9_2주차.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "9-2주차.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOoDfRK514NDvQkkVkECyDJ",
      "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/yujungha/AI-Study/blob/main/9_2%EC%A3%BC%EC%B0%A8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiX619fM_N6Z"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTtI3uYh_VSY",
        "outputId": "a1b1fab3-1fb1-4715-e118-03ae8bd79e83"
      },
      "source": [
        "df = pd.read_csv('train.csv')\n",
        "df2 = df[['Survived', 'Pclass', 'Sex', 'Age', 'Fare']].reset_index()\n",
        "\n",
        "df2.loc[:,'Sex'] = df2.loc[:,'Sex'].str.replace('female', '0')\n",
        "df2.loc[:,'Sex'] = df2.loc[:,'Sex'].str.replace('male', '1')\n",
        "df2['Sex'] = df2['Sex'].astype(int)\n",
        "df2['Pclass'] = round(df2['Pclass'] / 3, 1)\n",
        "df2['Age'] = round(df2['Age'] / 80 , 1)\n",
        "df2 = df2.dropna(subset=['Age'])\n",
        "df2['Fare'] = round(df2['Fare'] / df2['Fare'].max() , 3)\n",
        "df3 = df2['Survived'].head().values\n",
        "df3 = [[x] for x in df3]\n",
        "del df2['index']\n",
        "del df2['Survived']\n",
        "\n",
        "x_train = torch.FloatTensor(df2.head().values)\n",
        "y_train = torch.FloatTensor(df3)\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(4, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 1)\n",
        ")\n",
        "\n",
        "learning_rate = 0.02\n",
        "epochs = 10000\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "cost = nn.MSELoss()\n",
        "\n",
        "for i in range(epochs + 1):\n",
        "  y = model(x_train)\n",
        "  loss = cost(y, y_train)\n",
        "\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  if i % 1000 == 0:\n",
        "    print(i, loss.item())\n",
        "\n",
        "print(model(x_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.5029163956642151\n",
            "1000 1.3080169082968496e-05\n",
            "2000 4.411121246050698e-09\n",
            "3000 5.777600891199858e-14\n",
            "4000 2.612541259716661e-11\n",
            "5000 8.074800462054554e-06\n",
            "6000 5.615417464355232e-09\n",
            "7000 3.560887762432685e-06\n",
            "8000 9.555600755106752e-10\n",
            "9000 0.0\n",
            "10000 0.0\n",
            "tensor([[0.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [0.]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iymTxNdN6VFR",
        "outputId": "b745928b-a463-4b6c-ca27-3dc0c2f70157"
      },
      "source": [
        "df = pd.read_csv('train.csv')\n",
        "df2 = df[['Survived', 'Pclass', 'Sex', 'Age', 'Fare']].reset_index()\n",
        "\n",
        "df2.loc[:,'Sex'] = df2.loc[:,'Sex'].str.replace('female', '0')\n",
        "df2.loc[:,'Sex'] = df2.loc[:,'Sex'].str.replace('male', '1')\n",
        "df2['Sex'] = df2['Sex'].astype(int)\n",
        "df2['Pclass'] = round(df2['Pclass'] / 3, 1)\n",
        "df2['Age'] = round(df2['Age'] / 80 , 1)\n",
        "df2 = df2.dropna(subset=['Age'])\n",
        "df2['Fare'] = round(df2['Fare'] / df2['Fare'].max() , 3)\n",
        "df3 = df2['Survived'].values\n",
        "df3 = [[x] for x in df3]\n",
        "del df2['index']\n",
        "del df2['Survived']\n",
        "\n",
        "x_train = torch.FloatTensor(df2.values)\n",
        "y_train = torch.FloatTensor(df3)\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Linear(4, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 50),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(50, 1)\n",
        ")\n",
        "\n",
        "learning_rate = 0.01\n",
        "epochs = 10000\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "cost = nn.MSELoss()\n",
        "\n",
        "for i in range(epochs + 1):\n",
        "  y = model(x_train)\n",
        "  loss = cost(y, y_train)\n",
        "\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  if i % 1000 == 0:\n",
        "    output = torch.abs(torch.round(model(x_train)))\n",
        "    print(i, loss.item(), '%.2f%%'%((y_train == output).float().mean().numpy() * 100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.5415535569190979 59.38%\n",
            "1000 0.09538930654525757 87.96%\n",
            "2000 0.08877456933259964 88.52%\n",
            "3000 0.07861620932817459 89.92%\n",
            "4000 0.07276275008916855 90.76%\n",
            "5000 0.06602360308170319 90.76%\n",
            "6000 0.07021220773458481 91.04%\n",
            "7000 0.06427787244319916 91.46%\n",
            "8000 0.06796039640903473 91.32%\n",
            "9000 0.07403182238340378 90.62%\n",
            "10000 0.09004572778940201 88.80%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RRAcbXmvyqwX",
        "outputId": "d30cf615-4d74-412c-bede-c22a534d52cb"
      },
      "source": [
        "output = torch.abs(torch.round(model(x_train)))\n",
        "print('%.2f%%'%((y_train == output).float().mean().numpy() * 100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87.68%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vocbRtgRyn4d",
        "outputId": "f9bd8de7-715a-46d4-9457-f6c9687a62fe"
      },
      "source": [
        "print()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "metadata": {
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          "execution_count": 54
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    }
  ]
}