZnbmqLo33JNuaapjF1ySGiklSV1Nfbo3l8UGsaOZf8a2w+eYn315zg7bu70qWZFwB7o1NwtLejW3Pvaq/zZxvPkK83mD6jrgOWTScvASpTtT8mlYHt/Or084VtOxaXYXq8/lgiY/qFWq8w4ppWrV5Cc+bMITQ0FBcXFyIiIti1a1eZ8y5cuBCdTmdxc3GxrAfNzMxk4sSJNG/eHFdXVzp16sTcuXOrUzTRALg62ZuClcowVqP8/s9F5m46w+CZG1i47Rx6g4ZBg0/+Os39c7eTlm3ZE0nTNOZvPccfB+NwsNPh7+lMWk4BP+yMKfVz3vnzOP+cT+PdVccBOHMpkwfm7WDEvB0lll1Z8Wm5/Lgr1vT879NJ1VpOdeXk6y16X+05l1LO3KIuZeQWMP6bPSzaEW3Vchy9aB5CYN2xhArnz8wrJCUrvzaLJK5RVQ5YFi9ezOTJk3n11VfZt28f3bt3Z8iQISQmJpb5Hk9PT+Li4ky36GjLP+DkyZNZtWoVixYt4tixYzz77LNMnDiR5cuXV32NxDXnulZNaN7YlYzcQt758ziXswto27QRix6N4JORPfB0ceBAbCpzN58xvefg+VRGfL6DN/84CsCUoR2YfLPqdv3l32fJK7QcTffwhTSOxqkd95ZTSZyIz+DTDWfQGzRyCvSsLurtVFXzNqvsSkgTlWbfFZVSpyP57oxKJr/QYHq++9zlOvtsUb4l+y6w9mgC7685YbXrcOUW6DlTbHTqHWeTyShnCAK9QeO+z7Zxw8yNXMrIq4siimtIlQOWDz74gPHjxzNu3DhTJsTNzY358+eX+R6dTkdAQIDp5u9v2W1127ZtjBkzhsGDBxMaGsrjjz9O9+7dy83cCGFkZ6djTN9QAJo3dmXGfd34c9L1DGjry7DuQcy8vzsAi3ZEk5lXyL6Yy9zz6TZ2RaXg7GDHMze15bHrWzK8RzMCPF1ISM/j+yuyLL/sPW/x/O2Vx1h64ILp+e8HL1a53EmZ5s95a3gXAjxdyCs0mNoM1IXNJ1VGp1eIavuzP/ayRQAjrOePot/U5ewCTl/lJS2q63RiJoUGDW83R1r5uVOg10y/mdLsOJvM8fgM0nIKWHO0ekF8TTkRn3FV/6XcAn2JE5fatmhHNAPe/Yu90XLiUJoqBSz5+fns3buXyMhI8wLs7IiMjGT79u1lvi8zM5OQkBCCg4O56667OHLkiMXr/fr1Y/ny5Vy4cAFN09iwYQMnT57klltuKXV5eXl5pKenW9zEte2x61uy6tnr+es/g7m/VzAOxdqrRHb0p5WfOxm5hXyz/RxTfjlIoUHj+ra+/PX8YCbf3A6dToezgz3jB7YC4PXfj/Lh2pMYDBp5hXpTcPL0jerijZtPXkJv0OgQ4AHAtjPJJGXmkZKVz+ebzzBnw2m+3naOX/eeZ/WReGKSs0uUeen+C+QVGujW3IsBbXwZ0NYXgL9P1V210OZTqv3KuP4taezmSG6BgSMXKz/6cHEGg8aPu2IqlW2qyc6Jq4/Ec+fsvy2qLuq7+LRci2zXrijrVNUZs4odAzyJ7KhONMurFlqyzxzErz1acfVRbUnNzue+z7YxYt52TidmVPyGK2TkFnDjzI0M++RvCvS1E8DnFuh58deDzFh9nNwCPX+fSuKVZYc5fzmHt1YcrdH/SENRpYAlKSkJvV5fIkPi7+9PfHzpO6n27dszf/58li1bxqJFizAYDPTr14/z581nrJ988gmdOnWiefPmODk5MXToUObMmcPAgQNLXeb06dPx8vIy3YKDg6uyGqIB0ul0dAjwLLXti52djsevV4HIjNUnOJWYiW8jJz5+sAfNvF0t5h3TN4SxRY0KP1p/ige/2MH7a06Sml1AgKcLz0a2o1tzL9P8r93ZmW7NvdAbNH7de56Hv9zJtJXHmbH6BK8uP8J/fv6Hf3+7lyGzNhOdnGXxWb8W7dwf6BWMTqfj+qKAZctVBCyapnH4QhofrD3JY1/v4fCFsoOPC6k5nE7MxE4HA9r4Eh7iA8CeYgfKlKx83vzjaIXBQF6hnmd+3M+LSw7xxKK9/BObCsDlrHy+3X6OE/HqoLHxRCI3f7CJYbP/rna7n+IMBo23Vxzj4Pk0/rf0kM3u5Av0hnKrUq604lCcxfO6zLoVZ9zunYI8ualDUwDWH0sgPi23xLzZ+YWsOmwu97bTyWTmFdZKuXIL9OTkl539mP93FBl5hRg0WLw7tsz5yrLyUBwX03I5mZDJuloKvF7//Sg/7o5lzoYz3P3pNib+sA9jzd/+mFQ2X7EfeG35EW75cJOpg0BtSMnK50BsaoUjj1tLrQ/N37dvX0aPHk1YWBiDBg1iyZIl+Pn5MW/ePNM8n3zyCTt27GD58uXs3buX999/nwkTJrBu3bpSlzl16lTS0tJMt9jYqv8gxbVleI9m+DZyxng8e/3OLjR2dyoxn4O9Ha/d2ZmZ93fHycGOXVEpfL75LAD3hjfD3k5nGqDuulY+RLT0YVi3IADeWXWco3HpNHF34oFezbmtawDXt/WlmbcrOQV6/rf0sOmAevRiOsfi0nGyt+OObqoXVP82KmA5GpdOUmbF9f8XUnN44tu9vPTbIf44eJFPN57mxvc3cccnf/Px+lOsO5bAv7/dS1pO6Tuf9UVnymHB3ni5OdI7VFULGQ+Omqbx31/+4au/o3jyu71ltq3Jyitk7Pzd/HEwruh98PKyw2TkFvDwVzt5edkRhszazKAZGxi7YDenEjM5fCGdyT8dqLBthsGgkZBe8uBotOV0EjEpKnu1LyaVNUcTyMor5JVlh3nkq53856d/WLQjGn0NtQEp1Bs4fCGNwiqcdWuaxr8W7ua6aevLDSCLW1FUHXRzJ3VyuNtKGZZjxgxLoCfhIY1p4eNGem4h93y6lZMJlpmLNUcSyMrXE+zjSmgTN/L1BjYX9UCrSZez8rn5w00MmrGBC6k5JV5Pzy1gQbHxnJbsu1BqNWeB3sBH604x5ZeDZOdbBlbFM0WLdkaXeN+iHdGsOBhXpSC0uD8OXuSHXTHodODl6sixuHRSswvo1tyLR64LAeDDtSdN+4sNxxNZuO0cJxMyeXTh7hoJ9q+UW6Bn+JytDJ+zlW6vreGm9zeatr+tqFLA4uvri729PQkJlhFnQkICAQEBlVqGo6MjPXr04PTp0wDk5OTwf//3f3zwwQcMGzaMbt26MXHiREaMGMHMmTNLXYazszOenp4WNyHK4+Joz+MDWwJwa5cAbuta/u/1vvDmrJ88iH8PbIWXqyMeLg482LsFALd1DWTJU/2Y90gvdDodtxcFHJoGTg52fD66F+/d151PR4Xz7aMRfPdYBM4Odmw5lWSqWlqyT2UYb+rYFG83FTj5NnKmU1GX5ukrj5dbf56cmccjX+5k1ZF4vtsZw8Tv9/PeqhNEJWXh6mjPkM7+NG/syoXUHKYuOVgi85CZV8jsv9R/8PaigKtXaFGGJVq1Y1n+z0XWHVON6aOT1cB+VzIYNJ5bfIDtZ5Nxd7Jn1ogwPJwdOHg+jds+3sKRi+m4O9njYKcjOjkbezsdI3oF4+Rgx/rjiXy68XSZ63g5K58Rn28nYtp6nly0l/OXS1arGXvQNHZTY8e8u+o4D325k2+2R7PlVBK/7jvP/5Ye5tMNZX/O0YvpfPV3lCkwOnoxnRd+/odvt58zfW+J6bnMWH2cfu/8xR2f/M2UXw+Z3p+YkWvKIJVm97nLbDmVRFa+nsk/HaiwXcSF1Bz2xaSi08FLt3XE3k7HxbRczl/OJjo5i293RJebXSiPwaBZBJ4Gg8bGE4nEpZU88GuaZqoS6hToiYO9Hd89FkErP3cupuVy32fbOHg+1TT/kv3qt31Pj+amQKsq1UJlBYGnEzP5v98O8U9sKpqm8b9lh4lNySExI4+nFu0t8X1+vfUcGbmFtG3aCD8PZ5Kz8vnreAJpOQXM3XSG5f9c5FRCBqO/2sWH606yeE8sH607ZXp/bEo2O6NS0OlAp4Otp5M5W9SGSNM0Xvz1EP9bepgJ3++j55trefbH/VVqKB+bks3Uot/PU4Nbs/rZgdzYoSkdAz2Z+3A4T9/UBhdHOw7EprL2aALZ+YX8b+lhQA2UeTYpi6e+31tuVdXF1By+3X6u1ICuLPO3RhGTov6jAGcuZfHirwet1uC7NFUah8XJyYnw8HDWr1/P8OHDATAYDKxfv56JEydWahl6vZ5Dhw5x2223AVBQUEBBQQF2dpaxk729PQaDNP4TNeexAa3o1tybni0aV2pE3GAfN6be1pHJt7SjUK/hXmxsluID1AV5uzK4vR+bT15i5v3dCQ9pbLGcUF93nrmpLTNWn+DNP47h6ujA0gPqDPrenpYj2/57UCueXXyAX/ed5/SlTPq1bkJ8Wi6+jZwY1K4p7fwbkZCex0tLD3E2KYtm3q7c3MmfXVEpeLg4cG94c27vGoi7s+oZdd9n21h5KJ7315zk34NamQaFm7PhNIkZeYQ2cePh61Qg1rWZF+5O9qRk5XPrR5u5XHQW1yfUh13nUpj912m6Nfdi9ZF4UrMLuLdnc/bHXGbN0QSc7O345tE+hIf4cDk7n9d/P0psSg7ODnZ8+1gEIT5ubD2TTMcAD9r6exAe0pj//nqQ99eexN/Thft7WVbrnk7MZPw3e0zp7z8Px/PX8URCm7jj4mTPda18uD+8uSlL9NXY3vxr4W7TJR683RyZfHM7zl7KYuG2c8xaf4p+bXxpH+DBqsPxdG3mRfsADw7EpjLqix1k5euZvvIY3YO9TQ0ef957nu1nk+kd6sP7a05aVG/8uu889/dqTjNvV4bN/pvU7AI+GdmDYd2DSvyOPi/WO+1kQiYfrTvFf4d2KPN3t3BrlOl7D/V1p0uQJ/+cT2Pt0QTmbTpLfHouqw/H8+WYXrg42pOcmYe7swMujuWP0HzofBrP/XSAuNQc3ruvO0M6+zPl10P8uu88ro72PBvZlociWpCSlY+dTodOBxm5hTja62jTVA2+GOzjxq9P9OOxb/awN/oyYxfs5pcn+nIyIZO/i9pD3dOzGQnpeXyxJYq/jiey9XQSB2JT6RzkyfVt/UwHxOLWHIln8k//ENHSh/cf6G4K4rPyCnns692cS87mlz3nGd4jiBUH47C30+HuZM8/59N4/fejTLu7K6ACy6+Kvr+JN7bhWFwGczed4YstUcxcc5LTiZaNl50d7MgrNPDl31HcGRZE5yAvlhYFXv1aN8HFwZ71xxP5bmcML9/RiZlrTvDrvvPY2+lo4eNGVFIWSw9cJD23kLkPh+PkYIemaWw+lcSCrVF0a+7NpJvaWqzzWyuOkpFXSM8W3jwb2Q5He7sSI0w/HBHCl39H8e9Fe2nt14gLqTk083bl45FhPPLVLraeTubhL3fy4Ygw7O10fL8zhkuZeQQ3diP2cjY/74mlQK8xbeVxJt7YhkHt/LiUkUezxq608/co8f0nZ+bx6Qb1O515fzf6tGzCkA8388/5NH7bf4F7rTACd2mqPDT/4sWLGTNmDPPmzaNPnz7MmjWLn376iePHj+Pv78/o0aNp1qwZ06dPB+CNN97guuuuo02bNqSmpjJjxgyWLl3K3r176dSpEwCDBw8mKSmJ2bNnExISwqZNm3jyySf54IMPePLJJysskwzNL6wtJ1/P5ex8gq5oE2NUoDcw7JO/OV7sTLyJuxM7/u+mEgPabTyRyKQfD5RZlWPk4+7Ez0/0LXck33mbzjD9TzVujLuTPbd3C6Rrc2/e/P0o+XoDX47uRWQnc5u09ccSmPLrQZIy1TgaHQI8WD5xAA99sYM95fRcmHFfN1PQUag3cM9n2zh8IY2PR/bgjm4lD+IALy89zLdFGZLnItsR0sSNnVHJ7DibYgpUmnm78vIdnVi4LYodZy2rRRzsdBQaNCJa+rD4332Z/3cUb/xxlEAvF759tA9tmnqgaRrPLj7AsgMX8W3kTF6hnozcQux0cE/P5qw9qs68m7g7kVxs7JDr2/qy42wyBXrz7rF7cy/+Pag1m09e4sfdsbT398DeTmfKQjg52PHD+OssAtbTiRlEfrAZnQ7+O6QD7646jp1OXftqRK8WeLk5Uqg3YKfTYWenY9mBC0z68QAAcx/uydAugby94ihfbIkyrW/xMro62rP2WALBjd34akwv2pZyMNI0jc82neGDNSct3t+lmSeHL5Sd8m/p605UUhYdAz35c9L1Fq9l5hXy4OfbOXwhHQ9nBzKKgrnIjv58OaYXeoNGr7fWmoJeowBPFyI7NaVzkBfdmnvRMcCTnVEpjFmwy1Rt07yxKx+P7EGPYG/++8tBft57vsS6PxvZlrBgb8Yt3I2mwR3dAhnbL5RJPx7gQmoObZo2YvWzA4lJyeaGmRtN72vq4YyfhzNH49Jp7deIT0f15KN1p1hxKI5uzb34cnQvRny+g6ikLN6/vzs+7k6MW7gbNyd7Ar1cOFMUEL97b1dG9G7B1tNJPPr1bnILDFzf1pfWfo3453wq+2NSTZ95cyd/PnowDDcnB7adSeKhL3Zib6dj1aTrS91eoBr9Tvn1ICsPmduGfjWmFzd19GfD8UQmfL+P7Hw9jZwdyCvUW/xOjQK9XIgrpa3RhBta81xkOw5dSGPN0QSauDuxPzbVdHHZZRP6Y2en47ONZ3h31XGaejgzf2xvDl1IIyUrnwk3tCm1zNVV69cSmj17NjNmzCA+Pp6wsDA+/vhjIiIiAExdkxcuXAjAc889x5IlS4iPj6dx48aEh4fz1ltv0aOHeaTS+Ph4pk6dypo1a0hJSSEkJITHH3+c5557rlJnwhKwiPrgUkYeX245y5L9F7iUkcfTN7bhP2VcBiAmOZsv/z6LnU6Hv6cLUUmZbDp5icSMPHwbOdPS151X7uhkGnG3LJqmsXh3LF/+HVXi7PL6tr58868+Jf5jaTkFfLj2JLvPpTDjvu50CvLk8IU07vl0G4UGA0M6BxDg5cIve86TkVfIowNa8vIdnSyWkZlXyOWsfIJ9yh7GXdM03l11grmbzpR4zU4H/Vr78sED3Wnq6WKqnricVUBiRi6z1p0ytV0xZjY0TWPbmWQ6BnriU6x9UnpuAbd/vIXYFJUeb+rhTGKxMUJ6tvDm20cjOHspix1nk+nfxpdOQZ7sjb7MxO/3kZlbyAtD2zMqIgR7Ox2Xs/K58f2NpoNxE3cnOjfzYvPJS/i4O/HirR0Y2iUAZwc7/m/JYX7dd55bOvnz+eheTF58wFR14uRgh5uTPanZBXi6OBDRqgmbTl4iv9DAE4Na8+KtKguz5kg8j3+7FwBHex1Tb+3Iu6uOk3dFuwwPZwem3tYRezvI12vc2KEpTdydeOGXg/z+j8ro3dY1gKYeLqZrdjna6/hkZA8ycguZ/udxUrLycXG0o0Cvmdr93NOzGR88EFZiGyVl5nHfZ9s4l5yNTgf/HtiayTe3MzV8f+uPo3z5dxS+jZzo2aIxu86lkHpFAOPbyImcfD1Z+Xqub+tLdHK2abu28HEjJkUt+/vHrmNnVDKz1p2iV0hjfnj8Ohzt7fjq7yjeXnGU4rUWoU3c+PpffQhp4g7AiHnb2RmVQsdAT+aP7UWglyu5BXqcHezQ6XQkpudy0webyMg1Z9BcHe3Z879IXBztufH9jUQX9fLT6eD5W9pbHLQ3nbzEY1/vtgganBzsuL1rICsOxZFfaKBDgAf/d1tH3vlTtXN75LoQ3hzepcR3eqUT8Rks3BZFsI8bTw02f2ZUUhaTftzPwfOqTVSfUB/6tPThQmoOBk1jVEQIvUMbs+zARWatO0l2vh5vN0dOJqh9wJUButEP46+jb+smgGpIf/MHm03bA8DF0Y5Drw0pddTw6pKLHwphwwr1Bs4lZ9HSt1Gp6fGyaJo6iDhUY2ehaRrbzySz6eQldkalkJFbwBeje9GqgussFReTnI2Tgx0BXmqk6sy8Qk4nZtK9uVe1LzoJ8PW2c8xcfYKWfu5c16oJ17XyoVeoD57lXNMot0DPl1vOkpFXyAu3tK/wOzmZkMH8v6O4sUNTIjv6q2Bs9QlcHO2ZM6onXq6lf1Z+oQGDppWobvlhVwxTlxzC3k7Hokcj6B7sxYh5OzhU1Kj2yozAL0/0pVeoDwV6A4t3x7JoR7RFtq24mzv5M+/hcOyKfhuXs/Lp8eZawHxV8i2nLvHWH8cID23MvT2b8e6fJ9h1RU8inU5lNOLScnGw0/Hm8C482Fv1SPtxVww/7YllUmQ7BhVdiqFQbyC7QI+HswNxabl8vf0cu6JSeHVYZ8KCvUst6/nL2SzYeo4hnQPo09LH4rVCvYH49FyCvFyxs9ORV6hnw/FL7I+5zJGL6eyPuUxWUVuciJY+fP2vPuQVGHhl+WH+PBRvulxF8cA+MSOXxm5OFgfMg+dT+e8vBzken0H35l58NbY3vo2cTa/HpmTz1/FE7g1vXuolNwDWHU3g1eVHiEvLwaDBuP6hvDqsMwBnL2Wy42wKwT6qOqW0K1bvikph2YELeLo64u/hzNAugQR4ubA3OoXx3+y1GPnXw8WBTS/cYBFUV0eB3sAfBy/SyrcR3cvYPlf64+BFXvz1EJl5hTg52DGkcwB6g4EziVlc39aX/11x4vHX8QQe+3oPTg52hAV70yvEx6JquSZIwCKEELXIYNCYvzWK0Cbupiq19NwCvt0ezdL9FzhVlM1ydbRnWPdA3ruvu8X7NU3jdGImGqpqLzYlm79PJZGRV8ikm9patJcCWLA1iqTMPCbf3L7UIDevUM/7a06yKyoFH3cnMnMLTQGMp4sDcx8Op19RLzRbkV9oYF/MZc5cymR4WDOLdU7PLWD9sQTScwoZFdGiwoA0v9DAgdhUujX3qrAtT3n0Bo2M3AK8XB2vKggv7lJGHp9uPM13O2LI1xv43+0deaxomAVriE3JZvvZZCI7+lcqaLqclY+7s0OVLpdSFRKwCCGElWiaxvnLObg52ePj7lRjB76qik7O4q/jidzYoampekRYT3xaLqcTM+nfponVfhO2SAIWIYQQQti8qhy/a33gOCGEEEKIqyUBixBCCCFsngQsQgghhLB5ErAIIYQQwuZJwCKEEEIImycBixBCCCFsngQsQgghhLB5ErAIIYQQwuZJwFIegx6STkHUZmuXRAghhLimlX4lKKHkpsHsXurxSwngWPKiV0IIIYSofZJhKY9rY3AsugZH+gXrlkUIIYS4hknAUh6dDryaq8dp561bFiGEEOIaJgFLRSRgEUIIIaxOApaKSMAihBBCWJ0ELBXxClb3abHWLYcQQghxDZOApSKSYRFCCCGsTgKWikjAIoQQQlidBCwVKR6waJp1yyKEEEJcoyRgqYhnEKCDwhzITrZ2aYQQQohrkgQsFXFwhkb+6rE0vBVCCCGsQgKWypB2LEIIIYRVScBSGRKwCCGEEFYlAUtlSMAihBBCWJUELJUhg8cJIYQQViUBS2VIhkUIIYSwKglYKkMCFiGEEMKqJGCpDGOVUGYCFOZZtyxCCCHENUgClspw8wEHV/U4/YJ1yyKEEEJcgyRgqQydzlwtlCoNb4UQQoi6JgFLZfm0UvcJR6xbDiGEEOIaJAFLZbWIUPcx26xbDiGEEOIaJAFLZYX0V/fR2+WqzUIIIUQdk4ClsoJ6gL0zZCdB0ilrl0YIIYS4pkjAUlkOztC8l3os1UJCCCFEnZKApSpC+qn7aAlYhBBCiLokAUtVtOir7qO3W7ccQgghxDWmWgHLnDlzCA0NxcXFhYiICHbt2lXmvAsXLkSn01ncXFxcSsx37Ngx7rzzTry8vHB3d6d3797ExMRUp3i1J7gP6OwgLUbGYxFCCCHqUJUDlsWLFzN58mReffVV9u3bR/fu3RkyZAiJiYllvsfT05O4uDjTLTo62uL1M2fOMGDAADp06MDGjRs5ePAgL7/8cqmBjVU5e0Bgd/U4RrIsQgghRF3RaVrV+uhGRETQu3dvZs+eDYDBYCA4OJinn36aF198scT8Cxcu5NlnnyU1NbXMZT744IM4Ojry7bffVq30RdLT0/Hy8iItLQ1PT89qLaPSVr8E22dD6xvh4SVqFFwhhBBCVFlVjt9VyrDk5+ezd+9eIiMjzQuwsyMyMpLt28vOOGRmZhISEkJwcDB33XUXR46YR4s1GAysWLGCdu3aMWTIEJo2bUpERARLly4tc3l5eXmkp6db3OpM+Fiwd4Izf8GhX+ruc4UQQohrWJUClqSkJPR6Pf7+/hbT/f39iY+PL/U97du3Z/78+SxbtoxFixZhMBjo168f58+fByAxMZHMzEzeeecdhg4dypo1a7j77ru555572LRpU6nLnD59Ol5eXqZbcHBwVVbj6vi2hYH/VY9XvQjZKXX32UIIIcQ1qtZ7CfXt25fRo0cTFhbGoEGDWLJkCX5+fsybNw9QGRaAu+66i+eee46wsDBefPFF7rjjDubOnVvqMqdOnUpaWprpFhtbxw1g+0+Cpp3UIHJrX6nbzxZCCCGuQVUKWHx9fbG3tychIcFiekJCAgEBAZVahqOjIz169OD06dOmZTo4ONCpUyeL+Tp27FhmLyFnZ2c8PT0tbnXKwQlufU89PrIUioIuIYQQQtSOKgUsTk5OhIeHs379etM0g8HA+vXr6du3b6WWodfrOXToEIGBgaZl9u7dmxMnTljMd/LkSUJCQqpSvLrVoi84uEJ+BiTLUP1CCCFEbXKo6hsmT57MmDFj6NWrF3369GHWrFlkZWUxbtw4AEaPHk2zZs2YPn06AG+88QbXXXcdbdq0ITU1lRkzZhAdHc1jjz1mWuYLL7zAiBEjGDhwIDfccAOrVq3i999/Z+PGjTWzlrXB3gGCwlT35gv7wK+9tUskhBBCNFhVDlhGjBjBpUuXeOWVV4iPjycsLIxVq1aZGuLGxMRgZ2dO3Fy+fJnx48cTHx9P48aNCQ8PZ9u2bRZVQHfffTdz585l+vTpPPPMM7Rv355ff/2VAQMG1MAq1qKgnipgubgPwkZauzRCCCFEg1XlcVhsUZ2Ow1LcoV/g10ehWTiM/6vuPlcIIYRoAGptHBZxhWY91X38ISjMt25ZhBBCiAZMApar0bgluDYGfT4kHql4fiGEEEJUiwQsV0OnU+1YAC7stW5ZhBBCiAZMAparZawWurDfuuUQQgghGjAJWK5Ws3B1f3GfdcshhBBCNGASsFwtY5VQ4jHIy7BuWYQQQogGSgKWq+XhDz6tAA1OrlbT8jJg47uQfMaqRRNCCCEaCglYakLnu9X9kd/U/YZpsHEa/PWW9cokhBBCNCASsNSEzveo+1NrIf0i7F+kniedtF6ZhBBCiAZEApaa4N8ZfNuBPg9+Hgt56Wp6ylmo/wMJCyGEEFYnAUtN0Omgy73qcexO8/SCbMhMsE6ZhBBCiAZEApaaYqwWAnD2BI9A9Vga3gohhBBXTQKWmuLXDvy7qMc9HoGmHdXjlLPWK5MQQgjRQEjAUpNufx/Cx8LA54u6OiMBixBCCFEDHKxdgAalxXXqBuDTWt1LwCKEEEJcNcmw1BZThkXasAghhBBXSwKW2mIKWKKka7MQQghxlSRgqS2NQ0BnB/mZkHXJ2qURQggh6jUJWGqLgzN4NVePpWuzEEIIcVUkYKlN0lNICCGEqBESsNQmCViEEEKIGiEBS22qasCiaVCYX3vlEUIIIeopCVhqU1W7Ni99Cma0gfS42iuTEEIIUQ9JwFKbfNur+8RjkJlY8fwnV0FeGlzcX7vlEkIIIeoZCVhqU5PW0KwX6PNh57zy581JhZwU9TirEsGNEEIIcQ2RgKU26XTQ/xn1ePeXkJdZ9ryXo8yPM2XcFiGEEKI4CVhqW4c7VFuW3FTY/23Z86UUC1gkwyKEEEJYkIClttnZQ9+J6vH2T0FfWPp8FhkWCViEEEKI4iRgqQthD4GrD6TFQOyO0uexyLBIlZAQQghRnAQsdcHRFVoNUo+jt5c+z+Vz5scSsAghhBAWJGCpKyH91X3MttJfT6lmlZBBD2tfhZOrq182IYQQwsZJwFJXQvqp+9hdJduxFOZB+gXz89zUyo94G7sLts6CNS/XRCmFEEIImyQBS13x6wgu3pCfCfEHLV+7HA1o4NQI7BzUtMpWC2UmqPucyzVVUiGEEMLmSMBSV+zsoEVf9Tj6imohYw+hxi3B3U89rmzXZuNgc/nljPEihBBC1HMSsNSlkDICFmP7FZ9Qc8BS2cHjspPVfUF22V2mhRBCiHpOApa6ZGp4ux0MBvN049WcG7eERk3V48pmWLKLVQXlZ1x9GYUQQggbJAFLXQrsDo5uqhon6YR5urFKyKcluBcFLJXtKWSsEoLyh/4XQggh6jEJWOqSvSM0760e7ys2TH9KsTYsjYxtWKpYJQSQJxkWIYQQDVO1ApY5c+YQGhqKi4sLERER7Nq1q8x5Fy5ciE6ns7i5uLiUOf8TTzyBTqdj1qxZ1Sma7ev9qLrfMQf2L4LsFEiNVtN8WhVrw1LZKqHiGRYJWIQQQjRMVQ5YFi9ezOTJk3n11VfZt28f3bt3Z8iQISQmln2A9fT0JC4uznSLjo4udb7ffvuNHTt2EBQUVNVi1R+d7oKB/1WPf58EH3QEfT44uIJXc3OVUFV7CYG0YRFCCNFgVTlg+eCDDxg/fjzjxo2jU6dOzJ07Fzc3N+bPn1/me3Q6HQEBAaabv79/iXkuXLjA008/zXfffYejo2NVi1W/3PB/0OVeMBRCYS4EdIP7F6gLJTaqai8hybAIIYRo+ByqMnN+fj579+5l6tSppml2dnZERkayfXsZ18gBMjMzCQkJwWAw0LNnT6ZNm0bnzp1NrxsMBh555BFeeOEFi+llycvLIy8vz/Q8