notebooks/2020_0529komazawa_homework.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "2020-0529komazawa_homework.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.7"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/komazawa-deep-learning/komazawa-deep-learning.github.io/blob/master/notebooks/2020_0529komazawa_homework.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hqWLfwr4f5Th"
      },
      "source": [
        "\n",
        "# 2020年度 駒澤大学文学部心理学特講IIIA \n",
        "<div align=\"right\">\n",
        "<img src=\"https://koma-sso.komazawa-u.ac.jp/idp/images/header_logo.png\">\n",
        "</div>\n",
        "\n",
        "課題提出用\n",
        "- 作成者: 浅川伸一\n",
        "- [授業用ページ](https://komazawa-deep-learning.github.io/)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V039nHeuOqcn",
        "colab": {}
      },
      "source": [
        "#@title # facesheet { run: \"auto\", vertical-output: true, display-mode: \"form\" }\n",
        "date = '2020-05-29'  #@param {type: \"date\"}\n",
        "name = '氏名: '  #@param {type: \"string\"}\n",
        "id = '学生番号: '  #@param {type: \"string\"}\n",
        "\n",
        "#@markdown ---\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Eof2F-T-eFW3"
      },
      "source": [
        "# 課題ファイルの提出方法\n",
        "\n",
        "1. このファイルの自分の Google Drive に保存する (ウィンドウ左上の「ファイル」メニューから「ドライブにコピーを保存」を選ぶ)\n",
        "1. 上で保存したファイル名を次のように変更する '本日の日付(8桁yyyymmdd)-自分の学生番号(NBから始まる全6桁).ipynb' \n",
        "2. 変更したファイル名 で 自分の Google ドライブに保存する\n",
        "3. 下の課題をすべて解く\n",
        "4. Google ドライブからファイルを選んで，共有情報を入手する\n",
        "5. メールを開いて，以下のような宛先にする\n",
        "    - To: educ0233@komazawa-u.ac.jp\n",
        "    - Cc: 自分のメールアドレス\n",
        "6. メールの件名を 本日の日付(8桁:yyyymmdd)-自分の学生番号 とする\n",
        "7. 本文に，Google drive の共有情報を貼り付ける\n",
        "8. 送信ボタンを押下する\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK_-z1cBe5fb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title # 次の用語を簡単に解説せよ { run: \"auto\", vertical-output: true, display-mode: \"form\" }\n",
        "epoch = 'エポック epoch:'  #@param {type: \"string\"}\n",
        "lr = '学習率 learning rate: '  #@param {type: \"string\"}\n",
        "activation = '活性化関数 activation: '  #@param {type: \"string\"}\n",
        "regularization = '正則化係数 regularization:' #@param {type:\"string\"}\n",
        "batch = 'バッチ batch:' #@param {type:\"string\"}\n",
        "hidden_layer = \"隠れ層 hidden layers:\" #@param {type:\"string\"}\n",
