16_nlp_with_rnns_and_attention.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvGItwuDC45m"
      },
      "source": [
        "**16장 – RNN과 어텐션을 사용한 자연어 처리**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wu-DhphZC45p"
      },
      "source": [
        "_이 노트북에는 16장의 모든 샘플 코드와 연습 문제에 대한 해답이 포함되어 있습니다._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsf2r9PbC45p"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/rickiepark/handson-ml3/blob/main/16_nlp_with_rnns_and_attention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFXIv9qNpKzt",
        "tags": []
      },
      "source": [
        "# 설정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IPbJEmZpKzu"
      },
      "source": [
        "이 프로젝트에는 Python 3.7 이상이 필요합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "TFSU3FCOpKzu"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "assert sys.version_info >= (3, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJtVEqxfpKzw"
      },
      "source": [
        "그리고 TensorFlow ≥ 2.8:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0Piq5se2pKzx"
      },
      "outputs": [],
      "source": [
        "from packaging import version\n",
        "import tensorflow as tf\n",
        "\n",
        "assert version.parse(tf.__version__) >= version.parse(\"2.8.0\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDaDoLQTpKzx"
      },
      "source": [
        "이전 챕터에서 했던 것처럼 기본 글꼴 크기를 정의하여 그림을 더 예쁘게 만들어 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "8d4TH3NbpKzx",
        "outputId": "4cd017e2-a934-4e52-f807-c6faaaa3765c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 122518 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20180306-3_all.deb ...\n",
            "Unpacking fonts-nanum (20180306-3) ...\n",
            "Setting up fonts-nanum (20180306-3) ...\n",
            "Processing triggers for fontconfig (2.13.1-2ubuntu3) ...\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rc('font', size=14)\n",
        "plt.rc('axes', labelsize=14, titlesize=14)\n",
        "plt.rc('legend', fontsize=14)\n",
        "plt.rc('xtick', labelsize=10)\n",
        "plt.rc('ytick', labelsize=10)\n",
        "\n",
        "import sys\n",
        "# 코랩의 경우 나눔 폰트를 설치합니다.\n",
        "if 'google.colab' in sys.modules:\n",
        "    !sudo apt-get -qq -y install fonts-nanum\n",
        "    import matplotlib.font_manager as fm\n",
        "    font_files = fm.findSystemFonts(fontpaths=['/usr/share/fonts/truetype/nanum'])\n",
        "    for fpath in font_files:\n",
        "        fm.fontManager.addfont(fpath)\n",
        "\n",
        "# 나눔 폰트를 사용합니다.\n",
        "import matplotlib \n",
        "\n",
        "matplotlib.rc('font', family='NanumBarunGothic')\n",
        "matplotlib.rcParams['axes.unicode_minus'] = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcoUIRsvpKzy"
      },
      "source": [
        "그리고 `images/nlp` 폴더를 만들고(아직 존재하지 않는 경우), 이 노트북을 통해 책에 사용할 그림을 고해상도로 저장하는 데 사용되는 `save_fig()` 함수를 정의해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "PQFH5Y9PpKzy"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "IMAGES_PATH = Path() / \"images\" / \"nlp\"\n",
        "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
        "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
        "    if tight_layout:\n",
        "        plt.tight_layout()\n",
        "    plt.savefig(path, format=fig_extension, dpi=resolution)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTsawKlapKzy"
      },
      "source": [
        "이 챕터는 GPU가 없으면 매우 느려질 수 있으므로 GPU가 있는지 확인하거나 그렇지 않으면 경고를 표시합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ekxzo6pOpKzy"
      },
      "outputs": [],
      "source": [
        "if not tf.config.list_physical_devices('GPU'):\n",
        "    print(\"GPU가 감지되지 않았습니다. 신경망은 GPU가 없으면 매우 느릴 수 있습니다.\")\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        print(\"런타임 > 런타임 유형 변경으로 이동하여 하드웨어 가속기에서 GPU를 선택합니다.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19z42n_fC45t"
      },
      "source": [
        "# char-RNN을 사용하여 셰익스피어 같은 텍스트 생성하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0N_pUKKC45t"
      },
      "source": [
        "## 훈련 데이터셋 생성하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RuX8bfeoC45t"
      },
      "source": [
        "안드레이 카파시의 [char-rnn 프로젝트](https://github.com/karpathy/char-rnn/)에서 셰익스피어 데이터를 다운로드해 보겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "uAN_1XLxC45u",
        "outputId": "3d986e7e-95d6-4b21-a0aa-8b0f7c51c1c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://homl.info/shakespeare\n",
            "1115394/1115394 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "shakespeare_url = \"https://homl.info/shakespeare\"  # 단축 URL\n",
        "filepath = tf.keras.utils.get_file(\"shakespeare.txt\", shakespeare_url)\n",
        "with open(filepath) as f:\n",
        "    shakespeare_text = f.read()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "vbhZwby9C45u",
        "outputId": "ee03b9ea-312d-4a59-e0c5-541d1d454bef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First Citizen:\n",
            "Before we proceed any further, hear me speak.\n",
            "\n",
            "All:\n",
            "Speak, speak.\n"
          ]
        }
      ],
      "source": [
        "# 추가 코드 - 짧은 텍스트 샘플을 표시합니다.\n",
        "print(shakespeare_text[:80])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "SPcfVQbNC45u",
        "outputId": "80bbfeaf-3121-40c8-9e0b-1692de1922ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n !$&',-.3:;?abcdefghijklmnopqrstuvwxyz\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# 추가 코드 - 39개의 고유 문자를 모두 표시합니다(소문자로 변환 후).\n",
        "\"\".join(sorted(set(shakespeare_text.lower())))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "qCOCJWIIC45v"
      },
      "outputs": [],
      "source": [
        "text_vec_layer = tf.keras.layers.TextVectorization(split=\"character\",\n",
