DeepLearningMLP_practical_course.ipynb

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        "# Deep Neural Network practical course\n",
        "\n",
        "For this practical course we will use a deep multilayer perceptron to classify patients into two classes: chronic kidney disease, no chronic kidney disease.\n",
        "The dataset can be downloaded from: https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease#\n"
      ]
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      "source": [
        "## The Data\n",
        "\n",
        "We will use a data frame with 400 instances on the following 24 variables.\n",
        "* Attribute Information:\n",
        "We use 24 ( 11 numeric, 13 nominal)\n",
        "\n",
        "1.Age(numerical)\n",
        "age in years\n",
        "\n",
        "2.Blood Pressure(numerical)\n",
        "bp in mm/Hg\n",
        "\n",
        "3.Specific Gravity(nominal??)\n",
        "sg - (1.005,1.010,1.015,1.020,1.025)\n",
        "\n",
        "4.Albumin(nominal??)\n",
        "al - (0,1,2,3,4,5)\n",
        "\n",
        "5.Sugar(nominal??)\n",
        "su - (0,1,2,3,4,5)\n",
        "\n",
        "6.Red Blood Cells(nominal)\n",
        "rbc - (normal,abnormal)\n",
        "\n",
        "7.Pus Cell (nominal)\n",
        "pc - (normal,abnormal)\n",
        "\n",
        "8.Pus Cell clumps(nominal)\n",
        "pcc - (present,notpresent)\n",
        "\n",
        "9.Bacteria(nominal)\n",
        "ba - (present,notpresent)\n",
        "\n",
        "10.Blood Glucose Random(numerical)\n",
        "bgr in mgs/dl\n",
        "\n",
        "11.Blood Urea(numerical)\n",
        "bu in mgs/dl\n",
        "\n",
        "12.Serum Creatinine(numerical)\n",
        "sc in mgs/dl\n",
        "\n",
        "13.Sodium(numerical)\n",
        "sod in mEq/L\n",
        "\n",
        "14.Potassium(numerical)\n",
        "pot in mEq/L\n",
        "\n",
        "15.Hemoglobin(numerical)\n",
        "hemo in gms\n",
        "\n",
        "16.Packed Cell Volume(numerical)\n",
        "\n",
        "17.White Blood Cell Count(numerical)\n",
        "wc in cells/cumm\n",
        "\n",
        "18.Red Blood Cell Count(numerical)\n",
        "rc in millions/cmm\n",
        "\n",
        "19.Hypertension(nominal)\n",
        "htn - (yes,no)\n",
        "\n",
        "20.Diabetes Mellitus(nominal)\n",
        "dm - (yes,no)\n",
        "\n",
        "21.Coronary Artery Disease(nominal)\n",
        "cad - (yes,no)\n",
        "\n",
        "22.Appetite(nominal)\n",
        "appet - (good,poor)\n",
        "\n",
        "23.Pedal Edema(nominal)\n",
        "pe - (yes,no)\n",
        "\n",
        "24.Anemia(nominal)\n",
        "ane - (yes,no)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Import Libraries\n",
        "\n",
        "** Import the libraries you usually use for data analysis.**"
      ]
    },
    {
      "cell_type": "code",
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        "id": "9jwrfA3MRWbB"
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      "source": [
        "import tensorflow as tf\n",
        "from keras import layers\n",
        "from sklearn.datasets import make_blobs\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Input, Dense, Activation\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "#from keras.utils import np_utils\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 16,
      "outputs": []
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      "source": [
        "## Get the Data"
      ]
    },
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      "source": [
        "** Read in the \"chronic_kidney_disease\" file using read_csv.**"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "\n",
        "data = []\n",
        "with open('chronic_kidney_disease_full.arff', \"r\") as f:\n",
        "    for line in f:\n",
        "        line = line.replace('\\n', '')\n",
        "        data.append(line.split(','))\n",
        "\n",
        "\n",
        "names = ['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba',\n",
        "         'bgr', 'bu',  'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc',\n",
        "         'rbcc', 'htn', 'dm', 'cad', 'appet', 'pe', 'ane',\n",
        "         'class', 'no_name']\n",
        "\n",
        "df = pd.DataFrame(data[145:], columns=names)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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      "source": [
        "**Check the head of the data**"
      ]
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        "df.head()"
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      "execution_count": 4,
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6e3c2683-8f87-42dc-bdc9-061b83816dc5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  age  bp     sg al su     rbc        pc         pcc          ba  bgr  ...  \\\n",
              "0  48  80  1.020  1  0       ?    normal  notpresent  notpresent  121  ...   \n",
