Tensorflow - Breast Cancer Detection Using CNN.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sahug/ds-tensorflow-colab/blob/master/Tensorflow%20-%20Breast%20Cancer%20Detection%20Using%20CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZw_KK2sCEDV"
      },
      "source": [
        "**Tensorflow - Breast Cancer Detection Using CNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "lKV4-Sc4aLUK"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Flatten, Dense, Dropout, BatchNormalization\n",
        "from keras.layers import Conv1D, MaxPool1D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xdYB6iLKawy6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Vsch15Yxa0Yj"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "cL6jAd1Sa-r6"
      },
      "outputs": [],
      "source": [
        "from sklearn import datasets, metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Load Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "1EsDcFBubp9s"
      },
      "outputs": [],
      "source": [
        "cancer = datasets.load_breast_cancer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Data Descr**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2Tghnk-7bw15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry\n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        worst/largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
            "        10 is Radius SE, field 20 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ]
        }
      ],
      "source": [
        "print(cancer.DESCR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "vZ2TN92bcLhA"
      },
      "outputs": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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              "<p>5 rows × 30 columns</p>\n",
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            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
              "0                 0.07871  ...         25.38          17.33           184.60   \n",
              "1                 0.05667  ...         24.99          23.41           158.80   \n",
              "2                 0.05999  ...         23.57          25.53           152.50   \n",
              "3                 0.09744  ...         14.91          26.50            98.87   \n",
              "4                 0.05883  ...         22.54          16.67           152.20   \n",
              "\n",
              "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
              "0      2019.0            0.1622             0.6656           0.7119   \n",
              "1      1956.0            0.1238             0.1866           0.2416   \n",
              "2      1709.0            0.1444             0.4245           0.4504   \n",
              "3       567.7            0.2098             0.8663           0.6869   \n",
              "4      1575.0            0.1374             0.2050           0.4000   \n",
              "\n",
              "   worst concave points  worst symmetry  worst fractal dimension  \n",
              "0                0.2654          0.4601                  0.11890  \n",
              "1                0.1860          0.2750                  0.08902  \n",
              "2                0.2430          0.3613                  0.08758  \n",
              "3                0.2575          0.6638                  0.17300  \n",
              "4                0.1625          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = pd.DataFrame(data=cancer.data, columns=cancer.feature_names)\n",
        "x.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "gVqubYivckJ0"
      },
      "outputs": [
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              "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y = cancer.target\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "y8SuQVL4crVH"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cancer.target_names #0 is malignant and 1 is benign"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Split Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will split the datset in the pipeline. Check the pipeline, classifier for pipeline, below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "0-JP7IkrdBnZ"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((455, 30), (114, 30), (455,), (114,))"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0, stratify=y)\n",
        "# x_train.shape, x_test.shape, y_train.shape, y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Normalize Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(569, 30), dtype=float32, numpy=\n",
              "array([[ 1.0970638 , -2.073334  ,  1.2699338 , ...,  2.2960763 ,\n",
              "         2.7506218 ,  1.9370152 ],\n",
              "       [ 1.8298204 , -0.35363132,  1.6859545 , ...,  1.0870845 ,\n",
              "        -0.24388953,  0.2811903 ],\n",
              "       [ 1.5798881 ,  0.45618796,  1.5665032 , ...,  1.9550005 ,\n",
              "         1.1522547 ,  0.20139174],\n",
              "       ...,\n",
              "       [ 0.70228416,  2.045575  ,  0.6726759 , ...,  0.41406885,\n",
              "        -1.1045488 , -0.31840897],\n",
              "       [ 1.838341  ,  2.3364582 ,  1.9825244 , ...,  2.2899854 ,\n",