        "                                                   standardize=\"lower\")\n",
        "text_vec_layer.adapt([shakespeare_text])\n",
        "encoded = text_vec_layer([shakespeare_text])[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "L7SF571dC45v"
      },
      "outputs": [],
      "source": [
        "encoded -= 2  # 토큰 0(패딩)과 1(알 수 없음)을 드롭하는데, 이 토큰은 사용하지 않습니다.\n",
        "n_tokens = text_vec_layer.vocabulary_size() - 2  # 고유 문자 수 = 39\n",
        "dataset_size = len(encoded)  # 총 문자 수 = 1,115,394"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "VVI2turfC45v",
        "outputId": "2f593638-46a3-4236-d0d4-c3a5f0839fcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "n_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "vD5Y8YsDC45v",
        "outputId": "d2b3d596-a4d7-4fc8-eb2e-0a7530f57f00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1115394"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Xmj7G74fC45v"
      },
      "outputs": [],
      "source": [
        "def to_dataset(sequence, length, shuffle=False, seed=None, batch_size=32):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(sequence)\n",
        "    ds = ds.window(length + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda window_ds: window_ds.batch(length + 1))\n",
        "    if shuffle:\n",
        "        ds = ds.shuffle(100_000, seed=seed)\n",
        "    ds = ds.batch(batch_size)\n",
        "    return ds.map(lambda window: (window[:, :-1], window[:, 1:])).prefetch(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ZGQxvBcoC45v",
        "outputId": "062db663-b602-4ac1-de12-955bc5c7dfa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(<tf.Tensor: shape=(1, 4), dtype=int64, numpy=array([[ 4,  5,  2, 23]])>,\n",
              "  <tf.Tensor: shape=(1, 4), dtype=int64, numpy=array([[ 5,  2, 23,  3]])>)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# 추가 코드 - to_dataset()을 사용하는 간단한 예제\n",
        "# 이 데이터셋에는 하나의 샘플만 있습니다. 입력은 \"to b\"이고 출력은 \"o be\"입니다.\n",
        "list(to_dataset(text_vec_layer([\"To be\"])[0], length=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "HCJc9kWZC45w"
      },
      "outputs": [],
      "source": [
        "length = 100\n",
        "tf.random.set_seed(42)\n",
        "train_set = to_dataset(encoded[:1_000_000], length=length, shuffle=True,\n",
        "                       seed=42)\n",
        "valid_set = to_dataset(encoded[1_000_000:1_060_000], length=length)\n",
        "test_set = to_dataset(encoded[1_060_000:], length=length)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZM51bj5eC45w"
      },
      "source": [
        "## Char-RNN 모델 구축 및 훈련하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtwgJIltC45w"
      },
      "source": [
        "**경고**: 다음 코드는 GPU에 따라 실행하는 데 1~2시간이 걸릴 수 있습니다. GPU가 없는 경우 24시간 이상 걸릴 수 있습니다. 기다리지 않으려면 다음 두 코드 셀을 건너뛰고 아래 코드를 실행하여 사전 학습된 모델을 다운로드하세요."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0qsq8dcC45w"
      },
      "source": [
        "**참고**: (GPU가 있는 경우) `GRU` 클래스는 다음 매개변수의 기본값을 사용할 때 cuDNN 가속을 사용합니다: `activation`, `recurrent_activation`, `recurrent_dropout`, `unroll`, `use_bias`, `reset_after`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "PJEzwJ6vC45w",
        "outputId": "7d0d3ea7-f7cc-41c0-d303-93fdcd89f597",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "  31244/Unknown - 453s 14ms/step - loss: 1.4083 - accuracy: 0.5696"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 478s 14ms/step - loss: 1.4083 - accuracy: 0.5696 - val_loss: 1.6093 - val_accuracy: 0.5296\n",
            "Epoch 2/10\n",
            "31247/31247 [==============================] - ETA: 0s - loss: 1.3105 - accuracy: 0.5922"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 440s 13ms/step - loss: 1.3105 - accuracy: 0.5922 - val_loss: 1.5852 - val_accuracy: 0.5342\n",
            "Epoch 3/10\n",
            "31245/31247 [============================>.] - ETA: 0s - loss: 1.2921 - accuracy: 0.5961"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 471s 14ms/step - loss: 1.2921 - accuracy: 0.5961 - val_loss: 1.5798 - val_accuracy: 0.5374\n",
            "Epoch 4/10\n",
            "31242/31247 [============================>.] - ETA: 0s - loss: 1.2820 - accuracy: 0.5983"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 461s 14ms/step - loss: 1.2820 - accuracy: 0.5983 - val_loss: 1.5721 - val_accuracy: 0.5412\n",
            "Epoch 5/10\n",
            "31247/31247 [==============================] - 457s 14ms/step - loss: 1.2751 - accuracy: 0.5998 - val_loss: 1.5796 - val_accuracy: 0.5386\n",
            "Epoch 6/10\n",
            "31242/31247 [============================>.] - ETA: 0s - loss: 1.2711 - accuracy: 0.6005"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 467s 14ms/step - loss: 1.2711 - accuracy: 0.6005 - val_loss: 1.5732 - val_accuracy: 0.5423\n",
            "Epoch 7/10\n",
            "31243/31247 [============================>.] - ETA: 0s - loss: 1.2669 - accuracy: 0.6016"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 451s 14ms/step - loss: 1.2669 - accuracy: 0.6016 - val_loss: 1.5669 - val_accuracy: 0.5431\n",
            "Epoch 8/10\n",
            "31247/31247 [==============================] - 460s 14ms/step - loss: 1.2646 - accuracy: 0.6019 - val_loss: 1.5721 - val_accuracy: 0.5422\n",
            "Epoch 9/10\n",
            "31242/31247 [============================>.] - ETA: 0s - loss: 1.2616 - accuracy: 0.6025"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_layer_call_fn, gru_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r31247/31247 [==============================] - 456s 14ms/step - loss: 1.2616 - accuracy: 0.6025 - val_loss: 1.5698 - val_accuracy: 0.5454\n",
            "Epoch 10/10\n",
            "31247/31247 [==============================] - 454s 14ms/step - loss: 1.2620 - accuracy: 0.6026 - val_loss: 1.5659 - val_accuracy: 0.5430\n"
          ]
        }
      ],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=n_tokens, output_dim=16),\n",
        "    tf.keras.layers.GRU(128, return_sequences=True),\n",
        "    tf.keras.layers.Dense(n_tokens, activation=\"softmax\")\n",
        "])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model_ckpt = tf.keras.callbacks.ModelCheckpoint(\n",
        "    \"my_shakespeare_model\", monitor=\"val_accuracy\", save_best_only=True)\n",
        "history = model.fit(train_set, validation_data=valid_set, epochs=10,\n",
        "                    callbacks=[model_ckpt])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "sUjgq641C45w"
      },
      "outputs": [],
      "source": [
        "shakespeare_model = tf.keras.Sequential([\n",
        "    text_vec_layer,\n",
        "    tf.keras.layers.Lambda(lambda X: X - 2),  # <PAD>나 <UNK> 토큰 없음\n",
        "    model\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUBE7wDtC45w"
      },
      "source": [
        "훈련이 완료될 때까지 기다리기 싫으시다면 모델을 미리 훈련해 두었습니다. 다음 코드에서 다운로드할 수 있습니다. 위에서 학습된 모델 대신 이 모델을 사용하려면 마지막 줄의 주석 처리를 해제하세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0LZE1rbYC45x",
        "outputId": "307ad820-15ef-4ceb-a778-1f70a31358f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/ageron/data/raw/main/shakespeare_model.tgz\n",
            "352865/352865 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# 추가 코드 - 사전 훈련된 모델 다운로드\n",
        "url = \"https://github.com/ageron/data/raw/main/shakespeare_model.tgz\"\n",
        "path = tf.keras.utils.get_file(\"shakespeare_model.tgz\", url, extract=True)\n",
        "model_path = Path(path).with_name(\"shakespeare_model\")\n",
        "shakespeare_model = tf.keras.models.load_model(model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kl8RbP0sC45x",
        "outputId": "a4025263-46b2-4f4b-bd2b-4eb562cb6120",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 448ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'e'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "y_proba = shakespeare_model.predict([\"To be or not to b\"])[0, -1]\n",
        "y_pred = tf.argmax(y_proba)  # 가장 가능성이 높은 문자 ID 선택\n",
        "text_vec_layer.get_vocabulary()[y_pred + 2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEQn7SWOC45x"
      },
      "source": [
        "## 가짜 셰익스피어 텍스트 생성하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "iYshxcp-C45x",
        "outputId": "470d70e8-c163-4a08-a6c0-4cbc83dc0328",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 8), dtype=int64, numpy=array([[0, 0, 1, 1, 1, 0, 0, 0]])>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "log_probas = tf.math.log([[0.5, 0.4, 0.1]])  # 확률 = 50%, 40%, 10%\n",
        "tf.random.set_seed(42)\n",
        "tf.random.categorical(log_probas, num_samples=8)  # 샘플 8개를 뽑습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "H4RE41r8C45x"
      },
      "outputs": [],
      "source": [
        "def next_char(text, temperature=1):\n",
        "    y_proba = shakespeare_model.predict([text])[0, -1:]\n",
        "    rescaled_logits = tf.math.log(y_proba) / temperature\n",
        "    char_id = tf.random.categorical(rescaled_logits, num_samples=1)[0, 0]\n",
        "    return text_vec_layer.get_vocabulary()[char_id + 2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "BVKVaInnC45x"
      },
      "outputs": [],
      "source": [
        "def extend_text(text, n_chars=50, temperature=1):\n",
        "    for _ in range(n_chars):\n",
        "        text += next_char(text, temperature)\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "tQKB4HDuC45x"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "itJfQNjYC45y",
        "outputId": "f54a610c-f553-4888-e4f1-db788ecca74c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 57ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 65ms/step\n",
            "1/1 [==============================] - 0s 62ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 74ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 74ms/step\n",
            "1/1 [==============================] - 0s 59ms/step\n",
            "1/1 [==============================] - 0s 57ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "To be or not to be the duke\n",
            "as it is a proper strange death,\n",
            "and the\n"
          ]
        }
      ],
      "source": [
        "print(extend_text(\"To be or not to be\", temperature=0.01))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "s70j8ddxC45y",
        "outputId": "498925a7-25cd-413d-daf0-76daf2452781",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "To be or not to begg, i\n",
            "point and through you are to-morrow?\n",
            "\n",
            "romeo:\n"
          ]
        }
      ],
      "source": [
        "print(extend_text(\"To be or not to be\", temperature=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "2fjL8A9TC45y",
        "outputId": "87b86bf2-0b10-4547-b7fb-246fe9af48d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "To be or not to bepevicm-vilv!?$mz?gmjz :3?ljb'va;!td&\n",
            "i.ur3l'-j!3em\n"
          ]
        }
      ],
      "source": [
        "print(extend_text(\"To be or not to be\", temperature=100))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYzmlWapC45y"
      },
      "source": [
        "## 상태가 있는 RNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "OW48BH49C45y"
      },
      "outputs": [],
      "source": [
        "def to_dataset_for_stateful_rnn(sequence, length):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(sequence)\n",
        "    ds = ds.window(length + 1, shift=length, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda window: window.batch(length + 1)).batch(1)\n",
        "    return ds.map(lambda window: (window[:, :-1], window[:, 1:])).prefetch(1)\n",
        "\n",
        "stateful_train_set = to_dataset_for_stateful_rnn(encoded[:1_000_000], length)\n",
        "stateful_valid_set = to_dataset_for_stateful_rnn(encoded[1_000_000:1_060_000],\n",
