9-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MusV5YYT6kQF"
      },
      "source": [
        "# 순환 신경망으로 IMDB 리뷰 분류하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uYfRb5-6kQQ"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/9-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oBxL32NvSEVb"
      },
      "outputs": [],
      "source": [
        "# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.utils.set_random_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1A-DDIQ6kQQ"
      },
      "source": [
        "## IMDB 리뷰 데이터셋"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZoZm1ZO6kQR",
        "outputId": "e97cac1e-1def-42f3-b66a-12828383888e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = imdb.load_data(\n",
        "    num_words=200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTFjz6k06kQR",
        "outputId": "49c3c424-c31d-4e35-8cb9-3cf10f22c39b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25000,) (25000,)\n"
          ]
        }
      ],
      "source": [
        "print(train_input.shape, test_input.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jMLURzw6kQS",
        "outputId": "b7133130-9669-4f77-eda7-6ff2186c18e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "218\n"
          ]
        }
      ],
      "source": [
        "print(len(train_input[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdO834PL6kQT",
        "outputId": "275a5f71-90a9-46d6-9c73-5e94782145e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "189\n"
          ]
        }
      ],
      "source": [
        "print(len(train_input[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNuzGQ2c6kQT",
        "outputId": "5196b36e-f79d-4262-b76a-f223a03ea27c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 14, 22, 16, 43, 2, 2, 2, 2, 65, 2, 2, 66, 2, 4, 173, 36, 2, 5, 25, 100, 43, 2, 112, 50, 2, 2, 9, 35, 2, 2, 5, 150, 4, 172, 112, 167, 2, 2, 2, 39, 4, 172, 2, 2, 17, 2, 38, 13, 2, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 2, 4, 22, 71, 87, 12, 16, 43, 2, 38, 76, 15, 13, 2, 4, 22, 17, 2, 17, 12, 16, 2, 18, 2, 5, 62, 2, 12, 8, 2, 8, 106, 5, 4, 2, 2, 16, 2, 66, 2, 33, 4, 130, 12, 16, 38, 2, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 2, 28, 77, 52, 5, 14, 2, 16, 82, 2, 8, 4, 107, 117, 2, 15, 2, 4, 2, 7, 2, 5, 2, 36, 71, 43, 2, 2, 26, 2, 2, 46, 7, 4, 2, 2, 13, 104, 88, 4, 2, 15, 2, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 2, 22, 21, 134, 2, 26, 2, 5, 144, 30, 2, 18, 51, 36, 28, 2, 92, 25, 104, 4, 2, 65, 16, 38, 2, 88, 12, 16, 2, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"
          ]
        }
      ],
      "source": [
        "print(train_input[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "In8Qzltz6kQT",
        "outputId": "9719ddba-91bd-42e6-b351-329967a00238"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1]\n"
          ]
        }
      ],
      "source": [
        "print(train_target[:20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0-zjNgBw6kQU"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, val_input, train_target, val_target = train_test_split(\n",
        "    train_input, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "sRVvgntx6kQU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "lengths = np.array([len(x) for x in train_input])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p76m_61w6kQU",
        "outputId": "680e84a7-3b0c-4d24-bd27-5ec90cf04e42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "239.00925 178.0\n"
          ]
        }
      ],
      "source": [
        "print(np.mean(lengths), np.median(lengths))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "TVDrsRYF6kQV",
        "outputId": "3955327f-be50-41a7-bd34-2b592a59e5c0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+UXV1taqrq7VmzRpNmjRJY8aMaYweAQAAvM7jq+eeeuop7d+/X0OHDlVAwPcvr6mp0bhx4zinCQAANFseh6bAwEAtW7ZMTz31lL788kuFhISoT58+6tSpU2P0BwAA4BM8Dk21LrvsMl122WUN2QsAAIDP8jg0VVdXKzMzU9nZ2SotLVVNTY3b/Jo1axqsOQAAAF/hcWiaNGmSMjMzlZycrN69e8vhcDRGXwAAAD7F49C0dOlSvf322xoxYkRj9AMAAOCTPL7lQGBgoLp27doYvQAAAPgsj0PT1KlTtWDBAhljGqMfAAAAn+Txx3OfffaZPvnkE33wwQe6/PLL1aJFC7f5d955p8GaAwAA8BUeh6aIiAjdeuutjdELAACAz/L447klS5ac89HQ/v3vf+uOO+5Qu3btrBtpbtmyxZo3xmjWrFmKiYlRSEiIEhMTtXv3brdtlJWVKSUlRWFhYYqIiFBqaqqOHz/uVrNt2zYNHjxYwcHBio2N1Zw5cxp8LQAAoOnyODRJ0pkzZ/Txxx/r5Zdf1rFjxyRJhw8frhNEfqqjR4/quuuuU4sWLfTBBx/oq6++0rx589SmTRurZs6cOVq4cKEWL16s3NxctWzZUklJSTp16pRVk5KSop07dyorK0srV67U+vXrNXHiRGve5XJp2LBh6tSpk/Ly8jR37lw98cQTeuWVVxp0PQAAoOlyGA/P6D5w4ICGDx+uwsJCVVZW6ptvvtGll16qSZMmqbKyUosXL26w5mbMmKENGzbo008/rXfeGCOn06mpU6dq2rRpkqSKigpFRUUpMzNTY8aM0ddff61evXpp8+bNGjhwoCRp9erVGjFihA4dOiSn06lFixbpscceU3FxsQIDA633XrFihXbt2lXve1dWVqqystJ67nK5FBsbq4qKCoWFhTXYz6BW5xmrGnybjW3/7GRvtwAAwDm5XC6Fh4fb+v3t8ZGmSZMmaeDAgTp69KhCQkKs8VtvvVXZ2dmed3sO7733ngYOHKhf//rXioyMVL9+/fSXv/zFmt+3b5+Ki4uVmJhojYWHhys+Pl45OTmSpJycHEVERFiBSZISExPl5+en3Nxcq2bIkCFWYJKkpKQkFRQU6OjRo/X2lpGRofDwcOsRGxvboGsHAAC+xePQ9Omnn+r3v/+9W8CQpM6dO+vf//53gzUmSf/617+0aNEidevWTR9++KEeeOABPfTQQ3r99dclScXFxZKkqKgot9dFRUVZc8XFxYqMjHSbDwgIUNu2bd1q6tvG2e/xQzNnzlRFRYX1OHjw4E9cLQAA8GUeXz1XU1Oj6urqOuOHDh1S69atG6Sps99r4MCBeuaZZyRJ/fr1044dO7R48WKNHz++Qd/LU0FBQQoKCvJqDwAA4MLx+EjTsGHD9Pzzz1vPHQ6Hjh8/rscff7zBv1olJiZGvXr1chvr2bOnCgsLJUnR0dGSpJKSEreakpISay46OlqlpaVu82fOnFFZWZlbTX3bOPs9AADAxc3j0DRv3jxt2LBBvXr10qlTpzR27Fjro7lnn322QZu77rrrVFBQ4Db2zTffqFOnTpKkuLg4RUdHu51L5XK5lJubq4SEBElSQkKCysvLlZeXZ9WsWbNGNTU1io+Pt2rWr1+vqqoqqyYrK0vdu3d3u1IPAABcvDwOTR06dNCXX36p3/3ud3r44YfVr18/zZ49W1u3bq1z7tBP9fDDD+vzzz/XM888oz179uitt97SK6+8orS0NEnfH+WaPHmynn76ab333nvavn27xo0bJ6fTqZEjR0r6/sjU8OHDNWHCBG3atEkbNmxQenq6xowZI6fTKUkaO3asAgMDlZqaqp07d2rZsmVasGCBpkyZ0qDrAQAATZfH5zRJ359IfccddzR0L3VcddVVevfddzVz5kw9+eSTiouL0/PPP6+UlBSr5tFHH9WJEyc0ceJElZeXa9CgQVq9erWCg4OtmjfffFPp6ekaOnSo/Pz8NHr0aC1cuNCaDw8P10cffaS0tDQNGDBA7du316xZs9zu5QQAAC5uHt+n6W9/+9s558eNG/eTGmqqPLnPw/ngPk0AADQ8T35/exyafniOT1VVlU6ePKnAwECFhoaqrKzM846bAUJT80DQA4CLS6Pe3PLo0aNuj+PHj6ugoECDBg3S3//+9/NuGgAAwJed13fP/VC3bt00e/ZsTZo0qSE2BwAA4HMaJDRJ358cfvjw4YbaHAAAgE/x+Oq59957z+25MUZFRUV68cUXdd111zVYYwAAAL7E49BUe/+jWg6HQ5dccol+/vOfa968eQ3VFwAAgE85r++eAwAAuNg02DlNAAAAzZnHR5o8+WqR+fPne7p5AAAAn+RxaNq6dau2bt2qqqoqde/eXdL3X6Lr7++v/v37W3UOh6PhugQAAPAyj0PTzTffrNatW+v111+37g5+9OhR3X333Ro8eLCmTp3a4E0CAAB4m8fnNM2bN08ZGRluX6fSpk0bPf3001w9BwAAmi2PQ5PL5dKRI0fqjB85ckTHjh1rkKYAAAB8jceh6dZbb9Xdd9+td955R4cOHdKhQ4f0//7f/1NqaqpGjRrVGD0CAAB4ncfnNC1evFjTpk3T2LFjVVVV9f1GAgKUmpqquXPnNniDAAAAvsDj0BQaGqo///nPmjt3rvbu3StJ6tKli1q2bNngzQEAAPiK8765ZVFRkYqKitStWze1bNlSxpiG7AsAAMCneByavv32Ww0dOlSXXXaZRowYoaKiIklSamoqtxsAAADNlseh6eGHH1aLFi1UWFio0NBQa/y2227T6tWrG7Q5AAAAX+HxOU0fffSRPvzwQ3Xo0MFtvFu3bjpw4ECDNQYAAOBLPD7SdOLECbcjTLXKysoUFBTUIE0BAAD4Go9D0+DBg/W3v/3Neu5wOFRTU6M5c+boxhtvbNDmAAAAfIXHH8/NmTNHQ4cO1ZYtW3T69Gk9+uij2rlzp8rKyrRhw4bG6BEAAMDrPD7S1Lt3b33zzTcaNGiQbrnlFp04cUKjRo3S1q1b1aVLl8boEQAAwOs8OtJUVVWl4cOHa/HixXrssccaqycAAACf49GRphYtWmjbtm2N1QsAAIDP8vjjuTvuuEOvvvpqY/QCAADgszw+EfzMmTN67bXX9PHHH2vAgAF1vnNu/vz5DdYcAACAr7AVmrZt26bevXvLz89PO3bsUP/+/SVJ33zzjVudw+Fo+A4BAAB8gK3Q1K9fPxUVFSkyMlIHDhzQ5s2b1a5du8buDQAAwGfYOqcpIiJC+/btkyTt379fNTU1jdoUAACAr7F1pGn06NG6/vrrFRMTI4fDoYEDB8rf37/e2n/9618N2iAAAIAvsBWaXnnlFY0aNUp79uzRQw89pAkTJqh169aN3RsAAIDPsH313PDhwyVJeXl5mjRpEqEJAABcVDy+5cCSJUsaow8AAACf5vHNLQEAAC5GhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA1NKjTNnj1bDodDkydPtsZOnTqltLQ0tWvXTq1atdLo0aNVUlLi9rrCwkIlJycrNDRUkZGReuSRR3TmzBm3mrVr16p///4KCgpS165dlZmZeQFWBAAAmoomE5o2b96sl19+WX379nUbf/jhh/XPf/5Ty5cv17p163T48GGNGjXKmq+urlZycrJOnz6tjRs36vXXX1dmZqZmzZpl1ezbt0/Jycm68cYblZ+fr8mTJ+vee+/Vhx9+eMHWBwAAfFuTCE3Hjx9XSkqK/vKXv6hNmzbWeEVFhV599VXNnz9fP//5zzVgwAAtWbJEGzdu1Oeffy5J+uijj/TVV1/pjTfe0JVXXqmbbrpJTz31lF566SWdPn1akrR48WLFxcVp3rx56tmzp9LT0/WrX/1Kzz333H/tqbKyUi6Xy+0BAACaryYRmtLS0pScnKzExES38by8PFVVVbmN9+jRQx07dlROTo4kKScnR3369FFUVJRVk5SUJJfLpZ07d1o1P9x2UlKStY36ZGRkKDw83HrExsb+5HUCAADf5fOhaenSpfriiy+UkZFRZ664uFiBgYGKiIhwG4+KilJxcbFVc3Zgqp2vnTtXjcvl0nfffVdvXzNnzlRFRYX1OHjw4HmtDwAANA0B3m7gXA4ePKhJkyYpKytLwcHB3m7HTVBQkIKCgrzdBgAAuEB8+khTXl6eSktL1b9/fwUEBCggIEDr1q3TwoULFRAQoKioKJ0+fVrl5eVuryspKVF0dLQkKTo6us7VdLXPf6wmLCxMISEhjbQ6AADQlPh0aBo6dKi2b9+u/Px86zFw4EClpKRYf27RooWys7Ot1xQUFKiwsFAJCQmSpISEBG3fvl2lpaVWTVZWlsLCwtSrVy+r5uxt1NbUbgMAAMCnP55r3bq1evfu7TbWsmVLtWvXzhpPTU3VlClT1LZtW4WFhem3v/2tEhISdM0110iShg0bpl69eunOO+/UnDlzVFxcrN///vdKS0uzPl67//779eKLL+rRRx/VPffcozVr1ujtt9/WqlWrLuyCAQCAz/Lp0GTHc889Jz8/P40ePVqVlZVKSkrSn//8Z2ve399fK1eu1AMPPKCEhAS1bNlS48eP15NPPmnVxMXFadWqVXr44Ye1YMECdejQQX/961+VlJTkjSUBAAAf5DDGGG830Ry4XC6Fh4eroqJCYWFhDb79zjM46nUh7J+d7O0WAAAXkCe/v336nCYAAABfQWgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABp8OTRkZGbrqqqvUunVrRUZGauTIkSooKHCrOXXqlNLS0tSuXTu1atVKo0ePVklJiVtNYWGhkpOTFRoaqsjISD3yyCM6c+aMW83atWvVv39/BQUFqWvXrsrMzGzs5QEAgCbEp0PTunXrlJaWps8//1xZWVmqqqrSsGHDdOLECavm4Ycf1j//+U8tX75c69at0+HDhzVq1Chrvrq6WsnJyTp9+rQ2btyo119/XZmZmZo1a5ZVs2/fPiUnJ+vGG29Ufn6+Jk+erHvvvVcffvjhBV0vAADwXQ5jjPF2E3YdOXJEkZGRWrdunYYMGaKKigpdcskleuutt/SrX/1KkrRr1y717NlTOTk5uuaaa/TBBx/oF7/4hQ4fPqyoqChJ0uLFizV9+nQdOXJEgYGBmj59ulatWqUdO3ZY7zVmzBiVl5dr9erVtnpzuVwKDw9XRUWFwsLCGnztnWesavBtoq79s5O93QIA4ALy5Pe3Tx9p+qGKigpJUtu2bSVJeXl5qqqqUmJiolXTo0cPdezYUTk5OZKknJwc9enTxwpMkpSUlCSXy6WdO3daNWdvo7amdhv1qayslMvlcnsAAIDmq8mEppqaGk2ePFnXXXedevfuLUkqLi5WYGCgIiIi3GqjoqJUXFxs1ZwdmGrna+fOVeNyufTdd9/V209GRobCw8OtR2xs7E9eIwAA8F1NJjSlpaVpx44dWrp0qbdbkSTNnDlTFRUV1uPgwYPebgkAADSiAG83YEd6erpWrlyp9evXq0OHDtZ4dHS0Tp8+rfLycrejTSUlJYqOjrZqNm3a5La92qvrzq754RV3JSUlCgsLU0hISL09BQUFKSgo6CevDQAANA0+faTJGKP09HS9++67WrNmjeLi4tzmBwwYoBYtWig7O9saKygoUGFhoRISEiRJCQkJ2r59u0pLS62arKwshYWFqVevXlbN2duorandBgAAgE9fPffggw/qrbfe0j/+8Q91797dGg8PD7eOAD3wwAN6//33lZmZqbCwMP32t7+VJG3cuFHS97ccuPLKK+V0OjVnzhwVFxfrzjvv1L333qtnnnlG0ve3HOjdu7fS0tJ0zz33aM2aNXrooYe0atUqJSUl2eqVq+fgLVzxBwDnr9lcPbdo0SJVVFTohhtuUExMjPVYtmyZVfPcc8/pF7/4hUaPHq0hQ4YoOjpa77zzjjXv7++vlStXyt/fXwkJCbrjjjs0btw4Pfnkk1ZNXFycVq1apaysLF1xxRWaN2+e/vrXv9oOTAAAoPnz6SNNTQlHmuAtHGkCgPPXbI40AQAA+ApCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAGwhNAAAANhCaAAAAbCA0AQAA2EBoAgAAsIHQBAAAYAOhCQAAwAZCEwAAgA2EJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMCGAG83AOCn6Txjlbdb8Nj+2cnebgEAPMaRJgAAABsITQAAADYQmgAAAGwgNAEAANhAaAIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYwBf2Arjg+JJhAE0RR5oAAABsIDQBAADYQGgCAACwgXOaAMAGzsMCwJEmAAAAGwhNP/DSSy+pc+fOCg4OVnx8vDZt2uTtlgAAgA8gNJ1l2bJlmjJlih5//HF98cUXuuKKK5SUlKTS0lJvtwYAALyM0HSW+fPna8KECbr77rvVq1cvLV68WKGhoXrttde83RoAAPAyTgT/P6dPn1ZeXp5mzpxpjfn5+SkxMVE5OTl16isrK1VZWWk9r6iokCS5XK5G6a+m8mSjbBdA89VY/z0CmpPafyfGmB+tJTT9n//85z+qrq5WVFSU23hUVJR27dpVpz4jI0N//OMf64zHxsY2Wo8A4Inw573dAdB0HDt2TOHh4eesITSdp5kzZ2rKlCnW85qaGpWVlaldu3ZyOBw/efsul0uxsbE6ePCgwsLCfvL2mgLWfHGsWbo4182aWXNz1dTXbIzRsWPH5HQ6f7SW0PR/2rdvL39/f5WUlLiNl5SUKDo6uk59UFCQgoKC3MYiIiIavK+wsLAm+Zfwp2DNF4+Lcd2s+eLAmpuWHzvCVIsTwf9PYGCgBgwYoOzsbGuspqZG2dnZSkhI8GJnAADAF3Ck6SxTpkzR+PHjNXDgQF199dV6/vnndeLECd19993ebg0AAHgZoekst912m44cOaJZs2apuLhYV155pVavXl3n5PALISgoSI8//nidjwCbM9Z88bgY182aLw6suXlzGDvX2AEAAFzkOKcJAADABkITAACADYQmAAAAGwhNAAAANhCafNRLL72kzp07Kzg4WPHx8dq0aZO3WzovGRkZuuqqq9S6dWtFRkZq5MiRKigocKu54YYb5HA43B7333+/W01hYaGSk5MVGhqqyMhIPfLIIzpz5syFXIptTzzxRJ319OjRw5o/deqU0tLS1K5dO7Vq1UqjR4+uc1PVprTeWp07d66zbofDobS0NEnNYz+vX79eN998s5xOpxwOh1asWOE2b4zRrFmzFBMTo5CQECUmJmr37t1uNWVlZUpJSVFYWJgiIiKUmpqq48ePu9Vs27ZNgwcPVnBwsGJjYzVnzpzGXtp/da41V1VVafr06erTp49atmwpp9OpcePG6fDhw27bqO/vxuzZs91qmsqaJemuu+6qs57hw4e71TSn/Syp3n/bDodDc+fOtWqa2n4+LwY+Z+nSpSYwMNC89tprZufOnWbChAkmIiLClJSUeLs1jyUlJZklS5aYHTt2mPz8fDNixAjTsWNHc/z4cavm+uuvNxMmTDBFRUXWo6Kiwpo/c+aM6d27t0lMTDRbt24177//vmnfvr2ZOXOmN5b0ox5//HFz+eWXu63nyJEj1vz9999vYmNjTXZ2ttmyZYu55pprzLXXXmvNN7X11iotLXVbc1ZWlpFkPvnkE2NM89jP77//vnnsscfMO++8YySZd999121+9uzZJjw83KxYscJ8+eWX5pe//KWJi4sz3333nVUzfPhwc8UVV5jPP//cfPrpp6Zr167m9ttvt+YrKipMVFSUSUlJMTt27DB///vfTUhIiHn55Zcv1DLdnGvN5eXlJjEx0Sxbtszs2rXL5OTkmKuvvtoMGDDAbRudOnUyTz75pNu+P/u/AU1pzcYYM378eDN8+HC39ZSVlbnVNKf9bIxxW2tRUZF57bXXjMPhMHv37rVqmtp+Ph+EJh909dVXm7S0NOt5dXW1cTqdJiMjw4tdNYzS0lIjyaxbt84au/76682kSZP+62vef/994+fnZ4qLi62xRYsWmbCwMFNZWdmY7Z6Xxx9/3FxxxRX1zpWXl5sWLVqY5cuXW2Nff/21kWRycnKMMU1vvf/NpEmTTJcuXUxNTY0xpvnt5x/+YqmpqTHR0dFm7ty51lh5ebkJCgoyf//7340xxnz11VdGktm8ebNV88EHHxiHw2H+/e9/G2OM+fOf/2zatGnjtubp06eb7t27N/KKflx9v0x/aNOmTUaSOXDggDXWqVMn89xzz/3X1zS1NY8fP97ccsst//U1F8N+vuWWW8zPf/5zt7GmvJ/t4uM5H3P69Gnl5eUpMTHRGvPz81NiYqJycnK82FnDqKiokCS1bdvWbfzNN99U+/bt1bt3b82cOVMnT5605nJyctSnTx+3m4wmJSXJ5XJp586dF6ZxD+3evVtOp1OXXnqpUlJSVFhYKEnKy8tTVVWV2/7t0aOHOnbsaO3fprjeHzp9+rTeeOMN3XPPPW5fYN3c9vPZ9u3bp+LiYrd9Gx4ervj4eLd9GxERoYEDB1o1iYmJ8vPzU25urlUzZMgQBQYGWjVJSUkqKCjQ0aNHL9Bqzl9FRYUcDked7+KcPXu22rVrp379+mnu3LluH7s2xTWvXbtWkZGR6t69ux544AF9++231lxz388lJSVatWqVUlNT68w1t/38Q9wR3Mf85z//UXV1dZ27kEdFRWnXrl1e6qph1NTUaPLkybruuuvUu3dva3zs2LHq1KmTnE6ntm3bpunTp6ugoEDvvPOOJKm4uLjen0ftnK+Jj49XZmamunfvrqKiIv3xj3/U4MGDtWPHDhUXFyswMLDOL5SoqChrLU1tvfVZsWKFysvLddddd1ljzW0//1Btj/Wt4ex9GxkZ6TYfEBCgtm3butXExcXV2UbtXJs2bRql/4Zw6tQpTZ8+XbfffrvbF7c+9NBD6t+/v9q2bauNGzdq5syZKioq0vz58yU1vTUPHz5co0aNUlxcnPbu3avf/e53uummm5STkyN/f/9mv59ff/11tW7dWqNGjXIbb277uT6EJlwwaWlp2rFjhz777DO38YkTJ1p/7tOnj2JiYjR06FDt3btXXbp0udBt/mQ33XST9ee+ffsqPj5enTp10ttvv62QkBAvdnbhvPrqq7rpppvkdDqtsea2n+GuqqpKv/nNb2SM0aJFi9zmpkyZYv25b9++CgwM1H333aeMjIwm+dUbY8aMsf7cp08f9e3bV126dNHatWs1dOhQL3Z2Ybz22mtKSUlRcHCw23hz28/14eM5H9O+fXv5+/vXuZqqpKRE0dHRXurqp0tPT9fKlSv1ySefqEOHDuesjY+PlyTt2bNHkhQdHV3vz6N2ztdFRETosssu0549exQdHa3Tp0+rvLzcrebs/dvU13vgwAF9/PHHuvfee89Z19z2c22P5/q3Gx0drdLSUrf5M2fOqKysrEnv/9rAdODAAWVlZbkdZapPfHy8zpw5o/3790tqmms+26WXXqr27du7/V1ujvtZkj799FMVFBT86L9vqfntZ4nQ5HMCAwM1YMAAZWdnW2M1NTXKzs5WQkKCFzs7P8YYpaen691339WaNWvqHJqtT35+viQpJiZGkpSQkKDt27e7/Ueo9j/MvXr1apS+G9Lx48e1d+9excTEaMCAAWrRooXb/i0oKFBhYaG1f5v6epcsWaLIyEglJyefs6657ee4uDhFR0e77VuXy6Xc3Fy3fVteXq68vDyrZs2aNaqpqbFCZEJCgtavX6+qqiqrJisrS927d/fJjy9qA9Pu3bv18ccfq127dj/6mvz8fPn5+VkfYTW1Nf/QoUOH9O2337r9XW5u+7nWq6++qgEDBuiKK6740drmtp8lccsBX7R06VITFBRkMjMzzVdffWUmTpxoIiIi3K4qaioeeOABEx4ebtauXet2GerJkyeNMcbs2bPHPPnkk2bLli1m37595h//+Ie59NJLzZAhQ6xt1F6KPmzYMJOfn29Wr15tLrnkEp+6FP1sU6dONWvXrjX79u0zGzZsMImJiaZ9+/amtLTUGPP9LQc6duxo1qxZY7Zs2WISEhJMQkKC9fqmtt6zVVdXm44dO5rp06e7jTeX/Xzs2DGzdetWs3XrViPJzJ8/32zdutW6Umz27NkmIiLC/OMf/zDbtm0zt9xyS723HOjXr5/Jzc01n332menWrZvbpejl5eUmKirK3HnnnWbHjh1m6dKlJjQ01GuXZZ9rzadPnza//OUvTYcOHUx+fr7bv/HaK6Q2btxonnvuOZOfn2/27t1r3njjDXPJJZeYcePGNck1Hzt2zEybNs3k5OSYffv2mY8//tj079/fdOvWzZw6dcraRnPaz7UqKipMaGioWbRoUZ3XN8X9fD4ITT7qhRdeMB07djSBgYHm6quvNp9//rm3Wzovkup9LFmyxBhjTGFhoRkyZIhp27atCQoKMl27djWPPPKI2/17jDFm//795qabbjIhISGmffv2ZurUqaaqqsoLK/pxt912m4mJiTGBgYHmZz/7mbntttvMnj17rPnvvvvOPPjgg6ZNmzYmNDTU3HrrraaoqMhtG01pvWf78MMPjSRTUFDgNt5c9vMnn3xS79/n8ePHG2O+v+3AH/7wBxMVFWWCgoLM0KFD6/wsvv32W3P77bebVq1ambCwMHP33XebY8eOudV8+eWXZtCgQSYoKMj87Gc/M7Nnz75QS6zjXGvet2/ff/03Xnt/rry8PBMfH2/Cw8NNcHCw6dmzp3nmmWfcAoYxTWfNJ0+eNMOGDTOXXHKJadGihenUqZOZMGFCnf+pbU77udbLL79sQkJCTHl5eZ3XN8X9fD4cxhjTqIeyAAAAmgHOaQIAALCB0AQAAGADoQkAAMAGQhMAAIANhCYAAAAbCE0AAAA2EJoAAABsIDQBAADYQGgC0OzccMMNmjx5srfb0Nq1a+VwOOp8QTOAponQBAANwFeCGoDGQ2gCAACwgdAEoFmrrKzUtGnT9LOf/UwtW7ZUfHy81q5da81nZmYqIiJCH374oXr27KlWrVpp+PDhKioqsmrOnDmjhx56SBEREWrXrp2mT5+u8ePHa+TIkZKku+66S+vWrdOCBQvkcDjkcDi0f/9+6/V5eXkaOHCgQkNDde2116qgoOACrR5AQyI0AWjW0tPTlZOTo6VLl2rbtm369a9/reHDh2v37t1WzcmTJ/U///M/+t///V+tX79ehYWFmjZtmjX/7LPP6s0339SSJUu0YcMGuVwurVixwppfsGCBEhISNGHCBBUVFamoqEixsbHW/GOPPaZ58+Zpy5YtCggI0D333HNB1g6gYQV4uwEAaCyFhYVasmSJCgsL5XQ6JUnTpk3T6tWrtWTJEj3zzDOSpKqqKi1evFhdunSR9H3QevLJJ63tvPDCC5o5c6ZuvfVWSdKLL76o999/35oPDw9XYGCgQkNDFR0dXaePP/3pT7r++uslSTNmzFBycrJOnTql4ODgxlk4gEZBaALQbG3fvl3V1dW67LLL3MYrKyvVrl0763loaKgVmCQpJiZGpaWlkqSKigqVlJTo6quvtub9/f01YMAA1dTU2Oqjb9++btuWpNLSUnXs2NHzRQHwGkITgGbr+PHj8vf3V15envz9/d3mWrVqZf25RYsWbnMOh0PGmAbr4+ztOxwOSbIduAD4Ds5pAtBs9evXT9XV1SotLVXXrl3dHvV9jFaf8PBwRUVFafPmzdZYdXW1vvjiC7e6wMBAVVdXN2j/AHwLR5oANFuXXXaZUlJSNG7cOM2bN0/9+vXTkSNHlJ2drb59+yo5OdnWdn77298qIyNDXbt2VY8ePfTCCy/o6NGj1lEjSercubNyc3O1f/9+tWrVSm3btm2sZQHwEo40AWjWlixZonHjxmnq1Knq3r27Ro4cqc2bN3t0PtH06dN1++23a9y4cUpISFCrVq2UlJTkdiL3tGnT5O/vr169eumSSy5RYWFhYywHgBc5TEN+cA8AF4Gamhr17NlTv/nNb/TUU095ux0AFwgfzwHAjzhw4IA++ugjXX/99aqsrNSLL76offv2aezYsd5uDcAFxMdzAPAj/Pz8lJmZqauuukrXXXedtm/fro8//lg9e/b0dmsALiA+ngMAALCBI00AAAA2EJoAAABsIDQBAADYQGgCAACwgdAEAABgA6EJAADABkITAACADYQmAAAAG/4/1r4OtBuzCzgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(lengths)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fbLSFToP6kQV"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "train_seq = pad_sequences(train_input, maxlen=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c86x0Yj6kQV",
        "outputId": "2c1f132f-2e10-40aa-8c6e-e1776a61075f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 100)\n"
          ]
        }
      ],
      "source": [
        "print(train_seq.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuP0QJaU6kQW",
        "outputId": "5391f674-b925-4e4e-8adf-c9fff7c298e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 10   4  20   9   2   2   2   5  45   6   2   2  33   2   8   2 142   2\n",
            "   5   2  17  73  17   2   5   2  19  55   2   2  92  66 104  14  20  93\n",
            "  76   2 151  33   4  58  12 188   2 151  12   2  69   2 142  73   2   6\n",
            "   2   7   2   2 188   2 103  14  31  10  10   2   7   2   5   2  80  91\n",
            "   2  30   2  34  14  20 151  50  26 131  49   2  84  46  50  37  80  79\n",
            "   6   2  46   7  14  20  10  10   2 158]\n"
          ]
        }
      ],
      "source": [
        "print(train_seq[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t8kaDji6kQW",
        "outputId": "7a469d2f-e117-41fe-ca81-92e85379e5cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6, 2, 46, 7, 14, 20, 10, 10, 2, 158]\n"
          ]
        }
      ],
      "source": [
        "print(train_input[0][-10:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Emmoztwa6kQX",
        "outputId": "46af2ab3-2f43-4324-f66b-ff9abfa24647",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   0   0   0   1   2 195  19  49   2   2 190   4   2   2   2 183  10\n",
            "  10  13  82  79   4   2  36  71   2   8   2  25  19  49   7   4   2   2\n",
            "   2   2   2  10  10  48  25  40   2  11   2   2  40   2   2   5   4   2\n",
            "   2  95  14   2  56 129   2  10  10  21   2  94   2   2   2   2  11 190\n",
            "  24   2   2   7  94   2   2  10  10  87   2  34  49   2   7   2   2   2\n",
            "   2   2   2   2  46  48  64  18   4   2]\n"
          ]
        }
      ],
      "source": [
        "print(train_seq[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ZeFmsjOd6kQX"
      },
      "outputs": [],
      "source": [
        "val_seq = pad_sequences(val_input, maxlen=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xab0nu_6kQX"
      },
      "source": [
        "## 순환 신경망 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "45BtHGKX6kQX",
        "outputId": "f8568ff4-c856-4bfa-a8ec-60480ac5d9d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ],
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(keras.layers.SimpleRNN(8, input_shape=(100, 200)))\n",
        "model.add(keras.layers.Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "oXIWyOuw6kQY"
      },
      "outputs": [],
      "source": [
        "train_oh = keras.utils.to_categorical(train_seq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VD94WiRT6kQY",
        "outputId": "c9dc6e48-9fea-474e-f413-9e1504690fb1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 100, 200)\n"
          ]
        }
      ],
      "source": [
        "print(train_oh.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIVSHwJg6kQY",
        "outputId": "f26c6768-2b43-4f7e-ef14-f9f6b0f0655b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n"
          ]
        }
      ],
      "source": [
        "print(train_oh[0][0][:12])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CP3ivii46kQZ",
        "outputId": "b55b3e40-6064-4a3e-8645-74b98d40f653"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ],
      "source": [
        "print(np.sum(train_oh[0][0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "vgijH6m26kQZ"
      },
      "outputs": [],
      "source": [
        "val_oh = keras.utils.to_categorical(val_seq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "0V0Svpuf6kQZ",