              "         1.9190826 ,  2.2196357 ],\n",
              "       [-1.8084016 ,  1.2217933 , -1.8143885 , ..., -1.7450628 ,\n",
              "        -0.0481384 , -0.75120634]], dtype=float32)>"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def normalizeDataset(ds):\n",
        "    normalizer = tf.keras.layers.Normalization()    \n",
        "    normalizer.adapt(ds)    \n",
        "    return normalizer(ds)\n",
        "\n",
        "x_train_n = normalizeDataset(x)    \n",
        "x_train_n.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(30, 1)"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "input_shape = x_train.shape[1], 1\n",
        "input_shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {},
      "outputs": [],
      "source": [
        "from keras import activations\n",
        "from keras.backend import int_shape\n",
        "\n",
        "def buildModel():\n",
        "\n",
        "    inputs = keras.Input(shape=(input_shape))\n",
        "\n",
        "    x = keras.layers.Conv1D(filters=32, kernel_size=2, activation=activations.relu)(inputs)\n",
        "    x = keras.layers.BatchNormalization()(x)\n",
        "    x = keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "    x = keras.layers.Conv1D(filters=64, kernel_size=2, activation=activations.relu)(x)\n",
        "    x = keras.layers.BatchNormalization()(x)\n",
        "    x = keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "    x = keras.layers.Flatten()(x)\n",
        "    x = keras.layers.Dense(64, activation=activations.relu)(x)\n",
        "    x = keras.layers.Dropout(0.5)(x)\n",
        "\n",
        "    outputs = keras.layers.Dense(1,  activation=activations.sigmoid)(x)\n",
        "    \n",
        "    model = keras.Model(inputs=inputs, outputs=[outputs], name=\"conv1d\")\n",
        "\n",
        "    # Compile \n",
        "    loss = keras.losses.BinaryCrossentropy()\n",
        "    optim = keras.optimizers.Adam(learning_rate=0.00005)\n",
        "    # metrics = [keras.metrics.SparseCategoricalAccuracy()]\n",
        "    metrics = [\"accuracy\"]\n",
        "\n",
        "    model.compile(loss=loss, optimizer=optim, metrics=metrics)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Classifier for Pipeline**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {},
      "outputs": [],
      "source": [
        "from scikeras.wrappers import KerasClassifier\n",
        "\n",
        "kwargs = dict(\n",
        "    model=buildModel(),\n",
        "    epochs=10,\n",
        "    batch_size = 128,\n",
        "    verbose=1,\n",
        "    random_state=0,\n",
        ")\n",
        "# Here we are splitting the dataset to train and test as well.\n",
        "clf = KerasClassifier(model=buildModel(), epochs=5, validation_split=0.2, batch_size=8, verbose = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Pipeline**\n",
        "\n",
        "The class below is used to create a pipeline using your custom methods. Here we are using Normalization layer in the pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {},
      "outputs": [],
      "source": [
        "class DataframeFunctionTransformer():\n",
        "    def __init__(self, func):\n",
        "        self.func = func\n",
        "\n",
        "    def transform(self, input_df, **transform_params):\n",
        "        return self.func(input_df)\n",
        "\n",
        "    def fit(self, X, y=None, **fit_params):\n",
        "        return self"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 {color: black;background-color: white;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 pre{padding: 0;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-toggleable {background-color: white;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-item {z-index: 1;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-parallel-item:only-child::after {width: 0;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-450fdb95-07bc-4f18-9715-88c3095aa4e3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-450fdb95-07bc-4f18-9715-88c3095aa4e3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Normalization&#x27;,\n",
              "                 &lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;),\n",
              "                (&#x27;model&#x27;,\n",
              "                 KerasClassifier(epochs=5, model=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1cbcc670-71be-4f51-89aa-e270d91db625\" type=\"checkbox\" ><label for=\"1cbcc670-71be-4f51-89aa-e270d91db625\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Normalization&#x27;,\n",
              "                 &lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;),\n",
              "                (&#x27;model&#x27;,\n",
              "                 KerasClassifier(epochs=5, model=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ff486167-bcf0-4e8e-8b4a-bcf2821bdc2b\" type=\"checkbox\" ><label for=\"ff486167-bcf0-4e8e-8b4a-bcf2821bdc2b\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DataframeFunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>&lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b6ee86ad-9c7e-4042-87e9-6c1ff39da28f\" type=\"checkbox\" ><label for=\"b6ee86ad-9c7e-4042-87e9-6c1ff39da28f\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KerasClassifier</label><div class=\"sk-toggleable__content\"><pre>KerasClassifier(\n",