        "                                                 length)\n",
        "stateful_test_set = to_dataset_for_stateful_rnn(encoded[1_060_000:], length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "55ixV5oIC45y",
        "outputId": "85d1c115-91e9-450b-f253-a6c8c1110ae2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(<tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[0, 1, 2]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[1, 2, 3]], dtype=int32)>),\n",
              " (<tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[3, 4, 5]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[4, 5, 6]], dtype=int32)>),\n",
              " (<tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[6, 7, 8]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(1, 3), dtype=int32, numpy=array([[7, 8, 9]], dtype=int32)>)]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# 추가 코드 - to_dataset_for_stateful_rnn()을 사용한 간단한 예제\n",
        "list(to_dataset_for_stateful_rnn(tf.range(10), 3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBgLUp5mC45y"
      },
      "source": [
        "배치당 두 개 이상의 윈도가 있다면 `to_dataset_for_stateful_rnn()` 대신 `to_batched_dataset_for_stateful_rnn()` 함수를 사용할 수 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Fphrw1mvC45y",
        "outputId": "d41a2565-bc51-4dd5-9dc8-c4706eeb7570",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 0,  1,  2],\n",
              "         [10, 11, 12]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 1,  2,  3],\n",
              "         [11, 12, 13]], dtype=int32)>),\n",
              " (<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 3,  4,  5],\n",
              "         [13, 14, 15]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 4,  5,  6],\n",
              "         [14, 15, 16]], dtype=int32)>),\n",
              " (<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 6,  7,  8],\n",
              "         [16, 17, 18]], dtype=int32)>,\n",
              "  <tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
              "  array([[ 7,  8,  9],\n",
              "         [17, 18, 19]], dtype=int32)>)]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# 추가 코드 - 상태가 있는 RNN을 위해 배치 데이터셋을 준비하는 한 가지 방법을 보여줍니다.\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def to_non_overlapping_windows(sequence, length):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(sequence)\n",
        "    ds = ds.window(length + 1, shift=length, drop_remainder=True)\n",
        "    return ds.flat_map(lambda window: window.batch(length + 1))\n",
        "\n",
        "def to_batched_dataset_for_stateful_rnn(sequence, length, batch_size=32):\n",
        "    parts = np.array_split(sequence, batch_size)\n",
        "    datasets = tuple(to_non_overlapping_windows(part, length) for part in parts)\n",
        "    ds = tf.data.Dataset.zip(datasets).map(lambda *windows: tf.stack(windows))\n",
        "    return ds.map(lambda window: (window[:, :-1], window[:, 1:])).prefetch(1)\n",
        "\n",
        "list(to_batched_dataset_for_stateful_rnn(tf.range(20), length=3, batch_size=2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "18lvduzKC45z"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=n_tokens, output_dim=16,\n",
        "                              batch_input_shape=[1, None]),\n",
        "    tf.keras.layers.GRU(128, return_sequences=True, stateful=True),\n",
        "    tf.keras.layers.Dense(n_tokens, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "AdzjBI3WC45z"
      },
      "outputs": [],
      "source": [
        "class ResetStatesCallback(tf.keras.callbacks.Callback):\n",
        "    def on_epoch_begin(self, epoch, logs):\n",
        "        self.model.reset_states()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "9XzXh37RC45z"
      },
      "outputs": [],
      "source": [
        "# 추가 코드 - 다른 디렉터리를 사용하여 체크포인트를 저장합니다.\n",
        "model_ckpt = tf.keras.callbacks.ModelCheckpoint(\n",
        "    \"my_stateful_shakespeare_model\",\n",
        "    monitor=\"val_accuracy\",\n",
        "    save_best_only=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwCT5BAMC45z"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 1시간 정도 소요될 수 있음)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "FcgJ7-qHC45z",
        "outputId": "2490516e-eb1c-4597-8e42-0b28480baaf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "   9999/Unknown - 90s 9ms/step - loss: 1.8903 - accuracy: 0.4442"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 96s 9ms/step - loss: 1.8903 - accuracy: 0.4442 - val_loss: 1.7161 - val_accuracy: 0.4859\n",
            "Epoch 2/10\n",
            "9999/9999 [==============================] - ETA: 0s - loss: 1.5704 - accuracy: 0.5262"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 96s 10ms/step - loss: 1.5704 - accuracy: 0.5262 - val_loss: 1.6189 - val_accuracy: 0.5150\n",
            "Epoch 3/10\n",
            "9997/9999 [============================>.] - ETA: 0s - loss: 1.4856 - accuracy: 0.5485"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 94s 9ms/step - loss: 1.4856 - accuracy: 0.5485 - val_loss: 1.5805 - val_accuracy: 0.5291\n",
            "Epoch 4/10\n",
            "9997/9999 [============================>.] - ETA: 0s - loss: 1.4455 - accuracy: 0.5584"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 94s 9ms/step - loss: 1.4455 - accuracy: 0.5584 - val_loss: 1.5584 - val_accuracy: 0.5339\n",
            "Epoch 5/10\n",
            "9993/9999 [============================>.] - ETA: 0s - loss: 1.4210 - accuracy: 0.5646"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 95s 9ms/step - loss: 1.4210 - accuracy: 0.5646 - val_loss: 1.5520 - val_accuracy: 0.5367\n",
            "Epoch 6/10\n",
            "9993/9999 [============================>.] - ETA: 0s - loss: 1.4046 - accuracy: 0.5687"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 94s 9ms/step - loss: 1.4046 - accuracy: 0.5688 - val_loss: 1.5477 - val_accuracy: 0.5381\n",
