모두의_딥러닝_폐암_수술_환자_생존율_예측.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "모두의 딥러닝 - 폐암 수술 환자 생존율 예측.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP2T+A2yl3KZ2YC7X7BgrUX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/symaeng98/Artificial_Intelligence/blob/main/%EB%AA%A8%EB%91%90%EC%9D%98_%EB%94%A5%EB%9F%AC%EB%8B%9D_%ED%8F%90%EC%95%94_%EC%88%98%EC%88%A0_%ED%99%98%EC%9E%90_%EC%83%9D%EC%A1%B4%EC%9C%A8_%EC%98%88%EC%B8%A1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tTN-SErT4LrR"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/taehojo/data.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xdajBAF4XOb",
        "outputId": "d572fd8b-6d54-4267-91da-afeb1fef19da"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "Unpacking objects: 100% (21/21), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data_set = np.loadtxt(\"./data/ThoraricSurgery3.csv\", delimiter=\",\")\n",
        "\n",
        "X = Data_set[:, 0:16]\n",
        "y = Data_set[:, 16]"
      ],
      "metadata": {
        "id": "lIi_-6o74byT"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(30, input_dim=16, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "# 0인지 1인지 판단하기 위해서 마지막 레이어는 sigmoid 사용"
      ],
      "metadata": {
        "id": "jV_EPB2z4seT"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "metrics=['accuracy']\n",
        "history = model.fit(X, y, epochs=5, batch_size=16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKZyKfFb44Jd",
        "outputId": "259b0897-7143-4b17-9cfc-721ccda0de9b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "30/30 [==============================] - 1s 3ms/step - loss: 0.4546\n",
            "Epoch 2/5\n",
            "30/30 [==============================] - 0s 3ms/step - loss: 0.4475\n",
            "Epoch 3/5\n",
            "30/30 [==============================] - 0s 3ms/step - loss: 0.4515\n",
            "Epoch 4/5\n",
            "30/30 [==============================] - 0s 3ms/step - loss: 0.4392\n",
            "Epoch 5/5\n",
            "30/30 [==============================] - 0s 4ms/step - loss: 0.4335\n"
          ]
        }
      ]
    }
  ]
}