PT29KqthfTodDP8MQq8Hvw7Q4jo1DaqWYdEXqlFxjaTRrRBCiAaqShmWpKQk9Hp9iQyJv78/8fHxpb6nffv2zJ8/n2XLlrFo0SIMBgP9+vXj/PnzpnneffddHBwceOaZZypVjunTp+Pl5WW6BQcHV2U1bIODM/Qap8ZmMQYrYO7WnJ1S8bgqxYMVkAyLEEKIBqvWewn17duX0aNHExYWxqBBg1iyZAl+fn7MmzcPgL179/LRRx+ZGudWxtSpU0lLSzPdYmNja3MV6pZbE9DZARpkJ5U/b/HqIJCARQghRINVpYDF19cXe3t7EhISLKYnJCQQEBBQqWU4OjrSo0cPTp8+DcCWLVtITEykRYsWODg44ODgQHR0NP/5z38IDQ0tdRnOzs54enpa3BoMO3sVtEDFXZtzrghYpNGtEEKIBqpKAYuTkxPh4eGsX7/eNM1gMLB+/Xr69u1bqWXo9XoOHTpEYGAgAI888ggHDx7kwIEDpltQUBAvvPACq1evrkrxGo7ig8dpWtnzFR+DBSTDIoQQosGqUqNbgMmTJzNmzBh69epFnz59mDVrFllZWYwbNw6A0aNH06xZM6ZPnw7AG2+8wXXXXUebNm1ITU1lxowZREdH89hjjwHQpEkTmjRpYvEZjo6OBAQE0L59+6tdv/qpkR8kAltnwc/joNdYuPmNkvOVqBKSRrdCCCEapioHLCNGjODSpUu88sorxMfHExYWxqpVq0wNcWNiYrCzMyduLl++zPjx44mPj6dx48aEh4ezbds2OnXqVHNr0dAYMyxRm9X90WWlByymKiEdoEmGRQghRIOl07Ty6hzqh/T0dLy8vEhLS2sY7Vm2fADrXwfvFpAaoxrhvhSvehYVt/ZVlYXxCIKMi9CiH/zrT6sUWQghhKiqqhy/5VpCtqj/JBj/Fzy9D5w8QDOYrzdUnLENS+MQdS+NboUQQjRQErDYIjt7aBauLpbo20ZNSz5dcr6cy+reuyhgkSohIYQQDZQELLauSTkBi7HRrXcLdS+NboUQQjRQErDYuvICFmOj28ah6l4yLEIIIRooCVhsXbkZlivasOjzoDC/bsolhBBC1CEJWGxdWQGLppnbsHgVu5ZSvlQLCSGEaHgkYLF1TVqr+6xLkJNqnp6XDoaiiyM2agoOrubpQgghRAMjAYutc/aARkXXaUo5Y55urA5ycAVHVzUfSDsWIYQQDZIELPWBqVqoeMBSVB1kvFCiKWCRKiEhhBANjwQs9YGxWijplHmasYeQW2N179xI3UuGRQghRANU5WsJCSvwbavuk09DzA44twV1/SDA1UfdOxcNaSyj3QohhGiAJGCpD4xVQqfWwtGlaqh+I7eigMVJMixCCCEaLqkSqg+MAUt+hgpW/DqaX/MIUvfS6FYIIUQDJhmW+qBxqOoplJcOt38AYSMh/hCcXAVhD6t5pNGtEEKIBkwClvrA3hGe2q4Gi3Mv6hUU0FXdjKTRrRBCiAZMApb6wthWpSzGDIs0uhVCCNEASRuWhsJJ2rAIIYRouCRgaSik0a0QQogGTAKWhkIa3QohhGjAJGBpKIo3uo0/BOvfgIJc65ZJCCGEqCHS6LahMI50m3MZFj8Cl6PAqzn0+pd1yyWEEELUAMmwNBTGkW4zLqpgBeDCXuuVRwghhKhBErA0FMY2LMXF/VP35RBCCCFqgQQsDUXxgMWYbUk8Ju1YhBBCNAgSsDQUxiAFYODz6irOhkJIPGK9MgkhhBA1RAKWhsLODrreD817Q+/xEBSmpl88YM1SCSGEEDVCegk1JPd+aX4cGAZn/oK4A9YqjRBCCFFjJMPSUBkzLNLwVpRF0yDpNBgM1i6JEEJUSAKWhiowTN0nHIXCPKsWRdiow7/C7HDYPMPaJRFCiApJwNJQebcAF28wFEDiUWuXxvakxsplDC7uV/f7vpYsixDC5knA0lDpdOZqoX8Wq1vyGasWyWakx8HHPWDRvdYuiXVlJan79AsyyKAQwuZJwNKQGauFdn4Gvz0O84fI1ZwBkk6qzFPCYWuXxLqyk82Pjy61WjGEEFaWclZd1sXGScDSkIU9BIHdIaAbuDWBrEuw/VNrl8r6clLUfX7mtT2wnkXAskw1whVCXFvSL8LsPvDNcGuXpEISsDRkfu3h35vhiS1w+/tq2rZPzFUBV8pNh/zsuiuftWSnFHucXPZ8DV12sd9BWqxUCwnblHkJtn5c9n5LXJ2LB8xtHW38pEUClmtFx7tUFVF+Bmx5v+TrmZdUu47PB5XMOmganN0EmYl1UtRaZxGwXMM7QeP3ENRD3R/5zXplEaIsOz6FtS/DznnWLknDlHxa3evzIS/dumWpgAQs1wo7O4h8VT3e9QVs+QAK882v75qnDt5JJ2HPfMv3bvsEvrkTFj9Sd+WtTTnFApZr9aytIFdViQH0elTdn15nvfIIUZa0WHWffsG65Wiokk+ZH9v4/lAClmtJqxugy70q/bf+dZh3PSSdUt17d31hnm/L++bGubG71LwAsTvUuC71nVQJmdfbzgGCI9Tj9IvVW1Z+Fqx73dxNWoialJmg7m38YFpvFe89mnXJeuWoBAlYriU6Hdz7FQyfC26+cOm4ami16V3ITQWfVuqWnaQa5yYcgV/+pS6iaOeolrH/W2uuQc2wpQzL/u/gvBXajhirwtyaQKOm6nFeOhTkVH1Zx36Hvz+Av96qufIJYZRZdBC18YOpSX1rB5hULMNi49X+ErBca3Q6CBsJT+2AJm0h/Txs+1i91u8ZuOEl9XjjNPisn0rH+rSCe4rqj//5of6PnFs8q2LNNixx/8Cyp1SX87pm/A7cfMHFC+yd1fPq7LBSY9R9mqTsGzR9ISx/GpY+VbeNM7OKfpP1ob3Z0eUwvRnsWWDtklRObpr5+wWbDwqrFbDMmTOH0NBQXFxciIiIYNeuXWXOu3DhQnQ6ncXNxcXF9HpBQQFTpkyha9euuLu7ExQUxOjRo7l4sZrpaVE5jfzgkd/AI0g9d/eD7iOh8z3QLFxNc3SDVoNh5I/QabiaN+cyHP/DWqWuGdk2kmG5dFLdXz5X9yPNZhkDFh8VxBqzLNXZYRnbFmTZ9tmZuErrX4d938CB78xBam3TF5iD66x6UH17fAVoBojabO2SVI6xwa2RtTPOFahywLJ48WImT57Mq6++yr59++jevTtDhgwhMbHsnZWnpydxcXGmW3R0tOm17Oxs9u3bx8svv8y+fftYsmQJJ06c4M4776zeGonK8w6G0Uuh9Y1w20xwdFGNc0cvh6d2wouxMHqZ6h5tZw89Hlbv21dKtZCxHUN9aLiZYyNtWFLPqXtDYd2fPRrX29236N5P3Vcnw2Js+5KdrA4wouE5usyciYW6awBb/ABakFW9Ksu6FHdA3WfEWbUYlXbl6OcNLcPywQcfMH78eMaNG0enTp2YO3cubm5uzJ8/v8z36HQ6AgICTDd/f3/Ta15eXqxdu5YHHniA9u3bc9111zF79mz27t1LTEwdRfHXMr/2KtPSebh5mnMjaNoB7B0s5zUGLGc3WKb/87Ph+xGqHcOyibbdl19fqNKgRlYNWIr9vuu6B4SpSqiJujdlWKoTsBTbOdv4GZqohvQ4VQ0EoCs6ZNRV9d+Vv0db/n3lZ6lellD9Bux1zZhh0dmr+4YUsOTn57N3714iIyPNC7CzIzIyku3bt5f5vszMTEJCQggODuauu+7iyJEj5X5OWloaOp0Ob2/vUl/Py8sjPT3d4ibqQOMQaNFXPT62XN0X5MAPD8K5Lep5RlzJNKMtuXL4aWvuAC+bM40WB/26YGp0e2WG5SqqhMDco0PULE2z3gUqz21RXeCbdoLOd6tp6efr5rOv/D3a8gE1/rCqDgK1H7TlEzcjY4PbwG7q3pYDQqoYsCQlJaHX6y0yJAD+/v7Ex8eX+p727dszf/58li1bxqJFizAYDPTr14/z50v/wefm5jJlyhRGjhyJp6dnqfNMnz4dLy8v0y04OLgqqyGuhnGHdXiJut/wNkRtAqdG4NNaTYvaZJ2yVUbx6iCwbkO+1OIBSz3NsBTkWH6nNt7LoF7SNFh4O3zW13LspLpizBoE9wHvEPW4rjIsVwbAtjwMgbE6CNQgbLZcViPjyWWLfurextuh1Xovob59+zJ69GjCwsIYNGgQS5Yswc/Pj3nzSo5aWFBQwAMPPICmaXz22WdlLnPq1KmkpaWZbrGxsbW5CqK4jncCOji/Sw3lvutLNf2eL1SjXbDtBmfGBrfORcFwzmVVTVTXDHpIKxa0V6bO+9AvsPqlmjnTNjZgdC8KWNyLApaqBhxXpr5tfIdXL2VdguitahiClLN1//nGgMW3HXg1U4/LC7AzEiAlqmY+uz5VCcX9Y/nc1quFNM3chqXFdereljNYVDFg8fX1xd7enoQEy6g3ISGBgICASi3D0dGRHj16cPq0ZbWBMViJjo5m7dq1ZWZXAJydnfH09LS4iTriGQghRdH4j6OgMAea9YL2t0LLgWp61Bbrpa8rYjzradIa0KnHV2Zd6kL6BdXY1vS8Eju3VS/C9tlwfvfVf36JDEtRlVBVd1hXlvvKM+IL++DrOyHxWNXLKJRLJ8yP66p3jsXnFwtYPJurx2llVAlpGnw9DOYOqJls25VVQrbctfniAcvntt7wNiNONWTW2UPz3mpazmWbbjhfpYDFycmJ8PBw1q9fb5pmMBhYv349ffv2rdQy9Ho9hw4dIjAw0DTNGKycOnWKdevW0aRJk6oUS9Q1Y7WQ8Q85eKrqGtusJzi6qwAgsfx2SkRtVoOm1TVjcOLuB66N1WNrnLUVb78CFQcsOanmYOJy0dmrpqngMLsaAVeJNizGDEsV26CUCFiuOMBsnK6qCHd/WfUyCsWY4QDLasS6oC+ElKKz8MpkWLIuQdIJ1eYldufVf74xw2LnYF6+LSrIURkwgICi9iAV/adz0+DDrvDDQ7VbtrIY2680DlVVwsYG1TZclVXlKqHJkyfzxRdf8PXXX3Ps2DGefPJJsrKyGDduHACjR49m6tSppvnfeOMN1qxZw9mzZ9m3bx8PP/ww0dHRPPbYY4AKVu677z727NnDd999h16vJz4+nvj4ePLzrVBfKyrW8U7zj7t5b2hzk3ps72jOvpRXLZSfDT+MVIOmRW2p3bJeyXhwd/Uxd+m1xlmb8cDj4KruK9q5Fa8KuHxO3Z9ZD1/fAX88V7XPNhjM38OVbViq2ug2o5wMS14GnN2oHicer9pyhZk1A5bUaNUew8EVvILBsyhgyU4uvYtxYrFLd9TEpRqMv6cmbdW9rY7FknAENL0K/I3jWFX0nz6/B9Ji4MRK86VQMhJUcF8XbZWM1xDybauGrTCevNhqUEg1ApYRI0Ywc+ZMXnnlFcLCwjhw4ACrVq0yNcSNiYkhLs6cCrt8+TLjx4+nY8eO3HbbbaSnp7Nt2zY6deoEwIULF1i+fDnnz58nLCyMwMBA023btm01tJqiRnn4Q7tbVdBy4/9UdsXIVC1UTsByarX5wnuVGer/zAaYe33JOuLqMGZY3HzMB2trZFiMqf3mvdR9+sXyexWUFrBc2Kfuq1pFlJuqdq5g/g6MvYTy0kperbs8xp2ysTFm8WqA0+vUwQ7gklQJVZs1q4RM7VfaqDGaXBurASWh9ANy8cD0yiqS6jAG0E07qntbrRIyBmeB3cGzaDDOK4P5K5m2q2a+RtvaV2DFf2DHnFopZqmf71sUDLpXs1q4DlWr0e3EiROJjo4mLy+PnTt3EhERYXpt48aNLFy40PT8ww8/NM0bHx/PihUr6NGjh+n10NBQNE0r9TZ48OBqr5ioZfd+Cc8cUCPhFtfyenV/bmvZgYCxhxGoAalyUtUYBjvnmevLi9s6C+IPwrbZV19uU9uNYgGLNVKgxiohYzfxgqzyL+1uEbAUvdeY0k2/YDm2TEWKNzx2cFKPXRubrxdVlR2W8aAVVPSfLt5I8vjKYp+ZXL0u0/WVptVct9bi13qxWsDSTt3rdOYsS2ntWK7MsFT0HRTkqsA2P6v0142/p6adip5bMWApzC9qn6cv+Zqxh1BQmDlgqWiogkvFgrv4g+o+uugk/dTast93bit8fsPVB4TGdmXG79aYcbbh/6lcS0hUj5ObGpflSgHdwKsF5GfAVzeX7NWQlwGn1qjHbk2gMBcO/Qy/joc//wtfRqpUqVFBDkQXjfFzel3pO4uqyC4ah8WiSsgKAYsxtd+0A7h4q8fl7eCKj0hpzLAUvyx8Vapcil/40EinK3aGVYXGksa2DMaAxZjC1xfAydXqsTEQqq0sS+xu+GkMZJQ+tEKdi90FbwfAxz3gzymWAUdV5WVajnlyZdun2nbpioAFzO1YSg1Yim3jnBR1LbLyrHkJFt0Ln/SCfxZbNtYvPiy/McNizbP/rR+pKtitH5V87WJR9