Lecture_10.ipynb

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      "name": "Lecture9.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHVgFVIA08Qv",
        "colab_type": "text"
      },
      "source": [
        "### 乳腺癌（诊断）数据集\n",
        "预测癌症是良性还是恶性\n",
        "https://www.kaggle.com/uciml/breast-cancer-wisconsin-data\n",
        "\n",
        "此数据集sklearn中自带"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLjMZQc_1Gxd",
        "colab_type": "code",
        "outputId": "d31797f7-a8b2-4494-b24e-0439906ffbc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "mELIkkC2gIwl",
        "colab_type": "code",
        "outputId": "6f0dfea5-bd71-4e5c-fbef-61c31b491eb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dICbCNbigW0X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import datasets, metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2AOafnCgkSl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer = datasets.load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1vF-ngahGD5",
        "colab_type": "code",
        "outputId": "35213ff3-0e8f-45da-f3be-145e9298990a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(cancer.DESCR)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "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",
            "        largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
            "        13 is Radius SE, field 23 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PhPKig1hKwE",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "# 显示列名\n",
        "x = pd.DataFrame(data=cancer.data, columns=cancer.feature_names)\n",
        "x.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</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",
              "    </tr>\n",
              "    <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>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</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",
              "    </tr>\n",
              "    <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>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</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>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</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",
              "    </tr>\n",
              "    <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>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "3        11.42         20.38  ...          0.6638                  0.17300\n",
              "4        20.29         14.34  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0wRNHyohl2F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHA7xSdRijjn",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "y "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       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])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3EBPtCXip6s",
        "colab_type": "code",
        "outputId": "5b116e45-ba3f-4ad9-b5a7-e003689d1f88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cancer.target_names"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id1OXQNGiv8h",
        "colab_type": "code",
        "outputId": "7a482fef-e95d-497a-e8d3-1afda86dae8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.shape, y.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((569, 30), (569,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iFh8pkWi0mw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0, stratify=y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zuCHRpNjc1H",
        "colab_type": "code",
        "outputId": "5e5cf1c3-50a9-4ca3-8c19-2fa0cd765a80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(x_train.shape, y_train.shape)\n",
        "print(x_test.shape, y_test.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(455, 30) (455,)\n",
            "(114, 30) (114,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3H1dduOSjuNJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 数据标准化，也能返回numpy的数据类型\n",
        "scaler = StandardScaler()\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.fit_transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQKOn8llk8gB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.reshape(455, 30, 1)\n",
        "x_test = x_test.reshape(114, 30, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIQtlThWl845",
        "colab_type": "text"
      },
      "source": [
        "### 建立模型& 训练模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7ewWlz-vjhL",
        "colab_type": "text"
      },
      "source": [
        "#### conv1D, 只在一个方向上移动，只看纵向上滑动"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpF3ko4pk-WA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32, 2, activation='relu', input_shape=(30, 1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Conv1D(64, 2, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qedINP7UnRzI",
        "colab_type": "code",
        "outputId": "6a313e8b-5ca3-4ecb-d12b-80ad5e996327",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d (Conv1D)              (None, 29, 32)            96        \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 29, 32)            128       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 29, 32)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 28, 64)            4160      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 28, 64)            256       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 28, 64)            0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1792)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                114752    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 119,457\n",
            "Trainable params: 119,265\n",
            "Non-trainable params: 192\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6NppQDTnbj2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer=tf.optimizers.Adam(lr=0.00005),\n",
        "              loss='binary_crossentropy', metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlBje0bYqvRB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0YdbZLgoP8W",
        "colab_type": "code",
        "outputId": "2b111d99-b790-4fd6-d2d7-45d83b16ff93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(x_train, y_train, epochs=epochs, validation_data=(x_test, y_test))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "15/15 [==============================] - 0s 29ms/step - loss: 1.4247 - acc: 0.4681 - val_loss: 0.6897 - val_acc: 0.5088\n",
            "Epoch 2/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 1.1093 - acc: 0.5297 - val_loss: 0.6645 - val_acc: 0.8333\n",
            "Epoch 3/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.9718 - acc: 0.5912 - val_loss: 0.6363 - val_acc: 0.9211\n",
            "Epoch 4/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.7710 - acc: 0.6923 - val_loss: 0.6042 - val_acc: 0.9298\n",
            "Epoch 5/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.6039 - acc: 0.7385 - val_loss: 0.5706 - val_acc: 0.9123\n",