        "outputId": "65bf201d-7008-4686-d407-fa99f01a60f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ simple_rnn (\u001b[38;5;33mSimpleRNN\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │           \u001b[38;5;34m1,672\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m9\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ simple_rnn (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,672</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,681\u001b[0m (6.57 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,681</span> (6.57 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,681\u001b[0m (6.57 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,681</span> (6.57 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S22Hc-7L6kQZ"
      },
      "source": [
        "## 순환 신경망 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcwM299t6kQZ",
        "outputId": "0f4beed4-5bda-407d-bf9f-9d3b448bc715"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 39ms/step - accuracy: 0.4945 - loss: 0.7135 - val_accuracy: 0.4924 - val_loss: 0.7053\n",
            "Epoch 2/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 18ms/step - accuracy: 0.5020 - loss: 0.7038 - val_accuracy: 0.4960 - val_loss: 0.7010\n",
            "Epoch 3/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5059 - loss: 0.6993 - val_accuracy: 0.4962 - val_loss: 0.6984\n",
            "Epoch 4/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 21ms/step - accuracy: 0.5124 - loss: 0.6964 - val_accuracy: 0.5038 - val_loss: 0.6962\n",
            "Epoch 5/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5154 - loss: 0.6943 - val_accuracy: 0.5110 - val_loss: 0.6939\n",
            "Epoch 6/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5195 - loss: 0.6925 - val_accuracy: 0.5120 - val_loss: 0.6925\n",
            "Epoch 7/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 23ms/step - accuracy: 0.5228 - loss: 0.6915 - val_accuracy: 0.5162 - val_loss: 0.6917\n",
            "Epoch 8/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 20ms/step - accuracy: 0.5255 - loss: 0.6905 - val_accuracy: 0.5198 - val_loss: 0.6910\n",
            "Epoch 9/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 16ms/step - accuracy: 0.5305 - loss: 0.6897 - val_accuracy: 0.5232 - val_loss: 0.6904\n",
            "Epoch 10/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5355 - loss: 0.6888 - val_accuracy: 0.5256 - val_loss: 0.6899\n",
            "Epoch 11/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.5392 - loss: 0.6881 - val_accuracy: 0.5256 - val_loss: 0.6893\n",
            "Epoch 12/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5413 - loss: 0.6873 - val_accuracy: 0.5272 - val_loss: 0.6888\n",
            "Epoch 13/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5443 - loss: 0.6866 - val_accuracy: 0.5328 - val_loss: 0.6883\n",
            "Epoch 14/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5470 - loss: 0.6859 - val_accuracy: 0.5344 - val_loss: 0.6878\n",
            "Epoch 15/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5505 - loss: 0.6851 - val_accuracy: 0.5372 - val_loss: 0.6873\n",
            "Epoch 16/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5537 - loss: 0.6844 - val_accuracy: 0.5432 - val_loss: 0.6867\n",
            "Epoch 17/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.5544 - loss: 0.6837 - val_accuracy: 0.5466 - val_loss: 0.6861\n",
            "Epoch 18/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5559 - loss: 0.6829 - val_accuracy: 0.5478 - val_loss: 0.6856\n",
            "Epoch 19/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.5578 - loss: 0.6821 - val_accuracy: 0.5490 - val_loss: 0.6850\n",
            "Epoch 20/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.5599 - loss: 0.6813 - val_accuracy: 0.5522 - val_loss: 0.6845\n",
            "Epoch 21/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 29ms/step - accuracy: 0.5630 - loss: 0.6805 - val_accuracy: 0.5524 - val_loss: 0.6839\n",
            "Epoch 22/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 29ms/step - accuracy: 0.5656 - loss: 0.6797 - val_accuracy: 0.5530 - val_loss: 0.6832\n",
            "Epoch 23/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5670 - loss: 0.6789 - val_accuracy: 0.5538 - val_loss: 0.6825\n",
            "Epoch 24/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.5691 - loss: 0.6780 - val_accuracy: 0.5578 - val_loss: 0.6819\n",
            "Epoch 25/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.5706 - loss: 0.6771 - val_accuracy: 0.5604 - val_loss: 0.6812\n",
            "Epoch 26/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.5730 - loss: 0.6761 - val_accuracy: 0.5622 - val_loss: 0.6804\n",
            "Epoch 27/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 18ms/step - accuracy: 0.5747 - loss: 0.6750 - val_accuracy: 0.5632 - val_loss: 0.6797\n",
            "Epoch 28/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5747 - loss: 0.6740 - val_accuracy: 0.5634 - val_loss: 0.6787\n",
            "Epoch 29/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.5768 - loss: 0.6728 - val_accuracy: 0.5662 - val_loss: 0.6774\n",
            "Epoch 30/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.5796 - loss: 0.6713 - val_accuracy: 0.5716 - val_loss: 0.6759\n",
            "Epoch 31/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 18ms/step - accuracy: 0.5801 - loss: 0.6695 - val_accuracy: 0.5790 - val_loss: 0.6737\n",
