Demo20200328.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Demo20200328",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUvvQgcF6RHz",
        "colab_type": "text"
      },
      "source": [
        "## 使用Keras 分類 Iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ETTg4bwygbZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_xASkMYzSAJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "851c8f61-bac8-49f8-ec6c-d933605ab6de"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5qxcHsXzehG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "936bb3cd-aa5b-4f40-85f4-0c28ada3faca"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DOXklEizkZI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_scaled = sc.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7THXbS0Iz1R5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#X_scaled"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NMnrZQkz3UA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOeamSpI0d_x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lfyzrx-j0f43",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential() \n",
        "# Dense: x * w + b \n",
        "model.add(tf.keras.layers.Dense(units = 5,  activation = 'relu', input_dim = 4))\n",
        "model.add(tf.keras.layers.Dense(units = 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9vhk75W1bxI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential( [\n",
        "# Dense: x * w + b \n",
        "tf.keras.layers.Dense(units = 5,  activation = 'relu', input_dim = 4),\n",
        "tf.keras.layers.Dense(units = 3)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0J232Xzx1u9J",
        "colab_type": "text"
      },
      "source": [
        "- 如果目標資料是 one-hot 編碼, 使用 categorical_crossentropy.\n",
        "\n",
        "```\n",
        "one-hot 編碼範例:\n",
        "[1,0,0]\n",
        "[0,1,0]\n",
        "[0,0,1]\n",
        "```\n",
        "\n",
        "- 如果目標是整數 integers,使用sparse_categorical_crossentropy\n",
        "\n",
        "```\n",
        "整數範例\n",
        "1\n",
        "2\n",
        "3\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckA6zMxY1mXK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(loss=loss_fn,\n",
        "                  optimizer='sgd',\n",
        "                  metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRPGKl7S2Uzi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = model.fit(X_scaled, y,\n",
        "                    batch_size = 30,\n",
        "                    epochs=1000,\n",
        "                    verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IUTi5sp26z_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "288bd46a-327c-40ea-dce4-a13f9c8c0148"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSaBUsVl3uE8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "37b73f03-6878-4380-a12f-636264379865"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(history.history.get('loss'))), history.history.get('loss'))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff83cccc18>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
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9nhFZiWQkxjAiq6M7qDAjgbT4mLDtClLQi0ifl5YQw0XDBnLRsIGnzG9saaP8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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSMl1Dq34DFF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2638a3f1-170a-45d2-974c-2b920507f9a6"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(history.history.get('accuracy'))), history.history.get('accuracy'))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff83c31860>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ByZKmA0iaK2lb8hl/GhEHqr+BpBWStkjacvDgwaGew6jwGL2ZtapGlai3AJdKegK4FOgF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZqgcIqw4Psc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "y_pred = np.argmax(model(X_scaled).numpy(), axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4j5l-Oj4W6H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "86cc5597-12b7-4756-caa1-e1aabc0eff6c"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y, y_pred)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_GpIL8a4vFE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "fbb06bf9-e1c2-4af9-b56d-4e7ec64e2f1b"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y, y_pred)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[50,  0,  0],\n",
              "       [ 0, 47,  3],\n",
              "       [ 0,  2, 48]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkF3-M6r41Ig",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_classes  = 3\n",
        "y_onehot = tf.keras.utils.to_categorical(iris.target, n_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXtCOG385NrK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2 = tf.keras.models.Sequential( [\n",
        "# Dense: x * w + b \n",
        "tf.keras.layers.Dense(units = 5,  activation = 'relu', input_dim = 4),\n",
        "tf.keras.layers.Dense(units = 3, activation='softmax')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQSbt-GD5ahx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model2.compile(loss='categorical_crossentropy',\n",
        "                  optimizer='sgd',\n",
        "                  metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_owSFds5z3G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "6d43dd27-0ae7-4aa4-a3ae-54ffd03293e2"
      },
      "source": [
        "history = model2.fit(X_scaled, y_onehot,\n",
        "                    batch_size = 30,\n",
        "                    epochs=10,\n",
        "                    verbose=1)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0647 - accuracy: 0.9733\n",
            "Epoch 2/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0646 - accuracy: 0.9733\n",
            "Epoch 3/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0646 - accuracy: 0.9733\n",
            "Epoch 4/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0644 - accuracy: 0.9733\n",
            "Epoch 5/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0645 - accuracy: 0.9733\n",
            "Epoch 6/10\n",
            "5/5 [==============================] - 0s 1ms/step - loss: 0.0644 - accuracy: 0.9733\n",
            "Epoch 7/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0644 - accuracy: 0.9733\n",
            "Epoch 8/10\n",
            "5/5 [==============================] - 0s 1ms/step - loss: 0.0644 - accuracy: 0.9733\n",
            "Epoch 9/10\n",
            "5/5 [==============================] - 0s 1ms/step - loss: 0.0643 - accuracy: 0.9733\n",
            "Epoch 10/10\n",
            "5/5 [==============================] - 0s 2ms/step - loss: 0.0645 - accuracy: 0.9733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQu2YO-E56tz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2w_Bxaf7IUC",
        "colab_type": "text"
      },
      "source": [
        "## 客戶流失分析"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eG_gdEfQBar_",
        "colab_type": "text"
      },
      "source": [
        "### 資料讀取"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g__oaddq7GyJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "917cfec8-f052-45db-ed4f-852f9d1803a0"
      },
      "source": [
        "import pandas\n",
        "df = pandas.read_csv('https://github.com/ywchiu/tibamedl/raw/master/Data/customer_churn.csv', index_col=0, header = 0)\n",
        "df.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>area_code_408</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account_length  ... number_customer_service_calls churn\n",
              "1    KS             128  ...                             1    no\n",
              "2    OH             107  ...                             1    no\n",
              "3    NJ             137  ...                             0    no\n",
              "4    OH              84  ...                             2    no\n",
              "5    OK              75  ...                             3    no\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMQFM2ptBWQ7",
        "colab_type": "text"
      },
      "source": [
        "### 資料預處理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fd51xZB27kHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "91ea6de5-4b65-4a5a-f9e1-a6a22476b267"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "state                            0\n",
              "account_length                   0\n",
              "area_code                        0\n",
              "international_plan               0\n",
              "voice_mail_plan                  0\n",
              "number_vmail_messages            0\n",
              "total_day_minutes                0\n",
              "total_day_calls                  0\n",
              "total_day_charge                 0\n",
              "total_eve_minutes                0\n",
              "total_eve_calls                  0\n",
              "total_eve_charge                 0\n",
              "total_night_minutes              0\n",
              "total_night_calls                0\n",
              "total_night_charge               0\n",
              "total_intl_minutes               0\n",
              "total_intl_calls                 0\n",
              "total_intl_charge                0\n",
              "number_customer_service_calls    0\n",
              "churn                            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEUw2K2M8R2o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 根據位置篩選資料 .iloc[列,欄]\n",
        "df = df.iloc[:,3:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoNjtK7N8xYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "3f3114c4-2408-45b0-975c-dc0ebba20333"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  international_plan voice_mail_plan  ...  number_customer_service_calls  churn\n",
              "1                 no             yes  ...                              1     no\n",
              "2                 no             yes  ...                              1     no\n",
              "3                 no              no  ...                              0     no\n",
              "4                yes              no  ...                              2     no\n",
              "5                yes              no  ...                              3     no\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2HYuCe-820B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 建立虛擬變量 (create dummy variable)\n",
        "house = pandas.DataFrame([{'區域':'大安區'},\n",
        "                      {'區域':'中山區'},\n",
        "                      {'區域':'大同區'},\n",
        "                      {'區域':'中山區'},\n",
        "                      {'區域':'大安區'}])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUoiIbWP9g-_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "63e411fb-288e-472d-d7c8-7261adc3d94f"
      },
      "source": [
        "area = pandas.get_dummies(house['區域'])\n",
        "del area['中山區']\n",
        "area\n",
        "#house"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>大同區</th>\n",
              "      <th>大安區</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   大同區  大安區\n",
              "0    0    1\n",
              "1    0    0\n",
              "2    1    0\n",
              "3    0    0\n",
              "4    0    1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT0-Dcut9hhY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "d33bc1bd-feff-4cbf-8256-cbe76d674d38"
      },
      "source": [
        "cat_var = ['international_plan','voice_mail_plan', 'churn']\n",
        "\n",
        "for var in cat_var:\n",
        "    df[var] = df[var].replace({'yes': 1, 'no': 0})\n",
        "df.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   international_plan  voice_mail_plan  ...  number_customer_service_calls  churn\n",
              "1                   0                1  ...                              1      0\n",
              "2                   0                1  ...                              1      0\n",
              "3                   0                0  ...                              0      0\n",
              "4                   1                0  ...                              2      0\n",
              "5                   1                0  ...                              3      0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVEzRqo1_Gyq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 今天天氣真好\n",
        "# 今天會下大雨\n",
        "\n",
        "# 今天 天氣 真好 會 下大雨\n",
        "#  1   1    1   0    0\n",
        "#  1   0    0   1    1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7w8yUJ17_pu3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = df.iloc[:,:-1]\n",
        "y = df.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnMuydMSBQSo",
        "colab_type": "text"
      },
      "source": [
        "### 切訓練與測試資料集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpO5OSUX_0dT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_x, test_x, train_y, test_y = train_test_split(x,y, test_size = 0.33, random_state = 123 )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UeRbsdvAAs5M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "db5fc63b-daf7-40d1-b59e-487869cedf5f"
      },
      "source": [
        "x.shape, y.shape"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3333, 16), (3333,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENd-7ww5AvBB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f1749e83-f9b7-4d43-8ecf-19c27846adb9"
      },
      "source": [
        "train_x.shape, train_y.shape"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2233, 16), (2233,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PPisBnUNA0I-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "de64c1a0-8ba6-4568-f563-f06c9d3443c0"
      },
      "source": [
        "test_x.shape, test_y.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1100, 16), (1100,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZThpGoYBytu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "defa8dbb-bc70-4143-99ea-c5480e35d210"
      },
      "source": [
        "y.value_counts()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2850\n",
              "1     483\n",
              "Name: churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOAhgMCFA8cL",
        "colab_type": "text"
      },
      "source": [
        "### 資料標準化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SGTZYTaA3la",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "train_x = sc.fit_transform(train_x)\n",
        "test_x = sc.transform(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JbD01AqCmcZ",
        "colab_type": "text"
      },
      "source": [
        "### 建立模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0cGUWIQBNQ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPMpqSftCqaB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6TMQe9OCykJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential([                                 \n",