              "\tmodel=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;\n",
              "\tbuild_fn=None\n",
              "\twarm_start=False\n",
              "\trandom_state=None\n",
              "\toptimizer=rmsprop\n",
              "\tloss=None\n",
              "\tmetrics=None\n",
              "\tbatch_size=None\n",
              "\tvalidation_batch_size=None\n",
              "\tverbose=1\n",
              "\tcallbacks=None\n",
              "\tvalidation_split=0.0\n",
              "\tshuffle=True\n",
              "\trun_eagerly=False\n",
              "\tepochs=5\n",
              "\tclass_weight=None\n",
              ")</pre></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "Pipeline(steps=[('Normalization',\n",
              "                 <__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0>),\n",
              "                ('model',\n",
              "                 KerasClassifier(epochs=5, model=<keras.engine.functional.Functional object at 0x000001A4194336A0>))])"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn import set_config\n",
        "set_config(display=\"diagram\")\n",
        "pipeline = Pipeline(steps=[\n",
        "                (\"Normalization\", DataframeFunctionTransformer(normalizeDataset)),\n",
        "                (\"model\", clf)\n",
        "])\n",
        "pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Train**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "15/15 [==============================] - 1s 4ms/step - loss: 1.0720 - sparse_categorical_accuracy: 0.3736\n",
            "Epoch 2/5\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.6676 - sparse_categorical_accuracy: 0.3736\n",
            "Epoch 3/5\n",
            "15/15 [==============================] - 0s 5ms/step - loss: 0.5314 - sparse_categorical_accuracy: 0.3736\n",
            "Epoch 4/5\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.3538 - sparse_categorical_accuracy: 0.3736\n",
            "Epoch 5/5\n",
            "15/15 [==============================] - 0s 4ms/step - loss: 0.3940 - sparse_categorical_accuracy: 0.3736\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-39d98549-33cc-4bce-9080-4a418fc4359f {color: black;background-color: white;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f pre{padding: 0;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-toggleable {background-color: white;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-estimator:hover {background-color: #d4ebff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-item {z-index: 1;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-parallel-item:only-child::after {width: 0;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-39d98549-33cc-4bce-9080-4a418fc4359f div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-39d98549-33cc-4bce-9080-4a418fc4359f\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Normalization&#x27;,\n",
              "                 &lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;),\n",
              "                (&#x27;model&#x27;,\n",
              "                 KerasClassifier(epochs=5, model=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"edf7acb1-be2c-4e2e-a98b-79d6066eedcf\" type=\"checkbox\" ><label for=\"edf7acb1-be2c-4e2e-a98b-79d6066eedcf\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Normalization&#x27;,\n",
              "                 &lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;),\n",
              "                (&#x27;model&#x27;,\n",
              "                 KerasClassifier(epochs=5, model=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"55851258-de3a-4019-9284-1999569836c2\" type=\"checkbox\" ><label for=\"55851258-de3a-4019-9284-1999569836c2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DataframeFunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>&lt;__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0&gt;</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d762dff3-cb0a-4cda-b85b-12b63fcaba8a\" type=\"checkbox\" ><label for=\"d762dff3-cb0a-4cda-b85b-12b63fcaba8a\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KerasClassifier</label><div class=\"sk-toggleable__content\"><pre>KerasClassifier(\n",
              "\tmodel=&lt;keras.engine.functional.Functional object at 0x000001A4194336A0&gt;\n",
              "\tbuild_fn=None\n",
              "\twarm_start=False\n",
              "\trandom_state=None\n",
              "\toptimizer=rmsprop\n",
              "\tloss=None\n",
              "\tmetrics=None\n",
              "\tbatch_size=None\n",
              "\tvalidation_batch_size=None\n",
              "\tverbose=1\n",
              "\tcallbacks=None\n",
              "\tvalidation_split=0.0\n",
              "\tshuffle=True\n",
              "\trun_eagerly=False\n",
              "\tepochs=5\n",
              "\tclass_weight=None\n",
              ")</pre></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "Pipeline(steps=[('Normalization',\n",
