TFT_mobile_win_or_lose_prediction

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TFT모바일 승방률 승패예측",
      "provenance": [],
      "authorship_tag": "ABX9TyPFhT9nQuqv4frvFwU5iEoD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sejeong-park/2020-AI/blob/master/TFT%EB%AA%A8%EB%B0%94%EC%9D%BC_%EC%8A%B9%EB%B0%A9%EB%A5%A0_%EC%8A%B9%ED%8C%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",
      "metadata": {
        "id": "dxHahNbuFee2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "16fb24d0-d92b-4bfa-95a3-8f773c157896"
      },
      "source": [
        "!pip uninstall kaggle\n",
        "!pip install --upgrade pip\n",
        "!pip install kaggle==1.5.6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling kaggle-1.5.6:\n",
            "  Would remove:\n",
            "    /usr/local/bin/kaggle\n",
            "    /usr/local/lib/python3.6/dist-packages/kaggle-1.5.6.dist-info/*\n",
            "    /usr/local/lib/python3.6/dist-packages/kaggle/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled kaggle-1.5.6\n",
            "Collecting pip\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/43/84/23ed6a1796480a6f1a2d38f2802901d078266bda38388954d01d3f2e821d/pip-20.1.1-py2.py3-none-any.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 2.8MB/s \n",
            "\u001b[?25hInstalling collected packages: pip\n",
            "  Found existing installation: pip 19.3.1\n",
            "    Uninstalling pip-19.3.1:\n",
            "      Successfully uninstalled pip-19.3.1\n",
            "Successfully installed pip-20.1.1\n",
            "Collecting kaggle==1.5.6\n",
            "  Downloading kaggle-1.5.6.tar.gz (58 kB)\n",
            "\u001b[K     |████████████████████████████████| 58 kB 1.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (1.24.3)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (1.12.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (2020.4.5.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (2.8.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (4.41.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle==1.5.6) (4.0.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle==1.5.6) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle==1.5.6) (2.9)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle==1.5.6) (1.3)\n",
            "Building wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.6-py3-none-any.whl size=72859 sha256=8a7fb3fbaa9e39c899ccc0e651b0a55cb87a4438a3a8a870bebabcdadf189039\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/3e/ff/77407ebac3ef71a79b9166a8382aecf88415a0bcbe3c095a01\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "Successfully installed kaggle-1.5.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1NVhqnRF1Km",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e5b26246-0acb-47ba-8aad-71de964ab63d"
      },
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle\n",
        "!ls -lha kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 67 Jun 15 08:28 kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PSUqeJkF1RX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "dfeee598-809e-4149-931c-0d7ac14afc92"
      },
      "source": [
        "! kaggle competitions download -c tft-hyona"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading tft-hyona.zip to /content\n",
            "\r  0% 0.00/218k [00:00<?, ?B/s]\n",
            "\r100% 218k/218k [00:00<00:00, 70.0MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gh0_zHp7F1Ug",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "65778381-e19b-4a16-bfd2-7f5058f08e7c"
      },
      "source": [
        "!unzip tft-hyona.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  tft-hyona.zip\n",
            "  inflating: moral_TFT_test.csv      \n",