              "1   7  50  1.020  4  0       ?    normal  notpresent  notpresent    ?  ...   \n",
              "2  62  80  1.010  2  3  normal    normal  notpresent  notpresent  423  ...   \n",
              "3  48  70  1.005  4  0  normal  abnormal     present  notpresent  117  ...   \n",
              "4  51  80  1.010  2  0  normal    normal  notpresent  notpresent  106  ...   \n",
              "\n",
              "   wbcc rbcc  htn   dm cad appet   pe  ane class no_name  \n",
              "0  7800  5.2  yes  yes  no  good   no   no   ckd    None  \n",
              "1  6000    ?   no   no  no  good   no   no   ckd    None  \n",
              "2  7500    ?   no  yes  no  poor   no  yes   ckd    None  \n",
              "3  6700  3.9  yes   no  no  poor  yes  yes   ckd    None  \n",
              "4  7300  4.6   no   no  no  good   no   no   ckd    None  \n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ty1ulxA3RWbM"
      },
      "source": [
        "** Check the info() and describe() methods on the data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UONs9szkRWbN",
        "outputId": "e4dd977c-b8f2-456a-e03e-4d59627b7e98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 402 entries, 0 to 401\n",
            "Data columns (total 26 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   age      402 non-null    object\n",
            " 1   bp       400 non-null    object\n",
            " 2   sg       400 non-null    object\n",
            " 3   al       400 non-null    object\n",
            " 4   su       400 non-null    object\n",
            " 5   rbc      400 non-null    object\n",
            " 6   pc       400 non-null    object\n",
            " 7   pcc      400 non-null    object\n",
            " 8   ba       400 non-null    object\n",
            " 9   bgr      400 non-null    object\n",
            " 10  bu       400 non-null    object\n",
            " 11  sc       400 non-null    object\n",
            " 12  sod      400 non-null    object\n",
            " 13  pot      400 non-null    object\n",
            " 14  hemo     400 non-null    object\n",
            " 15  pcv      400 non-null    object\n",
            " 16  wbcc     400 non-null    object\n",
            " 17  rbcc     400 non-null    object\n",
            " 18  htn      400 non-null    object\n",
            " 19  dm       400 non-null    object\n",
            " 20  cad      400 non-null    object\n",
            " 21  appet    400 non-null    object\n",
            " 22  pe       400 non-null    object\n",
            " 23  ane      400 non-null    object\n",
            " 24  class    400 non-null    object\n",
            " 25  no_name  3 non-null      object\n",
            "dtypes: object(26)\n",
            "memory usage: 81.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezsdOY4iRWbP",
        "outputId": "e7f0342c-dac1-4fa5-a70b-302d13c36d36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age   bp     sg   al   su     rbc      pc         pcc          ba  \\\n",
              "count   402  400    400  400  400     400     400         400         400   \n",
              "unique   78   11      6    7    7       3       3           3           3   \n",
              "top      60   80  1.020    0    0  normal  normal  notpresent  notpresent   \n",
              "freq     19  116    106  199  290     201     259         354         374   \n",
              "\n",
              "        bgr  ... wbcc rbcc  htn   dm  cad appet   pe  ane class no_name  \n",
              "count   400  ...  400  400  400  400  400   400  400  400   400       3  \n",
              "unique  147  ...   93   50    3    7    4     4    4    3     4       2  \n",
              "top       ?  ...    ?    ?   no   no   no  good   no   no   ckd          \n",
              "freq     44  ...  105  130  251  257  362   316  322  339   248       2  \n",
              "\n",
              "[4 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ed4f7e9-ea14-4f5e-8e5b-00e83ddadae7\" 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",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>rbc</th>\n",
              "      <th>pc</th>\n",
              "      <th>pcc</th>\n",
              "      <th>ba</th>\n",
              "      <th>bgr</th>\n",
              "      <th>...</th>\n",
              "      <th>wbcc</th>\n",
              "      <th>rbcc</th>\n",
              "      <th>htn</th>\n",
              "      <th>dm</th>\n",
              "      <th>cad</th>\n",
              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>class</th>\n",
              "      <th>no_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>402</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>...</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>400</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>78</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>147</td>\n",
              "      <td>...</td>\n",
              "      <td>93</td>\n",