        "#@markdown ---"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoE7c7fa7ELJ",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title 授業の評価\n",
        "\n",
        "Q1 = '本日の感想' #@param {type:\"string\"}\n",
        "Q2 = '難易度:分かりやすかった' #@param{type:'string'} [\"難易度:分かりやすかった\", \"難易度:どちらとも言えない\", \"難易度:分かりにくかった\"]\n",
        "Q3 = '進み方:速かった' #@param{type:'string'}['進み方:速かった', '進み方:ちょうどよかった', '進み方:ゆっくりすぎた']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t3gVOhGn40F0",
        "colab": {}
      },
      "source": [
        "#@title ## 本日の授業でのトラブルについて {display-mode: \"form\"}\n",
        "#@markdown ### 本日の授業でのトラブルについて，ご意見や改善提案をお聞かせください\n",
        "opinion = \"意見\" #@param {type: \"string\"}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSYQD8sE7YyR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title ## 印象 {display-mode: \"form\"}\n",
        "#@markdown ### 問 1-1. 授業で取り上げた話題の中で <font color='red'>もっとも印象に残っている</font> 話題を一つ挙げよ。また簡単に，1行程度でその理由を記せ\n",
        "topic2_1 = '話題1: '  #@param {type: \"string\"}\n",
        "reason2_1 = '理由1:'  #@param {type: \"string\"}\n",
        "\n",
        "#@markdown ### 問 1-2. 授業で取り上げた話題の中で <font color='red'>2番目に印象に残っている</font> 話題を一つ挙げよ。また簡単に，1行程度でその理由を記せ\n",
        "topic2_2 = '話題2:'  #@param {type: \"string\"}\n",
        "reason2_2 = '理由2:'  #@param {type: \"string\"}\n",
        "\n",
        "#@markdown ### 問 1-3. 授業で取り上げた話題の中で <font color='red'>もっとも興味が持てなかった</font> 話題を一つ挙げよ。また簡単に，1行程度でその理由を記せ\n",
        "topic2_3 = '話題3:'  #@param {type: \"string\"}\n",
        "reason2_3 = '理由3:'  #@param {type: \"string\"}\n",
        "#@markdown ---\n",
        "x1 = \"\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzkyxDEkLw4Y",
        "colab_type": "code",
        "outputId": "fe44522d-bfd7-4a69-f836-8ebe3630f79e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "from IPython.display import YouTubeVideo, display\n",
        "#video_id = 'cofokoZJsA8'\n",
        "video_id = 'KiT_DrDjdDE'  # RT foodly\n",
        "display(YouTubeVideo(video_id, width=600, height=400))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"600\"\n",
              "            height=\"400\"\n",
              "            src=\"https://www.youtube.com/embed/KiT_DrDjdDE\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f5d9466a3c8>"
            ],