jcwDDyK2mdWVCVUPHMWf1BVB6UVBaSxu8zVRFfaMA0u7oO9V3m9IlPAUvTdNtQMixBlsrOHh39VQUvKWfjqFnPVBcCJVSpI8WkF1z+vpq35H5xYoR7npakrSMfsUM+jt4G+6GKLOSmWwUx1WFQJGetsa/CsLfMSbPnAHFSUxXjg8Q4pdkZWTlfRlGIBS8ZFFcgVPxBWJRi4soeQUWXbsRxfCVveVwcXU4YlTN3npqmLY577W21Ldz916QeovXYsq6fC0aWw64vaWX5VbXhb/cYvR8HOuTB/SPWv4GsMSp081H1OStkHsspY8z/4tG/lhx64MsMC4FXUU+jK36ummbMGxmEDymvHoi9QXfVB/aZ/exzWvWJ+3fi/1NmrEbnBug1Cz25Q90eWWE4vyDX//ypbJVT8uwKIPwQXiu3bDAUqk3Kl9DjVxR2uroo881LRiYsOfIu+WwlYxDXJrx08thYCuqof/8I7zCnOI7+p+873QLcR6uy7sKjNxK0zIGSAys78+JA6uzTuJIxOrrq6shVvbFobjW43TlcXiftsgDpjLE1hnrmHVfGApbxukFdmqmJ3mdsBQdW6DV95HSGjygwepy+AJY/D+jfg2DJzm5Wmnc2ZlMxEdRE4UN3d/YtSzrWRYbkcbW7Dc7FYYLxnARz+teY/ryJxB1VDY5093DVHVZ9kJ5tHhq4qY4YjsJu5V1t1q4UK82Dn56ra5us7VXuJ8sYg0jTV4wcsA5ayujanX1DVmnYO0OEONa28aouzm1R7KvemMPC/atq+b8zdao2/Q3df88E0P7NqbayM63G1mVmD3rwu8Ycs1z3xiBqiwK2JCuaM/+fcNMtANT9b/W81TWUic1PNryUchZgrRos/81fJchxdBhRVsyUcqX4XZON/sXGoypaDeX9gw2PdSMAiaodHAIxdCa1uUO0zvh8BH4WZA44u96hBy7rco573eRwiHodRP6vsS3Yy7PsazmxUr7cdou6N1UlQvTYCxoO1ay1kWAwG1eIfVND12+MwJwJ+HgvHfjfPlxoLaKrxortvxSnknFRzuRuHqvvTV9RxVyVgySqlSggqN3jc+T1q3QA2vw9oYO+k1qORv/n9xuqg9reDX1HKuTYyLMYAGFQmT9PU5/zxLPz6GCTV0GUiYnerQfsq6kK+7RN133m4uvZWuOo9yf5F1fvc4hkO7xbqcXUDlrh/VLZSZw9oqmpjV8kBPE2yLhW1jdJBkzbm6WV1bTZu3yZt1Ki4UH6G5WjRtus4DAa/qH6PuWnm7Kox0+feFFy8zAFxdpLKEBl/YxXZ9B681VRtw+q6dELtx4yKf7YxkAkMU1Wrzp5qeAcwn4QU5qnxab66WQUdxuxK45Yqe6bPg4M/q2nGfV1pAUvx7I4+37JaqSqM28pYHQSSYRHXOBdPFYB0H6l6pVyOUvfNws0NvW5/H8b8DkPfVc+d3KD/JPX47w8h4ZB6fMubgA4SDqtszWf9YcFtakdQWZpmvpaQm4+6Qc2lmeMOqB2UozsMelEdGC4dVwfVxQ+bd5jGqzR7h1g2YiyrSsiYXWnkD/5d1ONT68zTwDK9XJEyq4QqscMydlMG87bxCFTrYXx/zHZVF2/nqBphN+1QVMZjNX99leI78NxU9V0Zy6gZ1G+oJqx8Xg3a99PosrMSaefNWZ1+T6v7sJGATl2Ppzqj1BbPcBh7YlU3YDGewbcbCpGvqcfGKplSP7soWGocoq7kbmRqdHtlwFLU4Navg7mKMO5A6dtcX2DOwnUerqqS296inhtPaoztoRr5FbWxKjrBSDql2r18/4Bqz1Heb6ogV/W4MRRWrkdiWYpn74qXEcxVM4Hd1b1OpwbYBPN/+s8p5iqfgz+ZA42mnSCg6D+dWdQG6/rJat+RfMpyW6edLxrbRmfOeF1ZLfT3h6rKr/j7YneVPBkqvq2MTBlWCVjEtcreEYZ/Bk9ug3+tgfEbYOwKc1doZw/VFdqu2E+x+0h1EDT+cQK6qjps42iM392nApeYbbCn7KuEl5CbZu7OW7zRbU1lWE78qe7b3AQ3TIXJR+Ghn6DNzWr6X2+qe+NZv7HRsmnnVkaVkPFA59PKnGExpnTb36ruMxMqP4BcWQFLZQaPMwYDxoG8wHwAMwZPB4uqwoIjwMm9aAwNnQoWsy6p9PeZvyC3EhctTYlSvcNKa1eTfEbtsHX25nE6LuyzvJbVwR/LbqiaElW5IeTTLph7gZzbAuteLX2+bZ+o31fo9eZGyF7NzW14Dnxf8WddydhOya9YhuVytKpOeTtI9ayrrJiigdxaXAdhDwM6dSBOLaNh7KViwVJxZbVhKd7rpGknlXnLuWw5dszJ1ep/cmaDes3NF0L6q9faDS2apygYMFYJGX9Xxozoge/M3eU3vQsrXyh7ZO1Ta8w96E6tNQc3WcmqyrmyLuxV98b/8tlN5p5NxXsIGRXvKbR/kWUD2TPrzcvza6/2b0ZOHmo/ZxzuYPVLKju9+GFYVTS+WUg/czmKBywFObB5pgpGdnxmLudXN8Oi+ywDO+MJjvHEESTDIgSgghP/ztAiomg0XNfy53dwhr4TzM9b3aDu291inmbceW96r3JdegtyzQ1uHd3VGaNxB5idXDOXEjBWB3W4Xd17BEC7IXDHByrbELVJNQzdME29HtRT3ZsyLGVUCRl7CPm0NgcsRkE9zN9FZaqFDHrzBc/KasNSPDjIy4AN09WBMzfd3F7E2GAazDtn4/uNXTRbD1b3Tm7mch/8CT4fDN/eDe+GwFdDys48GAzq8g9rXoLZ4apdSvHtZOwe32qwefDC2J2qwS+o78VQqLrFXykzUY3tM29gxUPIGw+gxh369tmWXfNBBaHG0XwHXDGQX4+H1f3uL2HOdfBWABytRJsWfaF521tkWKLVmXRBFqx9teyAozhNg9iiqpYW16mshXGQx+N/lJzfoDcHWMUPamD+vealWwadxiC6aUf1Hza+z9hV9/Q6lRX54UF1A1UdZGevHre+Uf1Pkk+r79NUJVT0vRuve2X87oJ6AjrY/YU5SL5S8ekZF1W7j9QY+DhMHcgr2wbE2HGgxyj1u9LnqWCgMM88hkpgmHl+j6L/ROwOWFH0X7nhJVUFVJhr/v34dTCPjAvQrIf6PoxB7rHl6vd37HdzO6jOd5uzOcUDllNrze3aDnyv9nnGHk2JR8xBkqaZMyxNi2VYTCdwl2z2StMSsAjbFD7O3MiwTaS6Dxul/qj9J8GE3WonnpOiGg/+/izMaKseF2/opi+En8fBe63gwA9qmrEqyPgH1fTwwwhVx1zdLqiXo1XWR1cstW3k3QJ6/Us9Xvm86j0THGGu+ireqyA7RV2y4JdHVbfYPfPNB/QmrUoGLE3amg8MxcfAKE1ehhph2Bh0GLMARqV1a/7rbdj0Dnx3v2o3o+nVTrf/M+oK3cXLb8zQGBkDTTDXla/5n9rZOzVSVTaxO+Dru0q/+u+JFeZLPOSmqXYpS59UO9PcNDhQ1C6ky73m4O/QT+pA6uIFdxaN27N/kTq4FLdznmqLk5duPhs10jTVniD+cFE5ijJn1z1lDkbWvmI5Gunal1Vw1OZmc/Bk1OF29VvOuawO6oU5al2uzIgZ9JbTUs6o3iKObqqhqzEjd36POdNVmKM+uyLJp1Vg7uBiPth1HKbui7evMto5V1VhOHtCxL8tX3NupL5fMGdZDIZi1RxF27pdUVuM9W+qLOafL6rndo7mTKexDRuoKuTQomzLyT+LZViKflfG36ex1+AdH8IN/6ceb/2o5EE2O8Xc1sSYgTu1BrbPUds98agaUqEiBbkq0AH1OzNmgk6sVCcJhgI1NIHxxAHMWdM989U2Cr1eBfmd7lTTDUWB0pUZFmMWuedoNUZT+9thyHS46VUVmLe6Abo9YN6G8YfMDYqvrB7d9I7K5hj9U7T/y4hX/5/imUkwf7+FuZYN+m2IBCzCNjk3glG/qoOOcfRczyD492a4+Q2VIbmpKDW/d6FKuWYlqh3XZ/3UTtigVwHCkSXqbHTze2p+YyDk4GyuFjm1RjXk+3ls1XohpMao9iTGlG+LvuaAqLjr/2Meht87BB783twuwNjoNjsZZrZTlyw4/IsKVFY8bz44+ZQSsPi2NddDG9O8uWkqKzJvEJwu2mHlZxX11lqtDlr3f62yXsWZMixFB4qsoobPoNof/f6setxqsKrqCR+rnhvT18bUPYCzl+UZp6muXFNn6M8egmf2q6xRWgx8c6fa+RoPOpqmsmcA/Z+FIdPUDvbgj6pqbfEjquu4R6A68BqHQzdm20KvV7+b1jeq6oNv7lLBl75QVQXsLtYFeveXqmGz0a4vYMljasyN5DPmLsDtb4VBU6BRgBpfxNgm4swGdfDS2cOQtynBwVl93wOegwe+VQFmdrIK3owK81XPnRmtVQBVkAvLn1GvBYapKlPjATEzXn2Pvu3UaNNHfqv4EhfGhqxBPVV5wNyTJ3qbZZYp5awKMkC1HTMGpMUZewoZG5ye2wwF2WDvrAJaUNvNp5UKxD+/QbXJcPeDycfgvvlw9+fm/7ZRu6Iqzj3zzWU2BsJuxTKCns3VQbvP46oa5dKxkoOtHflNBQb+XeG6J9S0w7+qnkhGm2eW3SbpcrQaGyXhsFqOm6/aBsbv7eBic8ATFGY54rdHse/M3hnumKW2Yce7in1AUVuUph3NVazNiv5LnkHwr1Uw8nvo+5Rq1zJ6GYxeqoJF37Zqf1KQpbZXXqYaMgJUkAPm9lvG383hX1VGyHhi49PKsm2Sk7t5FGPjsmyMBCzCdjUPh56PWO4Iiutwu3mk3dY3wm0z1cHwcpSq853ZriiQ0Kk/p1HxgOLuedB3Itz6ntohJRyGda+pg9TXw9SAdmVdv+T4Svi4J3x3r3nnYGxTciUPfxg6XQU0o36xrI5xbawO8GDewV7/PLS/TZ2JGhvj+bRW13MxXmXa2UsdAIxntNHbYc3LMKubOruKO6AGVLt0Ul1vKO6ACtDGrlQNHa9kPDDkpqoD6M656iDUqOhK7MaReFsXZU5ufgOe2qGuLQXmgAdUY1v7Yu1cjGXU2cO9X6lt4NNK7YS9glUGYO4AmN1bBRa7vlBVS47u0O8ZVUV4R9F3vOV9Vb3m1Ei1EXLxVMsyfocALQep382I79TZKpoKWL+/X7U1yU1T36dfR7VexuqclLPmNio5l9VvQJ+nAkW/Dqo68/rJ5nJc2GcOLPqMN48XcqVWg1RD1053wrCP1DY88J2qJgEVvET/rbJOq16Euf1V9snZS1UpQtG2L2bgC+bM3bKnzD0/ojar30HcQfO8xoN/C/Oo5HgHF2XZNHMD2MvR8OPDKivQciD0HFP6+rS4Tt0vnwgr/6vaWYCqtjVudyc31bUbnXlAtMjXVHVUl3uh+4iSy21flL1IOWsedM4YpBurhED993U6cPWGXmPVtK0fqUDv8K8qm7N5pprefYS5zUfCYfWbbtpZtWNLOWOZmdA0Vc3y02j4qBt80lONqwSqOlunU99L21tUILy9KItXPDgHyyBv4PPq0gagvm9jlVrjEPUdOThDr0eheZ+SAVxZ7OzNjXXj/lHVRoU5Kli8fWZRT7Aid3+uAvucyyrjZGq/0rHkcjsNV/dLHlNV16VdL8qKHCqeRQgbpdPByMXqbNXY1bLbCBU87F1gHl/l1vfUjnDu9epg7FosYGl7s7qB2jF+/wDs/EzdjKI2m6+ZlJ+tDo45l1X1hKGgqK2EXqXPu95fdnl7jVO30tbjns9VF9BOd5ozH7npqo2F8erMxjMizyCVivdto95r3PEkHjFXofi2VwfXuAMw/xZVXp29OsNvHl56+Vwbq4aS+nxYNcXc4+W291QGav8iQKeyF6B2msV3esUzLFdesqHD7eog1W4ohBS7srt3sOoltvZlOLlGnYUbM2EAfR4zH6jCx6jv4u8P1brcv1CNTwLq7LVZD3M2qtUgde/kBnd+AqED4fdnVGNfY3fRfk+rs8ol42HHp+r73f2lOqAFdlcBgLHKo/1t5sC55xj4e5Z67YsbAU39BgZNKf17vVJwHxVo7PlKNYZsNchc7m4j1Jl78mlVdfLgIvN37NxIBdXZSSqQ6ThMVZeeXqeyTV9Gqu/W2PV/28fq9XZDzVmiFn0ty9JxmPrd/TlFDUh2ep36rbj7wbCPyz5ZGPK2mu/IEnPX6Ha3qoNjcSH9IOIJ9X9qFg7dHyr/u2kcqrIvcQdVJtCnlbmLtLHKAsztxAAinoQdc1XA9357y/FNHFzVf9IjQAWcxoP19ZPVd/bXmyqbdGGvylKc22zZwyY/0zyopTGLp9PB7R+oIQuMXZ2NVTRGgd3U9vPrYK76haIsyzB1MlC8h85t71Flgd1V9e6JleaGsl3uUfuH9reqtknNe6vfRLcHVED39wfmdjulBSx3fqIyODs/Uw2at82GNjeqYNnOQW2TG1+qellriAQson5zdDEHK6DOtiNfhUH/VY3zHJxUIzWA+xeos8Eu95a+rHZDVIp51+fqz9njYZW1SDqhziRL0/keuOcLy2xCdbQfaj67LL4u9y+EBbeqFLBzUZuRxqFFAUtR7w2/jiorkxajzvw63KF2itnJqoGr8aAb+aq5jUBp7OxUZmfjNHPvqyZtocMwFYCkX1TBVGlVXmCZYTE2GjRyclcHotL4tIQRi1SAdnKVSuWfXqd2nH2ftpz3xlfMjY+N160yCuqpDvyNAkr2bOl2v8p+/PiQOnN391O90ewc1Mi0l8/BL0XBpKM7PPCNqlY0Vtu0K7ZtHF3UAW/l84Cmvpt755f9vZTm5tdV+6sjv5mDlev/Aze9ooKMbR+rDMqVZ9zeLVTA0vU+FZA6usJj61UVWcw2Fazo7FVgErNNfY/GLA6Y20gYhY9T6f/zu4pVb/SEEd+aewOVxtFVbc+mHVUGoucjqq1Faf+DW95UVSatbrDsDViWLveW/h81Biwu3uYGw6D+/90eUBmr3FRVXdTxDnVADx2gghVQ/41Lx9V32Gm4Cky3faL+Nzvnmpdn56CyhgOeU1WzxoarxqpPUIF25KvqciJg2UMI1GdMOqBOAoxVcEb9n1VVcNc9WfF3UR5jkFR8gMTORW2CjN3WBxWVr/tItR7Fx8W5MngFtf1ufUct+6+31GUhirdxsne2asCi0zQbbQ5cBenp6Xh5eZGWloanp6e1iyPqM32B+oMGhamzu/xsNXpt9DbzzifplDoD7nq/SnlfbbBSkcxL6gBhDFh+f1ZlkG5+UzV+NdK0kmfEF/bC4tHqLKm8M+bizvwFyyepHfm9X6mDY2UY9LDoHhVo3P915T6rLMYup8Z1royL+1WWof+zcFMZDVEzL8H2T1Q2wJjpSYlSAcKFverxre+p8VMMelj+tGqsfN8Cy+1cmK9GNG7UVFUpGnu6VFXcP+pA4uINt82oeDl7F6rqsge+gSatLcvz1xsqOzDoRTW6cMpZlRVLPK4et74Rhk4ruUxNUw159y1U5bjxZcu2DRUx6Ku//lWRn62yYe1vNfe8MspOUdUzzXqpE4/SypN2AVZMVhkfY7Xmhb0qs2coUIFecB91IC/+uzu9TmV8+j9rGXAZ9Kqq1c5eZVyu5vdeHdkpKlDNuaz2D6EDVKBSVjn+fFH9R1oNhg63lcwKXUnTVIb2zF/qP6AvUMu+5a0aXY2qHL8lYBGiOkoLDupKepzq4hj2kBrHpiLVKWt+dlEWp23F89qSwnx1hlyZM3khhNVV5fgtVUJCVIe1ghVQXSav7GpanuqU1cmt/gUroKoAhRANkpyGCCGEEMLmScAihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLmScAihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLmScAihBBCCJsnAYsQQgghbJ4ELEIIIYSweRKwCCGEEMLmNYirNWuaBqjLVAshhBCifjAet43H8fI0iIAlIyMDgODgYCuXRAghhBBVlZGRgZeXV7nz6LTKhDU2zmAwcPHiRTw8PNDpdDW67PT0dIKDg4mNjcXT07NGl20rGvo6NvT1A1nHhqChrx/IOjYENb1+mqaRkZFBUFAQdnblt1JpEBkWOzs7mjdvXquf4enp2SB/fMU19HVs6OsHso4NQUNfP5B1bAhqcv0qyqwYSaNbIYQQQtg8CViEEEIIYfMkYKmAs7Mzr776Ks7OztYuSq1p6OvY0NcPZB0bgoa+fiDr2BBYc/0aRKNbIYQQQjRskmERQgghhM2TgEUIIYQQNk8CFiGEEELYPAlYhBBCCGHzJGARQgghhM2TgKUCc+bMITQ0FBcXFyIiIti1a5e1i1Qt06dPp3fv3nh4eNC0aVOGDx/OiRMnLOYZPHgwOp3O4vbEE09YqcRV99prr5Uof4cOHUyv5+bmMmHCBJo0aUKjRo249957SUhIsGKJqyY0NLTE+ul0OiZMmADUz+23efNmhg0bRlBQEDqdjqVLl1q8rmkar7zyCoGBgbi6uhIZGcmpU6cs5klJSWHUqFF4enri7e3No48+SmZmZh2uRfnKW8eCggKmTJlC165dcXd3JygoiNGjR3Px4kWLZZS27d955506XpPSVbQNx44dW6LsQ4cOtZinPm9DoNT/pU6nY8aMGaZ5bHkbVub4UJn9Z0xMDLfffjtubm40bdqUF154gcLCwhorpwQs5Vi8eDGTJ0/m1VdfZd++fXTv3p0hQ4aQmJho7aJV2aZNm5gwYQI7duxg7dq1FBQUcMstt5CVlWUx3/jx44mLizPd3nvvPSuVuHo6d+5sUf6///7b9Npzzz3H77//zs8//8ymTZu4ePEi99xzjxVLWzW7d++2WLe1a9cCcP/995vmqW/bLysri+7duzNnzpxSX3/vvff4+OOPmTt3Ljt37sTd3Z0hQ4aQm5trmmfUqFEcOXKEtWvX8scff7B582Yef/zxulqFCpW3jtnZ2ezbt4+XX36Zffv2sWTJEk6cOMGdd95ZYt433njDYts+/fTTdVH8ClW0DQGGDh1qUfYffvjB4vX6vA0Bi3WLi4tj/vz56HQ67r33Xov5bHUbVub4UNH+U6/Xc/vtt5Ofn8+2bdv4+uuvWbhwIa+88krNFVQTZerTp482YcIE03O9Xq8FBQVp06dPt2KpakZiYqIGaJs2bTJNGzRokDZp0iTrFeoqvfrqq1r37t1LfS01NVVzdHTUfv75Z9O0Y8eOaYC2ffv2OiphzZo0aZLWunVrzWAwaJpW/7cfoP3222+m5waDQQsICNBmzJhhmpaamqo5OztrP/zwg6Zpmnb06FEN0Hbv3m2a588//9R0Op124cKFOit7ZV25jqXZtWuXBmjR0dGmaSEhIdqHH35Yu4WrAaWt35gxY7S77rqrzPc0xG141113aTfeeKPFtPqyDTWt5PGhMvvPlStXanZ2dlp8fLxpns8++0zz9PTU8vLyaqRckmEpQ35+Pnv37iUyMtI0zc7OjsjISLZv327FktWMtLQ0AHx8fCymf/fdd/j6+tKlSxemTp1Kdna2NYpXbadOnSIoKIhWrVoxatQoYmJiANi7dy8FBQUW27NDhw60aNGiXm7P/Px8Fi1axL/+9S+LK5TX9+1XXFRUFPHx8RbbzMvLi4iICNM22759O97e3vTq1cs0T2RkJHZ2duzcubPOy1wT0tLS0Ol0eHt7W0x/5513aNKkCT169GDGjBk1mmqvbRs3bqRp06a0b9+eJ598kuTkZNNrDW0bJiQksGLFCh599NESr9WXbXjl8aEy+8/t27fTtWtX/P39TfMMGTKE9PR0jhw5UiPlahBXa64NSUlJ6PV6iy8fwN/fn+PHj1upVDXDYDDw7LPP0r9/f7p06WKa/tBDDxESEkJQUBAHDx5kypQpnDhxgiVLllixtJUXERHBwoULad++PXFxcbz++utcf/31HD58mPj4eJycnEocBPz9/YmPj7dOga/C0qVLSU1NZezYsaZp9X37Xcm4XUr7Dxpfi4+Pp2nTphavOzg44OPjUy+3a25uLlOmTGHkyJEWV8J95pln6NmzJz4+Pmzbto2pU6cSFxfHBx98YMXSVs7QoUO55557aNmyJWfOnOH//u//uPXWW9m+fTv29vYNbht+/fXXeHh4lKhuri/bsLTjQ2X2n/Hx8aX+V42v1QQJWK5BEyZM4PDhwxbtOwCLOuOuXbsSGBjITTfdxJkzZ2jdunVdF7PKbr31VtPjbt26ERERQUhICD/99BOurq5WLFnN++qrr7j11lsJCgoyTavv2+9aV1BQwAMPPICmaXz22WcWr02ePNn0uFu3bjg5OfHvf/+b6dOn2/w1ax588EHT465du9KtWzdat27Nxo0buemmm6xYstoxf/58Ro0ahYuLi8X0+rINyzo+2AKpEiqDr68v9vb2JVpBJyQkEBAQYKVSXb2JEyfyxx9/sGHDBpo3b17uvBEREQCcPn26LopW47y9vWnXrh2nT58mICCA/Px8UlNTLeapj9szOjqadevW8dhjj5U7X33ffsbtUt5/MCAgoEQj+MLCQlJSUurVdjUGK9HR0axdu9Yiu1KaiIgICgsLOXfuXN0UsAa1atUKX19f0++yoWxDgC1btnDixIkK/5tgm9uwrONDZfafAQEBpf5Xja/VBAlYyuDk5ER4eDjr1683TTMYDKxfv56+fftasWTVo2kaEydO5LfffuOvv/6iZcuWFb7nwIEDAAQGBtZy6WpHZmYmZ86cITAwkPDwcBwdHS2254kTJ4iJial323PBggU0bdqU22+/vdz56vv2a9myJQEBARbbLD09nZ07d5q2Wd++fUlNTWXv3r2mef766y8MBoMpYLN1xmDl1KlTrFu3jiZNmlT4ngMHDmBnZ1eiKqU+OH/+PMnJyabfZUPYhkZfffUV4eHhdO/evcJ5bWkbVnR8qMz+s2/fvhw6dMgi+DQG3506daqxgooy/Pjjj5qzs7O2cOFC7ejRo9rjjz+ueXt7W7SCri+efPJJzcvLS9u4caMWFxdnumVnZ2uapmmnT5/W3njjDW3Pnj1aVFSUtmzZMq1Vq1bawIEDrVzyyvvPf/6jbdy4UYuKitK2bt2qRUZGar6+vlpiYqKmaZr2xBNPaC1atND++usvbc+ePVrfvn21vn37WrnUVaPX67UWLVpoU6ZMsZheX7dfRkaGtn//fm3//v0aoH3wwQfa/v37TT1k3nnnHc3b21tbtmyZdvDgQe2uu+7SWrZsqeXk5JiWMXToUK1Hjx7azp07tb///ltr27atNnLkSGutUgnlrWN+fr525513as2bN9cOHDhg8d809qzYtm2b9uGHH2oHDhzQzpw5oy1atEjz8/PTRo8ebeU1U8pbv4yMDO3555/Xtm/frkVFRWnr1q3TevbsqbVt21bLzc01LaM+b0OjtLQ0zc3NTfvss89KvN/Wt2FFxwdNq3j/WVhYqHXp0kW75ZZbtAMHDmirVq3S/Pz8tKlTp9ZYOSVgqcAnn3yitWjRQnNyctL69Omj7dixw9pFqhag1NuCBQs0TdO0mJgYbeDAgZqPj4/m7OystWnTRnvhhRe0tLQ06xa8CkaMGKEFBgZqTk5OWrNmzbQRI0Zop0+fNr2ek5OjPfXUU1rjxo01Nzc37e6779bi4uKsWOKqW716tQZoJ06csJheX7ffhg0bSv1djhkzRtM01bX55Zdf1vz9/TVnZ2ftpptuKrHuycnJ2siRI7VGjRppnp6e2rhx47SMjAwrrE3pylvHqKioMv+bGzZs0DRN0/bu3atFRERoXl5emouLi9axY0dt2rRpFgd8aypv/bKzs7VbbrlF8/Pz0xwdHbWQkBBt/PjxJU766vM2NJo3b57m6uqqpaamlni/rW/Dio4Pmla5/ee5c+e0W2+9VXN1ddV8fX21//znP1pBQUGNlVNXVFghhBBCCJslbViEEEIIYfMkYBFCCCGEzZOARQghhBA2TwIWIYQQQtg8CViEEEIIYfMkYBFCCCGEzZOARQghhBA2TwIWIYQQQtg8CViEEEIIYfMkYBFCCCGEzZOARQghhBA27/8BpAdqiuYVfPgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Choose features\n",
        "wineFeaturesSimple = wineFeatures[['alcohol','volatile acidity','chlorides','density']]\n",
        "# Define\n",
        "model = None\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSimple.shape[1],\n",
        "                             activation='relu',\n",
        "                             input_dim=wineFeaturesSimple.shape[1]))\n",
        "# Add more layers here\n",
        "model.add(keras.layers.Dense(1,activation='linear'))\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSimple,\n",
        "                         wineLabels,\n",
        "                         epochs=200,\n",
        "                         batch_size=100,\n",