              "      <td>50</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>60</td>\n",
              "      <td>80</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>?</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>19</td>\n",
              "      <td>116</td>\n",
              "      <td>106</td>\n",
              "      <td>199</td>\n",
              "      <td>290</td>\n",
              "      <td>201</td>\n",
              "      <td>259</td>\n",
              "      <td>354</td>\n",
              "      <td>374</td>\n",
              "      <td>44</td>\n",
              "      <td>...</td>\n",
              "      <td>105</td>\n",
              "      <td>130</td>\n",
              "      <td>251</td>\n",
              "      <td>257</td>\n",
              "      <td>362</td>\n",
              "      <td>316</td>\n",
              "      <td>322</td>\n",
              "      <td>339</td>\n",
              "      <td>248</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ed4f7e9-ea14-4f5e-8e5b-00e83ddadae7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2ed4f7e9-ea14-4f5e-8e5b-00e83ddadae7 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-2ed4f7e9-ea14-4f5e-8e5b-00e83ddadae7');\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",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1dca3d99-00f8-40cb-853c-7d3f6bb2c0fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1dca3d99-00f8-40cb-853c-7d3f6bb2c0fd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      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",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1dca3d99-00f8-40cb-853c-7d3f6bb2c0fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3FP-azRBUEn"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azrow683BUEo"
      },
      "source": [
        "# Separate the labels from the variables\n",
        "data = df.drop(\"class\", axis =1)\n",
        "labels = df[\"class\"]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kvUNC4JOhvHx"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dpcFNo_ckuYP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCZ6r_yVBUEo"
      },
      "source": [
        "# Replace missing values with a median (or means , zeros, etc.)\n",
        "\n",
        "\n",
        "from sklearn.impute import SimpleImputer\n",
        "import numpy as np\n",
        "\n",
        "imputer = SimpleImputer(strategy=\"median\")\n",
        "\n",
        "#Create a copy without text attribute\n",
        "#\"sg\", \"al\", \"su\"\n",
        "#Nominal attributes:\"rbc\",\"pc\",\"pcc\",\"ba\",\"htn\",\"dm\", \"cad\", \"appet\", \"pe\", \"ane\"\n",
        "data_num = data[[\"age\", \"bp\",\"sg\", \"al\", \"su\", \"bgr\", \"bu\", \"sc\", \"sod\", \"pot\", \"hemo\",\"pcv\", \"wbcc\", \"rbcc\"]]\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
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        "id": "tz0PpxQIBUEo",
        "outputId": "48ffab0b-e6f7-4b98-ffcf-7da3558be2de",
        "colab": {
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      "source": [
        "data_num.describe()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age   bp     sg   al   su  bgr   bu   sc  sod  pot hemo  pcv wbcc rbcc\n",
              "count   402  400    400  400  400  400  400  400  400  400  400  400  400  400\n",
              "unique   78   11      6    7    7  147  119   87   35   43  119   45   93   50\n",
              "top      60   80  1.020    0    0    ?    ?  1.2    ?    ?    ?    ?    ?    ?\n",
              "freq     19  116    106  199  290   44   19   40   87   88   52   70  105  130"
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              "      <td>402</td>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_num\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          78,\n          \"19\",\n          \"402\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11,\n          \"116\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sg\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6,\n          \"106\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"al\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"199\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"su\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"290\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bgr\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          147,\n          \"44\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bu\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          119,\n          \"19\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          87,\n          \"40\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sod\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35,\n          \"87\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pot\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          43,\n          \"88\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hemo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          119,\n          \"52\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pcv\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          45,\n          \"70\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wbcc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          93,\n          \"105\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rbcc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          50,\n          \"130\",\n          \"400\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrQip_urBUEo",