            "image/jpeg": 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ILtRluVGrNlEpH5EpJVUHyg5iCmyiUGLc7KumuJoXLsO5ryTHLeq5O0aeoY/sH7Lo5T2gnRShzY2vcs9e3J/2kj9CrNDa4qGMLmngVuG3ncD3KCpZtDZcwAqdaJlv1VEV+KyNsX7jDG5MOp48upaFxS7lGzZuP1hIU45SXaMpbFDZNiazxPqD1j+i7EKC0tixoDW9GNwT/OjQyDwIVs8rkphrHpInSowU6Vm1OlEpsoUB0oTUIHJU1EoHISSklA5M/P1D6n9ksprTm484+SCRCSUIFQkQg5m7a9lRzaj8RbqAIH1KhNYk5TIzKz31naGZOnEnitGhZ1cLXlufAHPtXRLJNbYWbuzxdF3IKxRtKpzDSOBJhR06TRnVcym0Z9JwHYrf+I0T6tfH/sBd/wCoVcstejHDftP+HqYcyCVXJS/i59Vld3/HD/7EJj/Ou0pAc31APkAVjZW8LKSVI63dG4qAlVSqzDz1pKzy0y0JLnJ08QmNeV1TuSubKdrDa+IAnIncnU6eI5nLhxVXEp6LslGU1E4Ttb85wSudLc81C+oA3ESABqTkAse927+Wj8Z/QLKXXbazc012hsyFb87I5jesXZ73OoMcTJznjkSFcFc6AZrqxu5txZzV0kdWkwclJdSWtO8KNlk8/wAx3RAzHOFM9wc0jPJRnqpw2SmIACfKiad3BOlctnbsxu4fKfQ9dvWoZUtu6HgqcZuwy6jRqFS29cs0VSo4poqFdlm5pyS67a9WsSMic1TFUk5azCZSrZR3KgK5a6RxVccNQzy22beqWuwvOuYS3N61gzIWDdX5Lstdyz7uq9+vWonH5XZctR0da5DmjPOEynXDDiGqxKdRrQCCTOvJTNry5o3LXw1NVTy73G7VvAxjYMk5qGkwuBe8mPmVXpU8VQBSXFXE7CzRo1481x5TV07sbubJinPREpjWka71JTYXGAqfaacxpcYClbTAMHM9cKR1MMblu39irWVTFJnLUnetccJrdc+XJfpbADdwk8pU1Ns7klGlOe75qyBCWyelJLfalXe+nOGCCqDqzXes0ha1cZKk6iFHVXxtnpUijuJHegPpg+ufmrP4YcFBc2wjRR4xbzqWnXYNHjvRXug+ADIHJUm0gFMxqnxkRcrYe7Iy0dyma6VEDHUm1XxDhu1SzZjlpZQmNdIkJZWbc5CSUSoDkJspZQLKJSIQLKbTOU8SfqgmBPBJTENA5KUpJQmoUB0oTUIMU0KL4Iaw9bYI7k1+zKD5xMJP+93yEwmNqObp1KWnWaXQ7oHcd37LsuMce6fSsqTB0KLBG/CB+ilY5uUghPcx2QkEDTPVR4DvzVPGfpPlf2lEZap8t4qo/oAkjojPqVMbVpwHQYnKBl2KmUxjXCZ5eo1wWTGJSOsQ8ceYWHdX7CZDoiCciIUlh5QicLToOuVHjjfVW1nJbZ1Em0dj1gAWDGBw17t6yASMjkRxXZW21WPAxdEnu7068sKNcdNoJ9oa96TePVV6y7cZ5xNqXzaQJOZ3AK/tLYFWkC6mcbe4hc3caEb+CtbLCTs27v31fWMN3NGirApEB0a6LFu66zpebosbvDRPWcz81ZtwNd6YIcAQZBEgpjnhv5leZRz3Gr9WqBlOQElUS8/5hkCYpsG/moLvpNyd3FQsuHYwXSQ3T9FrjqxSzTQ3yddCOanbTcVVtqwiT1pfxQxDpEtO79Fb8Uyqv5LjOlgMPLvT8Bb0imPrYhkITDcmC07wk4pLuJvJb1V4PIylO85xaFWD5aDyTw+VqokxjdwKoV3xCmqE7lRunlWkZ5XR7CBLjvVapcDf9PqonVSWwqlwYyzCtLo9rFO5kuGkZ/dW6FcAYu5YwMZgx9FPQJMEu4ZCd5jPgo3v2Wfp0FCqTnJlbFG1p5HzjTvIxQsGg4Ngn1RGJLcXJrHC0YKY0aN/M8VzcmO66OLLUa9V7Hvw03Yg3KdyvUGBogLMsKIpMAVs14WetLZZeSa+rBrQMt/0VOwrNxREA6hV7+rkqWz6h844cp+imXpSzt1ra7DoU+VgY44qe3e6RmYlUsXnbTqqAhVKW0Cbl9FzCAGhzTlmNDoeMKV11BLY3pLtfPjuHtOAo67ck5tdvFRXFYAKWdVnNEpBrCjqVgFG2tnzU1WLZhAA3Kv5yVKyqArSIqYHckITmtkYgm1KzY5qKSBh3cEqipVJlSLG+3Vj6OQkQoSdKJTZQiSVT0Tzy78k+VHUOnX9M05A5EpqWVIVCSUKBhPIBwlMqN3ldFc2THjNoKyLq282CDJG4ndyXXM5enNcNdqgunsENII4HMJf8SqQThpnsIP1SPaMGIZwVb8xScwOa0ED1m4jIVtKKTr97jBptz3SqFa0pEYSx7M/y1PECt1ttSJkSD1z3plfZ7Do8jskSq2S9VbHPLDvG6YptKZGtSOYB+hCfQ2fTaZa8iP6PstJtlBjG094T/wbiMnCN8EKs45