            "Epoch 6/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.5279 - acc: 0.7934 - val_loss: 0.5371 - val_acc: 0.9035\n",
            "Epoch 7/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.4175 - acc: 0.8264 - val_loss: 0.5028 - val_acc: 0.9035\n",
            "Epoch 8/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.4706 - acc: 0.7912 - val_loss: 0.4682 - val_acc: 0.9035\n",
            "Epoch 9/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.3532 - acc: 0.8396 - val_loss: 0.4364 - val_acc: 0.9035\n",
            "Epoch 10/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.3874 - acc: 0.8527 - val_loss: 0.4054 - val_acc: 0.9211\n",
            "Epoch 11/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.3162 - acc: 0.8725 - val_loss: 0.3751 - val_acc: 0.9211\n",
            "Epoch 12/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.3314 - acc: 0.8549 - val_loss: 0.3468 - val_acc: 0.9211\n",
            "Epoch 13/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.3240 - acc: 0.8769 - val_loss: 0.3215 - val_acc: 0.9211\n",
            "Epoch 14/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2955 - acc: 0.8703 - val_loss: 0.2993 - val_acc: 0.9211\n",
            "Epoch 15/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2653 - acc: 0.8769 - val_loss: 0.2800 - val_acc: 0.9211\n",
            "Epoch 16/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.2721 - acc: 0.8791 - val_loss: 0.2612 - val_acc: 0.9298\n",
            "Epoch 17/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.2432 - acc: 0.8879 - val_loss: 0.2452 - val_acc: 0.9298\n",
            "Epoch 18/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2324 - acc: 0.8967 - val_loss: 0.2293 - val_acc: 0.9298\n",
            "Epoch 19/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2343 - acc: 0.8945 - val_loss: 0.2157 - val_acc: 0.9298\n",
            "Epoch 20/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2683 - acc: 0.8989 - val_loss: 0.2053 - val_acc: 0.9298\n",
            "Epoch 21/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1862 - acc: 0.9209 - val_loss: 0.1973 - val_acc: 0.9298\n",
            "Epoch 22/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1801 - acc: 0.9231 - val_loss: 0.1918 - val_acc: 0.9298\n",
            "Epoch 23/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.2254 - acc: 0.9011 - val_loss: 0.1848 - val_acc: 0.9298\n",
            "Epoch 24/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2031 - acc: 0.9341 - val_loss: 0.1780 - val_acc: 0.9298\n",
            "Epoch 25/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1913 - acc: 0.9297 - val_loss: 0.1692 - val_acc: 0.9386\n",
            "Epoch 26/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1902 - acc: 0.9363 - val_loss: 0.1635 - val_acc: 0.9386\n",
            "Epoch 27/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1636 - acc: 0.9407 - val_loss: 0.1590 - val_acc: 0.9474\n",
            "Epoch 28/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1720 - acc: 0.9363 - val_loss: 0.1525 - val_acc: 0.9561\n",
            "Epoch 29/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.2204 - acc: 0.9099 - val_loss: 0.1485 - val_acc: 0.9649\n",
            "Epoch 30/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1700 - acc: 0.9297 - val_loss: 0.1428 - val_acc: 0.9649\n",
            "Epoch 31/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1477 - acc: 0.9253 - val_loss: 0.1377 - val_acc: 0.9649\n",
            "Epoch 32/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1565 - acc: 0.9341 - val_loss: 0.1338 - val_acc: 0.9649\n",
            "Epoch 33/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1350 - acc: 0.9407 - val_loss: 0.1304 - val_acc: 0.9649\n",
            "Epoch 34/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1703 - acc: 0.9341 - val_loss: 0.1269 - val_acc: 0.9649\n",
            "Epoch 35/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1714 - acc: 0.9209 - val_loss: 0.1232 - val_acc: 0.9737\n",
            "Epoch 36/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1367 - acc: 0.9363 - val_loss: 0.1219 - val_acc: 0.9737\n",
            "Epoch 37/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1486 - acc: 0.9319 - val_loss: 0.1203 - val_acc: 0.9737\n",
            "Epoch 38/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1259 - acc: 0.9429 - val_loss: 0.1193 - val_acc: 0.9737\n",
            "Epoch 39/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1367 - acc: 0.9538 - val_loss: 0.1194 - val_acc: 0.9737\n",
            "Epoch 40/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1516 - acc: 0.9451 - val_loss: 0.1193 - val_acc: 0.9737\n",
            "Epoch 41/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1450 - acc: 0.9341 - val_loss: 0.1207 - val_acc: 0.9737\n",
            "Epoch 42/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1321 - acc: 0.9495 - val_loss: 0.1216 - val_acc: 0.9737\n",
            "Epoch 43/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1288 - acc: 0.9495 - val_loss: 0.1207 - val_acc: 0.9737\n",
            "Epoch 44/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1062 - acc: 0.9670 - val_loss: 0.1195 - val_acc: 0.9737\n",
            "Epoch 45/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1225 - acc: 0.9429 - val_loss: 0.1190 - val_acc: 0.9737\n",
            "Epoch 46/50\n",
            "15/15 [==============================] - 0s 9ms/step - loss: 0.1439 - acc: 0.9429 - val_loss: 0.1179 - val_acc: 0.9737\n",
            "Epoch 47/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.0947 - acc: 0.9648 - val_loss: 0.1176 - val_acc: 0.9737\n",
            "Epoch 48/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1326 - acc: 0.9473 - val_loss: 0.1174 - val_acc: 0.9737\n",
            "Epoch 49/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.1306 - acc: 0.9385 - val_loss: 0.1170 - val_acc: 0.9737\n",
            "Epoch 50/50\n",
            "15/15 [==============================] - 0s 8ms/step - loss: 0.0982 - acc: 0.9692 - val_loss: 0.1169 - val_acc: 0.9737\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "truatwJ4q5Z0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_learningCurve(history, epoch):\n",
        "  # Plot training & validation accuracy values\n",
        "  epoch_range = range(1, epoch+1)\n",
        "  plt.plot(epoch_range, history.history['acc'])\n",
        "  plt.plot(epoch_range, history.history['val_acc'])\n",
        "  plt.title('Model accuracy')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc='upper left')\n",
        "  plt.show()\n",
        "\n",
        "  # Plot training & validation loss values\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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oq6_rhf5wi-Y",
        "colab_type": "code",
        "outputId": "34a7ba9c-becb-4ac2-eecf-e9cf498a7b80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "plot_learningCurve(history, epochs)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsNIepCxwriP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}