        "outputId": "488e1819-266c-428d-e0fe-8b6fcb54d424",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_num = data_num.apply(pd.to_numeric, errors='coerce')#fit the imputer instance to the data\n",
        "imputer.fit(data_num)\n",
        "#imputer.statistics_\n",
        "data_num.median().values"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.500e+01, 8.000e+01, 1.020e+00, 0.000e+00, 0.000e+00, 1.210e+02,\n",
              "       4.200e+01, 1.300e+00, 1.380e+02, 4.400e+00, 1.265e+01, 4.000e+01,\n",
              "       8.000e+03, 4.800e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7x_q171BUEp"
      },
      "source": [
        "#trasform the dataset by replacing the missing values by the medians\n",
        "X = imputer.transform(data_num)\n",
        "data_tr = pd.DataFrame(X, columns = data_num.columns)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMvQrSCdBUEp",
        "outputId": "a9e4a463-c659-4215-b92c-3090b8949843"
      },
      "source": [
        "data_tr.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
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              "      <th>pcv</th>\n",
              "      <th>wbcc</th>\n",
              "      <th>rbcc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.00000</td>\n",
              "      <td>400.000000</td>\n",
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              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>51.562500</td>\n",
              "      <td>76.575000</td>\n",
              "      <td>1.017712</td>\n",
              "      <td>0.90000</td>\n",
              "      <td>0.395000</td>\n",
              "      <td>145.062500</td>\n",
              "      <td>56.693000</td>\n",
              "      <td>2.997125</td>\n",
              "      <td>137.631250</td>\n",
              "      <td>4.577250</td>\n",
              "      <td>12.54250</td>\n",
              "      <td>39.082500</td>\n",
              "      <td>8298.500000</td>\n",
              "      <td>4.737750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.982996</td>\n",
              "      <td>13.489785</td>\n",
              "      <td>0.005434</td>\n",
              "      <td>1.31313</td>\n",
              "      <td>1.040038</td>\n",
              "      <td>75.260774</td>\n",
              "      <td>49.395258</td>\n",
              "      <td>5.628886</td>\n",
              "      <td>9.206332</td>\n",
              "      <td>2.821357</td>\n",
              "      <td>2.71649</td>\n",
              "      <td>8.162245</td>\n",
              "      <td>2529.593814</td>\n",
              "      <td>0.841439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.10000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.015000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.87500</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>6975.000000</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>12.65000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>4.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>61.750000</td>\n",
              "      <td>2.725000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>14.62500</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>9400.000000</td>\n",
              "      <td>5.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>17.80000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>26400.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age          bp          sg         al          su         bgr  \\\n",
              "count  400.000000  400.000000  400.000000  400.00000  400.000000  400.000000   \n",
              "mean    51.562500   76.575000    1.017712    0.90000    0.395000  145.062500   \n",
              "std     16.982996   13.489785    0.005434    1.31313    1.040038   75.260774   \n",
              "min      2.000000   50.000000    1.005000    0.00000    0.000000   22.000000   \n",
              "25%     42.000000   70.000000    1.015000    0.00000    0.000000  101.000000   \n",
              "50%     55.000000   80.000000    1.020000    0.00000    0.000000  121.000000   \n",
              "75%     64.000000   80.000000    1.020000    2.00000    0.000000  150.000000   \n",
              "max     90.000000  180.000000    1.025000    5.00000    5.000000  490.000000   \n",
              "\n",
              "               bu          sc         sod         pot       hemo         pcv  \\\n",