            "Epoch 32/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 20ms/step - accuracy: 0.5860 - loss: 0.6671 - val_accuracy: 0.5836 - val_loss: 0.6705\n",
            "Epoch 33/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.5962 - loss: 0.6634 - val_accuracy: 0.5974 - val_loss: 0.6647\n",
            "Epoch 34/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.6079 - loss: 0.6564 - val_accuracy: 0.6116 - val_loss: 0.6511\n",
            "Epoch 35/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.6356 - loss: 0.6393 - val_accuracy: 0.6616 - val_loss: 0.6188\n",
            "Epoch 36/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 20ms/step - accuracy: 0.6708 - loss: 0.6103 - val_accuracy: 0.6684 - val_loss: 0.6126\n",
            "Epoch 37/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 19ms/step - accuracy: 0.6774 - loss: 0.6029 - val_accuracy: 0.6758 - val_loss: 0.6062\n",
            "Epoch 38/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.6811 - loss: 0.5968 - val_accuracy: 0.6796 - val_loss: 0.6015\n",
            "Epoch 39/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.6876 - loss: 0.5911 - val_accuracy: 0.6864 - val_loss: 0.5966\n",
            "Epoch 40/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.6914 - loss: 0.5860 - val_accuracy: 0.6854 - val_loss: 0.5926\n",
            "Epoch 41/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 19ms/step - accuracy: 0.6976 - loss: 0.5811 - val_accuracy: 0.6904 - val_loss: 0.5889\n",
            "Epoch 42/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7006 - loss: 0.5762 - val_accuracy: 0.6974 - val_loss: 0.5852\n",
            "Epoch 43/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7060 - loss: 0.5720 - val_accuracy: 0.6988 - val_loss: 0.5817\n",
            "Epoch 44/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.7100 - loss: 0.5681 - val_accuracy: 0.6990 - val_loss: 0.5787\n",
            "Epoch 45/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.7115 - loss: 0.5654 - val_accuracy: 0.7022 - val_loss: 0.5757\n",
            "Epoch 46/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7131 - loss: 0.5625 - val_accuracy: 0.7028 - val_loss: 0.5734\n",
            "Epoch 47/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7149 - loss: 0.5601 - val_accuracy: 0.7058 - val_loss: 0.5717\n",
            "Epoch 48/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 18ms/step - accuracy: 0.7164 - loss: 0.5574 - val_accuracy: 0.7084 - val_loss: 0.5693\n",
            "Epoch 49/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7152 - loss: 0.5561 - val_accuracy: 0.7106 - val_loss: 0.5675\n",
            "Epoch 50/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7186 - loss: 0.5541 - val_accuracy: 0.7120 - val_loss: 0.5662\n",
            "Epoch 51/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 22ms/step - accuracy: 0.7204 - loss: 0.5524 - val_accuracy: 0.7126 - val_loss: 0.5653\n",
            "Epoch 52/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7213 - loss: 0.5511 - val_accuracy: 0.7152 - val_loss: 0.5647\n",
            "Epoch 53/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7232 - loss: 0.5497 - val_accuracy: 0.7156 - val_loss: 0.5632\n",
            "Epoch 54/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7241 - loss: 0.5483 - val_accuracy: 0.7166 - val_loss: 0.5624\n",
            "Epoch 55/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.7257 - loss: 0.5472 - val_accuracy: 0.7204 - val_loss: 0.5615\n",
            "Epoch 56/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7263 - loss: 0.5463 - val_accuracy: 0.7210 - val_loss: 0.5605\n",
            "Epoch 57/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7265 - loss: 0.5451 - val_accuracy: 0.7202 - val_loss: 0.5601\n",
            "Epoch 58/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7279 - loss: 0.5440 - val_accuracy: 0.7214 - val_loss: 0.5590\n",
            "Epoch 59/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 16ms/step - accuracy: 0.7287 - loss: 0.5431 - val_accuracy: 0.7224 - val_loss: 0.5579\n",
            "Epoch 60/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.7301 - loss: 0.5422 - val_accuracy: 0.7210 - val_loss: 0.5584\n",
            "Epoch 61/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7279 - loss: 0.5410 - val_accuracy: 0.7238 - val_loss: 0.5574\n",
            "Epoch 62/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7286 - loss: 0.5400 - val_accuracy: 0.7270 - val_loss: 0.5558\n",
            "Epoch 63/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7319 - loss: 0.5391 - val_accuracy: 0.7256 - val_loss: 0.5556\n",
            "Epoch 64/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 16ms/step - accuracy: 0.7310 - loss: 0.5384 - val_accuracy: 0.7262 - val_loss: 0.5544\n",
            "Epoch 65/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7310 - loss: 0.5375 - val_accuracy: 0.7292 - val_loss: 0.5532\n",
            "Epoch 66/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7332 - loss: 0.5369 - val_accuracy: 0.7298 - val_loss: 0.5530\n",