            "  inflating: moral_TFT_train.csv     \n",
            "  inflating: sample.csv              \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_azx3gzG5BJ"
      },
      "source": [
        "import random\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "random.seed(777)\n",
        "torch.manual_seed(777)\n",
        "if device == 'cuda':\n",
        "  torch.cuda.manual_seed_all(777)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEFcTZLgF1ep"
      },
      "source": [
        "import torch\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler # For Normalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMwBJ8b1F10C"
      },
      "source": [
        "train_csv = pd.read_csv('moral_TFT_train.csv')\n",
        "train_D = train_csv.drop('Ranked',axis = 1)\n",
        "train_L = train_csv.Ranked\n",
        "test_D = pd.read_csv('moral_TFT_test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyT5KtxpF1xy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "6f2bc3ea-862f-4c96-c71b-e2700d2fdfdd"
      },
      "source": [
        "train_D"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6695</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6696</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6697</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6698</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6699</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6700 rows × 75 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      JarvanIV  Sona  Rakan  XinZhao  ...  Set3_Void  Sniper  Vanguard  ManaReaver\n",
              "0          3.0   3.0    3.0      3.0  ...        0.0     0.0       0.0         0.0\n",
              "1          0.0   2.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "2          0.0   0.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "3          0.0   0.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "4          0.0   1.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "...        ...   ...    ...      ...  ...        ...     ...       ...         ...\n",
              "6695       0.0   0.0    0.0      0.0  ...        0.0     0.0       1.0         2.0\n",
              "6696       0.0   0.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "6697       0.0   0.0    0.0      0.0  ...        0.0     0.0       0.0         0.0\n",
              "6698       0.0   0.0    0.0      0.0  ...        3.0     0.0       0.0         0.0\n",
              "6699       0.0   0.0    0.0      0.0  ...        1.0     0.0       0.0         0.0\n",
              "\n",
              "[6700 rows x 75 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjqvg09gF1u1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c7bcd85f-6956-43df-f983-552a5ba9a5e2"
      },
      "source": [
        "train_L"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0,  ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuH2BhQeF1sI"
      },
      "source": [
        "train_D= torch.FloatTensor(np.array(train_D))\n",
        "train_L = torch.LongTensor(np.array(train_L))\n",
        "test_D = torch.FloatTensor(np.array(test_D))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k404nlwlF1pv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a02d851-7b17-4964-9b3a-78faef862df8"
      },
      "source": [
        "train_L"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0,  ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycc-pehaF1nJ"
      },
      "source": [
        "data_loader = torch.utils.data.DataLoader(dataset=torch.utils.data.TensorDataset(train_D, train_L),\n",
        "                                          batch_size=100,\n",
        "                                          shuffle=True,\n",
        "                                          drop_last=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyvpJBhOF1j_"