              "count  400.000000  400.000000  400.000000  400.000000  400.00000  400.000000   \n",
              "mean    56.693000    2.997125  137.631250    4.577250   12.54250   39.082500   \n",
              "std     49.395258    5.628886    9.206332    2.821357    2.71649    8.162245   \n",
              "min      1.500000    0.400000    4.500000    2.500000    3.10000    9.000000   \n",
              "25%     27.000000    0.900000  135.000000    4.000000   10.87500   34.000000   \n",
              "50%     42.000000    1.300000  138.000000    4.400000   12.65000   40.000000   \n",
              "75%     61.750000    2.725000  141.000000    4.800000   14.62500   44.000000   \n",
              "max    391.000000   76.000000  163.000000   47.000000   17.80000   54.000000   \n",
              "\n",
              "               wbcc        rbcc  \n",
              "count    400.000000  400.000000  \n",
              "mean    8298.500000    4.737750  \n",
              "std     2529.593814    0.841439  \n",
              "min     2200.000000    2.100000  \n",
              "25%     6975.000000    4.500000  \n",
              "50%     8000.000000    4.800000  \n",
              "75%     9400.000000    5.100000  \n",
              "max    26400.000000    8.000000  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvy5dqHDBUEp"
      },
      "source": [
        "Handling categorical attributes (optional)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcgXcytQBUEp"
      },
      "source": [
        "data_cat = data[[\"rbc\",\"pc\",\"pcc\",\"ba\",\"htn\",\"dm\", \"cad\", \"appet\", \"pe\", \"ane\"]]\n",
        "#Use the pandas functions : factorize(), Imputer with the \"most_frequent\" strategy, OneHotEncoder()\n"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c9Hy6J5NxlBe"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAM3SjZjRWbS"
      },
      "source": [
        "## Data Visualization\n",
        "\n",
        "It's time to create some data visualizations!\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wulbE9pgRWbS",
        "outputId": "bbaf83e3-9ebe-4a6b-b1e2-83d0dd68f395",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "#Plot a histogram for each attribute\n",
        "data_tr.hist(bins=50)\n",
        "plt.show()\n",
        "#\"age\", \"bp\",\"sg\", \"al\", \"su\", \"bgr\", \"bu\", \"sc\", \"sod\", \"pot\", \"hemo\",\"pcv\", \"wbcc\", \"rbcc\""
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HbiQxx2MxJ2d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqRyGip6BUEq"
      },
      "source": [
        "# Data normalization\n",
        "scaler =  preprocessing.StandardScaler().fit(data_tr)\n",
        "data_tr = scaler.transform(data_tr)\n",
        "\n",
        "# transform the labels\n",
        "classes = labels.map(lambda x :0 if x=='ckd' else 1)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pf-6nVs3BUEq",
        "outputId": "9eeacedf-9d47-444e-db10-77b36ac9d8d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Data spliting\n",
        "x_train, x_test, y_train, y_test = train_test_split(data_tr, classes, test_size=0.2, random_state=42)\n",
        "print(y_train.shape)\n",
        "print(x_train.shape)\n",
        "print(x_train.shape[1])\n",
        "\n",
        "classes_train = tf.keras.utils.to_categorical(y_train)\n",
        "classes_test = tf.keras.utils.to_categorical(y_test)\n",
        "print(classes_train.shape)\n",
        "print(classes_test.shape)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(321,)\n",
            "(321, 14)\n",
            "14\n",
            "(321, 2)\n",
            "(81, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QurCSiVgBUEr"
      },
      "source": [
        "# Model learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZ5ci2k5BUEr"
      },
      "source": [
        "**You will apply the different deep learning tools that allow to improve the results**\n",
        "1. Built a deeep learning model with 5 hidden layers\n",
        "2. Apply the early stopping function that allows to stop the training step when the test error starts increasing\n",
        "3. Apply L1, then L2 regularization\n",
        "4. Apply Dropout\n",
        "5. Change the gradient descent optimization algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqLoRUPGBUEr"
      },
      "source": [
        "# Import the early stopping library\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "\n",
        "# Import the dropout library\n",
        "\n",
        "\n",
        "# Import the library of regulizers\n",
        "\n",
        "\n",
        "#parameters\n",
        "\n",
        "\n",
        "\n",
        "# Create & train the Deep MLP model with 5 hidden layers\n",
        "\n",
        "\n",
        "# Add the hidden layers and the regulizers\n",
        "\n",
        "# Add dropout for each hidden layer\n",
        "\n",
        "\n",
        "# Compile the model for a multi-class classification problem\n",