            "Epoch 67/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7335 - loss: 0.5361 - val_accuracy: 0.7324 - val_loss: 0.5524\n",
            "Epoch 68/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7333 - loss: 0.5355 - val_accuracy: 0.7312 - val_loss: 0.5516\n",
            "Epoch 69/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7334 - loss: 0.5351 - val_accuracy: 0.7324 - val_loss: 0.5515\n",
            "Epoch 70/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7343 - loss: 0.5345 - val_accuracy: 0.7330 - val_loss: 0.5510\n",
            "Epoch 71/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7349 - loss: 0.5336 - val_accuracy: 0.7322 - val_loss: 0.5504\n",
            "Epoch 72/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7360 - loss: 0.5329 - val_accuracy: 0.7300 - val_loss: 0.5501\n",
            "Epoch 73/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7365 - loss: 0.5324 - val_accuracy: 0.7330 - val_loss: 0.5501\n",
            "Epoch 74/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 16ms/step - accuracy: 0.7364 - loss: 0.5318 - val_accuracy: 0.7326 - val_loss: 0.5500\n",
            "Epoch 75/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7363 - loss: 0.5314 - val_accuracy: 0.7298 - val_loss: 0.5494\n",
            "Epoch 76/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 20ms/step - accuracy: 0.7373 - loss: 0.5307 - val_accuracy: 0.7310 - val_loss: 0.5496\n",
            "Epoch 77/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 17ms/step - accuracy: 0.7378 - loss: 0.5303 - val_accuracy: 0.7286 - val_loss: 0.5494\n",
            "Epoch 78/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7385 - loss: 0.5298 - val_accuracy: 0.7316 - val_loss: 0.5491\n",
            "Epoch 79/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7382 - loss: 0.5294 - val_accuracy: 0.7308 - val_loss: 0.5493\n",
            "Epoch 80/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7386 - loss: 0.5291 - val_accuracy: 0.7318 - val_loss: 0.5492\n",
            "Epoch 81/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7386 - loss: 0.5285 - val_accuracy: 0.7320 - val_loss: 0.5491\n",
            "Epoch 82/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.7387 - loss: 0.5281 - val_accuracy: 0.7324 - val_loss: 0.5491\n",
            "Epoch 83/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7383 - loss: 0.5275 - val_accuracy: 0.7324 - val_loss: 0.5495\n",
            "Epoch 84/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 19ms/step - accuracy: 0.7389 - loss: 0.5270 - val_accuracy: 0.7330 - val_loss: 0.5497\n"
          ]
        }
      ],
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model.compile(optimizer=rmsprop, loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-simplernn-model.keras',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model.fit(train_oh, train_target, epochs=100, batch_size=64,\n",
        "                    validation_data=(val_oh, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YS_5tIG26kQa",
        "outputId": "9f91624e-236c-45fb-d1b7-5277b2103224"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_U_lVie6kQa"
      },
      "source": [
        "## 단어 임베딩을 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "Ofl-Ifev6kQa",
        "outputId": "743a68e3-96bd-4d7d-f3c2-34b32cf192f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m3,200\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_2 (\u001b[38;5;33mSimpleRNN\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │             \u001b[38;5;34m200\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m9\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,200</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,409\u001b[0m (13.32 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,409</span> (13.32 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,409\u001b[0m (13.32 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,409</span> (13.32 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model2 = keras.Sequential()\n",
        "\n",
        "model2.add(keras.layers.Embedding(200, 16, input_shape=(100,)))\n",
        "model2.add(keras.layers.SimpleRNN(8))\n",
        "model2.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model2.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78TUrJAs6kQa",
        "outputId": "dc28c110-0ec3-471f-f888-5669a0d55383"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.5096 - loss: 0.6937 - val_accuracy: 0.6108 - val_loss: 0.6764\n",
            "Epoch 2/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.6264 - loss: 0.6719 - val_accuracy: 0.6544 - val_loss: 0.6605\n",
            "Epoch 3/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.6600 - loss: 0.6577 - val_accuracy: 0.6786 - val_loss: 0.6466\n",
            "Epoch 4/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 22ms/step - accuracy: 0.6802 - loss: 0.6438 - val_accuracy: 0.6916 - val_loss: 0.6347\n",
            "Epoch 5/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.6897 - loss: 0.6316 - val_accuracy: 0.6596 - val_loss: 0.6366\n",