            "Epoch 7/10\n",
            "9993/9999 [============================>.] - ETA: 0s - loss: 1.3929 - accuracy: 0.5717"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 94s 9ms/step - loss: 1.3928 - accuracy: 0.5717 - val_loss: 1.5415 - val_accuracy: 0.5408\n",
            "Epoch 8/10\n",
            "9994/9999 [============================>.] - ETA: 0s - loss: 1.3842 - accuracy: 0.5742"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 97s 10ms/step - loss: 1.3842 - accuracy: 0.5742 - val_loss: 1.5374 - val_accuracy: 0.5409\n",
            "Epoch 9/10\n",
            "9993/9999 [============================>.] - ETA: 0s - loss: 1.3777 - accuracy: 0.5755"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 95s 10ms/step - loss: 1.3776 - accuracy: 0.5756 - val_loss: 1.5335 - val_accuracy: 0.5414\n",
            "Epoch 10/10\n",
            "9996/9999 [============================>.] - ETA: 0s - loss: 1.3726 - accuracy: 0.5768"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as gru_cell_2_layer_call_fn, gru_cell_2_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r9999/9999 [==============================] - 93s 9ms/step - loss: 1.3725 - accuracy: 0.5768 - val_loss: 1.5277 - val_accuracy: 0.5419\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(stateful_train_set, validation_data=stateful_valid_set,\n",
        "                    epochs=10, callbacks=[ResetStatesCallback(), model_ckpt])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVCoCKLXC45z"
      },
      "source": [
        "**추가 자료: 상태가 있는 RNN을 상태가 없는 RNN으로 변환하여 사용하기**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPC6SI2NC45z"
      },
      "source": [
        "다른 배치 크기로 모델을 사용하려면 상태가 없는 모델 복사본을 만들어야 합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "ssf4bcKJC45z"
      },
      "outputs": [],
      "source": [
        "stateless_model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=n_tokens, output_dim=16),\n",
        "    tf.keras.layers.GRU(128, return_sequences=True),\n",
        "    tf.keras.layers.Dense(n_tokens, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hldkbULC45z"
      },
      "source": [
        "가중치를 설정하려면 먼저 모델을 빌드해야 합니다(가중치가 생성되도록):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "BilmycmEC450"
      },
      "outputs": [],
      "source": [
        "stateless_model.build(tf.TensorShape([None, None]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "BlxzIqNkC450"
      },
      "outputs": [],
      "source": [
        "stateless_model.set_weights(model.get_weights())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "bj0kvecZC450"
      },
      "outputs": [],
      "source": [
        "shakespeare_model = tf.keras.Sequential([\n",
        "    text_vec_layer,\n",
        "    tf.keras.layers.Lambda(lambda X: X - 2),  # <PAD>나 <UNK> 토큰 없음\n",
        "    stateless_model\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "0UB2s8INC450",
        "outputId": "51c79bb0-8613-4d58-f7db-a121f6bdd9be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 426ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 70ms/step\n",
            "1/1 [==============================] - 0s 69ms/step\n",
            "1/1 [==============================] - 0s 64ms/step\n",
            "1/1 [==============================] - 0s 61ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "to be or not to be so\n",
            "have the will be so have the will be so have t\n"
          ]
        }
      ],
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "print(extend_text(\"to be or not to be\", temperature=0.01))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpEKVsmWC450"
      },
      "source": [
        "# 감성 분석"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Jvqu6p-GC450",
        "outputId": "c0657a41-a09b-4a35-bf1c-c09e4a4cd8e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
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            "0c0dea3a8d434eb3887138090e95c56a",
            "7c367a8f7b4f413c88e742584f6370dd",
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            "c450d51da59a41c1b9b055e9a4ddcf63",
            "703d9e2ce5d64f9fbc22e148546de897",
            "19fe3cdf77dd4d8992f86920ff37cbea",
            "207914d54d9741e9a7ea69163d677691",
            "a1fc3eeb799546e8934bf6bf8811c901",
            "27574da917a54bf080c1b1f9ac696a9b",
            "6f0aada00c3e44f39ea9316d485ca4db",
            "6d95b2b860b044eab4d94bda8d55ad99",
            "3244ac580c614d64abdecfd87b31a5ca",
            "8afdccb3ecbc4e6e81fe5bd3707099c5",
            "4beb7c668ee340e4955711875c934429",
            "d2f69c7bc6e4442b946e8e380fb35431",
            "c285e56af29f4fc9a5c8868d2a53cc78",
            "2bb57e79447e49a9941126ff61eb3b7c",
            "fb6bba69558942f6979bc67f770f6210",
            "72ea156e70274947b27ffd033d547038",
            "0acdf8fcd89d48f39fb81bac174418e1",
            "6e5ede8abe804d72a9391f739413b0bd"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2adf3a3bbedd419e8d26050faa84f656"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf2384d93c824436be5a64c66ba7aa94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6185d080efc4d5aad0609d00d8329ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca92b969bd4646de876ad14fbdad581d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNRMR3Z/imdb_reviews-train.tfrecord…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3f7df85b88f4bc0b9acd6ec2d7a0d1a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd2b0caef596448794426a2953f61782"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNRMR3Z/imdb_reviews-test.tfrecord*…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cf77a7335f2b471eb7ed6d407605bbf0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating unsupervised examples...: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6479034d83944fce844e0836fb1f9aa3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteNRMR3Z/imdb_reviews-unsupervised.t…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d95b2b860b044eab4d94bda8d55ad99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "import tensorflow_datasets as tfds\n",