LvS95sspq1Bb1Qw+uCO39QrBq42OYKjmc6b8JHzUL7R+5nbKRlsRqO8LXXTLfakbN1IOe+q6sNYeSSR1yVs7Js9n3A6FJpczVrhmJHz61VFrieA4ENMA9ROfylatvsSzoCQxone55z7zC5OTjky3HTx5bxu/ahtnYFm4SHsoP3Q4AH/iuMurN9I9IGJgOg4THAnVegv2rs+hIFagCNzIJ/wDqsvae07a8aGsYagEmSC0jq3rPKzGbq0UKT+gMPqwNOrgjHxUTbS6yNG3fAyAkF0cwFfOzrgMDqlB7Z4dKOsDMKZZfRYrFgTPNg7z3KRpjqU4AKtLZ6RZKp1qDgBhJI1S2jHEy7Qbsldw5KGo1dGPI58sCuqgaD5qK4qtc3ImfomuKjdbucCR3b1bzkR4Wtml6o4Kw3RVbcw0dQUnnFoobWEzG7PuVeo4OM5GVLVqYGl/BVKTwdDMmerkm+0ZTraOtR4KC3plz8AZie6cI0GTSYns+a0m0+OSiAwua8ahwI7FFysRjGKylmD8ju5LX2Q0CoWn1aoDHACZzy6s96dte3aHNexv8twygRBAzBVe36JBG7PuWUy/Lx7n2t3jk0m21RrnNDWloMSc5+ylo0IeZbGHgZBVlz+nLYz4acwpWsaRBJB48VTj5PPCWrWaVzWw6qB92dyLynwIVBz43hTUxPcXRcIMJlvf4GYQwTPrTqqj3yrVhbScR0GnMqt6i0m6sjaQHrtIPL7FWrfaNMloxAQfzZKvdWofmMnfXrWWWRkdQomrFrvGukZaD8SbjN0swiIIHUm1z0j1rnqb3N9Vxb1EhTtv6o1OLrCiY6Wz5PPW2viI3KOtVJEEwqlPag/M0jqMqzQc2uSGuiBvCspULqmespBWA0UlWi1v5gOtUKldjNDiKszXfPIdcZc1n07sO3QmVnFwws1KkaFhfl1UsBOGPmrhjEqGy7IszOpV5zTKrl7WSUndJWZVN4hT0qmIc1lY2wv0llEpJRKq0OlCaiUCO9YdR/RPlRg9I9QH996dKB0oTZRKkORKbKJUCtb7Ydo8jM5Egdynr1y9pDmt7lzV7/lO5Ce7NVGvPE96b0a21fwgxOhWLN4Y/TVQ7PuAWCdRkfnmp2MMyCAu3DubcufV0tuYIxNEA8N3WondeaktqkTu5JlalEuHaFOtqEZTkHLNS1KgbllOQACrNqkDKR2porHWVFxqJkDXwnLd3JX15aekVGY5KCWE9J3R5arPPKYTda8eFzuofSu3AQTKUV5kODYnQgaQs+u6BDC0xJxEkdUqpVruwwXDOII4z9PssMfk7dF+Nf20qmz7aqT5oMa8DeOifssa5s61GqCym5pA1a2QeeSs21SHktk7iTIjvXQUNpF0Dd9OpTj48s39ozl4r+4i2f5WvFLCaTcbHAEzAIjPLiuntts0H0WVXVGMDho5wBB3hY1KztqlQvq0zV5ycv+M6Lcs7O3a0GjTptB3taAq+FxvaZnLOmPtUW1YOdSp1HVSDD6TCATGUnIFYB84w4Kgwv3gx+i9ALFl7T2VSqS+o4MPtE/WVKHIGoeKQnmn7T81QMiqx4/okrOZtOmTGYHEqNpXxATsRGY7VG1wIkGQnNOoQX2OyB4wpGlRUY05fRWA1d2LjqltEkNBGca8O1Z9vXwHEBHJbVR2SoutqZM4RPLJRZ9m/pM65a5oIVepWJ3wEr6IjoiFWqtcDBB61PSl3GhaVQ9houIz9UmYB45LOfWwzBEgwmNJBBBgovq4fVD3ZuI6Ujhp8l5/NMuK/1vV/9dnxpjnf7RubLv8AzjWuJGOCHCIAE5fJX7qqC3LcuY2dXAqiCRMkt7P+lpvqy1W+P1j/APVefDxz6PuHYgqDqeal89PIpaVMvPAbytapDaNviMblpsaAIGiYxoaICdKzt23xx0fKo31HPGO1XJQ4SIOiiXVMpuMdOIS1qeEkcEN0WznNDEjmZKYIIUCtUbIULgrUZKvCsgjGgZqe2/zAq71at29IFQNynACla4QVTY/JPFVQseTITqOSiJUjdFFTLpYlKmA5JZWboOlCbKJUBGHXr/b9E9R0/VCfKBUJspZQKhIhBU2E9rXuxta4GAZAMaronWNE/wClTP8Awb9lwmzL2t5xpNEtpuydiyI4HNdpsy6noE6afZQIbrZFI5tptaeLWgLGdSNM4HRrkZ15LrnCVkbZtuhiAmCOzmujiy70yzx3GUyp08503KYOkSM93/aqNdmf710TWVIJ6106c6w6mGGRm08UPY3gnsuA7I5gqOqwt5hTKrYqXrGhmekiRyVHaF4KjMOEgHTJaNamXtI4rKuLVzYxThOQiADyXnfKwty8nf8AFzmM8UFKsWlsva5pno/ZPwdDGKcMJgHIdwPVuVuhcYQWim2B1fMqtesqup9NsNMYcjA/Zce+3dr7Q1M82uIdy0UlOk6AGCYzMn7qs55