        "  tf.keras.layers.Dense(8, activation='relu', input_dim = 16),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uF8Zsx4bDaj8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='sgd',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmcXG6k1DlWS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = model.fit(train_x, train_y,\n",
        "          batch_size=30,\n",
        "          epochs=100,\n",
        "          verbose = 0,\n",
        "          validation_data=(test_x, test_y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63Ho6h7wEl03",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cda29d69-d112-4734-e553-e4e3f8e3ed76"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeP0X_fmEBdj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b16e16c2-35ec-4322-d895-e04a51a55ff3"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(history.history.get('loss'))), history.history.get('loss'))\n",
        "plt.plot(range(len(history.history.get('val_loss'))), history.history.get('val_loss'))"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff7e5d5128>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FjpX5NCTJSm3R2ngqBXZLndWvEJoMPsHQ0W49wBHrHIe/1dRUlgVlhyBvk5U1VexWDaOt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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upzw6C1gErds",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "51b5d314-1214-4256-c7e3-4294881eef51"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(history.history.get('accuracy'))), history.history.get('accuracy'))\n",
        "plt.plot(range(len(history.history.get('val_accuracy'))), history.history.get('val_accuracy'))"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7eff7e57bc18>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6jNSYMJcd4M2TbQyPKO2Pr1lQaAGgWRC8VdNO39CIxwJfkBHrOgHsrWghNDjI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\nlAuAmVmh/gHia1sgUD6/hgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-h-pIqsFbNQ",
        "colab_type": "text"
      },
      "source": [
        "###評估預測結果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egfcOvz9E0GJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "51d0b579-4f1a-40e1-88e2-ecbf7e1b1e7d"
      },
      "source": [
        "predictions = model(test_x).numpy()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR! Session/line number was not unique in database. History logging moved to new session 61\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0O6rYOwFhrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "cab5133c-00ba-4a7f-b2d6-1ce3106c8403"
      },
      "source": [
        "predictions"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.7871417 ],\n",
              "       [0.01863903],\n",
              "       [0.02990454],\n",
              "       ...,\n",
              "       [0.03284294],\n",
              "       [0.00699295],\n",
              "       [0.11133883]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ri_C2_2rFnzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_y = (predictions > 0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpQKOsHaGRvG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0f85b69c-92d6-4755-fa17-dbbeeae4e984"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(test_y, pred_y )"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9218181818181819"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2Ba29oFGTqB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f28f0e1c-efd5-454d-df92-de246bf51965"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(test_y, pred_y )\n",
        "cm"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[930,  20],\n",
              "       [ 66,  84]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2OJVt-0-Gehe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "c0cd5bba-2225-47dc-d7a7-469c2437e9d3"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(test_y, pred_y))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.98      0.96       950\n",
            "           1       0.81      0.56      0.66       150\n",
            "\n",
            "    accuracy                           0.92      1100\n",
            "   macro avg       0.87      0.77      0.81      1100\n",
            "weighted avg       0.92      0.92      0.92      1100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOJrhyR6R7HH",
        "colab_type": "text"
      },
      "source": [
        "## 選擇優化器"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cfZHsIRHQp0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "ff67c08c-d5df-4491-afca-793c2ccffe51"
      },
      "source": [
        "import pandas\n",
        "df = pandas.read_csv('https://github.com/ywchiu/tibamedl/raw/master/Data/customer_churn.csv', index_col=0, header = 0)\n",
        "df.head()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>area_code_408</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account_length  ... number_customer_service_calls churn\n",
              "1    KS             128  ...                             1    no\n",
              "2    OH             107  ...                             1    no\n",
              "3    NJ             137  ...                             0    no\n",
              "4    OH              84  ...                             2    no\n",
              "5    OK              75  ...                             3    no\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLLulHYCR_X8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[:,3:]\n",
        "cat_var = ['international_plan','voice_mail_plan', 'churn']\n",
        "\n",
        "for var in cat_var:\n",
        "    df[var] = df[var].map(lambda e: 1 if e == 'yes' else 0)\n",
        "y = df.iloc[:,-1]\n",
        "x = df.iloc[:,:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxcJ14UPSVY2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 123)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPXKQYK-STHO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnbuEdE3SYT5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNzvB1WRSbhm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJ4KMEZBSdIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tarinProcess(optimizer):\n",
        "    model = tf.keras.models.Sequential()\n",
        "    model.add(tf.keras.layers.Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu', input_dim = 16))\n",
        "    model.add(tf.keras.layers.Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer=optimizer,\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "    history = model.fit(x_train, y_train,\n",
        "                        batch_size=30,\n",
        "                        epochs=100,\n",
        "                        verbose=0,\n",
        "                        validation_data=(x_test, y_test))\n",
        "    return history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvB2CEDSTQlh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history1 = tarinProcess('sgd')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14uDr3JCTVFi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history2 = tarinProcess('RMSprop')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88fNz1iXTYqh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history3 = tarinProcess('Adagrad')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ImlhAdyTcFC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history4 = tarinProcess('Adadelta')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ukdBd3BTfbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history5 = tarinProcess('Adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwwM2CBKTnwl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2298ec43-359c-49cd-e6a9-3cdfd32ac9a6"
      },
      "source": [
        "history1.history.keys()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5HMrEkyTvbr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "87cd83f1-f76b-4275-f0e6-57201102f240"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
        "plt.plot(history1.epoch, history1.history.get('accuracy'), label='SGD')\n",
        "plt.plot(history2.epoch, history2.history.get('accuracy'), label='RMSProp')\n",
        "plt.plot(history3.epoch, history3.history.get('accuracy'), label='AdaGrad')\n",
        "plt.plot(history4.epoch, history4.history.get('accuracy'), label='AdaDelta')\n",
        "plt.plot(history5.epoch, history5.history.get('accuracy'), label='Adam')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Na2vfT/z7tF+GoA7aZKx8/g4BKKQ22OSF7K+H6ykA8yDjJQ50+DQ3xs5Qg2l\nXhimkHEgYztkdNbCqz+GNx8kmUhT03EPcVeI7DN2kOs+tNnkDm+aqsxyzufqYSpWRhWHk6Qvj5Qn\n27oPgeB1O/F7HGS4Xfg9DpwOG4cSdzRh1DUO/FkqjdHQDNvrMXY+WjuO+KVMw4CAH/JzMSflkS7I\nwyzMg5xMzEAGZPit32f4Mfu+EsgAvw8GMdyaMk1SsTiEI9BrPYzk4WzJbWD6fdZrZ/is1weIRKE3\nCuFejHAEIpG+14pihHthX70Ufi9mRkbf+/KB18OAZc8y4pQHMm153bEbMkwTfvGv1vLA0tnc27gU\nZzKXDcf+gfsu/W8tORUZQ5Lt7fS+9hrhv/2N8N9eI3GQoVcjIwPvMcfgnvthnMFMHMEgjkDAegQD\nOIPBXT/z+/eamGt4vDgCAZzBAIbfj2FnoBIZwcZuyDAMOO8/wOnmmc5pRO57j7i3jdKTPQoYImOA\naZr0/v0fdPxyNT1/ft7aDAxwT6gi51OX4Zt2PAPuf24YuEvL8B47CVdxsYKByFEwdkMGwNSPEool\n+d2ql5hhOnil8hmuqvyk3VWJyGEyTZNURwc9z/6Jjl/+ktjmzQAEzjiDrI+cR8Ypp+KpOMRdM0Vk\n2IztkAGsfHIDU3sglNnJloI3ObXsB3aXJCJAor6enpdeItnQSKKpkWRTM8nmZsx0yhqKCFjDFYbX\nS7KtlWRjk/X7uDWz0hEMkvvZK8i7/HI8Eyfa+2ZEZJ/GdMh4q7aT+lcaycfFKxN+x/EF08jz5R38\niSIyLKwhjr/Tvno1oedf6B/iAHBkZuIqLsLh9ZLu7iG5o4FUOAyJBM6cHFzFxWSc8mHcxcX4ps8g\n+8KP4wiM/eXBIqPZmA0Z6bTJf/3mbc6IO/FVOHgv+DpLypbYXZbIuGOaJont2wn95WU6H32E2OYt\nYBgEzzyTnP/3STyTjsVdXLTfwGCmUhhOreARGY3GbMhwOAwuJINuuomfvA0aYF7ZPLvLEhkXUqEQ\noRdfIvy3Vwn/7W8kdzQAVm9F3pVXknv5p/FMmDCocylgiIxeYzZkmKbJtBPy6S7K4KHUg2S4Mqgu\nrLa7LJExLdnRQftDD9Hxy1+R7rZ20fIccwy5l19OYN6pBE49VUMcIuPImA0ZhmEw92PH0BPv4Ybf\nvMUZFWfgduoeByJDzTRNko2NtD/wIB2PPIIZieCZMIG8668jOH8+7lJ7bvsuIvYbsyFjp380/IOU\nmeK0stPsLkVk1EtHInQ9/TS9a9ZYq0Gamkg0NWFGIgB4p06lYNlSMj/yEQ1ziMjYDxmv7HgFQCFD\n5AjE6+ro+NWv6Xz8cdJdXUgHtPQAACAASURBVMCu1SAZJ56Iq6SEzHPPITh/vja7E5F+YzpkmKbJ\nqztepTKzksqsSrvLERl1IuvX0/qTuwk9/zyYpjW/4ktfIvvCj+PMzra7PBEZ4cZ0yNjevZ36UD2X\nTb3M7lJERpX4tm20/OhHdD/ze2u56fz55C76DIF589RTISKDNqZDxs6hEi1dFRmcREMDrfesovOx\nxyCVIjh/PoXXX4dv6lS7SxORUWhMh4y/7fgbLsPFySUn212KyIiVjkToee7PdD35JOG//Q3Safwn\nnUTRDSvImDPH7vJEZBQb0yFjWp61jXjQE7S7FBHbmclk32qQZpJNjSSamoht2kzPs8+SDoXA5SK4\nYAG5l11K4IwzNCwiIkdsTIeM5Scut7sEkREhumEDdV/+Com6ur1+5z3heHI+8QmyPv5xXHm6t4+I\nDJ0xHTJEBLr+939p+NYtkE6Te/nluCsqcBUX4S4uxl1WhruszO4SRWSMUsgQGaPMRIKm732Pjod+\ngau4mIof/wj/rFl2lyUi44hChsgYlGhuZscNX6V3zRr8HzqJih/+EFdBgd1licg4o5AhMoaYpknn\nY4/R/N3vke7pIXfRIopv+hqGW/ftEZGjTyFDZIyIbd1K47duoXfNGlyFhZTe+l9knXuu3WWJyDjm\nGOyBmzdvZt68eUyZMoW5c+eyfv36vY5Jp9OsWLGCE044gVmzZrFgwQK2bNkypAWLyEDJjg5a7rqL\nrRd9gt41a8j51GVMeub/FDBExHaDDhnLli1j6dKlbNq0iZtuuonFixfvdcxTTz3FK6+8wltvvcXb\nb7/N2Wefzc033zyU9YpIn+i777Lj3/6NLfMX0PrjO3FXVDBh9S8o/fa3cWZm2l2eiMjghkuam5t5\n/fXXefbZZwG45JJLWL58OVu2bGHy5Mn9xxmGQSwWIxqN4nK56O7upqKiYngqFxmnetesofmHdxB5\n4w0AfLNnkbdoEVkf+QiGx2NzdSIiuwwqZNTW1lJaWorLZR1uGAZVVVXU1NQMCBkXXnghL7zwAiUl\nJWRmZlJeXs5LL700PJWLjDPpWIyW//kh7Q8+iOFykX3RQnIXLcI/c6bdpYmI7NOgh0sG4/XXX+ed\nd96hvr6eHTt2cPbZZ3P11Vfv89iVK1dSUVHR/wiFQkNZisiYEt2wgW2f/CTtDzyAb9ZMJv3uKcr+\n+78VMERkRDNM0zQPdlBzczOTJ0+mvb0dl8uFaZqUlpby17/+dUBPxvLlyykrK+ufh7F+/XrOO+88\n6uvrD1pIRUUFdfvY8lhkPEs0NdP56KO0rloFpknh8mvJX7IEw6WFYSIyMhyo/R5UT0ZRURFz5sxh\n9erVADz++ONUVFQMCBgAkyZN4vnnnycejwPw9NNPM2PGjCOpXWTciW/fTtt997HtU59my1ln0Xrn\nnXgmVDHx4d9QcPXVChgiMmoMqicDYOPGjSxevJi2tjaysrK4//77mTlzJkuWLGHhwoUsXLiQWCzG\n8uXL+etf/4rb7aakpIR77rmHSZMmHfT86smQ8S7d28uOb9xMzx//CIAjI4Pg/LPIPOccgmefjcPr\ntblCEZG9Haj9HnTIGG4KGTKeJZqaqLvmi0Q3bCC4YAE5l11K4NRTFSxEZMQ7UPutflcRm0U3bKD2\nmi+SbGqi4EvLKfjiFzEMw+6yRESOmEKGiI16nn+e+q/eCIkEZd/7HtkXftzukkREhoxChogNTNOk\n/YEHaf7ud3Hm5FDxs5+SMWeO3WWJiAwphQyRo8xMJGj8z/+i8+GH8UyaROWqe/BUVtpdlojIkFPI\nEDmKUt3d1F93PeFXXyXj1FOouOMOnFlZdpclIjIsFDJEjpJ4XR21V19NfMv75Fx6KSXf/DcMt9vu\nskREho1ChshR0PP88+z4xs2ku7spuukm8hZfqRUkIjLmKWSIDCMzHqf5Bytpf/BBnNnZVNz9EzLn\nz7e7LBGRo0IhQ2SYxOvqqL9+BdF16/DPmUP5D76Pu7TU7rJERI4ahQyRYRD6y1+ov+GrpHt6yF+6\nlMIvLdf8CxEZdxQyRIZY++pf0nTrrTgzM6n86b0EzzjD7pJERGyhkCEyRMxkkqbb/5uO1avxTJxI\n5T1345k40e6yRERso5AhMgRSoTD1N6wg/NJfyDj5ZCp+dAfOnBy7yxIRsZVChsgRSjQ1U7tsGbH3\n3iP74osp/fYtGB6P3WWJiNhOIUPkCMQ+2ErtkiUkduyg8LrryF+2VPtfiIj0UcgQOUyRt96idtnV\npHp6KL31VnIu/le7SxIRGVEUMkQOQ+gvf6HuK9eBaVJx153aYEtEZB8cdhcgMtp0/+GP1F7zRRwe\nDxMeuF8BQ0RkP9STIXIIwn//BztuvBFnXi4THngA77HH2l2SiMiIpZ4MkUGKbtxI3bXXYng8VN17\nrwKGiMhBqCdDZBASO3ZQ+4WlpGMxqn56L77jj7e7JBGREU8hQ+QgUp2d1HxhKcnmZsp+8H0Cp5xi\nd0kiIqOChktEDiAdi1H7xWuJv/8+RV+/ieyPfczukkRERg2FDJH9MNNpGr5xM5E33yRv8WLyFy+2\nuyQRkVFFIUNkP1p+/GO6n3mGzHPPoehrN9pdjojIqKOQIbIPnb99kra778E3YwZl3/0uhkP/VxER\nOVT6yymyh/Df/0HDt76Fq7SUip/chcPvt7skEZFRSSFDZDexrVup+/KXcXg8VN5zD+6iIrtLEhEZ\ntbSEVaSPmUhQf8MNpHt6qFx1D76pU+wuSURkVFNPhkif1nvvJbbhXfKXLCF4xhl2lyMiMuopZIgA\n0XffpfXue/AedxwFy6+1uxwRkTFBIUPGPTMeZ8c3bgbTpPT223B4PHaXJCIyJihkyLjXes8qYu+9\nR8GyZfinT7e7HBGRMUMhQ8a1yPr1tK5ahXfaNAquXmZ3OSIiY4pChoxbZjxOw9e/AYZB2e23YWiY\nRERkSClkyLjV8cijxDZvpmDZMnzTptldjojImKOQIeNSKhSm9e67cRUWkn/V5+0uR0RkTFLIkHGp\n/YEHSLW1UbB8OY6MDLvLEREZkxQyZNxJtrXR/vOf45k4kZxLLra7HBGRMUshQ8ad1rvvId3bS+H1\n12O4tLO+iMhwUciQcSVeW0vHww/jmzWLzPPOtbscEZExTSFDxpWWO34EiQRFN9yAYRh2lyMiMqYp\nZMi4Ed2wge6nnyZw5hkEPnyy3eWIiIx5ChkyLpiJBI3f+Q8wDIpWrLC7HBGRcUEhQ8aF5h/+kMja\nteRdeaU23hIROUoGHTI2b97MvHnzmDJlCnPnzmX9+vV7HXP//fdTXV3d/ygoKODii7VEUOzV88IL\ntN/3c/yzZ1N0g3oxRESOlkGHjGXLlrF06VI2bdrETTfdxOLFi/c65nOf+xxr167tf5SUlPCZz3xm\nKOsVOSSJHTvY8fVv4MzOpvx/VmK43XaXJCIybgwqZDQ3N/P666+zaNEiAC655BJqa2vZsmXLfp/z\n97//nebmZhYuXDg0lYocIjMep/76FaS7uii9/TbcZWV2lyQiMq4MKmTU1tZSWlqKq2/jIsMwqKqq\noqamZr/Pue+++7jiiitw68pRbNL8Pz8k8tZb5F31eTIXLLC7HBGRcWdYtjsMh8P85je/4bXXXtvv\nMStXrmTlypX934dCoeEoRcap0Msv037//fhPPJGi666zuxwRkXFpUD0ZlZWVNDQ0kEwmATBNk5qa\nGqqqqvZ5/KOPPsr06dM54YQT9nvOFStWUFdX1/8IBoOHUb7I3lLd3TT82zdxBIOU/+D7mochImKT\nQYWMoqIi5syZw+rVqwF4/PHHqaioYPLkyfs8/r777uOqq64auipFDkHTbbeTbGqi+Btf1zwMEREb\nDXp1yapVq1i1ahVTpkzh9ttv5/777wdgyZIlPPXUU/3Hbdy4kbVr13LZZZcNfbUiB9Hzwgt0/fa3\nBM48g2wtnxYRsZVhmqZpdxEAFRUV1NXV2V2GjGKpzk4+uHAh6WiUSU//Dndxsd0liYiMeQdqv3Wf\naxkzGm+9lWRLi7VcVQFDRMR22lZcxoSe556j+6nfEVywgOyLLrK7HBERQSFDxoDI2rXsuOnrOLKz\nKfn3b+sW7iIiI4RChoxqkbffpmbJFzDTaSrv/DHuoiK7SxIRkT4KGTJqRdato+aqJZipFJWr7iFj\n7ly7SxIRkd1o4qeMSpF171Dz+aswEwkq77mHwMkn212SiIjsQSFDRp3YB1upueoqzHicynvuJnDK\nh+0uSURE9kEhQ0YV0zRpvOUW0j09VN57L4FTT7W7JBER2Q/NyZBRpfvpp+lds4acSy8leMbpdpcj\nIiIHoJAho0aqp4em//4uztxciq7XnVVFREY6hQwZNVru+BGp1laKvnoDzpwcu8sREZGDUMiQUSG6\nYQMdv/oV/hNPJPtf/9XuckREZBAUMmTEM9NpGv/9OwCU3PItDIf+sxURGQ3011pGvM7HHyfy1lvk\nXbEI37RpdpcjIiKDpJAhI1oqFKZl5f/gKiyk4EtfsrscERE5BAoZMqK1P/QgqY4OCr78JZzBoN3l\niIjIIVDIkBEr2dFB+8/vxzNhAjmf+ITd5YiIyCFSyJARq/2++0iHQhR8+UsYbrfd5YiIyCFSyJAR\nKdHUTPvqX+KdOpWsj37U7nJEROQwKGTIiNS26h7MaJTCr3xFS1ZFREYp/fWWESdeV0fHo4/hnz2b\n4IL5dpcjIiKHSSFDRpzWO++CRILC66/HMAy7yxERkcOkkCEjSmzLFrqeeorAvFMJnPJhu8sREZEj\noJAhI4ZpmjT993chnabwOt1lVURktFPIkBEj9MKLhF9+meyLFuKfNcvuckRE5AgpZMiIkI7FaLrt\nNhwZGRTecIPd5YiIyBBQyJARof3+B0jU1lJw7RdxFxXZXY6IiAwBhQyxXaKxkdZVq/BMnEjeFVfY\nXY6IiAwRhQyxXfN3v4cZiVD8/92M4fHYXY6IiAwRhQyxVe+aNXQ/8wzBBQsInnGG3eWIiMgQUsgQ\n25jpNI3/dSuGx0PxN75udzkiIjLEFDLENj1//jOx994j94pFeKqq7C5HRESGmEKG2MI0Tdru/SmG\nx0P+5z5ndzkiIjIMFDLEFr1//zvRdevIvuRiXAUFdpcjIiLDQCFDbNF270/B4SD/85+3uxQRERkm\nChly1EXeWU/41VfJuuACPJWVdpcjIiLDRCFDjrq2n/4UgPwvLLG5EhERGU4KGXJUxT7YSs+zzxI4\n60x8U6faXY6IiAwjhQw5qtp+fh+YJgVLl9pdioiIDDOFDDlqEk1NdP3vU/jnzCHjpJPsLkdERIaZ\nQoYcNe0PPgSJBPlLv2B3KSIichQoZMhRkQ6H6Xz0UTzHHkvwzDPtLkdERI4ChQw5KjqffJJ0Tw95\nV1yB4dB/diIi44H+2suwM9NpOh76BY7sbLIvWmh3OSIicpQoZMiwC/3lL8S3byf30ktx+P12lyMi\nIkfJoEPG5s2bmTdvHlOmTGHu3LmsX79+n8etW7eO+fPnc/zxx3P88cfzxBNPDFmxMjp1PPQQuFzk\nfuZyu0sREZGjyDXYA5ctW8bSpUtZvHgxjz32GIsXL2bNmjUDjunt7eWiiy7ioYce4vTTTyeVStHe\n3j7kRcvoEd20ifCrfyPrYx/DXVJidzkiInIUDaono7m5mddff51FixYBcMkll1BbW8uWLVsGHPer\nX/2KU045hdNPPx0Ap9NJYWHhEJcso0nHL34BQN6Vn7W5EhEROdoGFTJqa2spLS3F5bI6PgzDoKqq\nipqamgHHbdiwAa/Xy8c//nGqq6v57Gc/S0tLy9BXLaNCsr3d2nyruhr/rFl2lyMiIkfZkE78TCaT\nPPfcc6xatYp//vOflJeXc8011+zz2JUrV1JRUdH/CIVCQ1mKjACdjzyCGY+rF0NEZJwaVMiorKyk\noaGBZDIJgGma1NTUUFVVNeC4qqoqFixYQHl5OYZhsGjRIl577bV9nnPFihXU1dX1P4LB4BG+FRlJ\n0r29dPzyV7hKS8k891y7yxERERsMKmQUFRUxZ84cVq9eDcDjjz9ORUUFkydPHnDcpZdeypo1a+ju\n7gbgmWeeYfbs2UNcsox0pmmy4xs3k2xpIf/zn8dwDXp+sYiIjCGD/uu/atUqFi9ezK233kpWVhb3\n338/AEuWLGHhwoUsXLiQqqoqbr75ZubNm4fD4aC8vJx777132IqXkan17rvp+eMfyTz/fHIXfcbu\nckRExCaGaZqm3UUAVFRUUFdXZ3cZcoS6//Qn6r/0ZbzHH8/EX67GkZFhd0kiIjKMDtR+a8dPGTLR\njZvYcdPXceblUXnnjxUwRETGOQ2Wy5BIdnRQd+21mIkEFfeuwl1ebndJIiJiM/VkyJBo/Na3SNTV\nUfLNfyPjQx+yuxwRERkBFDLkiPWuWUPPn54j87zzyL30UrvLERGREUIhQ46ImU7T9N3vgdtN0Q0r\n7C5HRERGEIUMOSLdv/890XXryP30p/BMmGB3OSIiMoIoZMhhS8ditPxgJY7MTAr2s328iIiMXwoZ\nctg6Vv+SxI4dFFx9Na7cXLvLERGREUYhQw5LsqOD1nvuwV1Wpl09RURknxQy5LC03XMP6Z4eCles\nwOH12l2OiIiMQAoZcsjidXW0/+rX+GbMIOuCj9pdjoiIjFAKGXLI2u67DxIJilZcj+HQf0IiIrJv\naiHkkCRbWuh6/Al8M2eSceqpdpcjIiIjmEKGHJL2h36BGY+Tv/QLGIZhdzkiIjKCKWTIoKV6euj4\n9a/xTJpE5tln212OiIiMcAoZMmgdv/4N6VCI/CVLNBdDREQOSi2FDEo6GqX9oYdwlZSQ/fGP2V2O\niIiMAgoZMihdTz5JqrWV/M9/DsPjsbscEREZBRQy5KDMZJK2n92HMyeHnE9+0u5yRERklFDIkIPq\n/sMfSdTVkXvFIhwZGXaXIyIio4RChhxU+89/jpGRQe7ll9tdioiIjCIKGXJAkXXvEN2wgewLL9Sd\nVkVE5JAoZMgBdT7yCAC5l11qcyUiIjLaKGTIfqVCIbr+7//wzZiB74QT7C5HRERGGYUM2a/up/8P\ns7eXHPViiIjIYVDIkH0yTZOORx7GEQiQfcEFdpcjIiKjkEKG7FP0nfXENrxL1oUfxxEI2F2OiIiM\nQgoZsk+djzwMQO6lGioREZHDo5Ahe7EmfD6Db+ZMTfgUEZHDppAhe+l++mlrwuel/8/uUkREZBRT\nyJABTNOk4+FHNOFTRESOmEKGDBBdt47Yu++StfBCTfgUEZEjopAhA7SuuheA3E9/2uZKRERktFPI\nkH7Rd98l9Oc/k3nuufimTLG7HBERGeUUMqRf609+AkDBtV+0uRIRERkLFDIEgOjGjfT86TmC55yN\nb9o0u8sREZExQCFDAGi9q68X45prbK5ERETGCoUMIbppEz3PPktwwQL806fbXY6IiIwRChlC6913\nA1DwRc3FEBGRoaOQMc7Ftmyh5w9/JHDWmfhnzrC7HBERGUMUMsa51p/cDaZJ4bXX2l2KiIiMMQoZ\n41hs82a6f/97AmecgX/WLLvLERGRMUYhYxxruesnVi/Gl5bbXYqIiIxBChnjVHTjRnr+8AeC8+er\nF0NERIaFQsY41XrnnQAUqBdDRESGyaBDxubNm5k3bx5Tpkxh7ty5rF+/fq9jXnzxRfx+P9XV1f2P\nSCQypAXLkYusX0/Pn54j89xztC+GiIgMG9dgD1y2bBlLly5l8eLFPPbYYyxevJg1a9bsddzUqVNZ\nu3btkBYpQ6v1zrsAKFiuXgwRERk+g+rJaG5u5vXXX2fRokUAXHLJJdTW1rJly5ZhLU6GXuTttwm9\n8AKZ55+Pb+pUu8sREZExbFAho7a2ltLSUlwuq+PDMAyqqqqoqanZ69j333+fOXPmMHfuXH7Sd1dP\nGTlafnwnGAaFutOqiIgMs0EPlwzGnDlzqKurIzs7m7q6Oi644AIKCgq49NJL9zp25cqVrFy5sv/7\nUCg0lKXIPvS++U/CL79M1sc+hve44+wuR0RExrhB9WRUVlbS0NBAMpkEwDRNampqqKqqGnBcVlYW\n2dnZAFRUVPDpT3+al19+eZ/nXLFiBXV1df2PYDB4JO9DBqH1rrvA4aBAvRgiInIUDCpkFBUVMWfO\nHFavXg3A448/TkVFBZMnTx5wXENDA+l0GoCenh6efvppTjzxxCEuWQ5HZO1awq+8QtYFF+CdNMnu\nckREZBwY9BLWVatWsWrVKqZMmcLtt9/O/fffD8CSJUt46qmnACt8zJw5k9mzZ3PKKadw7rnn8rnP\nfW54KpdD0vKTn4BhUHDN1XaXIiIi44RhmqZpdxFgDa/U1dXZXcaYFHn7bbZdehlZF3yU8t3mwYiI\niBypA7Xf2vFzHGi9a2cvxjV2lyIiIuOIQsYYF3lnPaGXXiLzIx/RihIRETmqFDLGuNa+vUrUiyEi\nIkebQsYYFt2wgdDzz5N57rn4pk6xuxwRERlnFDLGsNa77wag4IvqxRARkaNPIWOMirz1Fj1/eo7g\n2WfjO/54u8sREZFxSCFjDDJNk6bvfg+cTopWXG93OSIiMk4pZIxBPc89R+SNN8j5f5/Ee+yxdpcj\nIiLjlELGGGMmErR8/wc4MjIoXL7c7nJERGQcU8gYYzoefoT49u3kf2EJroICu8sREZFxTCFjDEn1\n9NB65524iorIW7zY7nJERGScU8gYQ9ru/Smpzk4Kr7sOh99vdzkiIjLOKWSMEYkdO2h/8EG806aR\nfdFCu8sRERFRyBgrWn70Y8x4nKIbv4rhdNpdjoiIiELGWBDbsoWup54iMO9UgqedZnc5IiIigELG\nmNDyox9DOk3hddfZXYqIiEg/hYxRLvLOenqefZbgOWfjnzXL7nJERET6KWSMci133AGGQeGXv2x3\nKSIiIgMoZIxivWvWEH75ZbI+/nF8U3QrdxERGVkUMkYp0zRp/uEd4HJRuPxau8sRERHZi0LGKBV+\n+WXrJmiXXIJnwgS7yxEREdmLy+4C5NCZ6TTNP/whhsdDwRevsbscERHbpdNpTNO0u4wxyTAMHI7D\n65NQyBiFuv73KWIb3iXvc5/DXVxsdzkiIraJx+PU1NSQSCTsLmVMc7vdVFVV4fF4Dul5ChmjTKqn\nh+Yf/ABnXh4F11xtdzkiIraqqakhMzOT/Px8DMOwu5wxyTRN2traqKmpYfLkyYf0XIWMUab1J3eT\nam2l9D//A2dWlt3liIjYJp1Ok0gkyM/Px+VSczac8vPzaW9vJ51OH9LQiSZ+jiKx99+n/Re/wDdj\nBtkXX2x3OSIitto5B0M9GMNv52d8qPNeFDJGCdM0abr1NkgmKfm3/w/jMCfhiIjI8HriiSc46aST\nqK6uZtq0afzLv/xL/8TUO++8k5kzZzJt2jTmzJnDeeedxwsvvADAtm3bcDqdVFdX9x/zhS98gbq6\nOpvf0eFT/9IoEfrznwm/8grZn/gE/upqu8sREZF9aGhoYOnSpbzxxhtM6Nte4M0338QwDL75zW/y\n/PPP8/vf/56KigoA1qxZwxtvvMGCBQsAyMzMZO3atYA1qfU///M/mTdvHuvWrSM7O9ueN3UEdDk8\nCqSjUZpuux1HIEDRDSvsLkdERPajqakJp9NJXl5e/8/mzJlDOBzm+9//Pj//+c/7AwbA3Llzufrq\nfU/i93g8fOc736G8vJzVq1cPe+3DQT0Zo0DbT39Gor6eoq99DVdhod3liIiMSEseXMP2tt5hO/+E\n/Ax+duXcAx4za9YsTj/9dCZMmMBZZ53FvHnzuPzyy6mvr8fr9TJt2rRDft2TTz6Z9evXH27ZtlJP\nxgjX+89/0nrPPXiPO468RZ+xuxwRETkAh8PB448/zquvvsr555/PK6+8wvTp04GBE1QjkQjV1dWc\ncMIJnHPOOQc852jeZEw9GSNYqqeHHV+9EcPlonzlDzAOcRMUEZHx5GC9DEfTtGnTmDZtGsuWLeP8\n88/nD3/4A9FolI0bNzJ16lT8fj9r167lxRdf5LrrrjvgudasWcMVV1xxlCofWurJGKFM06Txlm+T\nqK+n+Os34T3uOLtLEhGRg6ivr+eVV17p/76jo4OtW7cye/ZsVqxYwZIlS6ivr+//fTgc3u+54vE4\n//7v/05dXR2f+czo7MlWT8YI1fXbJ+l+5hmC55xNzqc+ZXc5IiIyCMlkku985zts3bqVjIwMkskk\nV155JRdddBELFy7kRz/6Eeeffz6JRIKCggKCwSC33XZb//N7enqorq4mmUySSCQ444wzePXVV0fl\nyhIAwxwhgz0VFRWjei3wUIpt3crWSz6JMzOTY578La7cXLtLEhEZcVKpFJs2bWLKlCk4nU67yxnT\nDvRZH6j9Vk/GCGPG4+z46o2YkQhld9+tgCEiIqOW5mSMMC0/vpPo+vXkL1tK4MMn212OiIjIYVPI\nGEF633iDtp/9DN+MGRRee63d5YiIiBwRhYwRIhUKseNrN2F4vZR997sYbrfdJYmIiBwRhYwRounW\n26xdPW/8Kt5Jx9hdjoiIyBFTyBgBuv/0J7qeeILA6aeTe/nldpcjIiIyJBQybJZsaaHxW7fgzM6m\n9L/+a8C2syIiIqOZQobNGr/zHVIdHZR85zu4i4vsLkdERI7QxIkTmTp1KtXV1UydOpXbb78dgG3b\ntmEYBhdddNGA42+55RYMw+DJJ58EoLa2loULFzJz5kxmzpxJdXU1zz//PAAPPPAA2dnZVFdXM336\ndD760Y9SU1NzdN/gIVDIsFHvG2/Q86fnyDzvPLI+cp7d5YiIyBB5+OGHWbt2Lc8//zy33XYb//jH\nPwDIzs5m06ZNNDU1AZBOp/n1r3/NzJkz+597zTXXsGDBAtatW8e6det47rnnmDx5cv/vFyxYwNq1\na1m/fj1Tpkzh+uuv3+v1k8nkML/DwVHIsIlpmjR//wfgdFK0Yu//QEREZPQrLy9n2rRpbN++vf9n\nixYt4qGHHgLgueee/dbfUQAAGF1JREFU48QTTyQvL6//93V1dZSXl/d/X1BQQFVV1T7P/5GPfISN\nGzcC1l1eb7nlFubOncs3vvENmpubufjii5k5cyYzZsxg1apV/c+bOHEiN954IyeddBKTJ0/me9/7\n3pC+750GvePn5s2bufLKK2ltbSU7O5sHHnig//a1ezJNk7PPPps333yTzs7OISt2LAm98AKRf/6T\nnE9/Cs/EiXaXIyIy+v3qU9CxdfjOn3sMXP6bQ3rKe++9R1tbG/Pnz++/GdqVV17J+eefz4033sjP\nf/5zPv/5zw+4f8lNN93EVVddxR133MEpp5zCRRddxJlnnrnXuVOpFI8++ignnXRS/8+cTidr1qwB\n4LLLLmPq1Kk88cQTNDc3c9JJJzF79mxOOeUUAJqamnj99ddpa2tjzpw5nHbaaf9/e3cfFmWV9wH8\nO8wgKq8qYMQIhIA6os6AEBpiq7C01qoXabWLGgYr4KrbpkZv7pKaS11Fj2IpJZIiuqyBrpsVIbqa\n16WXuIuvqYgxMA+iuEC8JC8DnOcPH2clFDHnnsHx+/kL7vvMPb85nGvmx7nPnB8mTpx4z93Sk17P\nZMTHx2PBggUoKSlBUlISYmJi7tj2ww8/xPDhw40Rn0US7e2oTk2FbMAAuCxcaO5wiIjIyJ5//nmM\nGjUKKpUKixcvhouLi+GcUqmEUqnEF198gX/961+IiIjo8tjf/OY3qKiowNKlSwEAM2bM6DLTcODA\nAajVagQGBkImk+GDDz4wnHvppZcMP+/btw/x8fEAAFdXV0RFRWHfvn2G87GxsZDJZHB2du52zlh6\nNZNRXV2N48eP45tvvgEAPPvss1i0aBFKS0u73CcCgLNnz2L37t3IzMzEzp07jR6wJaj/+9/RVnoJ\nQxIToLhl4BER0X24x1kGKeXk5ECtVmPfvn349a9/jSlTpsDe3t5wfv78+Zg/fz4SEhJgZdX9//1B\ngwYhKioKUVFRCAoKwpo1a7B8+XIAN9Zk3Fwk+lN2dnZ3jOlu316U4tuNvZrJ0Ol0cHNzg0KhMATi\n4eHRbUWrXq/H7373O6Snp7Mi3h10trTgWtp6yJ2cMCQ21tzhEBGRhMLDw5GYmIi33nqry/GZM2di\n2bJlSEhI6PaYL774AtevXwdwY/lBcXHxz7o7EB4ejk8//RQAcO3aNeTl5XWZNfnss88AALW1tdi1\naxemTp16z89xN0atwvr2228jKioKo0aNglar7bFtamoqUlNTDb83NTUZM5Q+qy47G+1XrmDo669B\n3kPGSURElmHFihXw8fFBTU2N4ZiNjQ2SkpJu2/7gwYNYvnw5FAoFhBAYMWIE1q9ff8/Pu27dOiQm\nJmLMmDEQQuDNN9/E448/bjjv4uKCwMBA1NfXY9GiRUZfjwEAMiGEuFuj6upq+Pj4oLa21vCi3dzc\ncPjw4S63SyZNmoSKigrIZDK0t7fj8uXL8PDwQFFRUZf7UbfTUz16S9FRX4/SX0ZCbmsL76+/glW/\nfuYOiYjogdXR0YGSkhL4+flx9vweeXl5Yffu3VCr1b1q31Nf9/T53avbJa6urggICMC2bdsAALm5\nuVAqld3WY3z77bcoLy+HVqvF4cOH4eDgAK1We9cE42FRuzULnfX1cF6ymAkGERFZvF5/uyQ9PR3p\n6enw8/NDSkoKMjMzAQBxcXHYs2ePZAFaio6mJtRmZcHa0wOOzzxj7nCIiOghptVqez2LcT96vSZj\nxIgROHLkSLfjmzZtum17Ly8v7pFxi7odO9DZ0IChry6HTGHUpTBERER9Enf8NIHO5mbUZn4GhZsb\nHKdPN3c4REREJsEkwwR+2Pk5OmprMSQ2FjKuxSAioocEkwyJdba1oSYjA3JnZzjNetbc4RAREZkM\nkwyJ1e/ejfarVzFkfgys+vc3dzhERCSxxsZG2NnZIbaHDReXLVuG5OTkXl2voKAAYWFh8Pb2xvjx\n4xEcHIxPPvnEKLGuX7++xzIh94tJhoREeztqPt0EuaMjnJ5/wdzhEBGRCeTk5CAwMBB5eXn3vdHk\nN998gxdffBEpKSn4/vvvcfz4ceTl5aGysvK27ftKifebmGRIqOHLL6HX6TDoxXmQ29maOxwiIjKB\njIwMJCUlISwsDDk5OQCAqqoqREZGQqVSITw8vMvmVYWFhZgwYQI0Gg1Gjx6NjIwMw7mVK1fiT3/6\nU5fdOJVKJd5++23D7z8t8X769GmEhoYiICAAKpUKq1evNrRtbGw0VGcNDQ3F6dOnpewK424rTv/V\n0dSEa+s/gpWtLQZHR5s7HCIii7e4cDF0jTrJrj/MfhjSpqb12Oa7776DTqdDZGQk2tvbkZKSgtjY\nWCxZsgTBwcHIz89HZWUl1Go1Ro4cCQAICAjA4cOHIZfLUVtbC41Gg8jISCiVSvz73/9GWlrPzwl0\nLfHe2NiIwsJC2NjYoLm5GRMnTkR4eDhCQkKwcuVK2NjY4Pz582hoaEBISEiXrcaNjTMZEhBCoOr1\n16GvqIDLH5ZA7uho7pCIiMgEMjIyMG/ePMjlckybNg1lZWU4d+4cCgsLERcXBwBwd3fH9Fu2M6ip\nqcHs2bPh7++PKVOmoKamBmfOnLnt9aOjo6FWq/HII4+goaHBcPzWEu/Nzc2Ii4vDmDFjEBISgvLy\ncpw4cQLAjVmTmyXeHR0d8dvf/laKbjDgTIYEajZtQmPBPjhM+xUGzZ1r7nCIiB4Kd5tlkJper0dW\nVhasra2xfft2AMD169e73P646day6gkJCZg2bRpyc3Mhk8kQEBCAlpYWAIBGo8GxY8eg0WgAANnZ\n2YbHd3Z2Gq5xa4n3N954A87OziguLoZCoUBUVJThej3FIQXOZBjZj0eO4NqH/wMbXx+4rVol+R+Q\niIj6hj179sDb2xuVlZXQarXQarU4evQosrKyMHnyZGzevBnAjfUZt5bjqKurg6enJ2QyGQ4dOoST\nJ08azq1YsQIrV67E0aNHDcd+/PHHHuOoq6uDUqmEQqHAhQsXUFBQYDgXHh6OzMxMCCHQ0NCAHTt2\nGOvl3xZnMoxIf/kyKl9ZCquBA+G+bh2sbLnYk4joYZGRkYHon6zBGzVqFNzd3TFr1ixs3boVKpUK\n7u7umDJliqFNSkoKFi5ciFWrVkGtVndZI/HUU08hIyMDy5cvx+XLl+Hi4oJ+/fohLS0N9vb2t43j\nrbfewty5c7FlyxYMHz68y3OtWLECcXFxGDlyJFxcXBAaGorW1lYj98R/9arUuyk86KXeO1tbUR49\nBy1nzkD50XrYT51q7pCIiCwaS72bjqSl3qlnQq9H5dKlaDlzBkPi45lgEBERgUnGfRN6PSpfWYqm\nfYVweOYZuCxZbO6QiIiI+gQmGfdB6PWoXLYcjQUFcJg2DY+m/AUyTtkREREBYJLxs4n2dlS++ioa\n8/Nh/6un8Oh770Km4DpaIiKim5hk/AyiowOXk15D41dfw/6Xv4T7e+8xwSAiIvoJJhn3SAiBK8nJ\naNi7F/YR4XD/4H3IrK3NHRYREVGfwyTjHgghUP3ue/hh5+ewDQ3Fox98wASDiIjoDphk3IP/bNiA\n2s8+w4DAQCjT1sGqXz9zh0RERH1MY2Mj7OzsEBsbe8c2y5YtQ3Jy8l2vlZycDBcXF2g0Gvj5+SEo\nKAhr165FR0fHXR/7z3/+E2q1GgDwww8/ICUlpdevwViYZPRS7dat+M+6NNioRmHYxg2wGjDA3CER\nEVEflJOTg8DAQOTl5aGpqem+rxcdHY3i4mKUlJQgJycHOTk5+OMf/3hP12CS0UcJvR7/2bABV9f8\nBf28veGxaRPkd9jKlYiIKCMjA0lJSQgLC0NOTg6AG/VKIiMjoVKpEB4e3mWHzMLCQkyYMAEajQaj\nR4++bUG1m7y9vbF582Zs2LAB9fX1AID8/HyEhoYiMDAQwcHBOHDgQLfHJSQkoLGxEWq1GuPHjwcA\npKamIigoCGq1GkFBQThy5IgxuwEAa5f0qPnUKVSt+BNaL1xAv+HD4ZGxCYrBg80dFhER3YYucSHa\ndBWSXb/fMA8M2/Bxj22+++476HQ6REZGor29HSkpKYiNjcWSJUsQHByM/Px8VFZWQq1WY+TIkQCA\ngIAAHD58GHK5HLW1tdBoNIiMjIRSqbztc4wcORIDBw7EhQsX4OzsjOTkZOTn58PBwQGlpaWYNGkS\ntFptl8ds3LgRarXaUPIdAObOnYtXXnkFAHD06FHExMTg/Pnz99FD3THJuI3OH3/EtXXrUJu1DZDL\n4bwwEUPi42FlY2Pu0IiIqA/LyMjAvHnzIJfLMW3aNMTHx+PcuXMoLCzE+++/DwBwd3fH9OnTDY+p\nqalBbGwsSkpKoFAoUFNTgzNnztwxyQBufBEBAL7++muUlpYiLCzMcM7KygoVFXdPtoqLi/HOO++g\npqbGULG1ubkZA4y4HIBJxk/or1xB+dx50Ot0GKBWw23VStj4+po7LCIiuou7zTJITa/XIysrC9bW\n1ti+fTsA4Pr167e9/SGTyQw/JyQkYNq0acjNzYVMJkNAQABaWlru+Dw3k4GRI0eiqKgIERERhue7\nVWVl5R2v0dbWhqioKBw4cABBQUFoaGiAo6MjWltbjZpkcE3GLTp//BG6xIXQ63RwfS0JntuzmWAQ\nEVGv7NmzB97e3qisrIRWq4VWq8XRo0eRlZWFyZMnY/PmzQBurM/Ys2eP4XF1dXXw9PSETCbDoUOH\ncPLkyTs+h1arRWxsLBITE+Hg4IDIyEjs27cPp06dMrQ5duxYt8c5ODigubkZbW1tAICWlha0tbXB\nw8MDAJCWlmaUPvgpzmT8P9HRgcqly9B67hxcXv4DhsTEmDskIiJ6gGRkZCA6OrrLsVGjRsHd3R2z\nZs3C1q1boVKp4O7ujilTphjapKSkYOHChVi1ahXUajUef/zxLtfIzs7GwYMHcf36dTg4OCA6OhqL\nF98oxunj44Pt27cjPj4e169fR1tbGzQaTbeZjcGDB2PevHkYO3Ys7OzscPz4caxevRrBwcFwdnbG\nCy+8IEmfyMTNGztm1lM9elO48s4a1GVlwXHmTLj9ZU2XqSwiIup7Ojo6UFJSAj8/P8hZnFJSPfV1\nT5/fvF0CoHZbNuqysjAwOBhuK99mgkFERGQED32S0bh/P66uWYN+Xl5QrlsLGXfxJCIiMoqHOsn4\nYfdu/O+SP0Du6Ihh6Rshd3Iyd0hEREQW46FMMoQQqMnIQNVrr0Ph4gLPbVno5+lp7rCIiIgsykP3\n7RLR2Ynqd99D7ZYtsPH1xbBNn8J66FBzh0VERGRxHqoko7O1FVVvvImGvXsxYHwghn30EeSOjuYO\ni4iIyCI9NLdLWr8vg/a559Gwdy/spk69UeiMCQYRERmZMUu9P+geiiSjfs8elM2ahdaLF+G8eBGU\n69bCqn9/c4dFREQWyNil3h9kFp1kdDY34/Kbb+Lyq0mQ29rCIzMTLr//PWTctIWIiCRizFLvMTEx\nWLBgAcLDw/HYY4/hpZdewrFjx/Dkk0/C29vbUEW1r7LYNRmiowPlc+ai5exZ2IaG4tF3U6AYMsTc\nYRERkUT2fnwK9deaJbu+o8sAPL1wbI9tpCj1fvr0aRw4cABWVlZQqVSoq6tDQUEB2tra4O3tjdjY\nWIwePVqy130/LHYmQyaXw+m55+DyyisY9kk6EwwiIpLcT0u9l5WVGUq9x8XFAbh9qffZs2fD398f\nU6ZMMZR6v2nGjBno378/+vXrhzFjxiAyMhLW1tawtbWFSqXCxYsXTf46e8tiZzIAYNDzz5k7BCIi\nMpG7zTJITapS7/1vWUMol8u7/d7e3i7FyzEKi53JICIiMiVTlHp/0Fj0TAYREZGpSFXq/UHGUu9E\nRPRAYql302GpdyIiIupTmGQQERGRJHqdZFy8eBETJ06En58fgoKCcPbs2W5tjhw5ArVaDbVajdGj\nRyM+Ph6tra1GDZiIiIgeDL1OMuLj47FgwQKUlJQgKSkJMTEx3dqMGzcORUVFOHHiBE6fPo3q6mp8\n/PHHxoyXiIgIQNevgZJp3Guf9yrJqK6uxvHjxzFnzhwAwLPPPgudTofS0tIu7QYOHAhra2sAQFtb\nG5qbmzkIiIhIEjKZDDKZDHq93tyhWDy9Xm/o73vRq6+w6nQ6uLm5QaG40Vwmk8HDwwMVFRXw8fHp\n0lar1WLGjBm4dOkSnn76aSxcuPCeAiIiIuoNmUwGJycnXL16Fe7u7vynViJCCFy9ehVOTk7SJBn3\nwsvLCydPnkRTUxPmzJmDvLw8vPDCC93apaamIjU11fD7w16pjoiI7p2rqyvKy8v79NbalqB///5w\ndXW958f1ap+M6upq+Pj4oLa2FgqFAkIIuLm54fDhw91mMm7117/+FdnZ2fjHP/5x10C4TwYREf1c\nnZ2d6CPbPlkcmUwGK6s7r67o6fO7VzMZrq6uCAgIwLZt2xATE4Pc3FwolcpuCUZpaSk8PT1hbW2N\ntrY27Nq1C2PHmncveSIisnw9fQiS+fT6r5Keno709HT4+fkhJSUFmZmZAIC4uDjDHuz79++HRqPB\nuHHjoNFoMHToUKxYsUKayImIiKhP47biRERE9LP19PndZ5IMGxsbuLi4GP26TU1NsLOzM/p1qTv2\ntemwr02HfW067GvTMlZ/X7t27Y4bb/aZJEMqnCExHfa16bCvTYd9bTrsa9MyRX9zpQwRERFJgkkG\nERERSUKenJycbO4gpDZhwgRzh/DQYF+bDvvadNjXpsO+Ni2p+9vi12QQERGRefB2CREREUmCSQYR\nERFJwmKTjIsXL2LixInw8/NDUFAQzp49a+6QLEZLSwtmzpwJPz8/jBs3DhERESgtLQVwo87NU089\nBV9fX/j7++PQoUNmjtZyZGZmQiaTYffu3QDY11JobW3FokWL4OvrizFjxmDOnDkA+H4ihS+//BIB\nAQFQq9Xw9/fHli1bAHBcG8OSJUvg5eUFmUyGEydOGI73NI4lG+PCQv3iF78QmZmZQgghdu7cKcaP\nH2/egCxIc3Oz2Lt3r+js7BRCCJGWliYmT54shBBi/vz54s9//rMQQohjx44Jd3d30dbWZqZILUdZ\nWZmYMGGCCAkJEbt27RJCsK+l8PLLL4tFixYZxnZVVZUQgu8nxtbZ2SkGDRokTp48KYS4Mb5tbGxE\nQ0MDx7URHDx4UOh0OuHp6SmKi4sNx3sax1KNcYtMMq5evSrs7e2FXq8XQtwY0EOHDhUXL140c2SW\nqaioSHh6egohhLC1tTW8MQshRFBQkCgoKDBTZJaho6NDTJ06VRw/flxMnjzZkGSwr42rqalJ2Nvb\ni/r6+i7H+X5ifJ2dnWLw4MHi4MGDQgghTp48KR599FHR2trKcW1EtyYZPY1jKce4Rd4u0el0cHNz\ng0Jxo8isTCaDh4cHKioqzByZZVq7di1mzJiBmpoa6PV6PPLII4ZzXl5e7Pf7lJqaiieeeAKBgYGG\nY+xr47t06RIGDx6MNWvWYPz48Zg0aRIKCwv5fiIBmUyGnJwcREVFwdPTE6GhodiyZQsaGxs5riXS\n0ziWcoz3qtQ70Z2sWbMGpaWlKCwsRHNzs7nDsThnzpxBbm4u70ubQHt7O8rLy6FSqZCSkoLi4mJE\nRERg79695g7N4rS3t2P16tXIy8tDWFgYioqKMH369C7rB8gyWORMxrBhw1BVVYX29nYAgBACFRUV\n8PDwMHNkluX9999HXl4evvrqKwwcOBBDhgyBQqHAlStXDG20Wi37/T58++230Gq18PX1hZeXF44e\nPYoFCxbgb3/7G/vayDw8PGBlZYXo6GgAgEajwWOPPYby8nK+nxjZiRMncPnyZYSFhQEAgoKCoFQq\ncerUKY5rifT0uSjlZ6ZFJhmurq4ICAjAtm3bAAC5ublQKpXw8fExc2SWIzU1FTt27EBBQQGcnJwM\nx2fPno2NGzcCAIqKilBZWYnJkyebK8wHXmJiIqqqqqDVaqHVahESEoJPPvkEiYmJ7Gsjc3Z2xtSp\nU5Gfnw8AKCsrQ1lZGZ544gm+nxjZzQ+1c+fOAQBKS0tx6dIljBgxguNaIj19Lkr6mXnfqzr6qPPn\nz4uQkBDh6+srAgMDxalTp8wdksXQ6XQCgPD29hbjxo0T48aNE8HBwUIIIa5cuSIiIiKEj4+PUKlU\nYv/+/WaO1rLcuvCTfW18ly5dEk8++aTw9/cXY8eOFZ9//rkQgu8nUti+fbuhn/39/UV2drYQguPa\nGBYsWCDc3d2FXC4Xrq6uYvjw4UKInsexVGOc24oTERGRJCzydgkRERGZH5MMIiIikgSTDCIiIpIE\nkwwiIiKSBJMMIiIikgSTDCIiIpIEkwwiIiKSBJMMIiIikgSTDCIiIpLE/wHajHsW0wlvVwAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XerUuEtLVkkQ",
        "colab_type": "text"
      },
      "source": [
        "##k-fold cross validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OHNFlXdT1VV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "34c1416a-386f-4257-8807-17acc38fa614"
      },
      "source": [
        "import pandas\n",