      },
      "source": [
        "linear1= torch.nn.Linear(train_D.shape[1], 256, bias = True)\n",
        "linear2 = torch.nn.Linear(256,256,bias = True)\n",
        "linear3 = torch.nn.Linear(256,256,bias = True)\n",
        "linear4 = torch.nn.Linear(256,256,bias = True)\n",
        "linear5 = torch.nn.Linear(256,2, bias=  True)\n",
        "relu = torch.nn.ReLU()\n",
        "dropout = torch.nn.Dropout(p = 0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFlRX3NcF1iU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "63f91760-4c92-4090-c56b-ced38b8affbe"
      },
      "source": [
        "torch.nn.init.xavier_normal(linear1.weight)\n",
        "torch.nn.init.xavier_normal(linear2.weight)\n",
        "torch.nn.init.xavier_normal(linear3.weight)\n",
        "torch.nn.init.xavier_normal(linear4.weight)\n",
        "torch.nn.init.xavier_normal(linear5.weight)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Parameter containing:\n",
              "tensor([[-1.1016e-01,  3.8220e-02, -3.3337e-02,  1.9869e-02,  2.8343e-02,\n",
              "          2.0104e-01,  1.3599e-01, -8.4806e-02,  5.5926e-02,  8.8898e-03,\n",
              "         -5.2970e-04,  4.6597e-02,  3.5502e-02, -3.1195e-02, -7.5791e-02,\n",
              "         -3.7435e-02,  4.7421e-02,  9.3494e-02, -5.8333e-02, -4.8365e-02,\n",
              "         -1.5863e-01,  1.0921e-01,  2.3679e-01, -4.2331e-03, -1.3422e-02,\n",
              "         -3.5500e-02,  7.0703e-02, -1.2928e-01, -8.6596e-02,  1.0253e-02,\n",
              "          2.6316e-02,  1.9953e-02, -1.0991e-02, -1.1355e-02, -9.2926e-02,\n",
              "          4.1886e-02, -2.3508e-02, -8.7822e-02,  6.8931e-03,  8.4693e-02,\n",
              "          2.9858e-02, -6.7298e-02, -7.3263e-02, -3.0865e-02,  7.9273e-02,\n",
              "         -1.0065e-01, -9.0814e-02,  1.5167e-01,  2.4391e-01,  6.7645e-02,\n",
              "          4.4424e-02,  9.6180e-02, -4.5756e-02, -3.7919e-03, -2.4857e-02,\n",
              "         -1.7738e-02, -7.5452e-02,  8.1756e-02, -1.1287e-01,  5.2770e-03,\n",
              "         -5.8502e-03,  2.6415e-03, -6.4181e-02, -1.3450e-01, -4.2162e-02,\n",
              "          7.3240e-02,  2.0869e-02, -3.6336e-03, -5.1649e-02, -3.3694e-02,\n",
              "         -6.3380e-02, -3.0768e-02, -2.8585e-01, -3.7091e-02,  2.1320e-02,\n",
              "         -2.3589e-02, -9.4143e-02,  2.4378e-02, -7.9573e-02, -5.6095e-02,\n",
              "          2.8754e-02,  1.5190e-01,  5.7071e-02, -1.1550e-01,  7.9214e-02,\n",
              "          4.5424e-04,  1.9694e-02,  1.0931e-01,  9.1934e-02, -1.4968e-01,\n",
              "         -9.1575e-03,  9.7367e-03,  1.9552e-02,  7.6971e-02,  2.3501e-01,\n",
              "          1.3601e-02, -2.7778e-01,  2.7071e-01, -2.4997e-02, -1.1225e-01,\n",
              "         -9.8435e-02,  3.0788e-02,  8.4675e-02, -5.3032e-02,  2.2010e-01,\n",
              "         -7.7701e-02, -1.0093e-01,  2.3037e-02,  8.2155e-05, -1.8434e-02,\n",
              "         -6.7165e-02, -1.6142e-02, -1.4201e-01, -1.8961e-01,  8.2542e-02,\n",
              "         -1.4467e-01,  6.4550e-02,  2.9156e-02, -1.1965e-01,  1.7709e-02,\n",
              "          4.3596e-02,  7.3505e-02, -3.3482e-02,  1.9057e-02, -7.5904e-02,\n",
              "          5.4301e-02, -1.6954e-01, -1.8804e-02,  2.2275e-02, -1.0753e-02,\n",
              "         -1.0392e-01,  8.2631e-02,  5.6400e-02, -1.0301e-02, -2.0965e-02,\n",
              "         -7.7828e-02,  7.7673e-03, -1.5293e-01,  1.1310e-01,  1.4157e-01,\n",
              "         -4.2072e-02,  7.7665e-02,  1.6236e-01, -6.7566e-02,  3.7704e-02,\n",
              "         -8.9863e-02, -9.1044e-03, -1.1616e-01,  9.6816e-02, -1.7095e-01,\n",