ojTGYjoiYPBOpXzi3DhJdqTB+m5aS2dxncZeq2bK6wYQ/Q8DI71uULkfldhJzlpy7VxlrRqVJwTh3jetK1xUjiza0DLEc3fbrXRh8j6zc/J8ee8GltypduDfNXTmCIIbEHmHATK5K+tXziNZ1Rzc5qEkjtJK6iz2pikAAkZObM9oRc21G4IGGCNMtFrlx+feNYTO49ZRzuz7umXhlw2TIgGAJ57t+9dlU8m23DDjFNvskAOPXIjcubuvJ55cT54f04mx8wnbH2rXtWkCoSzc2oJaOYzkLly4scLvJ0YXyn9Gz6EtYP5VxUDuYBbpwWNd21e2g16eEEwHNILT3aLcsfLam+pgqUyABm5kuzjc0CVautt+daWstHPaR/rEMaezM/JbY7vpTLr2xKdRpAc0gzwVmnVBCou2W4PfWLmsmT5umIZ1Zoovnfmu7C79uLOa9LVZ05BRYCpm80Eq1ViuU9jpEFJUJUJdG9Z5drw2pRkyq4Y0uOe9W3VARG9Un0yJhZXCX20xzuPo+1ptADgOlESdVancorenADexaLGgaKtsx6iZLl3VajbEmXZD5q4AAICSUSsrbWuOMh0olNSyoWOlLKZKWUFe+ZInhqqtIZK/U0d1KkxvctcO4w5J2cAlKaXEJ9FpeYGqtpRARmVXLSCZBXS22xnHOB1lT3ew/ONjGB2Kep9quShXbdhIyVmrsJ7T6zSFYoWxpjPVRd/S+OvtVa/JKaiS8ZhOIeqdVXc9J2i9L9MyrQ0VCg7oyrLa2SikWWnJLKgFQBSys7NOjHLZ0pKhyKJTXnTrHyz/RVWPCVNlCByJSSiUCyhIhEsosV2xrxDZzGiqOe3jPUozWgyMo3kqkHaWlx5xk7xqpKjJEFc9sy/Bh7SDucAfkuia4OAI0KvKiuZ2mxlOoWtEce1U3tg9efWuqurYOBkArnrug5hzBwgf2V3cecymnLyYWdq0tjTNJ+OdSEQ1w5j5oEEAjMFUrp2avpntO7bWH/SYe8fJQVdsNd61BvH1ifqsuq8zqUwu3FVsWl/TXZtCkf9Bg0/7Vs7UaR02yDrnuXOtUzXTkVS4T9LzO/tvtuqLhlREZwYGaiq1qDhhdSMcnRCrUKgDQNycxvSzzVFtpGXVvSlopuHUU2pXoPJlj4c3c7NRVHNbJO4ZrNr3Bc6dOStMJfcVudn20WUrZhJa2q06Zu/dTm4psAgPJMRms+icWZRUqkxx3cua0xx0rld91p/4mz1YcRv0y6k23s6NV7nOpzwBJg840VCk0QrtvVc0ZK9x8ppn5+HbUp0mMENaGjkAEj67BvHYqBeTqSUgCtOH9ssvk/qJ69zLSANQRKotpARyUzyGiSQBzMKhX2vQZo7Gf6RPzV7OPGds5ly53caTXZa9+qPO7nCD/AHoueq7ce7/LY1vNxkqw+m+4pluMvdGXSgT1aLg5vkYYert3cfHnf9TTRNzTxYQ9pPCQmOzKqN8mHNZnUbj5Nkd+qdbWV60lraLqrRvGkcnFZYfJwzuo2y4LJtM8QkDio3V+kWPDmPGRa4QQnArfe2WtLNLUK5Ko0jvVwFY8ntrgeiU2UsrNoVEpJSSgdKVNlEoG1So2xCdUUa2x9OfP/RcMrb2NYNEVH67h+qxPORHWF0dvWAAcTkra62rtqKOq5o1KyL3bP5afes3z73mZJVPFPts13sOhVV2YVQSeKkokq8VsQ1myCNxWfUpEFa7woKoEK+tq7+ldghqG1NyjfUnJFFucKlidrVECRKtyqzaQBBCnlY5Onj9HSmn1h2olID0j2f381Vc+UspqEDkSmolA6USmyllEuMq7SqH80dQVR9ZztST1mUoYjzagWtk7RNCrJzpuyeOXHrC9E2bdgQ0mWuzBXmYpHguj8nr04fMvOnqH/wDKDv1DVogrEp+URbUwEBzW9GZzxBWDt7/4/wD7fsrS6RYz761NF53tccuSxLp4gxqV0VztRtQQ6kPiWJe2rHyWtLT/ALiV04801258uLvcYxpxm4phInJXf8OpmfWy/qTDs5nPvKv+SKeFVZzhTUi380lTNswPunG3HAd6XOU8UtG4YBkM+afSqTJVenbEuAiATqukt3tcwBga18erGSrJE22ObuGlx0MbslWbavdOFpcR2fVdLUuntPTptG71cj1KBl84jMMy5Zq3nIjW2NTtaoMCm6TxEfVFa3qCQWOHZP0Wyb95OQbw0CWpduAlzhmdwCmcsLhWRRpukZEDTQq+1hG49yPx1QuOYAzyACQ31T2vkFM5pGeXDckgYeB7k9tJ3snuVb8XU9s9iR1ep7bu9W/k/wDFf4v/AEt9sRtduIHDU4O0P2WI7yZudYbE73CVs+edM4j3pDUcTqT2rny8crt04+WM1GJV2FWawdEl0mQCDlzzVejc1bd+haeBC6ijWg5HMx9VoVralXYG1GA8xrmqZcWNi05L9l8lts29RobUEVfadmDwg6BdaIXnz9jmmJpkvYBEZT2qK18oK1k4knGxzhNMiCMoyPYufx8LrWnRuZTcu3c7S2TQumxVYCRo4ZOHUVx+0fJ25tiXUprUsz/U0DiPsum2T5SW10Bhfhf7Dsj+/YrF9tm2t/8ANqtB3NBlx6mjNXls9KXV9uDoXLS7CZa4atdkQtJrkbYuG3s+atcJP+tUhjvhGZ7VBb2xosh73P5xk39VfLdnas1L0soTRyMj+96UO7FmuVCEjnACTogVSNpkqZlNoGnelDYTaET6IDTxVUtV92nFU3Ba4Xpjye1Wu6IUou3lgZ3plx+XtTH1YGcdi6Z/lhrtLTEuAlaTKjGCFgNJLpmArTqo1JCpca121/xI4ZKP8WJmFluvARH0UVe4MCNE8VbWnVvAdFWqXHNZ7ahUgMqfSntYDp61NQYdYVdiu0K+g1VcomJmuIUgeOOaa6plmPkq/mG1JcCWnkcu4rHNvx3tclNZv6/2VcONMdM4hu4qZmgWbdJKWU1EoHSklIhA6USmolByTaACUs4QFLCIRCOErSQQQYIMghOhIgmtnQx87jPyTLfyibiwOaTnAIP1Utu+QWQM85jPqngqbNlMa/EOwIlpHbDdzHd4TDtf/wCM/F+yo1GwSOabCIWze45OGI5oN5loFWpDMjiCkhTMqiyJzenh80O2gTuHeVWeFA4w4dSt5o8Y17G5c98ZBoEwN/BXqTnB2sEEZ6RCzNkggkzmfstfzZwnic10YemOfVXKN2KjSyqBMa7iqN1bOpOkjonelDZgda0qNYOYWuGJgEH7q2UUjEY4AkprqhJnhkOSvXezy0Y2Q5h0PBZeJoBly5+TKYe2/Hhc/RQ/VJjUDyeGR0I0UVWpUGjS4f3ksby9bjacP9tVKLkyY0GucKRt4wgCY/vVMwHDB9Y8uj1KN1EkdKDnkJnrWX5btv8Ahx0tis2MylFSFC4tY0YhynTsUT3gAO1bkrY89+1MuDH6W2PzBAnqWnRuQelm3DMzyCymVMOjZns71WrVTiDSRmdAeCjHny32nL4+GunRMq6EFJc2lC6EVmgR+YZH5LDp3uFxGm6FqMuGwJK6ceSWbcufFcbqKw8lRTOLzjnM3YYxdp+yuW9nSp5saAd51cesnNUbfa9wx4eIFOSMJ4c1vtfSrgF38txGoMgqePPG+kcmGWPtSfUDcyQFWN3J6OnMaqxcbGe0F4OMcd6pERkRC68MMb/1x8nJlPrS2003/wBDuI0PXuS1KD26jEOLc/kqikpV3M0PZuVM/jS/5Ww+TZ/oo5H9UlMzUIJgwN/MqwK1Op64wu9oLO2rT81NTzjIyjOD3b1y5YXD3HXjnjn6rVbUPX8inioD18DkVy9PbpDoDS5vFa1ttKnUET2FV1Kt20HE7ktSwIzJz4blX8+GkHEOo5/P/tN2ptbHQLW+sTB35b4Krln4xHhvvShd3rXuDWDot0O8nioapOiXZFr56qAcmjMyMstynuR03HfJWvxc7Zcawzxk7Vy4xn3Ku+Vac0lRPYuvbK9m0yp2ncVCGGE9pyUqke3CeSc0pTUERqmNVKmLLHqai6Hg81Ua5TU3SoWada5yyjLVPoQWyN6zLgFrg4dRUv4khga3LifssM/Tfj9rF3XaMoBd8gpaROFs5mBKx3VOk1g1JC2VlG50olNlLKByE2UsoFQkRKDnNEiUpIRBEiWEiB1Aw8Ky4mRERvz+ipzBlW6vGCYIIAUJVLkdLrUasXQ0VcqQ6gemOtPcyFCDBB4LVt7UVazWF2EOOZiYylBmVRAJG5VXjNq9GZ5K2sZh7v8An9lKzyZsmx/IBji55+pQcVsbCXAE6Zx9lvtpzly/T9gtqpsS3/LRptPENE96xry3fRcDBwhdPFlL1XPyY33FQshPpVMst+qljEyRqPpxUNOmZ4Los6YSrttWLSd4gGFXutmsrdOmBjiSBkn6Zco7JTaRwvDgT/eX2WNxlnbWZWXcc/tClVpZGTJgngmsxPEjIBdbUYys3C+A7Sdx4rFu9lOoiGNy3CfouTk4Ot4Ozi+R3rNlecLfWcNMxxPJQVHOPTYQBvG7rVxpmQ7Xg7KFHcUmBrmtbEiCcxHMBcn2699KtO5c8ZjLTjmN6VpLnEYTj6iJyUpquosDaTct5jPmTxT6N7Mu6RjiP2UkV/5jXYYzOc6xClpswkuIlx0Lh6uXBILgnJ49Y5B2Wis06Bwy85btMktJFdtFr4dInLTRSEOz0gaE/sp2MkQ2MLcp3ntT227S0gkEiI0G7PtUeX0am9qtaqSIbE5QFYtL/BEyRvVHzMucAMLTqJJOXBW22/RDcwIzH7q2OXj6RlhM526GyvCB0ZIKtltGtk4BrjvXL21bB0RJI3KzT2o4ZGDJ0jMc11fmxnquH+Pld9NO52JUbmyHjkVi3Vyyj/mHCeG/uWpZbXcHCHZ6EHRX69rb3XQqsGI74HeDuXVjz3Tky+PJe3C3G2XuypNwjidU78M0w57sZO8nVaO1PIupTGO2d5xusGJjrXPY6lF0OaWkflcCuXmuef26cMcZP6un2TsS0cxzqgcTJyk5ZZBU7vYT2Omi4OaNA4w7qS2G3GAQRDvkepdXs20ZWph78y7MAOyA7N645eby1I6Z467cbUdUwhzgW57xw1zUFK7eSGjUr080GYcJaMOkEAjuVD/BLYNIbRYJMnLPsK3/ABo/JXK0rjzLSylJcfWcRr1Dclo0nESTLiZic/76lb2j5P1afSoOL8/VcQIHWscXhY8sqDC4GCCrcWHhd/bC479tA0yoiyU9l0Haw7r++qkGE6GOR+//AEuj8lZ/iivhIUbaZ3q6+nxy4ftxUZou3QVbHkUy41V45KOVbdSefylOp2Dic8lfyk+1fG36VAVJTfCtV7VrAOlnwhVy0LO8kXnFUoqmc9OaaSXGBmTOijxy5rd7jAlXQRTDg0buk46uMfRY27b446U7UYrhv9IJWysnZQmpUdwgLVULHAoTUSgfKJTUSgfKJTZQgwIQUqQoGpCnFPp0Hv8AVaT9O9BArQzYMyMokaqVuzY/zHhvIZlPfSa0ANmOJ3oKlRhe1uEEzEZQfmm/go9dzW8tT3KXF0TDpImSMo5KqSoEwNJujS88XZDuVy3qQ5j+orKLuqdwV23dNMaZEjLMIO1srzB0Xer9Fqgrk7G7FRsH1gMxx5rXsrzD0Xer9EGqo61EOEFSAoUjmb+0dRcS0dE9yhFPFDgddy6ivRD2lrhIKwbuzNKYmJyPDrXVx8m5qufPDvaJ1PokxmFA04SQVYp3EkT1FQ1GGJ/vkra0zqSmQZ+alpXEjBUEjPrCqU04ZEcP2UVO0d/skVWlzDI3EarnjsyriIDgeE5LpqVw5hkd24qR1uy4AcwxV1IKrcccvcXxzyx9OUrUa9PN1NpbxCkoXmUgjq/Zb9ZoBw1GYHcW6HsWbdWVMdPIxuIgrn5OCSbjp4/kZW6qlWbjjKRrmkrS2STMDKeKfXvGiMJ09Yx8lVq1C9hc6Q0nIZSuPt27hLa6GRdmOA6+Cfc7R6LoBHMBMohokBoHMGCpqlXDA1nj+qfaPpSsnnOoZiRHYtS6uXOAwkKOkC4GQ3LKAqF64s6QEhP9U9RJWrOGegncZ7k91yd+U74+STZ1qXZ1CN0AZ9qt1qhYXBrQctyXXokvtUta0VG8ZHVC3aF60uAzBBWJb4W1C7QxAH1S1HPLwWdUrXHks6Y58UvddbQu3MBLdAcwcwnXVva3jSKrAHRkevmse3u8DAHSZyKlZesOjoy3/JdMym9OS4XW2VtPyNrUpfQPnGaxlP7rNsNqV7R8SW8WOz+W5dxa7RcwZEEHMZ96LhtnfNcHgBw0Oh65SybRLdItleVVGtDah82/gdD1Fb4qAnJefXvky9suouD28DqFDY7ZubR2GSR7D9OwqLLF5ZfT0VzcoCo3uzqVZpa9oiCNMxOpB3Khs7yoo1SA/wDlv4OOXYVsF4InuUJcptDyaczp274AGbXEmSOBWP8Ain03YarSx3AiF1m1ts06HR9eodGN1PXwWC61fcvFS5geywbhwJUybRaShe5ZHLvHcrbKwPLqzHd9iqN3satTJcwY26jBqOUfZVKVcgHMSMo3qNp02KlaWkZjXMJG3wwhozdvMZKiLslkKOg6OsqLoi09+85lRkpJQoSbTP8AOb/S1x7dAr90cNPP1jqqdmQKlRzuAA+Z/VJe1y5pOmUAcFIubHbFIn2iSr0qvZMw0mDkp0DpRKSUSgWUspsoQOSymolBkUqLnmGgkq0NnBudV4byGZVIPLXsIJEktMZZEfcBWHOAzJQTg0WerTxHi77JtS6e7fA4DJVX1wNM1C6sTyUC1TrYHAlXrshzWuGh/v8ARYUrQsK2JppHXVv2QK5qzHawtwUCeSzdpW3mjj/KdeRQYTny8knMHQrW2ZVxMfmMnA5bpH7LOrMDzMK9s1uAP4QP+/74qRfZULXBzTBGi3LG9bVHB41H6jkuZqVojQSYE/ZKHOY4VGuONumkdvJQO8srzD0Xer9Fqgri7Pann6OKmP5gLQ9nDMSeqJzW/ZXmDou9X6INVMqUw4QRKeDKFIwb/ZGrqeR4Kg2qZLHCDkusc2Vm39gHiYzGhW2PJ+2WWErIGmSXCExxLHYXiOB3KXDktdysdaV3thLTERGoTnfRMAIzTQtmsyq0tqa7nb1Qu7VwbB6TDo790tR+ZKnsbtzXYTmw7oU+ofbmLjZrg6Wkjr0UJsKrhhIy5LsLm1D5NGAN7IVFlMaHou35ZFZXilu2s5bI5h9hWaMiDG/eomsql2FzCV1z2+00O/visuvk9xEADQT9VhzYTCbkb8Odzy1aZb29RrHZNE7ic1WDzUMOaO/Nuac67l0QQN+ajaXOeRRj+