        "\n",
        "raw_train_set, raw_valid_set, raw_test_set = tfds.load(\n",
        "    name=\"imdb_reviews\",\n",
        "    split=[\"train[:90%]\", \"train[90%:]\", \"test\"],\n",
        "    as_supervised=True\n",
        ")\n",
        "tf.random.set_seed(42)\n",
        "train_set = raw_train_set.shuffle(5000, seed=42).batch(32).prefetch(1)\n",
        "valid_set = raw_valid_set.batch(32).prefetch(1)\n",
        "test_set = raw_test_set.batch(32).prefetch(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "zGvb_8GHC450",
        "outputId": "9c5a3604-9304-40f5-9a48-9536c196dc48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting  ...\n",
            "레이블: 0\n",
            "I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However  ...\n",
            "레이블: 0\n",
            "Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Moun ...\n",
            "레이블: 0\n",
            "This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful perf ...\n",
            "레이블: 1\n"
          ]
        }
      ],
      "source": [
        "for review, label in raw_train_set.take(4):\n",
        "    print(review.numpy().decode(\"utf-8\")[:200], \"...\")\n",
        "    print(\"레이블:\", label.numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "oH5xJUJDC450"
      },
      "outputs": [],
      "source": [
        "vocab_size = 1000\n",
        "text_vec_layer = tf.keras.layers.TextVectorization(max_tokens=vocab_size)\n",
        "text_vec_layer.adapt(train_set.map(lambda reviews, labels: reviews))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12uj9IXmC451"
      },
      "source": [
        "**경고**: 다음 셀은 실행하는 데 몇 분 정도 걸리며 패딩 토큰을 마스킹하지 않았기 때문에 모델이 아무것도 학습하지 못할 수 있습니다(다음 섹션의 요점입니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "E6CJsFzzC451",
        "outputId": "f9d3ee71-79ea-4eea-fe56-cb0ec972c791",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "704/704 [==============================] - 65s 88ms/step - loss: 0.6934 - accuracy: 0.5001 - val_loss: 0.6931 - val_accuracy: 0.5012\n",
            "Epoch 2/2\n",
            "704/704 [==============================] - 28s 40ms/step - loss: 0.6929 - accuracy: 0.5051 - val_loss: 0.6936 - val_accuracy: 0.4992\n"
          ]
        }
      ],
      "source": [
        "embed_size = 128\n",
        "tf.random.set_seed(42)\n",
        "model = tf.keras.Sequential([\n",
        "    text_vec_layer,\n",
        "    tf.keras.layers.Embedding(vocab_size, embed_size),\n",
        "    tf.keras.layers.GRU(128),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(train_set, validation_data=valid_set, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9C5BTnbC451"
      },
      "source": [
        "## 마스킹"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqStWXt1C451"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸립니다(GPU를 사용하지 않는 경우 30분 정도 소요될 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "42P82ac7C451",
        "outputId": "67fe1967-ebd6-487c-d937-a3846b2559a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "704/704 [==============================] - 61s 77ms/step - loss: 0.5274 - accuracy: 0.7255 - val_loss: 0.3541 - val_accuracy: 0.8448\n",
            "Epoch 2/5\n",
            "704/704 [==============================] - 32s 45ms/step - loss: 0.3263 - accuracy: 0.8612 - val_loss: 0.3243 - val_accuracy: 0.8588\n",
            "Epoch 3/5\n",
            "704/704 [==============================] - 29s 41ms/step - loss: 0.2880 - accuracy: 0.8782 - val_loss: 0.2974 - val_accuracy: 0.8740\n",
            "Epoch 4/5\n",
            "704/704 [==============================] - 28s 40ms/step - loss: 0.2677 - accuracy: 0.8888 - val_loss: 0.3018 - val_accuracy: 0.8696\n",
            "Epoch 5/5\n",
            "704/704 [==============================] - 28s 39ms/step - loss: 0.2547 - accuracy: 0.8964 - val_loss: 0.3106 - val_accuracy: 0.8608\n"
          ]
        }
      ],
      "source": [
        "embed_size = 128\n",
        "tf.random.set_seed(42)\n",
        "model = tf.keras.Sequential([\n",
        "    text_vec_layer,\n",
        "    tf.keras.layers.Embedding(vocab_size, embed_size, mask_zero=True),\n",
        "    tf.keras.layers.GRU(128),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(train_set, validation_data=valid_set, epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lc04LwxmC451"
      },
      "source": [
        "또는 수동 마스킹을 사용합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "qv_uXWREC451"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "inputs = tf.keras.layers.Input(shape=[], dtype=tf.string)\n",
        "token_ids = text_vec_layer(inputs)\n",
        "mask = tf.math.not_equal(token_ids, 0)\n",
        "Z = tf.keras.layers.Embedding(vocab_size, embed_size)(token_ids)\n",
        "Z = tf.keras.layers.GRU(128, dropout=0.2)(Z, mask=mask)\n",
        "outputs = tf.keras.layers.Dense(1, activation=\"sigmoid\")(Z)\n",
        "model = tf.keras.Model(inputs=[inputs], outputs=[outputs])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkkWZbBkC451"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸립니다(GPU를 사용하지 않는 경우 30분 정도 소요될 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "iplBZbMOC451",
        "outputId": "8541a26c-6677-437f-a357-722d8a78464c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "704/704 [==============================] - 62s 81ms/step - loss: 0.4778 - accuracy: 0.7717 - val_loss: 0.3933 - val_accuracy: 0.8336\n",
            "Epoch 2/5\n",
            "704/704 [==============================] - 31s 43ms/step - loss: 0.3391 - accuracy: 0.8588 - val_loss: 0.3140 - val_accuracy: 0.8736\n",
            "Epoch 3/5\n",