              "                 <__main__.DataframeFunctionTransformer object at 0x000001A4194DBEB0>),\n",
              "                ('model',\n",
              "                 KerasClassifier(epochs=5, model=<keras.engine.functional.Functional object at 0x000001A4194336A0>))])"
            ]
          },
          "execution_count": 85,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline.fit(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Plot History**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "clf.history_.keys()\n",
        "\n",
        "epoch_range = range(1, 6)\n",
        "\n",
        "# Plot training and validation accuracy values\n",
        "# sparse_categorical_accuracy vs val_sparse_categorical_accuracy\n",
        "plt.plot(epoch_range, clf.history_[\"sparse_categorical_accuracy\"])\n",
        "plt.plot(epoch_range, clf.history_[\"val_sparse_categorical_accuracy\"])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation loss values\n",
        "# Loss vs Val_Loss\n",
        "plt.plot(epoch_range, clf.history_[\"loss\"])\n",
        "plt.plot(epoch_range, clf.history_[\"val_loss\"])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Approach 2**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.26670379,  0.64119581,  0.21021862, ...,  0.57195757,\n",
              "        -0.36743271, -0.29487344],\n",
              "       [-1.50538429, -0.78076949, -1.4608281 , ..., -0.14909852,\n",
              "         0.04677597,  1.00205472],\n",
              "       [ 1.50388483,  2.19180417,  1.67631803, ...,  1.18936185,\n",
              "        -0.13144928,  0.93430053],\n",
              "       ...,\n",
              "       [-0.30323355,  0.01865526, -0.38082036, ..., -1.55951427,\n",
              "        -0.75688789, -1.18922878],\n",
              "       [-0.07525861, -0.69347598, -0.14238757, ..., -0.89178629,\n",
              "        -0.88395589, -0.72398332],\n",
              "       [-0.63963558, -0.12147379, -0.56963008, ...,  0.59599277,\n",
              "         3.07495497,  3.17583505]])"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cancer = datasets.load_breast_cancer()\n",
        "\n",
        "x = pd.DataFrame(data=cancer.data, columns=cancer.feature_names)\n",
        "\n",
        "y = cancer.target\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0, stratify=y)\n",
        "\n",
        "# Converting Pandas to Numpy data to reshape the data later.\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.fit_transform(x_test)\n",
        "\n",
        "x_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "vH0SY5uvdmfr"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "15/15 [==============================] - 1s 16ms/step - loss: 0.3846 - accuracy: 0.8352 - val_loss: 0.3732 - val_accuracy: 0.9474\n",
            "Epoch 2/5\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.1653 - accuracy: 0.9473 - val_loss: 0.3008 - val_accuracy: 0.9474\n",
            "Epoch 3/5\n",
            "15/15 [==============================] - 0s 7ms/step - loss: 0.0952 - accuracy: 0.9648 - val_loss: 0.2920 - val_accuracy: 0.8947\n",
            "Epoch 4/5\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.0890 - accuracy: 0.9736 - val_loss: 0.2884 - val_accuracy: 0.8947\n",
            "Epoch 5/5\n",
            "15/15 [==============================] - 0s 6ms/step - loss: 0.0940 - accuracy: 0.9582 - val_loss: 0.2677 - val_accuracy: 0.8947\n"
          ]
        }
      ],
      "source": [
        "# Build Model\n",
        "epochs = 5\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv1D(filters=32, kernel_size=2, activation=\"relu\", input_shape=(30, 1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(filters=64, kernel_size=2, activation=\"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, activation=\"relu\"))\n",
        "\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(1, activation=\"sigmoid\")) # Only 1 output 0 or 1. sigmoid detects binary i.e. 0 or 1\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history = model.fit(x_train, y_train, epochs=epochs, validation_data=(x_test, y_test), verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "5kaTynlDoVE6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "epoch_range = range(1, epochs+1)\n",
        "\n",
        "# Plot training and validation accuracy values\n",
        "# sparse_categorical_accuracy vs val_sparse_categorical_accuracy\n",
        "plt.plot(epoch_range, history.history[\"accuracy\"])\n",
        "plt.plot(epoch_range, history.history[\"val_accuracy\"])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "aBBRExDMoc39"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation loss values\n",
        "# Loss vs Val_Loss\n",
        "plt.plot(epoch_range, history.history[\"loss\"])\n",
        "plt.plot(epoch_range, history.history[\"val_loss\"])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "name": "Tensorflow - Breast Cancer Detection Using CNN.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "0d7fd96aeadd740978789f2880dd43822d8681a39e327029c33ff72656207c4b"
    },
    "kernelspec": {
      "display_name": "Python 3.10.4 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}