        "                         validation_split=0.2,\n",
        "                         verbose=0)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gy6CkyIZSMN3"
      },
      "source": [
        "## Check for Implementation Bugs using Reduced Dataset\n",
        "\n",
        "Your loss isn't decreasing by much. Perhaps your model has an implementation bug. From the [Model Debugging](https://developers.google.com/machine-learning/testing-debugging/common/model-errors) guidelines, a quick test for implementation bugs is to obtain a low loss on a reduced dataset of, say, 10 examples. Remember, passing this test does not validate your modeling approach but only checks for basic implementation bugs. In your ML problem, if your model passes this test, then continue debugging your model to train on your full dataset.\n",
        "\n",
        "In the following code, experiment with the learning rate, batch size, and number of epochs. Can you reach a low loss? Choose hyperparameter values that let you iterate quickly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "A0WLrVA7SUJe",
        "outputId": "e41910f3-2df4-446a-88a8-39e360e742a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 11.7090482711792\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e3d355e1e70>]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Choose 10 examples\n",
        "wineFeaturesSmall = wineFeatures[0:10]\n",
        "wineLabelsSmall = wineLabels[0:10]\n",
        "\n",
        "# Define model\n",
        "model = None\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSmall.shape[1],\n",
        "                             activation='relu',\n",
        "                             input_dim=wineFeaturesSmall.shape[1]))\n",
        "model.add(keras.layers.Dense(wineFeaturesSmall.shape[1], activation='relu'))\n",
        "model.add(keras.layers.Dense(1, activation='linear'))\n",
        "\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')  # Set learning rate\n",
        "\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSmall,\n",
        "                        wineLabelsSmall,\n",
        "                        epochs=50,  # Replace with your desired number of epochs\n",
        "                        batch_size=32,  # Replace with your desired batch size\n",
        "                        verbose=0)\n",
        "\n",
        "# Plot results\n",
        "print(\"Final training loss: \" + str(trainHistory.history['loss'][-1]))\n",
        "plt.plot(trainHistory.history['loss'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uoUVsCIsYBVC"
      },
      "source": [
        "### Solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z5TWFqrZ-oyp"
      },
      "source": [
        "Run the following code cell to train the model using these hyperparameter values:\n",
        "\n",
        "* learning rate = 0.01\n",
        "* epochs = 200\n",
        "* batch size = 10\n",
        "\n",
        "You get a low loss on your reduced dataset. This result means your model is probably solid and your previous results are as good as they'll get."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "xzIcE6j_fQNf",
        "outputId": "2ddf4b3e-2133-4aa5-c75d-0db09627a0d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final training loss: 0.33318591117858887\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e3d441045e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Choose 10 examples\n",
        "wineFeaturesSmall = wineFeatures[0:10]\n",
        "wineLabelsSmall = wineLabels[0:10]\n",
        "# Define model\n",
        "model = None\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeaturesSmall.shape[1], activation='relu',\n",
        "                             input_dim=wineFeaturesSmall.shape[1]))\n",
        "model.add(keras.layers.Dense(wineFeaturesSmall.shape[1], activation='relu'))\n",
        "model.add(keras.layers.Dense(1, activation='linear'))\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(0.01), loss='mse') # set LR\n",
        "# Train\n",
        "trainHistory = model.fit(wineFeaturesSmall,\n",
        "                        wineLabelsSmall,\n",
        "                        epochs=200,\n",
        "                        batch_size=10,\n",
        "                        verbose=0)\n",
        "# Plot results\n",
        "print(\"Final training loss: \" + str(trainHistory.history['loss'][-1]))\n",
        "plt.plot(trainHistory.history['loss'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LK-CuHasqxt6"
      },
      "source": [
        "## Trying a Very Complex Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLiaqT8AKZ8o"
      },
      "source": [
        "Let's go all in and use a very complex model with all the features. For science! And to satisfy ourselves that a simple model is indeed better. Let's use all 11 features with 3 fully-connected relu layers and a final linear layer. The next cell takes a while to run. Skip to the results in the cell after if you like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "I5D1IyEZq1Ej",
        "outputId": "30ef02c6-dab9-4702-cedb-9b33e5cbc126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "52/52 [==============================] - 1s 8ms/step - loss: 10.1107 - val_loss: 5.9102\n",
            "Epoch 2/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 5.0580 - val_loss: 4.2809\n",
            "Epoch 3/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 3.0781 - val_loss: 1.6974\n",
            "Epoch 4/100\n",
            "52/52 [==============================] - 0s 5ms/step - loss: 1.5744 - val_loss: 1.0843\n",
            "Epoch 5/100\n",
            "52/52 [==============================] - 0s 5ms/step - loss: 1.1691 - val_loss: 0.8313\n",
            "Epoch 6/100\n",
            "52/52 [==============================] - 0s 5ms/step - loss: 0.9342 - val_loss: 0.7203\n",
            "Epoch 7/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.8001 - val_loss: 0.6452\n",
            "Epoch 8/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.7394 - val_loss: 0.5998\n",
            "Epoch 9/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6811 - val_loss: 0.5844\n",
            "Epoch 10/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.6714 - val_loss: 0.5682\n",
            "Epoch 11/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6571 - val_loss: 0.5636\n",