        "\n",
        "\n",
        "# Add early stoping\n"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the early stopping library\n",
        "from keras.callbacks import EarlyStopping\n",
        "\n",
        "# Import the dropout library\n",
        "from keras.layers import Dropout\n",
        "\n",
        "# Import the library of regulizers\n",
        "from keras import regularizers\n",
        "\n",
        "#parameters\n",
        "nb_features = 14\n",
        "learning_rate = 0.01\n",
        "opt = 'SGD'\n",
        "hidden1_drop = 0.3\n",
        "hidden2_drop = 0.3\n",
        "hidden3_drop = 0.3\n",
        "hidden4_drop = 0.3\n",
        "input_drop = 0.2\n",
        "lambda_l1 = 0.001\n",
        "lambda_l2 = 0\n",
        "size_batch= 250\n",
        "nb_epochs = 50\n",
        "\n",
        "# Create & train the Deep MLP model with 4 hidden layers\n",
        "model = Sequential()\n",
        "\n",
        "# Add the first hidden layer\n",
        "model.add(Dense(30, input_dim = nb_features, activation='relu', kernel_regularizer=regularizers.l1(lambda_l1)))\n",
        "#model.add(Activation('relu'))\n",
        "#model.add(Dropout(input_drop))\n",
        "\n",
        "# Add the second hidden layer\n",
        "model.add(Dense(20, activation='relu', kernel_regularizer=regularizers.l2(lambda_l1)))\n",
        "#model.add(Dropout(hidden1_drop))\n",
        "\n",
        "# Add the third hidden layer\n",
        "model.add(Dense(20, activation='relu', kernel_regularizer=regularizers.l1(lambda_l1)))\n",
        "#model.add(Dropout(hidden2_drop))\n",
        "\n",
        "# Add the fourth hidden layer\n",
        "model.add(Dense(20, activation='relu', kernel_regularizer=regularizers.l1(lambda_l1)))\n",
        "#model.add(Dropout(hidden3_drop))\n",
        "\n",
        "# Add the fifth hidden layer\n",
        "model.add(Dense(20, activation='relu', kernel_regularizer=regularizers.l1(lambda_l1)))\n",
        "#model.add(Dropout(hidden4_drop))\n",
        "\n",
        "# # Add the output layer\n",
        "model.add(Dense(2, activation='softmax', kernel_regularizer=regularizers.l1(lambda_l1)))\n",
        "\n",
        "# For a multi-class classification problem\n",
        "model.compile(optimizer=opt,\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "#  kernel_regularizer=regularizers.l2(0.01),\n",
        "               # activity_regularizer=regularizers.l1(0.01))))\n",
        "\n",
        "es = EarlyStopping(monitor='val_acc', mode='max', verbose=1, patience = 100)\n",
        "\n",
        "history = model.fit(x_train, classes_train, validation_data=(x_test, classes_test), batch_size = size_batch, epochs=nb_epochs, verbose=1, callbacks=[es])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIfhW-KIv-Uq",
        "outputId": "79145f79-1056-4665-fc4f-bed09b93e881"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 476ms/step - accuracy: 0.4128 - loss: 1.0643 - val_accuracy: 0.4568 - val_loss: 1.0472\n",
            "Epoch 2/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.4293 - loss: 1.0605 - val_accuracy: 0.4691 - val_loss: 1.0430\n",
            "Epoch 3/50\n",
            "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.4200 - loss: 1.0607"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/callbacks/early_stopping.py:155: UserWarning: Early stopping conditioned on metric `val_acc` which is not available. Available metrics are: accuracy,loss,val_accuracy,val_loss\n",
            "  current = self.get_monitor_value(logs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.4287 - loss: 1.0582 - val_accuracy: 0.4691 - val_loss: 1.0398\n",
            "Epoch 4/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.4319 - loss: 1.0540 - val_accuracy: 0.5556 - val_loss: 1.0361\n",
            "Epoch 5/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.3993 - loss: 1.0503 - val_accuracy: 0.5679 - val_loss: 1.0323\n",
            "Epoch 6/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - accuracy: 0.4297 - loss: 1.0459 - val_accuracy: 0.6049 - val_loss: 1.0288\n",
            "Epoch 7/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - accuracy: 0.4244 - loss: 1.0430 - val_accuracy: 0.6049 - val_loss: 1.0254\n",
            "Epoch 8/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.4389 - loss: 1.0399 - val_accuracy: 0.6173 - val_loss: 1.0220\n",
            "Epoch 9/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.4376 - loss: 1.0373 - val_accuracy: 0.6173 - val_loss: 1.0194\n",
            "Epoch 10/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - accuracy: 0.4729 - loss: 1.0344 - val_accuracy: 0.5802 - val_loss: 1.0167\n",
            "Epoch 11/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - accuracy: 0.4560 - loss: 1.0321 - val_accuracy: 0.5926 - val_loss: 1.0144\n",
            "Epoch 12/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.4910 - loss: 1.0298 - val_accuracy: 0.6420 - val_loss: 1.0116\n",