        "df = pandas.read_csv('https://github.com/ywchiu/tibamedl/raw/master/Data/customer_churn.csv', index_col=0, header = 0)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>area_code_408</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account_length  ... number_customer_service_calls churn\n",
              "1    KS             128  ...                             1    no\n",
              "2    OH             107  ...                             1    no\n",
              "3    NJ             137  ...                             0    no\n",
              "4    OH              84  ...                             2    no\n",
              "5    OK              75  ...                             3    no\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJdYb8YYVn2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[:,3:]\n",
        "cat_var = ['international_plan','voice_mail_plan', 'churn']\n",
        "\n",
        "for var in cat_var:\n",
        "    df[var] = df[var].map(lambda e: 1 if e == 'yes' else 0)\n",
        "y = df.iloc[:,-1]\n",
        "x = df.iloc[:,:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0mohTViVqRO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 123)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFzIDW8TVswi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtNWTWUgVvPU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2gkZvnLWI6u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1FTL-UbWKQB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def buildClassifier(optimizer):\n",
        "  model = tf.keras.models.Sequential([                                 \n",
        "  tf.keras.layers.Dense(8, activation='relu', input_dim = 16),\n",
        "  tf.keras.layers.Dense(8, activation='relu'),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "  ])\n",
        "  model.compile(optimizer=optimizer,\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4jOUOr4WTBC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dcDHMbaWZGc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn = buildClassifier, \n",
        "                                                            batch_size = 30, \n",
        "                                                            epochs = 100, \n",
        "                                                            verbose = 0, \n",
        "                                                            optimizer = 'adam')\n",
        "accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv = 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oBl_JesWyzS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b3ab1f57-2b54-4d86-b55f-4baeece0a34b"
      },
      "source": [
        "accuracies"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.90156597, 0.9194631 , 0.92393738, 0.9103139 , 0.93721974])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYclK2QKW6aI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9e80d6ee-03c6-44c3-c401-f032f50b1bad"
      },
      "source": [
        "accuracies.mean()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.918500018119812"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWlM7TMxXToE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "53a504cb-8b81-4783-f43f-2adc34edd944"
      },
      "source": [
        "accuracies.std()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01212183862748183"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47hiPeYzXWZk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def buildClassifierWithDropout(optimizer):\n",
        "  model = tf.keras.models.Sequential([                                 \n",
        "  tf.keras.layers.Dense(8, activation='relu', input_dim = 16),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(8, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "  ])\n",
        "  model.compile(optimizer=optimizer,\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j2GHdo4KYAFL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "classifier2 = tf.keras.wrappers.scikit_learn.KerasClassifier(\n",
        "    build_fn = buildClassifierWithDropout, \n",
        "    batch_size = 30, \n",
        "    epochs = 100, \n",
        "    verbose = 0, \n",
        "    optimizer='adam' )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7hCUY6gYImc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accuracies2 = cross_val_score(estimator = classifier2, X = x_train, y = y_train, cv = 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93H9zuRLYLMQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "96e5aaf4-005e-407d-8b0b-fa682cb50a1c"
      },
      "source": [
        "accuracies.mean(),accuracies2.mean()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.918500018119812, 0.9140076875686646)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vthxzAIXYTAz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6dcdf1ca-b647-4d13-ad6c-ca89e084cbae"
      },
      "source": [
        "accuracies.std(),accuracies2.std()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.01212183862748183, 0.012785336035607334)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlWZhaybpvDV",
        "colab_type": "text"
      },
      "source": [
        "Regularization (正則化)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4ax98nEq4il",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "?tf.keras.layers.Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJyabipcYZJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " def buildClassifierWithRegularizer(optimizer):\n",
        "  model = tf.keras.models.Sequential([                                 \n",
        "  tf.keras.layers.Dense(8, activation='relu', input_dim = 16),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(8, activation='relu',activity_regularizer=tf.keras.regularizers.l2(0.01)),\n",
        "  tf.keras.layers.Dropout(0.1),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "  ])\n",
        "  model.compile(optimizer=optimizer,\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21JEGWsDrRzS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "classifier3 = tf.keras.wrappers.scikit_learn.KerasClassifier(\n",
        "    build_fn = buildClassifierWithRegularizer, \n",
        "    batch_size = 30, \n",
        "    epochs = 100, \n",
        "    verbose = 0, \n",
        "    optimizer='adam' )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrDT1B09rXcc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "accuracies3 = cross_val_score(estimator = classifier3, X = x_train, y = y_train, cv = 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0n8nA-irf7L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "941db0a6-f1c6-47e8-e6c8-d201fd3e71c6"
      },
      "source": [
        "accuracies3"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.92841166, 0.92170024, 0.93288589, 0.90807176, 0.93946189])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNkDkA2xsvuQ",
        "colab_type": "text"
      },
      "source": [
        "##Grid Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZisjYsprqqJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "1a0c60bc-8c65-4a74-f281-f23c6f7ff3e6"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "classifier = tf.keras.wrappers.scikit_learn.KerasClassifier(\n",
        "    build_fn = buildClassifierWithDropout, \n",
        "    epochs = 10,verbose = 0)\n",
        "parameters = {'batch_size': [10, 15],\n",
        "              'optimizer': ['adam', 'rmsprop']}\n",
        "grid_search = GridSearchCV(estimator = classifier,\n",
        "                           param_grid = parameters,\n",
        "                           scoring = 'accuracy',\n",
        "                           cv = 2)\n",
        "grid_search     = grid_search.fit(x_train, y_train)\n",
        "best_parameters = grid_search.best_params_\n",
        "best_accuracy   = grid_search.best_score_"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py:241: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UBNMuz7tJep",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "613a2af1-10e7-4b2a-d3ad-c5c591d358b4"
      },
      "source": [
        "best_parameters"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 15, 'optimizer': 'rmsprop'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56WxyprGtTDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f799f89a-2ac5-416c-c479-33f91baf26ed"
      },
      "source": [
        "best_accuracy"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8584810985647038"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gVV-TTVtaeA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ebd660b7-7fc5-4751-b664-43bdbc48bc48"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import uniform\n",
        "iris = load_iris()\n",
        "logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,\n",
        "                               random_state=0)\n",
        "distributions = dict(C=uniform(loc=0, scale=4),\n",
        "                      penalty=['l2', 'l1'])\n",
        "clf = RandomizedSearchCV(logistic, distributions, random_state=0)\n",
        "search = clf.fit(iris.data, iris.target)\n",
        "search.best_params_"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 2.195254015709299, 'penalty': 'l1'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9CT1PKPcwx18",
        "colab_type": "text"
      },
      "source": [
        "##比較機器學習模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0SLPxEJwcmr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "e70ad0b3-f4d0-4e1a-94e4-4a16516e162c"
      },
      "source": [
        "import pandas\n",
        "df = pandas.read_csv('https://github.com/ywchiu/tibamedl/raw/master/Data/customer_churn.csv', index_col=0, header = 0)\n",
        "df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>account_length</th>\n",
              "      <th>area_code</th>\n",
              "      <th>international_plan</th>\n",
              "      <th>voice_mail_plan</th>\n",
              "      <th>number_vmail_messages</th>\n",
              "      <th>total_day_minutes</th>\n",
              "      <th>total_day_calls</th>\n",
              "      <th>total_day_charge</th>\n",
              "      <th>total_eve_minutes</th>\n",
              "      <th>total_eve_calls</th>\n",
              "      <th>total_eve_charge</th>\n",
              "      <th>total_night_minutes</th>\n",
              "      <th>total_night_calls</th>\n",
              "      <th>total_night_charge</th>\n",
              "      <th>total_intl_minutes</th>\n",
              "      <th>total_intl_calls</th>\n",
              "      <th>total_intl_charge</th>\n",
              "      <th>number_customer_service_calls</th>\n",
              "      <th>churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KS</td>\n",
              "      <td>128</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>25</td>\n",
              "      <td>265.1</td>\n",
              "      <td>110</td>\n",
              "      <td>45.07</td>\n",
              "      <td>197.4</td>\n",
              "      <td>99</td>\n",
              "      <td>16.78</td>\n",
              "      <td>244.7</td>\n",
              "      <td>91</td>\n",
              "      <td>11.01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OH</td>\n",
              "      <td>107</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>161.6</td>\n",
              "      <td>123</td>\n",
              "      <td>27.47</td>\n",
              "      <td>195.5</td>\n",
              "      <td>103</td>\n",
              "      <td>16.62</td>\n",
              "      <td>254.4</td>\n",
              "      <td>103</td>\n",
              "      <td>11.45</td>\n",
              "      <td>13.7</td>\n",
              "      <td>3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NJ</td>\n",
              "      <td>137</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>243.4</td>\n",
              "      <td>114</td>\n",
              "      <td>41.38</td>\n",
              "      <td>121.2</td>\n",
              "      <td>110</td>\n",
              "      <td>10.30</td>\n",
              "      <td>162.6</td>\n",
              "      <td>104</td>\n",
              "      <td>7.32</td>\n",
              "      <td>12.2</td>\n",
              "      <td>5</td>\n",
              "      <td>3.29</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OH</td>\n",
              "      <td>84</td>\n",
              "      <td>area_code_408</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>299.4</td>\n",
              "      <td>71</td>\n",
              "      <td>50.90</td>\n",
              "      <td>61.9</td>\n",
              "      <td>88</td>\n",
              "      <td>5.26</td>\n",
              "      <td>196.9</td>\n",
              "      <td>89</td>\n",
              "      <td>8.86</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7</td>\n",
              "      <td>1.78</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>OK</td>\n",
              "      <td>75</td>\n",
              "      <td>area_code_415</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>166.7</td>\n",
              "      <td>113</td>\n",
              "      <td>28.34</td>\n",
              "      <td>148.3</td>\n",
              "      <td>122</td>\n",
              "      <td>12.61</td>\n",
              "      <td>186.9</td>\n",
              "      <td>121</td>\n",
              "      <td>8.41</td>\n",
              "      <td>10.1</td>\n",
              "      <td>3</td>\n",
              "      <td>2.73</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  state  account_length  ... number_customer_service_calls churn\n",
              "1    KS             128  ...                             1    no\n",
              "2    OH             107  ...                             1    no\n",
              "3    NJ             137  ...                             0    no\n",
              "4    OH              84  ...                             2    no\n",
              "5    OK              75  ...                             3    no\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-6hmFQew12N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[:,3:]\n",
        "cat_var = ['international_plan','voice_mail_plan', 'churn']\n",
        "\n",
        "for var in cat_var:\n",
        "    df[var] = df[var].map(lambda e: 1 if e == 'yes' else 0)\n",
        "y = df.iloc[:,-1]\n",
        "x = df.iloc[:,:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUWBf4KAw30r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.33, random_state = 123)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjFNoIcmw5p5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqemKxYTw768",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnXFMvtaw979",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxhngKWaw_Sm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Dense(units = 8,  activation = 'relu', input_dim = 16))\n",