              "          3.7993e-02,  2.7080e-02, -6.4948e-02, -3.1670e-02,  8.0180e-02,\n",
              "         -6.3259e-02, -1.1436e-02,  1.6768e-01, -8.5386e-02, -8.1611e-03,\n",
              "         -3.0371e-02,  4.0405e-02,  8.8230e-02, -1.0993e-03,  4.7974e-02,\n",
              "          2.5297e-02, -7.8340e-02, -4.4663e-02,  3.0285e-02,  1.7409e-01,\n",
              "         -3.9373e-02, -4.4944e-02, -7.7938e-02, -8.2928e-02,  3.2249e-02,\n",
              "          5.7756e-02,  3.3129e-02,  4.4753e-02, -1.1093e-01,  3.7734e-02,\n",
              "          1.1255e-01, -2.9933e-02,  6.1286e-02,  2.8672e-02, -3.0858e-02,\n",
              "         -2.0266e-02,  4.0076e-02,  3.5128e-02, -1.1890e-02,  2.5095e-02,\n",
              "          3.3984e-02,  1.6191e-01, -7.2577e-02, -1.2561e-01, -6.0063e-02,\n",
              "         -8.3712e-02,  1.7598e-01, -2.4886e-01,  6.9699e-02, -1.3893e-01,\n",
              "          3.8708e-02, -3.3611e-02, -8.1782e-02,  9.8245e-03, -3.4508e-02,\n",
              "          1.0942e-01, -5.4821e-02, -6.6393e-02,  1.3169e-01, -8.8947e-03,\n",
              "         -1.0280e-01, -9.7619e-02, -1.8376e-01, -1.4666e-01,  7.6155e-02,\n",
              "         -1.2348e-01,  4.2685e-02, -4.5362e-02, -7.7784e-03, -2.6233e-02,\n",
              "          6.0467e-03,  1.1244e-01,  4.6385e-02,  4.3852e-02,  3.4218e-02,\n",
              "          9.0697e-02,  7.8300e-02,  3.0071e-02,  5.9283e-02, -4.0698e-03,\n",
              "          7.3753e-02, -1.6033e-01, -2.6660e-02, -4.3537e-03, -1.0265e-01,\n",
              "         -7.4789e-02,  1.8038e-02,  3.0873e-02, -2.6118e-01,  4.8446e-02,\n",
              "          5.9493e-02, -3.9220e-02,  1.0302e-01, -5.7478e-02,  3.3420e-02,\n",
              "         -1.6771e-02,  1.0249e-01, -9.1826e-02,  1.1251e-01, -5.3921e-02,\n",
              "          1.9998e-02, -3.5550e-02, -5.7960e-02, -1.1250e-02,  8.8703e-02,\n",
              "         -1.5926e-01],\n",
              "        [-1.8053e-03, -1.7358e-01,  9.6752e-02,  3.6792e-02, -5.2257e-02,\n",
              "          8.7829e-02, -9.2393e-02, -1.6658e-01, -9.3829e-03, -7.8173e-02,\n",
              "         -1.5934e-01, -4.5777e-03,  6.3636e-02,  2.0419e-02,  7.1958e-02,\n",
              "         -4.6765e-03, -9.6752e-02,  5.9000e-02,  1.1500e-01, -1.4617e-01,\n",
              "          4.0263e-02, -4.0670e-02,  7.7809e-03,  8.3549e-02, -1.3575e-01,\n",
              "         -2.0905e-01,  1.9229e-02,  6.6338e-02, -1.4002e-02, -8.5486e-02,\n",
              "         -5.1252e-02,  1.6538e-02,  7.1646e-02, -1.7241e-02, -1.9511e-01,\n",
              "         -1.5194e-01, -4.1774e-02, -3.2513e-02, -2.2755e-02, -8.6803e-02,\n",
              "          2.2741e-03, -2.0151e-02, -2.1716e-02,  1.3740e-01,  5.9337e-02,\n",
              "         -2.0271e-02, -1.2848e-02,  1.0130e-01, -8.9245e-02, -1.3896e-02,\n",
              "          1.2328e-01,  4.1206e-03, -4.4877e-02,  4.1340e-02,  7.4303e-02,\n",
              "          5.3511e-02,  1.2272e-01,  2.5163e-02,  2.9026e-02,  1.1731e-01,\n",
              "          6.7859e-02,  6.9560e-02, -4.4987e-02,  1.6699e-01,  8.4110e-02,\n",
              "         -3.3150e-02,  5.4566e-02,  1.9803e-02, -3.7614e-02,  4.7008e-02,\n",
              "         -1.4733e-01,  1.0909e-01, -1.1828e-01, -5.0477e-02,  9.1421e-02,\n",
              "         -1.5817e-01,  1.1305e-01,  1.6943e-02,  1.7818e-02,  3.1590e-02,\n",
              "         -9.7671e-02,  7.1687e-03, -3.2320e-02,  1.0489e-01,  7.6783e-02,\n",
              "         -1.4320e-02,  6.4423e-02, -1.7090e-01,  5.8459e-02, -9.2463e-02,\n",
              "          4.0487e-02,  1.7598e-02, -1.4082e-01,  9.4464e-02, -2.9816e-02,\n",