            "Epoch 6/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 18ms/step - accuracy: 0.6973 - loss: 0.6199 - val_accuracy: 0.7086 - val_loss: 0.6108\n",
            "Epoch 7/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7087 - loss: 0.6071 - val_accuracy: 0.7092 - val_loss: 0.6007\n",
            "Epoch 8/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7112 - loss: 0.5974 - val_accuracy: 0.7030 - val_loss: 0.5963\n",
            "Epoch 9/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7133 - loss: 0.5890 - val_accuracy: 0.7072 - val_loss: 0.5892\n",
            "Epoch 10/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 19ms/step - accuracy: 0.7199 - loss: 0.5801 - val_accuracy: 0.7044 - val_loss: 0.5841\n",
            "Epoch 11/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7235 - loss: 0.5722 - val_accuracy: 0.7128 - val_loss: 0.5747\n",
            "Epoch 12/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7259 - loss: 0.5665 - val_accuracy: 0.7192 - val_loss: 0.5670\n",
            "Epoch 13/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7283 - loss: 0.5611 - val_accuracy: 0.7186 - val_loss: 0.5631\n",
            "Epoch 14/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7310 - loss: 0.5568 - val_accuracy: 0.7182 - val_loss: 0.5598\n",
            "Epoch 15/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7324 - loss: 0.5531 - val_accuracy: 0.7210 - val_loss: 0.5565\n",
            "Epoch 16/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7318 - loss: 0.5498 - val_accuracy: 0.7224 - val_loss: 0.5535\n",
            "Epoch 17/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7342 - loss: 0.5469 - val_accuracy: 0.7242 - val_loss: 0.5511\n",
            "Epoch 18/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7338 - loss: 0.5445 - val_accuracy: 0.7260 - val_loss: 0.5494\n",
            "Epoch 19/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 19ms/step - accuracy: 0.7347 - loss: 0.5426 - val_accuracy: 0.7268 - val_loss: 0.5480\n",
            "Epoch 20/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 20ms/step - accuracy: 0.7345 - loss: 0.5410 - val_accuracy: 0.7284 - val_loss: 0.5466\n",
            "Epoch 21/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 18ms/step - accuracy: 0.7343 - loss: 0.5395 - val_accuracy: 0.7282 - val_loss: 0.5455\n",
            "Epoch 22/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7355 - loss: 0.5381 - val_accuracy: 0.7280 - val_loss: 0.5448\n",
            "Epoch 23/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 19ms/step - accuracy: 0.7356 - loss: 0.5368 - val_accuracy: 0.7290 - val_loss: 0.5442\n",
            "Epoch 24/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7364 - loss: 0.5356 - val_accuracy: 0.7294 - val_loss: 0.5437\n",
            "Epoch 25/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7362 - loss: 0.5345 - val_accuracy: 0.7294 - val_loss: 0.5432\n",
            "Epoch 26/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7363 - loss: 0.5334 - val_accuracy: 0.7296 - val_loss: 0.5427\n",
            "Epoch 27/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step - accuracy: 0.7350 - loss: 0.5323 - val_accuracy: 0.7308 - val_loss: 0.5423\n",
            "Epoch 28/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 18ms/step - accuracy: 0.7352 - loss: 0.5313 - val_accuracy: 0.7296 - val_loss: 0.5418\n",
            "Epoch 29/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7367 - loss: 0.5303 - val_accuracy: 0.7294 - val_loss: 0.5415\n",
            "Epoch 30/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 17ms/step - accuracy: 0.7369 - loss: 0.5294 - val_accuracy: 0.7290 - val_loss: 0.5415\n",
            "Epoch 31/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7387 - loss: 0.5287 - val_accuracy: 0.7306 - val_loss: 0.5413\n",
            "Epoch 32/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 19ms/step - accuracy: 0.7385 - loss: 0.5279 - val_accuracy: 0.7310 - val_loss: 0.5415\n",
            "Epoch 33/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 17ms/step - accuracy: 0.7394 - loss: 0.5273 - val_accuracy: 0.7316 - val_loss: 0.5416\n",
            "Epoch 34/100\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step - accuracy: 0.7395 - loss: 0.5268 - val_accuracy: 0.7322 - val_loss: 0.5417\n"
          ]
        }
      ],
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model2.compile(optimizer=rmsprop, loss='binary_crossentropy',\n",
        "               metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-embedding-model.keras',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model2.fit(train_seq, train_target, epochs=100, batch_size=64,\n",
        "                     validation_data=(val_seq, val_target),\n",
        "                     callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "brUvKwLt6kQb",
        "outputId": "4a836061-88a8-4b32-bc9b-ebb3b9c9d854",
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "9-2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}