        "model.add(tf.keras.layers.Dense(units = 8,  activation = 'relu'))\n",
        "model.add(tf.keras.layers.Dense(units = 1,  activation = 'sigmoid'))\n",
        "model.compile(loss='binary_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "                        batch_size=30,\n",
        "                        epochs=300,\n",
        "                        verbose=0,\n",
        "                        validation_data=(x_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh0lYtU7xH1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "c8104b08-eb22-422b-9252-2a3442e0bc6e"
      },
      "source": [
        "predictions = model(x_test).numpy()\n",
        "predictions"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.8825076 ],\n",
              "       [0.00800419],\n",
              "       [0.01472759],\n",
              "       ...,\n",
              "       [0.037984  ],\n",
              "       [0.02017074],\n",
              "       [0.08064306]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK5KMWf2xXcJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = (predictions > 0.5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FuFPuu1xiy-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b2b599ca-976e-447a-fbef-d82cac4358ae"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, y_pred )"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9463636363636364"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zfARXxfxldE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kv_haMPQx2Y_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a20538a1-2194-455c-a16c-9708a44241fe"
      },
      "source": [
        "cm"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[937,  13],\n",
              "       [ 46, 104]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wc3pFGOwx2_t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TP = cm[0,0]\n",
        "FP = cm[0,1]\n",
        "FN = cm[1,0]\n",
        "TN = cm[1,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dby17hmqzUY7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8eb99168-c2c4-49c7-cfb3-f68b032fb558"
      },
      "source": [
        "TPR = TP / (TP + FN)\n",
        "FPR = FP / (FP + TN)\n",
        "TPR, FPR"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.953204476093591, 0.1111111111111111)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8O_8eq8ztu8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "9b5c4eae-d22b-431b-9893-40d96821846a"
      },
      "source": [
        "import numpy as np\n",
        "TPR_ary = [1]\n",
        "FPR_ary = [1]\n",
        "for cutoff in np.arange(0,1,0.1):\n",
        "  y_pred = (predictions > cutoff)\n",
        "  cm = confusion_matrix(y_test, y_pred )\n",
        "  TP = cm[0,0]\n",
        "  FP = cm[0,1]\n",
        "  FN = cm[1,0]\n",
        "  TN = cm[1,1]\n",
        "  TPR = TP / (TP + FN)\n",
        "  FPR = FP / (FP + TN)\n",
        "  if pandas.notnull(TPR) and pandas.notnull(FPR): \n",
        "    TPR_ary.append(TPR)\n",
        "    FPR_ary.append(FPR)\n",
        "TPR_ary.append(0)\n",
        "FPR_ary.append(0)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in long_scalars\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvW7X5m80pmV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "c5e98884-24db-4e1a-97c2-67a3a2278354"
      },
      "source": [
        "TPR_ary"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 0.9654403567447045,\n",
              " 0.9612159329140462,\n",
              " 0.9586776859504132,\n",
              " 0.9548254620123203,\n",
              " 0.953204476093591,\n",
              " 0.9485368314833501,\n",
              " 0.9440559440559441,\n",
              " 0.9405351833498513,\n",
              " 0.9350393700787402,\n",
              " 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIEL55GOz3jo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "027d7809-6d13-4094-dc1c-c653bdd5c1a7"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(FPR_ary, TPR_ary)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f038c36c7f0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DjgCv63ygiNgdEZMRMTk1NXVeBV+zbhXb33olFf+/kKR5aov5ZJm5F9gLMD4+nufzGO99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\nYaBLUiH+H2/4jhVo4c/vAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTZAHHXk0RE_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "fpr_ary, tpr_ary, thresholds = roc_curve(y_test, predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbU-rLQr10Fd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "5264c5d7-49bc-43c8-eaf1-c268875d14e9"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(fpr_ary, tpr_ary, label='ROC curve')\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([-0.1, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver operating characteristic example')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VU92/oZslCWPKIFSwHjt2LI888giAJQkTNyIZ66mPiNRx7/9GRB4QkVb+h2ZM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\n/wG/NklmsbY8vgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gr1IzRyZ113n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "334410c4-2b3d-40fc-af90-590b97c3c2bb"
      },
      "source": [
        "from sklearn.metrics import auc\n",
        "auc(fpr_ary, tpr_ary)\n"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8910456140350878"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mn5rh5Ab2BWy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "63dc9197-0279-4a37-be9b-f904175423fb"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "clf1 = DecisionTreeClassifier()\n",
        "clf1.fit(x_train, y_train)\n",
        "\n",
        "clf2 = SVC(probability = True)\n",
        "clf2.fit(x_train, y_train)\n",
        "\n",
        "clf3 = LogisticRegression()\n",
        "clf3.fit(x_train, y_train)\n",
        "\n",
        "clf4 = RandomForestClassifier()\n",
        "clf4.fit(x_train, y_train)\n",
        "\n",
        "clf5 = GradientBoostingClassifier()\n",
        "clf5.fit(x_train, y_train)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
              "                           max_features=None, max_leaf_nodes=None,\n",
              "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                           min_samples_leaf=1, min_samples_split=2,\n",
              "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                           n_iter_no_change=None, presort='deprecated',\n",
              "                           random_state=None, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlHUxxJq3WUa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "08623017-03ca-4d83-cb7c-9833e9d49b2f"
      },
      "source": [
        "from sklearn.metrics import auc\n",
        "from sklearn.metrics import roc_curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize= [20,10])\n",
        "for clf, title in zip([model,clf1,clf2,clf3, clf4,clf5], \n",
        "                      ['ANN','Decision Tree', 'SVM', 'LogisticRegression', 'RandomForest', 'GBT']):\n",
        "    if title != 'ANN':\n",
        "        probas_ = clf.fit(x_train, y_train).predict_proba(x_test)\n",
        "        fpr, tpr, thresholds = roc_curve(y_test, probas_[:, 1])\n",
        "    else:\n",
        "        y_pred = clf.predict(x_test)\n",
        "        fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "    plt.plot(fpr, tpr, label='%s - AUC:%.2f'%(title, auc(fpr, tpr)) )\n",
        "    \n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.0])\n",
        "plt.xlabel('False Positive Rate', fontsize = 20)\n",
        "plt.ylabel('True Positive Rate', fontsize = 20)\n",
        "plt.title('Receiver operating characteristic example', fontsize = 20)\n",
        "plt.legend(loc=\"lower right\", fontsize = 20)\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lcQSfT/AFHBy/Yx/7vcd++9jndwiGffj8AW+5becEuh6ngjeOr2sbn7cPJ+25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hfPnyPProo7Rt25Z27dr5OzQRERERERER8YOSlYCKc63ErzvgFYljx44xZcoUpk2bxsmTJ2nT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\niEiRUgJKRERERERERESK1P8DzYCMwhg5KX4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpbG0-KdCxvS",
        "colab_type": "text"
      },
      "source": [
        "## 使用ANN 做物件辨識"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pS8g13f3lIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqmfgvAEDHbO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZksiJEBIocb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CNKb-G9IsNm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "9b7e88bf-752b-46e9-8703-17bcef36e590"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hdbGspRlkJjF3fw7YPQp9EZFmcZQdiaW5zsxeS043Uy8gmNkiM1tuZst7iK+fiEhzsHK+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\nSmIiUmhKYiJSaEpiIlJoSmIiUmj/H4BqExLuMX2fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWl-zSMEDXfv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIDRfaN8DfM0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "4f5a27ff-eb53-4219-8818-9f409847a77e"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bVt/XpLGiucwHSZIsA3AOgPNcFWc65x53zo0G8LxzroVz7s7CWnjNOXcUUHx9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z6Zq9LKPP/888lr1kk5TVPkYS3fr4ruU9Ob8CvcM7mquryNv8tX6/ZptyIlt3L2o48+Gm1fToJj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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5CPUcT2JDvn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1ZeB7RGE5hk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjAVOSlnFK4o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "63e93c6d-1113-47cd-ce4c-b114dba289b9"
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5033 - accuracy: 0.8235\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3753 - accuracy: 0.8648\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3369 - accuracy: 0.8768\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3113 - accuracy: 0.8853\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2950 - accuracy: 0.8913\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2792 - accuracy: 0.8961\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2669 - accuracy: 0.9004\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2591 - accuracy: 0.9043\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2471 - accuracy: 0.9074\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2370 - accuracy: 0.9113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f038854b080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jdOudCJFQjw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "fcc5b180-cf4a-4469-ce2f-168a0a0144cf"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.3585 - accuracy: 0.8773\n",
            "\n",
            "Test accuracy: 0.8773000240325928\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6i7SvmrJ6S1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "probability_model = tf.keras.Sequential([model, \n",
        "                                         tf.keras.layers.Softmax()])\n",
        "predictions = probability_model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpSKuEkOJ-L9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "5fb26ab8-14f2-4216-d3d4-f2b435f15520"
      },
      "source": [
        "predictions"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.8323528e-08, 3.5306927e-12, 1.4559151e-10, ..., 5.9732161e-03,\n",
              "        1.0710400e-09, 9.9132955e-01],\n",
              "       [2.2430732e-04, 2.4722572e-13, 9.9105096e-01, ..., 1.2501897e-15,\n",
              "        1.8866011e-12, 7.7832495e-14],\n",
              "       [2.4496296e-06, 9.9999714e-01, 6.1643521e-09, ..., 7.1885924e-26,\n",
              "        1.7752121e-12, 4.5080652e-15],\n",
              "       ...,\n",
              "       [4.1751950e-03, 1.5946413e-09, 3.6061283e-03, ..., 5.8726164e-06,\n",
              "        9.9139649e-01, 4.0149634e-10],\n",
              "       [1.8889942e-07, 9.9976796e-01, 7.5884905e-08, ..., 1.8256318e-14,\n",
              "        1.6620936e-08, 3.1251339e-08],\n",
              "       [2.4683669e-07, 1.2130281e-11, 3.1416228e-08, ..., 9.7032877e-05,\n",
              "        4.5942213e-07, 1.4377241e-06]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1Mi3t3WJ_LU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "y_pred = np.argmax(predictions,axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZJ8QHqtKINC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4ab032e0-a789-4dc7-b101-686778dea4d6"
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array, true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR! Session/line number was not unique in database. History logging moved to new session 60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQsTLYECKQFA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "c0860585-9516-484c-ba00-a121b54eae2e"
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npQ8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqmX3fy0KW4U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "9bb648d6-bd03-4fbc-9a9a-74430ddcfdf8"
      },
      "source": [
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f+a78xnKbmR5UdjH4RmU3v7slrTDTbyT9QcnrG2vsd6XqD63cb0IIDX0IOmO7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\n2ik4rw1mGmGm9pK+JOn3FU7xm5L+4MsXSlrgnEY4p+GSviJpYw3v7wr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ZS2U/SNzrnCs7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J9d/N2lnST90UyDJDllvyZVOUbS/so+u5VmOlnZsMZ/+f20kTQ22D797BYqG5rY4Djnbmyo2zWn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\nn7uLzhMfC4QAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPKlFVseNanw",
        "colab_type": "text"
      },
      "source": [
        "使用CNN 做手寫辨識"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNT-PG98KfRu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWO3ePvnLF5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist = keras.datasets.mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Uiw2-H3MEnZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "880007f3-5276-45b7-94ce-4dcdda5d660c"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFJDiFeSN0oI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b92291b8-31e5-44fb-f5dc-f583bd255aef"
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wp_82RVDN3O2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "717904a4-5555-41df-f286-6c4954885ecb"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IfScxM2viJGYzxtgZy5Px/3nnUcn4Kct3tYy1GwfWzif3nZqMH33P5mTcEpzEzCxrTmJm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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fE6DhYQkLQeq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape(60000,28,28,1)\n",
        "test_images = test_images.reshape(10000,28,28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALEx5HydLI9Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aRsboV-mMYEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = tf.keras.Sequential()\n",
        "# 1. Convolution\n",
        "classifier.add(tf.keras.layers.Conv2D(filters=16, \n",