              "          1.4635e-01, -1.8287e-01,  6.5578e-02,  3.7882e-02, -2.2395e-02,\n",
              "         -3.2949e-02, -1.1923e-01,  1.7599e-02,  1.3051e-01, -1.0869e-02,\n",
              "          5.9673e-02, -4.1002e-02,  3.8341e-02,  1.0696e-01, -3.0474e-02,\n",
              "          1.2523e-01, -1.8275e-01,  2.6245e-02, -6.8755e-03,  1.2260e-01,\n",
              "          1.4414e-02,  5.5016e-02, -1.2473e-01,  2.7919e-02, -8.3606e-02,\n",
              "          3.1307e-02,  6.9902e-02,  1.1680e-01, -1.8702e-01, -6.3816e-02,\n",
              "          7.3979e-03, -8.9209e-02,  3.3301e-03, -9.4994e-03,  6.4534e-02,\n",
              "          9.2160e-03,  3.5595e-02, -4.7825e-02, -4.7616e-02, -1.6132e-01,\n",
              "         -7.0177e-02,  1.1774e-01,  7.7155e-02,  1.1462e-01, -7.9427e-05,\n",
              "          1.0956e-02, -1.5916e-01, -8.1596e-02,  1.5147e-01,  1.2306e-01,\n",
              "         -4.2351e-02,  8.2863e-02, -1.1407e-01,  3.8941e-02,  3.8119e-02,\n",
              "         -3.9970e-02, -7.9847e-02,  1.7403e-02,  5.0627e-02, -1.1398e-01,\n",
              "          4.4564e-02, -4.5046e-02,  2.6599e-02,  1.6501e-01,  1.3593e-01,\n",
              "          3.6329e-02, -1.0093e-02, -7.6263e-03, -8.8739e-02, -3.1034e-03,\n",
              "         -7.1045e-02, -1.2806e-01,  1.8649e-02, -8.6884e-02, -6.7797e-02,\n",
              "         -1.0322e-01, -5.7439e-02, -2.7293e-02, -1.8725e-02, -1.4698e-01,\n",
              "          1.8977e-02,  1.8893e-01,  5.0376e-02, -2.5692e-02,  6.5490e-02,\n",
              "          1.6371e-01, -4.6621e-02,  8.3826e-03,  4.5340e-02,  1.7333e-01,\n",
              "         -1.7401e-02,  5.8703e-02, -7.1044e-02, -1.2776e-01, -8.7796e-02,\n",
              "          2.5041e-02,  1.2646e-02,  6.2898e-02, -1.1038e-01, -6.2619e-02,\n",
              "          6.7739e-03, -3.2036e-02, -1.4455e-01,  1.0694e-01, -7.6519e-02,\n",
              "          1.7134e-01, -9.0414e-02,  5.1620e-04, -1.4910e-01, -2.5930e-02,\n",
              "          7.4651e-02,  1.0775e-01, -9.8489e-02, -8.4931e-02, -6.6310e-02,\n",
              "          2.0953e-02,  1.8771e-01, -4.4058e-02, -1.6113e-01, -1.0972e-01,\n",
              "          3.4851e-02,  3.8986e-02, -5.5655e-02,  1.7966e-01, -6.7248e-02,\n",
              "          5.0445e-02, -7.2371e-02,  4.6843e-02,  2.5707e-03,  6.7539e-02,\n",
              "          7.4795e-02,  1.5559e-01, -8.6727e-02,  3.3788e-02, -3.4881e-03,\n",
              "          9.4899e-02,  5.3987e-03,  6.5399e-02,  5.9327e-02, -1.7357e-01,\n",
              "         -1.3840e-01, -1.5844e-01,  2.4976e-02,  1.3626e-01, -2.1197e-02,\n",
              "          3.8273e-02,  1.5126e-01, -2.4714e-02,  6.1758e-02, -5.7129e-03,\n",
              "          5.0038e-02, -3.5499e-02,  4.2930e-03,  4.2457e-02, -1.4458e-01,\n",
              "         -8.4239e-02, -1.8225e-03,  3.3734e-02,  8.6040e-02,  5.6382e-02,\n",
              "          1.4742e-01]], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw-TO0WkF1cR"
      },
      "source": [
        "model = torch.nn.Sequential(linear1, relu, dropout,\n",
        "                            linear2, relu, dropout,\n",
        "                            linear3, relu, dropout,\n",
        "                            linear4, relu, dropout,\n",
        "                            linear5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ad5fvvQZF1aM"
      },
      "source": [
        "loss =torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr = 0.001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reQW1nPhF1YR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "57a21f06-305d-4b5f-db11-6af2d9bdca15"
      },
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "total_batch = len(data_loader)\n",