onKVyadqwWFgIBAA0O5QvruDYAc4jgDCR1BzekX4hwG881aZc5QIiFAoVHFmZh3HiJOafSuWnIaDcpa/T0gnQqD8Aaghowf1FW6+0VadUO8iBpJ1VU1f5gAdzJ/QJ9xZwA0OJI4/snW1E0xJaHOk6cElk7Lu9NGi0uZOg+qiJh8NMYRuUDLpzmANbhE5jh2KMMcA5wMk6Slyyt3UTDGTUaltfvaBBkhy06tKlcM/mtEzuH14rl7as8gktjNatCvumDuXdx5+UefycfjVe/8mXAYqBxDXDuH6hVLCneMJpsc6m3fJBAnhzXQ0bgt3nWZ4qSrVY71x0zo4a9qvMJap52Rm2tgylJGbjq45kpbiuGZau4KWuH4YaRPHj1LMDOK2x49seTl0mtL2pTgAyOBV2o63uP81mF/taHv+6oBqVaZcGOTHHnyxMudluZPmzjHPI/Yqu1paIIg81ep1S3Q9m5XG3NKoMNRoH0+4XLnwZY+u3Xh8jHL30x5RK1K2yARNJ0jgfus2rReww9pH98Vzugihr5lreJUkpKEOrs4DNEtwCBHBCRClByRIhA5CaiUDkqbKVBhOrtD2tImSMu1Le4A1oOriCHSZz3f9LAO1qmIOwsxDMGD90yrtF73Bzg2R1xrPFEN8JQFiDbNT2Wdx+6Q7ZqnczuP3RLcMBM89hMjULE/xWpwZ3H7pRtWp7LO4/dB3tjcCtTDt+9Pr27XtLXCQRmuGtPKGtSJLQzPcQY+qtemFz7FH4XeJBf/AMKqB5aGyB+bQEdat0rHzYOJ4LiIgLAq+VNy7dTHUHD9VA3b1YGcLO533Qbb4ykTwUdRwwnfHOFhVtrVXiDhHUD91DSvHtmIzEQUG1Y13Unh7DBB7xwPcuxsL5tdmJuRHrN3j9l5s3aDxub3H7qe123WovD2YQRyMHkc0HrFleYOi71fotYGdF5H6aXPsUfhf4lYof8AkC8YIDKBHNr/ABKB6qmuErzH+I977q2+Cp40fxHvfdW3wVPGpHoF3Zh4zCx6tN1Exqz6Llj/AORrw/6Vt8D/ABqGp5e3ThBpW/wv8avjlpW47dbAcctFG85rjG+V1wDkyiP+LvEkd5W3B/JR+F3iW05MWN48nXObOe5EQuRPlZcexR+F3iSellx7FL4XeJLy4n48nXFxBBBg/wB5q3TuqdQBtQQfa3LhfSqv7FL4XeJJ6UV/YpdzvEo/JD8dd5XsiwFwzbyWFfWzjJaJ4jRY9DyzuWDCGUSOBa+P/ZMreVtd+Zp0QeIa/wASjLPHKaq2OOWN3CvoVp0AkhaVBkdGmMzn9yViHyjrEQWUjzwn7opeUVZghrKUdTvuuTkw3/l2cfJr/TYq21UAkkDPIBVWOeCWnI75yyOghVfSivvZSPW133UFxtypUIJZTaRvaHA/VZzDJp+XFsedDQAAA8xGXzU8FwIB/v8ARc3S2vUaScLCTvIJP1Ujtu1T+Vncfun46flxbrWwY/NqcpT6bTJIdAiBOvWuc/xirJPRM8j91INu1R+Wn3H7qPx5H5cWzXynpSTyTDWmQdFhVdqVHahvZP3T27WeDJp03dYd+hVpx1F5o3Gk6CTyGauWtnUMFwIO7isSn5VV2CG0qAHANd4kp8rbj2KXwu8S6MMMMfdc2eeWXUjqqNMYfWceIMBRVblrTDXYid2vzXIVdv13k4sOfI/dIzblVujWdx+66ZyYT057x5X26qpdPJgGBCiXN/49V9mn3H7pf8frezT7nfdaY8+EY8nBnl6dIhc3/j9b2afc77o9IK3s0+533Vv5GDP+NyOkQuc9IK3s0+533SekFb2afc77p/IwP43I6elVcwy0kK4zaDSIqtEbzu7QuM9IK3s0+533UVfbFV4g4QOQP3WXJyceU9NuPi5cb7a+2L6kHkUAY3ncepZ1vcuY7EHEHqVRm0HN0azuP3UzdruDXNNGi7FEktdIjgQ7JcfbsbtttcZY8v6m6dy1KVwHCQQRxH6jULhfxjuDfmn0toVGGWkN6p+6kd4HA6JVxw8o6/s0yeMGfkU8eU9f2aXwu+6DrULk/Siv7FLud4kelFf2KXc7xIOsSyuS9KK/sUu53iR6UV/YpdzvEgxEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQgEIQg//9k=\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vaEa3I0FgbvG",
        "colab_type": "text"
      },
      "source": [
        "# 上のビデオの感想を下のセルに書いてください"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDlL1gwtgmmU",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}