        "                                      kernel_size=(3, 3), \n",
        "                                      padding = 'same', \n",
        "                                      input_shape = (28, 28, 1), \n",
        "                                      activation = 'relu'))\n",
        "\n",
        "# 2. Max Pooling\n",
        "classifier.add(tf.keras.layers.MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# 3. Flattening\n",
        "classifier.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# 4. Fully Connection\n",
        "classifier.add(tf.keras.layers.Dense(units = 128, activation = 'relu')) \n",
        "classifier.add(tf.keras.layers.Dense(units = 10, activation = 'softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jBS947AOpgY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "706fb7fd-0285-442f-f6cb-d3ab63234780"
      },
      "source": [
        "classifier.summary()"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_39\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 28, 28, 16)        160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "flatten_12 (Flatten)         (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_84 (Dense)             (None, 128)               401536    \n",
            "_________________________________________________________________\n",
            "dense_85 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 402,986\n",
            "Trainable params: 402,986\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPDR7Mf2Mvex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier.compile(optimizer = 'adam', \n",
        "                   loss ='categorical_crossentropy', \n",
        "                   metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kg3fo8tRNB9U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "0f7719c8-0622-45db-9d53-feebe85a1633"
      },
      "source": [
        "history = model.fit(train_images, train_labels,\n",
        "                    batch_size=100,\n",
        "                    epochs=5,\n",
        "                    verbose=1,\n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0715 - accuracy: 0.9792 - val_loss: 0.0966 - val_accuracy: 0.9707\n",
            "Epoch 2/5\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0608 - accuracy: 0.9826 - val_loss: 0.0939 - val_accuracy: 0.9701\n",
            "Epoch 3/5\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0520 - accuracy: 0.9849 - val_loss: 0.0944 - val_accuracy: 0.9716\n",
            "Epoch 4/5\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0443 - accuracy: 0.9872 - val_loss: 0.0867 - val_accuracy: 0.9735\n",
            "Epoch 5/5\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0383 - accuracy: 0.9892 - val_loss: 0.0843 - val_accuracy: 0.9735\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_sHmJKSwP6m_",
        "colab_type": "text"
      },
      "source": [
        "## 使用CNN 做時尚數據辨識"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmcHrr14NNvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_Jk689GQDdv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "9d14c749-c18d-4be1-fb26-12e978770a81"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dS16ZtPBuGrjvzKyNSqK4390fSVY3fN8N1q9m2m9F1IgkNuKPIQyXmXUkF1wx\nsw7gK8DquNWoexxYmLxeCDzWwL7005cgEpfRoH1nZgbcA6xz9zuqQg3dd2n9apb9VlQNqdhPbiH/\nTz58DOHWUe/EIMzsZCpHX1B5JOuBRvbNzH4MnE9lqJZtwLeB/ws8BHwMeBu43N1H/QJ7St/Op3JK\n5MBG4Jqqa1Cj2bfzgF8Cq4C+kftupnL9qWH7LujXlTTBfisqPXYkIoWmC/siUmhKYiJSaEpiIlJo\nSmIiUmhKYiJSaEpiIlJoSmIiUmj/H4BqExLuMX2fAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RTRAyEPQHZk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape(60000,28,28,1)\n",
        "test_images = test_images.reshape(10000,28,28,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkIWUJ4oQMBa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "816RNG33QPEu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = tf.keras.Sequential()\n",
        "# 1. Convolution\n",
        "classifier.add(tf.keras.layers.Conv2D(filters=16, \n",
        "                                      kernel_size=(3, 3), \n",
        "                                      padding = 'same', \n",
        "                                      input_shape = (28, 28, 1), \n",
        "                                      activation = 'relu'))\n",
        "\n",
        "# 2. Max Pooling\n",
        "classifier.add(tf.keras.layers.MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# 3. Flattening\n",
        "classifier.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# 4. Fully Connection\n",
        "classifier.add(tf.keras.layers.Dense(units = 128, activation = 'relu')) \n",
        "classifier.add(tf.keras.layers.Dense(units = 10, activation = 'softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOCNsQkxQSrw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier.compile(optimizer = 'adam', \n",
        "                   loss ='categorical_crossentropy', \n",
        "                   metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8EeEz6llRyhi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "7eb79e43-a24a-40e6-e476-a9c28b7b4c99"
      },
      "source": [
        "history = model.fit(train_images, train_labels,\n",
        "                    batch_size=100,\n",
        "                    epochs=100,\n",
        "                    verbose=0,\n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-250-8961ccaf9757>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                     validation_data=(test_images, test_labels))\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    781\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    782\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5c8QUmJjQisb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "453fd1f0-5b62-4076-f0f0-f6b47b634458"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.9065 - accuracy: 0.8849\n",
            "\n",
            "Test accuracy: 0.8848999738693237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiDSct4_Qu-n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "probability_model = tf.keras.Sequential([model, \n",
        "                                         tf.keras.layers.Softmax()])\n",
        "predictions = probability_model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFRA-zc3Qyhz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array, true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keX9w4bARQ3A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_images2 = test_images.reshape((10000,28,28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lm3MfhA9Q0be",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "c2d147b7-0fa1-43c6-a17d-0e131d65ebd4"
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images2)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()\n"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAARWklEQVR4nO3de7CdVXnH8e+ThIQkBBMMhFQSAmgA\niaAYUEq5eAliS0E7nXopBam3qlgv0zq2OqZUoXamokAL9QKpVlQUoQVKRdRSqAKFBEiiMAEVIWCA\nDCbcQkKSp3+8b8rJ2Wvn7H1CskLy/cycyTlrr/W+a79Jfufd67J3ZCaSpK1vRO0OSNKOygCWpEoM\nYEmqxACWpEoMYEmqxACWpEpG1e6AVNvkyZNzxowZtbvx/HPHHbB2bW91R42CQw7Zsv3ZRs2fP395\nZu5eeswA1g5vxowZ3HrrrbW78fwT0XvdtWthB73GEfGrbo85BCFJlRjAklSJASxJlfQ1Buxkhbak\ne++9l+XLl/cxsCg9v/UVwE5WaEuaPXt27S5IW5VDEJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUY\nwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJU\niQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEs\nSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUYwJJUiQEsSZUY\nwJJUiQEsSZWMqt2Bbdn5559fLF+8eHHPdXuVmcXyiNis40radnkHLEmVGMCSVIkBLEmVGMCSVIkB\nLEmVbPYqiFWrVhXLx44du1nHGD169LD7tMHIkSN7rnvVVVd1lD344IPFunvssUdH2SmnnNJRduaZ\nZxbbT5s2raOsn9UO69at67luP9dA0tblHbAkVWIAS1IlBrAkVWIAS1Ilmz0JV5p8Ajj99NM7yo45\n5phi3X4m7LaU0lbiww8/vFi3NEG41157dZRdcsklxfalSbw3v/nNxboTJkzoKOs2sVaanOu2xXlz\nuUVa2nzeAUtSJQawJFViAEtSJQawJFViAEtSJX2tgli/fj1PPvnkRmVLly4t1r3iiis6yp566qli\n3VmzZnWU7bbbbsW648aNK/ar5L777usomzdvXrHunnvu2VE2efLkYt0rr7yyo+ykk07qKFuxYkWx\n/dVXX91RdtdddxXr7rvvvh1lc+bMKdbde++9i+Wbq7S6ots1HzGi83e626GlMu+AJakSA1iSKjGA\nJakSA1iSKulrEm7VqlXFTwQuGTxZB3DxxRcX6x588MEdZd3eD7hUfs899xTrLlq0qKNszZo1xbpH\nHXVUR9mCBQuKdd/whjd0lJUmB7s9h+OPP76j7OGHHy7WXbJkSUfZjTfeWKx74IEHdpQddNBBxbqz\nZ8/uKNt9992LdUuTaE6sSZvPO2BJqsQAlqRKDGBJqsQAlqRK+pqEW7duXcfurkcffbR84FGdh165\ncmWx7uWXX95RNmnSpGLdZ555pqOs9J65AEcccURH2cyZM4t1Szu4Sjv0AJYvX95RVtrl1203X+ma\nlSbxAKZPn95TGcBjjz3WUXbDDTcU695yyy0992HixIkdZd123ZXe6/iAAw4o1h0zZkyxXNpReAcs\nSZUYwJJUiQEsSZUYwJJUiQEsSZX0tQpixIgRjB8/fqOy0lZZgNNOO62jbMaMGcW6pVUBTz/9dLFu\naUZ+5513LtYtHWPhwoXFuiW77LJLsby0WqC0xXnZsmXF9qUtyrvuumuxbum4pdUOUH7/4m4rMUq6\nXfPSNukHH3ywWLd0bT7zmc8U65588skb/dzt/ZOl7ZV3wJJUiQEsSZUYwJJUiQEsSZX0NQm3YsWK\njg/bnDp1arFuaZKm2+RR6YMnu223Xbt2bU/nAli9enVHWekDJrvpNilU2lK90047dZSVtuVCf5Nw\nJd22DE+ZMqWjrNvzLU3udZvMLJV3+7ss/V1ERLHu2WefvdHPDz30ULGetL3yDliSKjGAJakSA1iS\nKjGAJakSA1iSKulrFcTq1as7PoF4v/32K9YtvZl5t09UXrp0aUdZP1td169fX6xb0q1uaVVAt09Q\nLs3ql95c/JFHHim2L9UdO3ZssW5pdUU3pTeK7/Z8H3/88Y6ybqs+SnW7bdMubWe+++67i3UHn6/b\n9Za2V94BS1IlBrAkVWIAS1IlBrAkVdL3+wEPngS76aabinX72epaqlv6lGEob9ktvQ8uwBNPPNFR\n1s9W5JEjRxbLS5/4XCorfdIylLcid1OahOs2AVZ6395u17G0lbjb+wGXPom69HyhvFW823HPOOOM\njX6eO3dusZ60vfIOWJIqMYAlqRIDWJIqMYAlqRIDWJIq6WsVxPTp0znvvPM6ykpKn8Zb2ioL5VUQ\n3VYKlGb1S5+qDDBhwoSOstIsPZRXLHSb6S9t7121alVHWbc3Ii89t27bcPvpVz91S38/pU+chvLq\nlW6ftrz//vt3lM2ZM6dYd7Bzzz23p3rS9sI7YEmqxACWpEoMYEmqxACWpEr6moQbOXIkkyZN2qjs\nrLPOek47JEk7Cu+AJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGA\nJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakS\nA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iS\nKjGAJakSA1iSKjGAJakSA1iSKjGAJakSA1iSKjGAJamSUf1Unj9//vKI+NWW6ox2eHvX7oC0NfUV\nwJm5+5bqiCTtaByCkKRKDGBJqsQAlqRK6gRwxCeI+CkRC4m4nYhXPUfHvY6I2cOqE3E6EfcQkURM\nHlAeRJzbPraQiEMHPHYqEXe3X6e2ZWOI+B4Ri4l4/4C6X9qobef530TEp9rv92/7eTsRdxLxpd4u\nQB96uVbd244m4noi+ppDkLSxrf8fKOII4ATgUDJXt2E3eqv3o9OPgauA6waVvxF4Sfv1KuAC4FVE\n7AbMBWYDCcwn4grgKOB/gLPaY55PxCHASDIXbOL8HwNObL8/F/g8mf8OQMTLNvfJPWciRpG5hogf\nAm8BLq7dpc01zNU9k4HlwzjdjtsuYuufc9to13V1T407mKnAcjJXA5D57BNp7gB/HxgL/AR4L5lJ\nxHXAzcBrgInAO8m8gYixwDzgEOCutt2GY10AHNaWXUrm3E32KvO2tt3gR04CvkZmAjcRMZGIqcCx\nwLVkPtq2uxY4HlgBjAN2AjYc7NPAn3U9d8RMYPWAazEVWDqgb4vaeu+gCelxwH7A5WR+rH3sOOAM\nYAzwc+A0Mp/oek2fPfcI4KL2fHOBz7bPbQzwT2R+kYhj2+fwG+AAYCbwb8DfsR0E8HBW90TErZnZ\n9ysI220756zxHAerMQTxfWAaEUuIOJ+IYwY89o9kHkbmLJrAOGHAY6PIPBz4ME1QALwPeIrMA9uy\nVw6o/wmai3QwcAwRBw+zvy8C7h/w89K2rFv5tcAM4CbgXCJOBBaQ+eAmznEkMPDu+PPAj4j4TyI+\nQsTEAY+9nObO82XAW4iY1r6K+CTwejIPBW4FPtrW3/Q1bQL0bjI/CbwTWEnmYTS/vN5NxD5t3UOB\nD5E5s/15cVtH0jBt/QDOfIImKN8DPAJc0t7ZAbyGiJuJWAS8FjhoQMvL2j/n0wQcwNHA19vjLgQW\nDqj/R0QsAG5rj/PS5/qpFGWuJfPtZL4C+A7NL4zPEXE2EZe2gTzYVJprseEY84AD2/bH0tx5j2kf\n/SGZK8l8GvgZzcubV9M8vx8TcTtwKs++7NnUNf0isJjMM9ufjwNOaY9xM/BCmqEXgP8l85cD+rgO\nWEPEhP4ukKQN6kyiNP95rwOua4PhVCK+BZwPzCbzfiL+Bth5QKvV7Z/rGKrfzV3bXwCHkfkbIv5l\n0LH68QAwbcDPe7VlD9CE48Dy6wa1fT/wNZqAXElz5/oj4IpB9VYBL9iopLljvgi4iIjFwKz2kdUD\nam24FkEzHPK2jY4RsTObvqY/oQnoz7WBHsAHybxm0HGOBZ6k0xjg6UL5jmC4E6O223bOWeM5bmTr\n3wE3M/wvGVDycuBXPBsMy4nYBfjDHo52PfD29rizaIYbAHalCYyVREyhmUgbrito7gqDiCZIM38N\nXAMcR8QkIibR3D0+G1xN2Qk0ATwOWE8zWTeWTncCLx7Q9ngidmq/35PmTvSBTfTxJuBIIl7cthnf\njisPdU0vBK4Gvt2uaLgGeN+Ac88kYnzxjBEvpBnLf2YT/dpuZeaw/hPabts5Z43nOFiNO+BdgPPa\ncc21wD3Ae8hcQcSXacYWlwG39HCsC4B5RNxJE2LzAci8g4jbaCbm7qdZjbBpEX9OsxJhT2AhEVeT\n+S6agPrdtp9PAae153iUiE8P6Off/v+EXONTwJlkrifiGuADwCLgnwtnv55mmCLaCbLjgHOI2HB3\n+ZdkLus6i5z5SDuM880BQxWfJHPJkNc082wiXgD8K/DHNMM7C4gImmGRN3W5Yq8B/qPLY5J6EAMn\nxFVRxDnAlWT+oHZXehJxGfBxMpfU7srWFBHHA+cAI4GvZOZne2x3Ec0rooezmRDt9XzTaF5FTaF5\nBfWlzDynh3Y70/xiH0Nzo3VpDrUSaOP2I2kmcx/IzBOGqt+2uRd4nGZobG2vKwWiuRn7Cs0wWwJ/\nmpk3DtFmf+CSAUX7Ap/KzC/0cL6PAO9qz7UIOC2bIbih2n0IeDfNUN2XeznXkDLTr23hC6YknFi9\nH731dXTCKdX7sZW/aEL35zT/2UcDdwAv7bHt0TQrSRb3ec6pwKHt9xOAJb2csw2JXdrvd6KZVH11\nH+f9KPAN4Ko+2twLTB7Gdf0q8K72+9HAxGH8vSwD9u6h7ouAXwJj25+/Dbyjh3azaF5JjqP5hfYD\n4MWb+2/KrcjbisyHyBw8ObdtylxD5tdqd6OCw4F7MvMXmbkG+BbNOvEhZeb1wKNDVuxs9+tsN/Bk\n5uM0Q20v6qFdZrPiCJoA3onmjm9IEbEX8Hs0d6VbVDTDX0fTzEeQmWsyc0Wfh3kd8PPM7HUzzShg\nbDTzHuOATS0R3eBA4ObMfCoz1wL/DfxBn/3sYABLveu29nuriIgZwCto7mZ7qT8ymiWFDwPXZmZP\n7YAv0MyHrO+ziwl8PyLmR8R7emyzD81cw7yIuC0ivhLdJn67eyvwzZ46mPkA8A/AfcCvgZWZ+f0e\nmi4GjoqIF0bEOJp5oWlDtBmSASw9D0SziuW7wIcz87Fe2mTmusx8Oc0SycOjWSk01Hk2jFPPH0Y3\nfyebjUBvBD4QEUf30GYUzdDMBdmsnX8S+HivJ4yI0TS7Q7/TY/1JNK9a9gF+CxgfEScP1S4z7wT+\nnmYj2feA22nGujeLASz1rtua8C0qmmWB3wUuzszLhqo/WPuS/r9otsoP5UjgxHZC7VvAayPi6z2e\n54H2z4eBy2mGbIayFFg64O78UppA7tUbgQWZ+VCP9V8P/DIzH8lmCeVlwG/30jAzL8zMV2bm0TTb\n8jd7AtoAlnp3C/CSiNinvfN6K52bap5T0SwHvBC4MzPP7qPd7u3qAqJ5z5Q5NMsyNykz/yoz98rM\nGTTP70eZOeQdYkSMj3ZXZDuEcBzNy/ahzrcMuL9d1QDNeO7Phmo3wNvocfihdR/w6ogY117b19GM\nqw8pIvZo/5xOM/77jT7OW+TbCUo9ysy1EXE6zYaVkcBFmfnTXtpGxDdpdk5OjoilwNzMvLCHpkcC\nfwIsasdzAf46M68eot1U4KvtcrIRwLcz86pe+jpMU4DLm0xjFPCNzPxej20/CFzc/lL7BRvW2g+h\nDfo5wHt77WRm3hwRl9K898pamrcq6HVjxXej2YD0DPCBYUwWdnAdsCRV4hCEJFViAEtSJQawJFVi\nAEtSJQawJFViAEtSJQawJFViAEtSJf8H5Rl7j4tWjecAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FILL-aKuS-v-",
        "colab_type": "text"
      },
      "source": [
        "## 獲取明星圖片"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-lapV7rQ2-w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "ecef6431-93e3-48a6-9cd1-fe57f8280d80"
      },
      "source": [
        "!  pip install google_images_download"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting google_images_download\n",
            "  Downloading https://files.pythonhosted.org/packages/18/ed/0319d30c48f3653802da8e6dcfefcea6370157d10d566ef6807cceb5ec4d/google_images_download-2.8.0.tar.gz\n",
            "Collecting selenium\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/80/d6/4294f0b4bce4de0abf13e17190289f9d0613b0a44e5dd6a7f5ca98459853/selenium-3.141.0-py2.py3-none-any.whl (904kB)\n",
            "\u001b[K     |████████████████████████████████| 911kB 7.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from selenium->google_images_download) (1.24.3)\n",
            "Building wheels for collected packages: google-images-download\n",
            "  Building wheel for google-images-download (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for google-images-download: filename=google_images_download-2.8.0-py2.py3-none-any.whl size=14550 sha256=2093097bae3264fa6976620323876edc729f940fc4ab0af74742d7ecaec17e10\n",
            "  Stored in directory: /root/.cache/pip/wheels/1f/28/ad/f56e7061e1d2a9a1affe2f9c649c2570cb9198dd24ede0bbab\n",
            "Successfully built google-images-download\n",
            "Installing collected packages: selenium, google-images-download\n",
            "Successfully installed google-images-download-2.8.0 selenium-3.141.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIMTsjW5TBqO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "f6272e69-de2e-4bf4-c11b-03458d465b29"
      },
      "source": [
        "! googleimagesdownload -k \"北极熊\" -l 50"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Item no.: 1 --> Item name = \\u5317\\u6781\\u718a\n",
            "Evaluating...\n",
