02_모두의딥러닝_02_00.ipynb

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        "<a href=\"https://colab.research.google.com/github/gesture07/ML_Agent_Study/blob/main/02_%EB%AA%A8%EB%91%90%EC%9D%98%EB%94%A5%EB%9F%AC%EB%8B%9D_02_00.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "t3qKPCqkIM8l"
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      "source": [
        "# 2장. 딥러닝 핵심 미리보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aGnqS4iIM8p"
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      "source": [
        "## 나의 첫 딥러닝: '10장 폐암 수술 환자의 생존율 예측' 코드 미리보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbVYiBTEIM8q"
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      "source": [
        "### 1. 환경 준비"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L4eubqQbIM8r"
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      "source": [
        "from tensorflow.keras.models import Sequential  # 텐서플로의 케라스 API에서 필요한 함수들을 불러옵니다.\n",
        "from tensorflow.keras.layers import Dense       # 데이터를 다루는 데 필요한 라이브러리를 불러옵니다.\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5TWyXHBIM8s"
      },
      "source": [
        "### 2. 데이터 준비"
      ]
    },
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        "id": "Xq0LZLNrIM8s",
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      "outputs": [
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          "text": [
            "Cloning into 'data'...\n",
            "remote: Enumerating objects: 21, done.\u001b[K\n",
            "remote: Counting objects: 100% (21/21), done.\u001b[K\n",
            "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
            "remote: Total 21 (delta 3), reused 20 (delta 2), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (21/21), 460.95 KiB | 9.81 MiB/s, done.\n",
            "Resolving deltas: 100% (3/3), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/taehojo/data.git   # 깃허브에 준비된 데이터를 가져옵니다.\n",
        "\n",
        "Data_set = np.loadtxt(\"./data/ThoraricSurgery3.csv\", delimiter=\",\")  # 수술 환자 데이터를 불러옵니다.\n",
        "X = Data_set[:,0:16]                                                 # 환자의 진찰 기록을 X로 지정합니다.\n",
        "y = Data_set[:,16]                                                   # 수술 후 사망/생존 여부를 y로 지정합니다."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWvYKam39_R_",
        "outputId": "9c55d86b-4b39-466a-b1a5-24b4bb8b7953"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1.    2.88  2.16  1.    0.    0.    0.    1.    1.    3.    0.    0.\n",
            "  0.    1.    0.   60.  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-X_6wZ6ZIM8t"
      },
      "source": [
        "### 3. 구조 결정"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BHUiEfJmIM8t"
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      "outputs": [],
      "source": [
        "model = Sequential()                                                  # 딥러닝 모델의 구조를 결정합니다.\n",
        "model.add(Dense(30, input_dim=16, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
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        "id": "Ya4u0abO-Y0v",
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 30)                510       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 541 (2.11 KB)\n",
            "Trainable params: 541 (2.11 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
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        "id": "dVbNJR7PIM8u"
      },
      "source": [
        "### 4. 모델 실행"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 1s 1ms/step - loss: 2.0996 - accuracy: 0.3787\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 1ms/step - loss: 0.4991 - accuracy: 0.8511\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 1ms/step - loss: 0.4547 - accuracy: 0.8511\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 1ms/step - loss: 0.4426 - accuracy: 0.8511\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 1ms/step - loss: 0.4378 - accuracy: 0.8511\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])  # 딥러닝 모델을 실행합니다.\n",
        "history=model.fit(X, y, epochs=5, batch_size=16)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dkqeM9JKhdRX"
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      "execution_count": null,
      "outputs": []
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