            "Epoch 13/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - accuracy: 0.5277 - loss: 1.0273 - val_accuracy: 0.6543 - val_loss: 1.0093\n",
            "Epoch 14/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - accuracy: 0.5474 - loss: 1.0243 - val_accuracy: 0.6420 - val_loss: 1.0073\n",
            "Epoch 15/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - accuracy: 0.5464 - loss: 1.0233 - val_accuracy: 0.6420 - val_loss: 1.0050\n",
            "Epoch 16/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - accuracy: 0.5735 - loss: 1.0206 - val_accuracy: 0.6543 - val_loss: 1.0029\n",
            "Epoch 17/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - accuracy: 0.5864 - loss: 1.0179 - val_accuracy: 0.6543 - val_loss: 1.0011\n",
            "Epoch 18/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.5845 - loss: 1.0167 - val_accuracy: 0.6543 - val_loss: 0.9990\n",
            "Epoch 19/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - accuracy: 0.5899 - loss: 1.0147 - val_accuracy: 0.6543 - val_loss: 0.9971\n",
            "Epoch 20/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - accuracy: 0.5885 - loss: 1.0124 - val_accuracy: 0.6667 - val_loss: 0.9952\n",
            "Epoch 21/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - accuracy: 0.5832 - loss: 1.0109 - val_accuracy: 0.6667 - val_loss: 0.9931\n",
            "Epoch 22/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.6014 - loss: 1.0074 - val_accuracy: 0.6667 - val_loss: 0.9916\n",
            "Epoch 23/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.5982 - loss: 1.0071 - val_accuracy: 0.6667 - val_loss: 0.9897\n",
            "Epoch 24/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.6042 - loss: 1.0043 - val_accuracy: 0.6667 - val_loss: 0.9880\n",
            "Epoch 25/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.6016 - loss: 1.0030 - val_accuracy: 0.6667 - val_loss: 0.9863\n",
            "Epoch 26/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.6056 - loss: 1.0009 - val_accuracy: 0.6667 - val_loss: 0.9847\n",
            "Epoch 27/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - accuracy: 0.6056 - loss: 0.9996 - val_accuracy: 0.6667 - val_loss: 0.9832\n",
            "Epoch 28/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6056 - loss: 0.9974 - val_accuracy: 0.6667 - val_loss: 0.9817\n",
            "Epoch 29/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6082 - loss: 0.9953 - val_accuracy: 0.6667 - val_loss: 0.9804\n",
            "Epoch 30/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6042 - loss: 0.9947 - val_accuracy: 0.6667 - val_loss: 0.9790\n",
            "Epoch 31/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6069 - loss: 0.9929 - val_accuracy: 0.6667 - val_loss: 0.9778\n",
            "Epoch 32/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.6016 - loss: 0.9927 - val_accuracy: 0.6667 - val_loss: 0.9764\n",
            "Epoch 33/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6096 - loss: 0.9898 - val_accuracy: 0.6667 - val_loss: 0.9752\n",
            "Epoch 34/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6082 - loss: 0.9886 - val_accuracy: 0.6667 - val_loss: 0.9739\n",
            "Epoch 35/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6096 - loss: 0.9874 - val_accuracy: 0.6667 - val_loss: 0.9726\n",
            "Epoch 36/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.5989 - loss: 0.9883 - val_accuracy: 0.6667 - val_loss: 0.9710\n",
            "Epoch 37/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.5989 - loss: 0.9873 - val_accuracy: 0.6667 - val_loss: 0.9695\n",
            "Epoch 38/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6056 - loss: 0.9837 - val_accuracy: 0.6667 - val_loss: 0.9681\n",
            "Epoch 39/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.5936 - loss: 0.9858 - val_accuracy: 0.6667 - val_loss: 0.9664\n",
            "Epoch 40/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.5962 - loss: 0.9828 - val_accuracy: 0.6667 - val_loss: 0.9648\n",
            "Epoch 41/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.6029 - loss: 0.9788 - val_accuracy: 0.6667 - val_loss: 0.9635\n",
            "Epoch 42/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 0.6082 - loss: 0.9774 - val_accuracy: 0.6667 - val_loss: 0.9622\n",
            "Epoch 43/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6122 - loss: 0.9750 - val_accuracy: 0.6667 - val_loss: 0.9611\n",
            "Epoch 44/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6042 - loss: 0.9754 - val_accuracy: 0.6667 - val_loss: 0.9597\n",
            "Epoch 45/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.6016 - loss: 0.9752 - val_accuracy: 0.6667 - val_loss: 0.9582\n",
            "Epoch 46/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6016 - loss: 0.9739 - val_accuracy: 0.6667 - val_loss: 0.9567\n",
            "Epoch 47/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.6002 - loss: 0.9722 - val_accuracy: 0.6667 - val_loss: 0.9552\n",