            "Starting Download...\n",
            "\n",
            "\n",
            "Unfortunately all 50 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!\n",
            "\n",
            "Errors: 0\n",
            "\n",
            "\n",
            "Everything downloaded!\n",
            "Total errors: 0\n",
            "Total time taken: 0.266970157623291 Seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3d4d3n7tTQUo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "google_url = 'https://www.google.com/search?ei=gAhqXciWMbCSr7wPgLaqiAE&yv=3&tbm=isch&q={}&vet=10ahUKEwjI1v2Bs6zkAhUwyYsBHQCbChEQuT0IWygB.gAhqXciWMbCSr7wPgLaqiAE.i&ved=0ahUKEwjI1v2Bs6zkAhUwyYsBHQCbChEQuT0IWygB&ijn=1&start={}&asearch=ichunk&async=_id:rg_s,_pms:s,_jsfs:Ffpdje,_fmt:pc'\n",
        "res = requests.get(google_url.format('翁滋蔓', 0))\n",
        "soup = BeautifulSoup(res.text, 'lxml' )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YufzsBuEUjQR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748
        },
        "outputId": "c29fe67f-fd0b-4ba3-83dc-be0b7aafaea1"
      },
      "source": [
        "for alink in soup.select('a img'):\n",
        "    if alink.get('src'):\n",
        "        print(alink.get('src'))\n",
        "        print('======================')"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ45QtaKPW6-SyFa8D--GdF5_CwJ7hz6Ux5O7t6eKrD3qKn9tEh_Q&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQEDrORxH2aOaahP1QKG_vWQmQQKwTdADza7uQ43EaD6SlUsRfdeg&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSBrz9X3hzdoLS-rVUYjBPH09xBLT04xl-tiHm0rsPzvKb3Kf2J&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTO-cVVVmzg-pbc3L9JHC8xTxI42ARmVOzjqjh_fhzRwnggqP1aLw&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTjTMwJS9SMMJEFLwf6E99lsaV6QB4vXQSaHs4f1YUTns2p4d0fYQ&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQRjUXconaY4FLwVji4QhbwS547lAJDLr2_MBWs4c3tr5yWpd97mw&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT_Fb8aGyxS1vgEN0aWwziQ6WXZU7xOB-S1C-wVctr3VARF_Yz0&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRmQCWI_ysc5OurhydYJ0j9JZ-S_zu8aO5Ig1V5BO8QCNt2jhpF&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSXxrzw3UHLkgpTPQmOz9plEs2hjFwrbzSWV35vtXql5lyyZum&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSlNqFemytn5Zvp1ZaH0KCypSTCRwIM5jNzdU8F2QRnA_SCPtcSZQ&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTduKfUKVdPClVCcoKB9ac9qBltcqKa-uY8aqXYFe7x7-ehJtgg&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRetKKIDnvCpeAktL0QxkntevzE34Z0klDpxG_M906KxXpEvkYS&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTbZjTlfuHDmRAWAR9XSZsqY2ZdtQwMh5IItccFwpH1vqpl3k1_&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSd1yRXYe4pefCsozze2FoSnGQWl1ktnPlmjQGlRRa4y5gXCddjzA&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDSGwuBTZMDjZIBxTL-wBia9MLnL95-Q6Vx6lpTseqoHL0DEZL1w&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ8LUC4NceD-whirjyM_ClEfiUS7ZLdlJNEpKUUcQyXYKwwot--&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSrFP6JrL3I1IoRMoZg9KHGFhdnLlRRkoNt1fTRwTvjP-0-qYCB&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTCdTAVj_g07X0Yyws_tOJ7_p6LPiIM0zd7n_TlCPq-4RptTJwl&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS5DIu0PFbzz1X5I_UkappkGSZ0fI6iQlGlyYc4VW0yOJ8pX4ya&s\n",
            "======================\n",
            "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSQszn0FaKGlOi_iZzm5UVX-PAbm0tezlkqw2jsO_FWzZoNT0Gz&s\n",
            "======================\n",
            "ERROR! Session/line number was not unique in database. History logging moved to new session 61\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GXSTEM-UlYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getPicture(path, url):\n",
        "    res = requests.get(url)\n",
        "    with open('{}/{}.jpg'.format(path, url.split('tbn:')[1]) , 'wb') as f:\n",
        "        f.write(res.content)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAX6gdIuU1sy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import os\n",
        "from bs4 import BeautifulSoup\n",
        "google_url = 'https://www.google.com/search?ei=gAhqXciWMbCSr7wPgLaqiAE&yv=3&tbm=isch&q={}&vet=10ahUKEwjI1v2Bs6zkAhUwyYsBHQCbChEQuT0IWygB.gAhqXciWMbCSr7wPgLaqiAE.i&ved=0ahUKEwjI1v2Bs6zkAhUwyYsBHQCbChEQuT0IWygB&ijn=1&start={}&asearch=ichunk&async=_id:rg_s,_pms:s,_jsfs:Ffpdje,_fmt:pc'\n",
        "\n",
        "def getIdolImage(path, term, pagenum):\n",
        "    if not os.path.exists(path):\n",
        "        os.mkdir(path)\n",
        "    for p in range(pagenum):\n",
        "        res = requests.get(google_url.format(term, p* 100))\n",
        "        soup = BeautifulSoup(res.text, 'lxml' )\n",
        "        for alink in soup.select('a img'):\n",
        "            if alink.get('src'):\n",
        "                getPicture(path, alink.get('src'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHpO4tZyU56V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "getIdolImage('zhiyu','周子瑜', 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1eWdQ0kZU9jV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "getIdolImage('mm','翁滋蔓', 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_U6uo-XVXQ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "getIdolImage('puff','郭雪芙', 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USjq17MvV_U6",
        "colab_type": "text"
      },
      "source": [
        "## 使用OpenCV 擷取人臉部分"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-dLCJXNVbuk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2 as cv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jVuEqcdxWCK6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "667cce16-c103-45ab-89f8-d4ad72af6d3f"
      },
      "source": [
        "from PIL import Image\n",
        "im = Image.open('//content/zhiyu/ANd9GcQ0jkg92jzLtEGY4GgvO-WNcWpwggOusYgoQbLPsoCqwybHg2eD&s.jpg')\n",
        "im"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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+1aSm2bGan/TsdH0SG9qFMxuXR/q05kG9Mk8cSD2/m0dmfbGnUbWNu57f48M291IRR1wH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\nt76iLb3jHe8gslmWEcOuENdu3JxMJu96z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7VN6x8/9z3DfXiTpskHdck8IhCbZsR5wLxK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\ndy//yUXR6xsPlj//f2D0zlkda+KXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=277x182 at 0x7F037F95DAC8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5eBnTYbuXCMF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "c6eb0e27-0c0a-4d7c-bc2c-97801eb6d3f0"
      },
      "source": [
        "! wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-28 08:57:27--  https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 930127 (908K) [text/plain]\n",
            "Saving to: ‘haarcascade_frontalface_default.xml’\n",
            "\n",
            "\r          haarcasca   0%[                    ]       0  --.-KB/s               \rhaarcascade_frontal 100%[===================>] 908.33K  --.-KB/s    in 0.07s   \n",
            "\n",
            "2020-03-28 08:57:27 (12.5 MB/s) - ‘haarcascade_frontalface_default.xml’ saved [930127/930127]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1-Rm3MvWQKM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "874d65f7-2234-463e-85d5-857e2bf3af3f"
      },
      "source": [
        "face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "img = cv.imread('//content/zhiyu/ANd9GcQ0jkg92jzLtEGY4GgvO-WNcWpwggOusYgoQbLPsoCqwybHg2eD&s.jpg')\n",
        "img.shape"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(182, 277, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQZPUd4VWcNj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5dc10b60-e9ba-42c5-a3bd-397ccc2f6312"
      },
      "source": [
        "faces = face_cascade.detectMultiScale(img, 1.3, 5)\n",
        "faces"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[101,  48,  72,  72]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jz1IhGjvWj9g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x,y,w,h = faces[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lEV5qMBXMMn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "box = (x, y, x+w, y+h)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I-Ywd6TVXNoH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "crpim = im.crop(box).resize((64,64))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJoplPomXPZ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "a048103f-161f-41aa-9a0d-264d26f2181d"
      },
      "source": [
        "crpim"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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GiISkKqoSBUQBiJRpaXn86CPvaeazyelx23hmzrLMqIj3narmeQaEUdLCEwUIXWNjZwxT\nFMuSaUzzU9OvH3voIiA/+NDF9z7+3qXx+Ob2TbYmy4rFamJsZl3hiqooezbLvUDTtNblveH4yaef\n+YmqrlEO7x0u9Vb/1b/8F3lROWf6Vd6eHvPNThHr8ZKYDGJkkxGgQZCYmJGYNtc3qtx9/0c/nE7b\nrgNrlJmMiIYYAdBZh4iSEhk21oQUve9Gi05BOlxeWhkNVwbDtbXlv/+3P7G+uZZX5ayZdr599LGH\nfPBGMlElJDbWWJtlWV7kiszEwMTOQgqKdGtndxinkKIr+mRzJfUBd+7t5I6XV5eLQV2OBoQZosbQ\nTqPJssIhgoKcHmvw89imIPuHk8XmHnww0rWpaxFUGI0z1hgiVq+QMAPHOM9cXrh8adBfHg3H/X4v\nL2MXdm/uqEoXPCCsrC4vDQaZ7xQY2ZiswqxOpu6wVkS1QsFmHJ0tSsr6eTW/u9+F5nQSEnRFFV3u\nLHF/vFzWQzauC8oFKymiSpyBUTLUhfa1mztHVFCS3Rkeei1yBubWexO7RoInXmwYTNaigkRgYEMO\ngi/yenk8Gvf6dVmWRWEMN/M5KWbWDuvecDjs9foud6IHCgTWscvQ5IFy1EwR1So5wtQxBS7EEc2h\n7ELw0SdMZKkoXOFcVZTIGdlCErFikkiZGVaOMAFK69vdk3lnK5OZN+7uJwAxlo1JoKaZN0kkywtr\njKhKSoaYGTCJSrTEdVGujpc2VlaXB6NB3avyHAWKPO+VdVGVxlkAEFCnhSiJcWQIiQAXcB4CoqIq\nKiHZPLfOZqaOwUdJQSMyWMe5tZl1TASAklLsvAkRbCKyUSKIVnntrPnQjz/zyksvX7123RqkBSCq\nYGbNPIkUzqomQrbGOOswpOA7DXFQVUv9wcby6tn1zaXhqFdWmbEikud5XVUuz0TVex+Dd1mGgIAW\njVVGJRRURmRDZDgKKYK1uWGkjFKKUVLQAKSG0DFbNiCSFFOIwQcMkUWQABUIuZk33vtvfOObaytr\njz360EsvvoJ4f+AnHwIyAkBMyRib5y63zAQxdKhxqd9fqntLvd6ornpFXmYuz93K0mjYr21mARVQ\nhBUMAbEiATEQ3ocAEIEJjVlAcoKYgJQMGSJrTGZsZm1mjTNsiFEJEUEIlCRBjJhS6DoCtuSOjo5+\n8Rd/6Q/+4L8vLS+PRqMF4ncfiwAAYwwgIGGWWWdZfPTNnDVV1o2relCUpXMZGUdsCZ0xdVUtMFQB\nTaLETKBtGxVIIbGqEgApErIhIgsgIqFtfOpCiOwUEUBQgJTuY/YQU8S0WK0I2TAgxJQXRROiYX7u\nued++md+Zm/3HiBev/G2dQ4IEQggGTJMlpHUZdZmTkVn06mfz2rjhr2qzlxhOEM0qBYhMya3HGOH\nTECkqlGCj74LARarOiBIUlVCYFQmQCZCEzymFBvfIWhFTIxERBYBmAEINIUIoo5yZkZmRAQRiZGI\nEfDFF18+mU7/wc/+3f/5x5+9fmN7PKgQSRFA0BhmYw1ZY5yJwccUQ9uA99YVVZYZBYNgETMmx+SY\nDKLGAGrQgICkGNq2aXyHPiowu5QVtWNEjURiUMkQuzzv5lPCrm2JMLOWgQEVgUGTKAQRkMRAoAKq\nqEoIwBxEXFZ471944YXnvv38+XPn/svv/eHKuKeAgKSqIkrAZNi4LEOCrp37riNSw2QJLCCLGiDL\nZIkNEQGAiCXDtOgukmJI3kfvQ9eBJktoGTR23exUullopwTJFHnO3CvL3Nrp5CS0ncaoIpAkxRRC\n13UtqBLhgv4RESAEQzbLEGA6mRwc3PvlX/6ln/+5T334I0/NmqCISUFEVZWQABmtM+8gh8CqFtUS\nOkPOsrNkGZmAUVGFQCUFiSEFH32bfJdiB8H3e71Rv1dYVt+obyh0YTppT46ha4DU9qulrbWzF89j\nSu18FtvGt23o2hg9JCVV1SgSFRRJie5zEioKgjvbO5nNPvWpT330J37qV3/1V7Myn82blBIiGjJk\njCE21pn7uJkm1USgGWFm2FhjjWFiBEBRkKQpSYwSQmq72HSx8xIixFRltpfnDhGDNxLBN9OjPW0n\nx3fvhNu30uE9nUwryyvjEYTUzdowb/y8jW0n0euCUUlJQe6DdoSwADPJvfba1YsXLoxG49bPH37k\nPZcvP9A2PokgEhMb65xzFgBFU5KgKVGKKEoAhpAtIQOgqKaUQgwAKgAoqFElpUgxGlVAcKhVxhll\nMSaTQoiSfOdVjbFek4+iinW/nxG0IqCaQAASgknKbExKAgwWnDLiwgHC6L3Jy5defPHewWFRFLkr\n7x0eTuZzNMBkCBlQjHWOrPExpBhjDBQC+mAUUfRdVFFUkyzMB0mJmZOqoIIIIWZsHZnZycn2jRsH\n+/t1r3f27FlnM++9ppBn1c729s3t22TsA1euiKgBBERQ0ChCSQgEEZIgQFK1iLCYaxBMnkHn/+zP\n/uKVV1/5nd/5nZ/4+Md/87d+6+rLV/vjkWGHAJLUWGus5WnXhBhCDBgiRW8TiURSUVCRlFKMMXkM\nmoSZrbUAoKBIaIkzaxVgf/vtWzff3tnZHQz6qWvrus6L0rX5kT/40cuvbN/ZLaueJT5z7mxhc1GI\nGFVQk0hCIQGRtCBQFvV9kQYCd+/cUUl1Xf3bX/t1/bV/x5kbrA6LrGLF6H1KYKx1IABRKQF0KTYd\nhwTOJMImJREQhZRijJ0jBTQqMfjAhgWR0AAbQWDmoiqvPPjg1taZJCn60LWdYTufNmjNmTNnHnrP\no0VVFWVd9Wo/T6JqyAhEFYGQCDRJYpMDqaqgMQCgAkjlV7721Zvbt5PK5uba/v7h+sZ623aWEEQB\nlQ0aItfN57mSRIrz5KctEEFlWssnIjEYEeu9+rYrLVlCkRhiRC4FDKAJol2IrrTlqNakiQEEuqZL\nSXf3DxDZFkVvOByvrlaDXkgpkswoMoEj5CCWUo5oFLxGJmtYgVJUYMpAMfjmK9/6+snsdDgYVEXZ\nVk1zOgEARXTWKiOWlkIIMcQQg48hxgiAUeJ81oQUrLMJRUEEJGqKKfkYQ4yi2radiDZtNz2diqpl\ni0CAmGVFUVbExnsvCooESKPx0nA0YjaqyMbk1hrg2PpmPk8xIZEPwWVZSsLGssva6RStRc5+9MPv\nfuubzy3IXWYuiqLrujzPY4xsrDGm6zoTQhBNBBBC8DEpgsQ0nc26NiBhlBgkhhRDpBADk6omwxxj\nFIxtEEEuOAtdTF0YDYd1Rd28iaUisggW9aDqVb1qoIqi4qwLUS2z73w7n0MMBiKBGKaYUt6vjXNA\nlOWltg3mw8//5bOvXX291+tba5umCSEMBoOUkjFGJBFRWRbGe2+YQKP3IaZIqkk0hjSfN/Om7XLt\nUvDJ+0g+RRNBNYWYUsIwP561sR4sGbbzJiz1ByTog/ddyLO8yMpeNVQkNNw2XZ3n1mRsDCctDV/f\n2b11481Bv67WlwE0ikCK/TynLPOgrsjBmHv3dp77zndVpN/vq0rXdSmlsiy99yJJJBHDaDgwolGV\nY0ohJQBIClFBACaz2f7hvfPluOnaNmS5M1E1IaBSnufOldu37/7ohy/N23jh8pX1jc39tw5FxBgz\nn8xOJ1NIMFpaPjk9nUznCXHz7NnR8go7t7q2/uW//sYbr18jkAcvX6ozG+sCiJZ7q1GTtYaIO++z\ncvDlr33u+z/4PjMyc0pgrbXWzmbTPM+PDu9dfvAyEZ6enhprbfS+bRoFBUJVRSRkmTbN7v5Bs9ab\nB9/FGEQEFQgBYDqdInb7+/u3bt364cs34KvfOnfu3I+dXV1ZWVkaL3WtX65G1lhRffj85cFoPOv8\nzdu333jl2le//rUbt3YKp+fOrj/04BUCbZpZ1SvLXsXWojWAitaaLHvj9au/+7u/f3Bw1O/1Y4zG\nGEQSEe99SrHuV+vra0tLox/84AeGCGMKPgQEVMS0IMbY+JhOJ9Pj2az2vksSF5MfGSQ1Sr7zm1ub\nP/ePPvnU03deufp62/rDnX2Yh8Nbu7nLH/nJj62urb1+7bq26XT/6Pnvff+vvvyljbPnHjxz8SNP\nPsOFbmyuDgd1jN5mDgk733HbDspSkqBx6vIv/O9nv/Llr4NwWVQAmudZCCHGWBR517XLK0tHR/fW\n1pbqujRt2xBzWVbz+TSJBBECJEIEmHfh5atvknGD/qAuZdZ1mTWls2TYqMWEAnDmzGZvMJycTouD\nw2Y2Pzg4mM1Ovvnlr1pjj49PYlJ2bjAef/rn//F4ebXo1czsbWsLG3xLDM7Zpmt61SirSxElYyjP\nr167+pv/4T/FCKNBNZvO8iIzxmZZ7r1t2jmglGWR567X643GQwP3uU+9Tz0DCiIiCQIAH52m7Tt7\nS6PlflFlpjUAWBWkCgmAmNkgUZ4blWKVVlOMD156oCxKVPQ+xCRZlhdVr+z3yLqoECUJYKwzchii\nidGLpoRqnLG5a31X1+sQw+svv3btjdu9Xp5nOed2wV3EGFWSc64sszx3Gxsb46Xh++rHjMhiMRBF\nUEQFRHjnF3HepRu3D4riRpXnjpctIqiUzloke18HhCU4y1gonZy0USgvM8cuhGhcZtgoURe6dj4J\nCnlRVHWfigQMrrSqad7OoorJMzRMhlGxmc6PD08ZYFhXqpQVOSEWRdG2bdOFLHeDQX8yOT2dVMaY\n9773A2YhjVuwk3r/De7/EQBB8BHu7N6r87dzJrc+NiC5YbDIBMySVK1RROplI69xOp3sHOxadqKK\nSGSypJIETJ6X/dqWGeUGKCWNBIyGbZ4VmRsMB2VVYVbu37x1PPPz6dQBsGAERaSiyEejpdPT48nk\nhD2kFIuy2N7enkyOb9++ZUSSKqqqvstoAwmQACRAJVNU/ZDk5q07OWlGYldHpTOWHaCqJtAFIIYi\nMFgf06k7OT6ZdhMAFAHAuYLaohpWvXxYYWkbDWwZNAUVSWKdXVlbqVeWkQwodV1XlzWJJoBm1uS9\nISiORqO6qufziTHc+m4w6J85u/n5z39he/vO+x5/zKiqqMj/E/eRwn1dDQIhmChoCJou7u4e9HKD\nsXOsBKWzZMghEbOxxjYz7zKXD+tA4luPQKIaoqCxriiwctEiMRjDyomRrbVK2u/3euuryGZy76iL\nsLa2Tv3lFCIAoCgTWWuXlpa97+ZNm+dFnPmu64yx/X5589abJycn5r6GBt4RdNz3ROE+1Gzn84Yy\nqisbVfYODjh141HlmIoiRybL2aLFDKpRCAE73zNDSRpjSjEpUF5VRa/n8pIsk7F5lh/ND61z9WCQ\nZyYfDXA4nN65s7O76yOMts6LD7d37hqAMssQwFrT7/e3t2/OptPhsBdis31r22Xuk5/8ua9+7UuH\nh0fGUgFAKSVJiITMXUxeYzQIiupTl2eWC4acIbct8O7JjN/YvnLhPJi6L9nQFqDZdOrNkqHCFXW/\nWogiFFU0yxyTZcNMjMgioiny6mZeV8jEhqkaNHePXvnW8+Xy0vrFixGT5HwiTQvQWMg11s6sL4+b\n6dH+rq3yvJ3nTZwd7B0Yzn7+k//0M3/0GXN/71oIbJSUEeRdbYcu1HL6TnIstDe7OzsssVfkK+PR\nYkQZj8dEyEzWWmPMguVWUWZGpAXvqgoL2VedO+dMZawbDU/vbD//7F8GwrMPXen1ellZnkynW1tb\ng2Hv+GiyNLZPPPn+5eWl3bt3BoPBZDq11vZ6az6FF1544ZlnnlZV8454Co1hhcQJBd8NpPvRtGgU\niMjMxiBEvXNnh0QM4vnz55l5Op0WWC9MZ2ZVVRVV8D4REbMwm0W1VhEKsZ/npt/fu/raD7/2VZF4\n4colx9S2M5tSlhcf+fDTv/Hr4y99+Ru3bt558MqDp9PjEMLS0pLcEybqYicik8lUAN773vcZUAQA\nImI2IomQiVhiUhBQZMKF/SpKRAucM8udBnt0dPTSSy+p6tbWFiCAIWutqqYkiJBSijGpguFFjjgi\nRkRV7efOEF5/4flrf/1cN5+dPX9m2K8lRUToJifF2uYHnnz8sfe+74kn33960p4cTZ9/YdcYe2fn\nzmA4eOThR97efnv/YH8wHG5v33788SfMO+JiMgZFDLHBFBDpXbHV4gUWr7QoC85QXuQSfNM0b7zx\nRtM04/HYZDbLsgVAAoAxxhAiIjKHJNliUCViVZ3c23/z6qtvvvxKaOaXL5xz1s5OT9eWxs45rHLQ\neHR08L3vvfTooz/2/g88MpsGn8JfffGLx8fHZVW+fv16v9/f3Nyqe9X1629tbm0ZXQiPCYgNpoRE\nQKi4EPHeFyP/zRAy1rHGaK01WSYi+/v7XdcljFVZ9fv9qq6tsSISo8SYmCj4rmMDiCoKAHuv3T09\nPS6d3drYIJVBr+Y8Y6YUfV7kQOQc11XWqzKIvuoNPvaxj33uz//8s5/9k6zMs+ms6donP/DkeDze\n3z8AIPNuqOP9j0AZgABE7yf4Oy4slPdEkoQQF3JGZgaAtm137tyu69r7LqWY5zkii2jXtbCAzeF+\nUKWU2mayOh5XeZax6feq5L2tCmscjUbTvbvVeLk3GKwuj+ftbGR4cnRvtLT0W7/927/w6U+/+NKP\nnv2rZ3f39lZXV4uyOHPu/MMPv+f/Aqe2X0YpAZKIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=64x64 at 0x7F03897C63C8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t23hgAsmXQXM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "\n",