            "Epoch 12/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6560 - val_loss: 0.5587\n",
            "Epoch 13/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6351 - val_loss: 0.5630\n",
            "Epoch 14/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6233 - val_loss: 0.5414\n",
            "Epoch 15/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6215 - val_loss: 0.5577\n",
            "Epoch 16/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6176 - val_loss: 0.5470\n",
            "Epoch 17/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6180 - val_loss: 0.5335\n",
            "Epoch 18/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6123 - val_loss: 0.5459\n",
            "Epoch 19/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6090 - val_loss: 0.5325\n",
            "Epoch 20/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6000 - val_loss: 0.5298\n",
            "Epoch 21/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5983 - val_loss: 0.5214\n",
            "Epoch 22/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5977 - val_loss: 0.5245\n",
            "Epoch 23/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5999 - val_loss: 0.5583\n",
            "Epoch 24/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5969 - val_loss: 0.5317\n",
            "Epoch 25/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.6043 - val_loss: 0.5147\n",
            "Epoch 26/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5909 - val_loss: 0.5159\n",
            "Epoch 27/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5897 - val_loss: 0.5121\n",
            "Epoch 28/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5932 - val_loss: 0.5663\n",
            "Epoch 29/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5919 - val_loss: 0.5229\n",
            "Epoch 30/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5835 - val_loss: 0.5169\n",
            "Epoch 31/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5797 - val_loss: 0.5089\n",
            "Epoch 32/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5781 - val_loss: 0.5065\n",
            "Epoch 33/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5766 - val_loss: 0.5060\n",
            "Epoch 34/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5791 - val_loss: 0.5149\n",
            "Epoch 35/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5989 - val_loss: 0.5289\n",
            "Epoch 36/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.6003 - val_loss: 0.5356\n",
            "Epoch 37/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5821 - val_loss: 0.5042\n",
            "Epoch 38/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5719 - val_loss: 0.5030\n",
            "Epoch 39/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5781 - val_loss: 0.5014\n",
            "Epoch 40/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5758 - val_loss: 0.5395\n",
            "Epoch 41/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5741 - val_loss: 0.5062\n",
            "Epoch 42/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5726 - val_loss: 0.5127\n",
            "Epoch 43/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5683 - val_loss: 0.5002\n",
            "Epoch 44/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5734 - val_loss: 0.5106\n",
            "Epoch 45/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5713 - val_loss: 0.5118\n",
            "Epoch 46/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5906 - val_loss: 0.4978\n",
            "Epoch 47/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5761 - val_loss: 0.5143\n",
            "Epoch 48/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5636 - val_loss: 0.5006\n",
            "Epoch 49/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5736 - val_loss: 0.5068\n",
            "Epoch 50/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5643 - val_loss: 0.5086\n",
            "Epoch 51/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5614 - val_loss: 0.4964\n",
            "Epoch 52/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5792 - val_loss: 0.4966\n",
            "Epoch 53/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5697 - val_loss: 0.5660\n",
            "Epoch 54/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5816 - val_loss: 0.4941\n",
            "Epoch 55/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5679 - val_loss: 0.5029\n",
            "Epoch 56/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5675 - val_loss: 0.4994\n",
            "Epoch 57/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5618 - val_loss: 0.4932\n",
            "Epoch 58/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5676 - val_loss: 0.4896\n",
            "Epoch 59/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5602 - val_loss: 0.4893\n",
            "Epoch 60/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5555 - val_loss: 0.5295\n",
            "Epoch 61/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5677 - val_loss: 0.5429\n",
            "Epoch 62/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5622 - val_loss: 0.4897\n",
            "Epoch 63/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5732 - val_loss: 0.4886\n",
            "Epoch 64/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5670 - val_loss: 0.4880\n",
            "Epoch 65/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5759 - val_loss: 0.4933\n",
            "Epoch 66/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5702 - val_loss: 0.5184\n",
            "Epoch 67/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5570 - val_loss: 0.4869\n",
            "Epoch 68/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5545 - val_loss: 0.4860\n",
            "Epoch 69/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5596 - val_loss: 0.4951\n",
            "Epoch 70/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5811 - val_loss: 0.5845\n",
            "Epoch 71/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5685 - val_loss: 0.4868\n",
            "Epoch 72/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5553 - val_loss: 0.4904\n",
            "Epoch 73/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5569 - val_loss: 0.4846\n",