            "Epoch 48/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.6109 - loss: 0.9678 - val_accuracy: 0.6667 - val_loss: 0.9540\n",
            "Epoch 49/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6042 - loss: 0.9689 - val_accuracy: 0.6667 - val_loss: 0.9525\n",
            "Epoch 50/50\n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6029 - loss: 0.9667 - val_accuracy: 0.6667 - val_loss: 0.9511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57nGj9-CBUEr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bba66dd-ab79-4882-b8d5-505905b404cd"
      },
      "source": [
        "# Evalute the model\n",
        "score = model.evaluate(x_test, classes_test, batch_size=size_batch, verbose=1)\n",
        "print(\"Scores on test set: loss=%s accuracy=%s\" % tuple(score))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - accuracy: 0.6667 - loss: 0.9511\n",
            "Scores on test set: loss=0.9511208534240723 accuracy=0.6666666865348816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64_DjQtMBUEs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "8f2623ae-ab5a-4c07-88dd-59b4a03a6abf"
      },
      "source": [
        "# plot the train loss and the validation loss\n",
        "plt.figure(1)\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "out = np.asarray(history.history['loss'])\n",
        "plt.plot(out, label='train loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "out = np.asarray(history.history['accuracy'])\n",
        "plt.plot(out, label='valid acc')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5viuf0zBUEs"
      },
      "source": [
        "**Compute the cross validation error**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit_learn\n",
        "!pip install tensorflow\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-r0pPkH6x2b",
        "outputId": "829a9547-84b0-46cc-984a-699221dce770"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (3.5.0)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (71.0.4)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.26.4)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.44.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (13.9.2)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow) (0.13.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.8.30)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__d_9NwGBUEs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "8f84fa9f-13fa-49b5-e8d7-820613ad535e"
      },
      "source": [
        "# Import the libraries\n",
        "\n",
        "\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
        "\n",
        "# create an MLP model\n",
        "def create_MLP_model():\n",
        "\n",
        "    return model\n",
        "\n",
        "estimators = []\n",
        "estimators.append(('standardize', preprocessing.StandardScaler()))\n",
        "estimators.append(('mlp', KerasClassifier(build_fn=create_MLP_model, epochs=nb_epochs, batch_size=size_batch, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "\n",
        "# Compute the cross validation error\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit_learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit_learn) (3.5.0)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'tensorflow.keras.wrappers'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-2fd2c09916c6>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pip install scikit_learn'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrappers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscikit_learn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mKerasClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# create an MLP model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.keras.wrappers'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P9rhEC4qRWb6"
      },
      "source": [
        "## Confusion matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNPawU-kRWcB"
      },
      "source": [
        "** Create a confusion matrix to see how well the MLP classify the different classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpTYOpo3RWcB"
      },
      "source": [
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIxtoaERBUEt"
      },
      "source": [
        "**Autoencoder**\n",
        "Autoencoders are similar to dimensionality reduction techniques like Principal Component Analysis (PCA) that projects the data from a higher dimension to a lower dimension using linear transformation.\n",
        "However, the major difference between autoencoders and PCA lies in the transformation part: as you already read, PCA uses linear transformation whereas autoencoders use non-linear transformations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_fOcxcrBUEt"
      },
      "source": [
        "# Build a sparse autoencoder\n",
        "\n",
        "# Build a denoising autoencoder\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}