        "def cropIdolFace(src, dest):\n",
        "    if not os.path.exists(dest):\n",
        "        os.mkdir(dest)\n",
        "    for filename in os.listdir(src):\n",
        "        img = cv.imread(src + filename)\n",
        "        faces = face_cascade.detectMultiScale(img, 1.3, 5)\n",
        "        if len(faces) == 1:\n",
        "            im      = Image.open(src + filename)\n",
        "            x,y,w,h = faces[0]\n",
        "            box     = (x, y, x+w, y+h)\n",
        "            crpim   = im.crop(box).resize((64,64))\n",
        "            crpim.save(dest + filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzbTki7OXd-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cropIdolFace('zhiyu/', 'zhiyu_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Edn0HK5iXgRr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cropIdolFace('mm/', 'mm_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wprl-Pp7XpHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cropIdolFace('puff/', 'puff_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtfOkPUYYqH6",
        "colab_type": "text"
      },
      "source": [
        "## 使用 CNN 建立人臉識別模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGB38qjdXrEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialising the CNN\n",
        "classifier = tf.keras.Sequential()\n",
        "\n",
        "# Convolution\n",
        "classifier.add(tf.keras.layers.Conv2D(filters=32, \n",
        "                                      kernel_size=(3, 3), \n",
        "                                      padding = 'same',\n",
        "                                      input_shape = (64, 64, 3), \n",
        "                                      activation = 'relu'))\n",
        "# Max Pooling\n",
        "classifier.add(tf.keras.layers.MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# Convolution\n",
        "classifier.add(tf.keras.layers.Conv2D(filters=32, \n",
        "                                      kernel_size=(3, 3), \n",
        "                                      padding = 'same', \n",
        "                                      activation = 'relu'))\n",
        "# Max Pooling\n",
        "classifier.add(tf.keras.layers.MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "# Flattening\n",
        "classifier.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# Fully Connected\n",
        "classifier.add(tf.keras.layers.Dense(units = 128, activation = 'relu')) \n",
        "classifier.add(tf.keras.layers.Dropout(rate=0.1)) \n",
        "classifier.add(tf.keras.layers.Dense(units = 128, activation = 'relu')) \n",
        "classifier.add(tf.keras.layers.Dropout(rate=0.1)) \n",
        "classifier.add(tf.keras.layers.Dense(units = 3, activation = 'softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-8BF28jZUAe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "bb6ec2ea-bef5-407d-fba5-27ed616a01b9"
      },
      "source": [
        "classifier.summary()"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_44\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_21 (Conv2D)           (None, 64, 64, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_18 (MaxPooling (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten_15 (Flatten)         (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_90 (Dense)             (None, 128)               1048704   \n",
            "_________________________________________________________________\n",
            "dropout_31 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_91 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_32 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_92 (Dense)             (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 1,075,747\n",
            "Trainable params: 1,075,747\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9svfDsnnZWg_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier.compile(optimizer = 'adam', \n",
        "                        loss ='categorical_crossentropy', \n",
        "                     metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82zLBrTNZzBH",
        "colab_type": "text"
      },
      "source": [
        "## 圖像增廣技術"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYapXncqZrBY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,     #x坐标保持不变，而对应的y坐标按比例发生平移\n",
        "                                   zoom_range = 0.2,      #可以让图片在长或宽的方向进行放大\n",
        "                                   horizontal_flip = True #水平翻转操作\n",
        "                                  )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xhs6lJ4JaJR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQ16ngg9aUUr",
        "colab_type": "text"
      },
      "source": [
        "## 將資料分為訓練與測試資料集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NvrIu5saLMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "if not os.path.exists('train/'):\n",
        "    os.mkdir('train/')\n",
        "if not os.path.exists('test/'):\n",
        "    os.mkdir('test/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEXA72fHaXVC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "def splitDataset(dirs):\n",
        "    dataset = list(os.listdir(dirs))\n",
        "    train_data, test_data = train_test_split(dataset, test_size= 0.2, random_state = 42)\n",
        "    \n",
        "    if not os.path.exists('train/'+dirs):\n",
        "        os.mkdir('train/'+dirs)\n",
        "    if not os.path.exists('test/'+dirs):\n",
        "        os.mkdir('test/'+dirs)\n",
        "\n",
        "    for f in train_data:\n",
        "        os.rename(dirs + f, 'train/'+ dirs + f)\n",
        "    for f in test_data:\n",
        "        os.rename(dirs + f, 'test/' + dirs +f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MidSNqjAaoJ5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "splitDataset('puff_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H61at9uhaplc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "splitDataset('zhiyu_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv2ikqdNaw5w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "splitDataset('mm_face/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBEv3v6waynR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a27225ba-cf6a-4c22-8d8f-42d08cbfbc3f"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory(\n",
        "    'train/', target_size = (64, 64),\n",
        "     batch_size = 16,\n",
        "     class_mode = 'categorical')"
      ],
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 142 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wudX_cya58D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "848e35c5-b335-4a55-8154-16686c657a31"
      },
      "source": [
        "test_set = test_datagen.flow_from_directory(\n",
        "    'test/', target_size = (64, 64),\n",
        "    batch_size = 30, \n",
        "    class_mode = 'categorical')"
      ],
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 37 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JURbmfCa9U3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "b9ab67eb-d371-4fca-c2e5-867198218ec3"
      },
      "source": [
        "history = classifier.fit_generator(training_set,\n",
        "                         epochs=10,\n",
        "                         validation_steps=16,\n",
        "                         verbose = 1,\n",
        "                         validation_data = test_set)"
      ],
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "    451/Unknown - 40s 89ms/step - loss: 0.0778 - accuracy: 0.9729"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-300-3cd032f19e84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m                          \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                          \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m                          validation_data = test_set)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    322\u001b[0m               \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m               instructions)\n\u001b[0;32m--> 324\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m     return tf_decorator.make_decorator(\n\u001b[1;32m    326\u001b[0m         \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1412\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1416\u001b[0m   @deprecation.deprecated(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    781\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    782\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    784\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4_tQGBTbHBm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "808bb63b-c945-46f6-dd31-b1149ffedd95"
      },
      "source": [
        "! wget https://attach.setn.com/newsimages/2018/09/06/1531445-XXL.jpg"
      ],
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-28 09:19:23--  https://attach.setn.com/newsimages/2018/09/06/1531445-XXL.jpg\n",
            "Resolving attach.setn.com (attach.setn.com)... 13.224.8.12, 13.224.8.86, 13.224.8.64, ...\n",
            "Connecting to attach.setn.com (attach.setn.com)|13.224.8.12|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20147 (20K) [image/jpeg]\n",
            "Saving to: ‘1531445-XXL.jpg’\n",
            "\n",
            "\r1531445-XXL.jpg       0%[                    ]       0  --.-KB/s               \r1531445-XXL.jpg     100%[===================>]  19.67K  --.-KB/s    in 0.1s    \n",
            "\n",
            "2020-03-28 09:19:24 (201 KB/s) - ‘1531445-XXL.jpg’ saved [20147/20147]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRUMW7zucFC4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "62c47052-7ad1-4d09-89a9-647faa08258a"
      },
      "source": [
        "img = cv.imread('1531445-XXL.jpg')\n",
        "img.shape"
      ],
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(287, 510, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_Sv0NFTcKzr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "faces = face_cascade.detectMultiScale(img, 1.3, 5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lKHC1xScMyV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "20ef59ef-b8c9-4703-d0a3-4f9afb6f336a"
      },
      "source": [
        "faces"
      ],
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[214,  37, 117, 117]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJJbRzqAcOHX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "ba4f4506-02a7-434e-973c-25f0b6e7c805"
      },
      "source": [
        "from PIL import Image\n",
        "im = Image.open('1531445-XXL.jpg')\n",
        "x,y,w,h = faces[0]\n",
        "box     = (x, y, x+w, y+h)\n",
        "crpim   = im.crop(box).resize((64,64))\n",
        "crpim"
      ],
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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dJGFHAABwMHN3NzNCinVVNfW0m95Zu//FX/3Fq5/91Iuf/WSr7etvvLqwMHjmmbPLBw/8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\nRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=64x64 at 0x7F037F913DA0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cc4ejd0ncRve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "crpim.save('predict_face.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iljWt90cWwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "121d6fc2-429d-47e2-e58a-368c65e84040"
      },
      "source": [
        "import numpy as np\n",
        "from keras.preprocessing import image\n",
        "test_image = image.load_img('predict_face.jpg', target_size= (64,64))\n",
        "test_image = image.img_to_array(test_image)\n",
        "test_image.shape"
      ],
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRJ1XlPYcYOz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a5677a7f-4d0e-4afc-fe48-a674c2e0d4fa"
      },
      "source": [
        "test_image = np.expand_dims(test_image, axis = 0)\n",
        "test_image.shape"
      ],
      "execution_count": 308,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9Ogv3M_ccE-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "78e1df7c-8032-49a0-d55a-bbe1fa88e5e1"
      },
      "source": [
        "classifier.predict_classes(test_image)"
      ],
      "execution_count": 309,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dg3KEDQQcfeO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a773e626-b64d-47b6-b58e-31e574809468"
      },
      "source": [
        "training_set.class_indices"
      ],
      "execution_count": 310,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mm_face': 0, 'puff_face': 1, 'zhiyu_face': 2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HVM2EGO3en5f",
        "colab_type": "text"
      },
      "source": [
        "### 作業\n",
        "\n",
        "- 目標: 建立一深度學習模型預測客戶是否流失\n",
        "- 問題一: 請將資料區分為訓練與測試資料集\n",
        "- 問題二: 請將資料標準化\n",
        "- 問題三: 使用Keras 建立深度學習模型預測客戶是否流失\n",
        "- 問題四: 評估模型準確度\n",
        "- 問題五: 請繪製ROC Curve, 並求出 AUC\n",
        "- 問題六: 請比較 ANN, SVM, Gradient Boosting, Random Forest, Logist Regression, Decision Tree 各模型的 AUC 與 ROC Curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAasyk_tcime",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "5400f193-bd86-432a-ba6c-d776ce27b484"
      },
      "source": [
        "import pandas\n",
        "df = pandas.read_csv('https://raw.githubusercontent.com/ywchiu/tibamedl/master/Data/Churn_Modelling.csv', header = 0 )\n",
        "df.head()"
      ],
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>608</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>Onio</td>\n",
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              "      <td>Boni</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   RowNumber  CustomerId   Surname  ...  IsActiveMember EstimatedSalary Exited\n",
              "0          1    15634602  Hargrave  ...               1       101348.88      1\n",
              "1          2    15647311      Hill  ...               1       112542.58      0\n",
              "2          3    15619304      Onio  ...               0       113931.57      1\n",
              "3          4    15701354      Boni  ...               0        93826.63      0\n",
              "4          5    15737888  Mitchell  ...               1        79084.10      0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9wyzfmfe29O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}