        "\n",
        "model.train()\n",
        "for e in range(15):\n",
        "\n",
        "  avg_cost= 0\n",
        "\n",
        "  for x, y in data_loader:\n",
        "\n",
        "    X = x.to(device)\n",
        "    Y=  y.to(device)\n",
        "\n",
        "    hypothesis= model(X)\n",
        "    cost = loss(hypothesis, Y)\n",
        "   \n",
        "    optimizer.zero_grad()\n",
        "    cost.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    avg_cost += cost / total_batch\n",
        "\n",
        "  print('Epoch {}'.format(e), 'cost {}'.format(avg_cost))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0 cost 0.6916729211807251\n",
            "Epoch 1 cost 0.6258869171142578\n",
            "Epoch 2 cost 0.5925130248069763\n",
            "Epoch 3 cost 0.5614397525787354\n",
            "Epoch 4 cost 0.5490742921829224\n",
            "Epoch 5 cost 0.527576744556427\n",
            "Epoch 6 cost 0.5144951939582825\n",
            "Epoch 7 cost 0.505511462688446\n",
            "Epoch 8 cost 0.49723097681999207\n",
            "Epoch 9 cost 0.49319297075271606\n",
            "Epoch 10 cost 0.48308154940605164\n",
            "Epoch 11 cost 0.477673202753067\n",
            "Epoch 12 cost 0.4728682041168213\n",
            "Epoch 13 cost 0.46600937843322754\n",
            "Epoch 14 cost 0.4658046066761017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFyZ2HLNI0Oy"
      },
      "source": [
        "test_D = torch.FloatTensor(np.array(test_D))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iC9HO7rTJfMf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "f65a4b4d-1a79-4514-d60f-6dacda5cfb25"
      },
      "source": [
        "test_D"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2., 3., 3.,  ..., 2., 0., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 1., 2.],\n",
              "        [0., 0., 0.,  ..., 0., 1., 2.],\n",
              "        ...,\n",
              "        [0., 0., 0.,  ..., 0., 1., 2.],\n",
              "        [0., 1., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 1., 2.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGPytzMuHVeu"
      },
      "source": [
        "with torch.no_grad():\n",
        "  model.eval()\n",
        "  test_D=test_D.to(device)\n",
        "  prediction=model(test_D)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMlsa-R5IqlT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "8ecaf9b1-cf17-4f89-f420-ef26260d1fd9"
      },
      "source": [
        "prediction[:,[-1]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.7346],\n",
              "        [ 0.6002],\n",
              "        [ 0.7821],\n",
              "        ...,\n",
              "        [ 0.9017],\n",
              "        [ 0.0718],\n",
              "        [ 1.2547]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2z_-MyjHckF"
      },
      "source": [
        "result = pd.read_csv('sample.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQ7QxNo1Hd6S"
      },
      "source": [
        "result['id'] = list(i for i in range(3300))\n",
        "result['result'] = torch.argmax(pred, 1).cpu()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7gDiCOZHgEs"
      },
      "source": [
        "\n",
        "result.to_csv('submission.csv', index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4L4Zta_Hmyh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "cb32494c-df5b-4c22-f4fe-5042afbcab8d"
      },
      "source": [
        "!kaggle competitions submit -c tft-hyona -f submission.csv -m \"17011885\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100% 36.5k/36.5k [00:00<00:00, 153kB/s]\n",
            "Successfully submitted to TFT 모바일 승방률(승/패)"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}