            "Epoch 74/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5619 - val_loss: 0.4842\n",
            "Epoch 75/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5606 - val_loss: 0.4840\n",
            "Epoch 76/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5609 - val_loss: 0.5185\n",
            "Epoch 77/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5972 - val_loss: 0.4855\n",
            "Epoch 78/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5677 - val_loss: 0.4825\n",
            "Epoch 79/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5563 - val_loss: 0.4875\n",
            "Epoch 80/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5526 - val_loss: 0.4960\n",
            "Epoch 81/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5552 - val_loss: 0.5070\n",
            "Epoch 82/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5535 - val_loss: 0.4903\n",
            "Epoch 83/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5566 - val_loss: 0.5203\n",
            "Epoch 84/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5538 - val_loss: 0.4860\n",
            "Epoch 85/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5700 - val_loss: 0.5153\n",
            "Epoch 86/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5555 - val_loss: 0.4886\n",
            "Epoch 87/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5510 - val_loss: 0.4816\n",
            "Epoch 88/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5529 - val_loss: 0.4927\n",
            "Epoch 89/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5492 - val_loss: 0.4852\n",
            "Epoch 90/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5496 - val_loss: 0.4937\n",
            "Epoch 91/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5633 - val_loss: 0.4809\n",
            "Epoch 92/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5701 - val_loss: 0.5083\n",
            "Epoch 93/100\n",
            "52/52 [==============================] - 0s 4ms/step - loss: 0.5765 - val_loss: 0.5725\n",
            "Epoch 94/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5581 - val_loss: 0.4898\n",
            "Epoch 95/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5595 - val_loss: 0.5327\n",
            "Epoch 96/100\n",
            "52/52 [==============================] - 0s 2ms/step - loss: 0.5616 - val_loss: 0.5250\n",
            "Epoch 97/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5724 - val_loss: 0.4854\n",
            "Epoch 98/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5451 - val_loss: 0.5053\n",
            "Epoch 99/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5511 - val_loss: 0.5023\n",
            "Epoch 100/100\n",
            "52/52 [==============================] - 0s 3ms/step - loss: 0.5540 - val_loss: 0.4844\n",
            "Final training loss: 0.5540224313735962\n",
            "Final Validation loss: 0.48444509506225586\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.4, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = None\n",
        "# Define\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Dense(wineFeatures.shape[1], activation='relu',\n",
        "                             input_dim=wineFeatures.shape[1]))\n",
        "model.add(keras.layers.Dense(wineFeatures.shape[1], activation='relu'))\n",
        "model.add(keras.layers.Dense(wineFeatures.shape[1], activation='relu'))\n",
        "model.add(keras.layers.Dense(1,activation='linear'))\n",
        "# Compile\n",
        "model.compile(optimizer=tf.optimizers.Adam(), loss='mse')\n",
        "# Train the model!\n",
        "trainHistory = model.fit(wineFeatures, wineLabels, epochs=100, batch_size=100,\n",
        "                         verbose=1, validation_split = 0.2)\n",
        "# Plot results\n",
        "showRegressionResults(trainHistory)\n",
        "plt.ylim(0.4,1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eClRDcGVudk2"
      },
      "source": [
        "### Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkM7MT8XLEd6"
      },
      "source": [
        "If you train for long enough, the minimum achievable MSE is around 0.52, which is a decrease of 0.04 from the previous best loss of 0.56. This decrease probably isn't worth the performance and complexity cost of using all your features and a deeper network. However, that decision depends on the objectives you're optimizing for.\n",
        "\n",
        "If you train the model long enough, the validation loss actually starts increasing while training loss continues to decrease. This divergence in loss curves means your model is overfitting. The overfitting results from the closer fit that your very complex model can learn. Stick with the simpler model. You'll be happier and live longer!\n",
        "\n",
        "If you do want to optimize your loss, then play with the model to find the minimum achievable training loss before overfitting sets in. Try playing with the network parameters in the code cell above to achieve a loss of 0.51. But be warned—optimizing your loss could take a lot of trial and error.."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yT7i3BdqvzKO"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "This Colab demonstrated the following principles:\n",
        "\n",
        "* The most important step in machine learning is understanding your data.\n",
        "* The largest gains come from the initial features and network.\n",
        "* Returns diminish as you add features and complexity.\n",
        "* Incremental development provides confidence in model quality and allows benchmarking against previous results.\n",
        "* Reproducing previous results is extremely important. Hence, always use version control."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "vrr7QBAu_oeC",
        "fko5u9Y3hiKW",
        "nWgwvf4qODQE",
        "w8qvAbVIsL4w",
        "_prjsbFc71O8",
        "SDLggOvfqFbe",
        "syvONtOvrkBV",
        "Nm1xp9DjCejl",
        "uoUVsCIsYBVC",
        "eClRDcGVudk2"
      ],
      "name": "Case Study: Debugging in Regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8.13 ('open-machine-learning-jupyter-book')",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
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    "vscode": {
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}