            "704/704 [==============================] - 28s 40ms/step - loss: 0.2920 - accuracy: 0.8803 - val_loss: 0.3230 - val_accuracy: 0.8628\n",
            "Epoch 4/5\n",
            "704/704 [==============================] - 29s 41ms/step - loss: 0.2717 - accuracy: 0.8897 - val_loss: 0.3121 - val_accuracy: 0.8668\n",
            "Epoch 5/5\n",
            "704/704 [==============================] - 28s 40ms/step - loss: 0.2548 - accuracy: 0.8976 - val_loss: 0.3218 - val_accuracy: 0.8668\n"
          ]
        }
      ],
      "source": [
        "# 추가 코드 - 평소와 같이 모델을 컴파일하고 훈련합니다.\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(train_set, validation_data=valid_set, epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bobIUkMCC451"
      },
      "source": [
        "**추가 자료: 래그드 텐서 사용하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "eyswunQDC452",
        "outputId": "f4c4e7f7-bb1a-4714-cc62-12bba21b962b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.RaggedTensor [[86, 18], [11, 7, 1, 116, 217]]>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "text_vec_layer_ragged = tf.keras.layers.TextVectorization(\n",
        "    max_tokens=vocab_size, ragged=True)\n",
        "text_vec_layer_ragged.adapt(train_set.map(lambda reviews, labels: reviews))\n",
        "text_vec_layer_ragged([\"Great movie!\", \"This is DiCaprio's best role.\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "gA9L8_b6C452",
        "outputId": "ed32887c-f9e7-4b0b-86df-cfd777367cb0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 5), dtype=int64, numpy=\n",
              "array([[ 86,  18,   0,   0,   0],\n",
              "       [ 11,   7,   1, 116, 217]])>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "text_vec_layer([\"Great movie!\", \"This is DiCaprio's best role.\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzBWyTakC452"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸립니다(GPU를 사용하지 않는 경우 30분 정도 소요될 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "gM9VkQddC452",
        "outputId": "c4b2e606-f977-4556-ae4a-aead02b80164",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "704/704 [==============================] - 61s 81ms/step - loss: 0.4700 - accuracy: 0.7751 - val_loss: 0.3720 - val_accuracy: 0.8416\n",
            "Epoch 2/5\n",
            "704/704 [==============================] - 33s 47ms/step - loss: 0.3254 - accuracy: 0.8623 - val_loss: 0.3254 - val_accuracy: 0.8632\n",
            "Epoch 3/5\n",
            "704/704 [==============================] - 30s 43ms/step - loss: 0.2940 - accuracy: 0.8782 - val_loss: 0.3091 - val_accuracy: 0.8656\n",
            "Epoch 4/5\n",
            "704/704 [==============================] - 30s 43ms/step - loss: 0.2648 - accuracy: 0.8935 - val_loss: 0.3181 - val_accuracy: 0.8672\n",
            "Epoch 5/5\n",
            "704/704 [==============================] - 29s 42ms/step - loss: 0.2497 - accuracy: 0.8987 - val_loss: 0.3505 - val_accuracy: 0.8636\n"
          ]
        }
      ],
      "source": [
        "embed_size = 128\n",
        "tf.random.set_seed(42)\n",
        "model = tf.keras.Sequential([\n",
        "    text_vec_layer_ragged,\n",
        "    tf.keras.layers.Embedding(vocab_size, embed_size),\n",
        "    tf.keras.layers.GRU(128),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(train_set, validation_data=valid_set, epochs=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-gBuEnOC452"
      },
      "source": [
        "## 사전 훈련된 임베딩 및 언어 모델 재사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kccHHpkC452"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 1시간 정도 소요될 수 있음)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "Dvv6wXCEC452",
        "outputId": "0013783f-63a2-497f-df53-34be37c4937c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "704/704 [==============================] - 38s 47ms/step - loss: 0.3812 - accuracy: 0.8393 - val_loss: 0.3328 - val_accuracy: 0.8508\n",
            "Epoch 2/10\n",
            "704/704 [==============================] - 32s 45ms/step - loss: 0.3251 - accuracy: 0.8608 - val_loss: 0.3228 - val_accuracy: 0.8516\n",
            "Epoch 3/10\n",
            "704/704 [==============================] - 33s 47ms/step - loss: 0.3207 - accuracy: 0.8639 - val_loss: 0.3228 - val_accuracy: 0.8520\n",
            "Epoch 4/10\n",
            "704/704 [==============================] - 32s 45ms/step - loss: 0.3160 - accuracy: 0.8665 - val_loss: 0.3207 - val_accuracy: 0.8548\n",
            "Epoch 5/10\n",
            "704/704 [==============================] - 33s 47ms/step - loss: 0.3119 - accuracy: 0.8686 - val_loss: 0.3191 - val_accuracy: 0.8560\n",
            "Epoch 6/10\n",
            "704/704 [==============================] - 32s 45ms/step - loss: 0.3080 - accuracy: 0.8696 - val_loss: 0.3159 - val_accuracy: 0.8576\n",
            "Epoch 7/10\n",
            "704/704 [==============================] - 34s 49ms/step - loss: 0.3031 - accuracy: 0.8708 - val_loss: 0.3285 - val_accuracy: 0.8496\n",
            "Epoch 8/10\n",
            "704/704 [==============================] - 33s 47ms/step - loss: 0.2983 - accuracy: 0.8740 - val_loss: 0.3169 - val_accuracy: 0.8556\n",
            "Epoch 9/10\n",
            "704/704 [==============================] - 32s 45ms/step - loss: 0.2940 - accuracy: 0.8759 - val_loss: 0.3210 - val_accuracy: 0.8548\n",
            "Epoch 10/10\n",
            "704/704 [==============================] - 33s 47ms/step - loss: 0.2891 - accuracy: 0.8787 - val_loss: 0.3129 - val_accuracy: 0.8568\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe4ee788a90>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "import os\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "os.environ[\"TFHUB_CACHE_DIR\"] = \"my_tfhub_cache\"\n",
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "model = tf.keras.Sequential([\n",
        "    # trainable=True로 할 경우 코랩에서 메모리 부족 에러가 발생합니다.\n",
        "    hub.KerasLayer(\"https://tfhub.dev/google/universal-sentence-encoder/4\",\n",
        "                   trainable=False, dtype=tf.string, input_shape=[]),\n",
        "    tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(train_set, validation_data=valid_set, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4E8G1YRrC452"
      },
      "source": [
        "# 신경망 기계 번역을 위한 인코더-디코더 네트워크"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "PaJrhKvTC453",
        "outputId": "8cb9670d-a2ad-4b80-9917-7abb2564a1dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\n",
            "2638744/2638744 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "url = \"https://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\"\n",
        "path = tf.keras.utils.get_file(\"spa-eng.zip\", origin=url, cache_dir=\"datasets\",\n",
        "                               extract=True)\n",
        "text = (Path(path).with_name(\"spa-eng\") / \"spa.txt\").read_text()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "OmMKDi4fC453"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "text = text.replace(\"¡\", \"\").replace(\"¿\", \"\")\n",
        "pairs = [line.split(\"\\t\") for line in text.splitlines()]\n",
        "np.random.seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "np.random.shuffle(pairs)\n",
        "sentences_en, sentences_es = zip(*pairs)  # 쌍을 2개의 리스트로 분리합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "ud_jKZDaC453",
        "outputId": "482f6414-a309-4dc1-aa5c-03a178f49d8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "How boring! => Qué aburrimiento!\n",
            "I love sports. => Adoro el deporte.\n",
            "Would you like to swap jobs? => Te gustaría que intercambiemos los trabajos?\n"
          ]
        }
      ],
      "source": [
        "for i in range(3):\n",
        "    print(sentences_en[i], \"=>\", sentences_es[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "WAvOGf7YC453"
      },
      "outputs": [],
      "source": [
        "vocab_size = 1000\n",
        "max_length = 50\n",
        "text_vec_layer_en = tf.keras.layers.TextVectorization(\n",
        "    vocab_size, output_sequence_length=max_length)\n",
        "text_vec_layer_es = tf.keras.layers.TextVectorization(\n",
        "    vocab_size, output_sequence_length=max_length)\n",
        "text_vec_layer_en.adapt(sentences_en)\n",
        "text_vec_layer_es.adapt([f\"startofseq {s} endofseq\" for s in sentences_es])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "RSMQbPbAC453",
        "outputId": "2524ed86-5516-4b00-e377-d88a54334a6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['', '[UNK]', 'the', 'i', 'to', 'you', 'tom', 'a', 'is', 'he']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "text_vec_layer_en.get_vocabulary()[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "9tHrSKlhC453",
        "outputId": "029c3e54-15cf-438e-f628-3869c6837c17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['', '[UNK]', 'startofseq', 'endofseq', 'de', 'que', 'a', 'no', 'tom', 'la']"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "text_vec_layer_es.get_vocabulary()[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "relGSfGWC453"
      },
      "outputs": [],
      "source": [
        "X_train = tf.constant(sentences_en[:100_000])\n",
        "X_valid = tf.constant(sentences_en[100_000:])\n",
        "X_train_dec = tf.constant([f\"startofseq {s}\" for s in sentences_es[:100_000]])\n",
        "X_valid_dec = tf.constant([f\"startofseq {s}\" for s in sentences_es[100_000:]])\n",
        "Y_train = text_vec_layer_es([f\"{s} endofseq\" for s in sentences_es[:100_000]])\n",
        "Y_valid = text_vec_layer_es([f\"{s} endofseq\" for s in sentences_es[100_000:]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "HLwrDO5HC453"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "encoder_inputs = tf.keras.layers.Input(shape=[], dtype=tf.string)\n",
        "decoder_inputs = tf.keras.layers.Input(shape=[], dtype=tf.string)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "aQ-1elyzC453"
      },
      "outputs": [],
      "source": [
        "embed_size = 128\n",
        "encoder_input_ids = text_vec_layer_en(encoder_inputs)\n",
        "decoder_input_ids = text_vec_layer_es(decoder_inputs)\n",
        "encoder_embedding_layer = tf.keras.layers.Embedding(vocab_size, embed_size,\n",
        "                                                    mask_zero=True)\n",
        "decoder_embedding_layer = tf.keras.layers.Embedding(vocab_size, embed_size,\n",
        "                                                    mask_zero=True)\n",
        "encoder_embeddings = encoder_embedding_layer(encoder_input_ids)\n",
        "decoder_embeddings = decoder_embedding_layer(decoder_input_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "7HuTQgTEC454"
      },
      "outputs": [],
      "source": [
        "encoder = tf.keras.layers.LSTM(512, return_state=True)\n",
        "encoder_outputs, *encoder_state = encoder(encoder_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "vRsCxv7GC454"
      },
      "outputs": [],
      "source": [
        "decoder = tf.keras.layers.LSTM(512, return_sequences=True)\n",
        "decoder_outputs = decoder(decoder_embeddings, initial_state=encoder_state)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "oht3tTSrC454"
      },
      "outputs": [],
      "source": [
        "output_layer = tf.keras.layers.Dense(vocab_size, activation=\"softmax\")\n",
        "Y_proba = output_layer(decoder_outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ghwg8FJbC454"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 몇 시간이 걸릴 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "0V-KsbIKC454",
        "outputId": "020fd7d6-7b5c-45ee-9fc0-61daa791dee1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "3125/3125 [==============================] - 122s 36ms/step - loss: 2.9134 - accuracy: 0.4284 - val_loss: 2.1610 - val_accuracy: 0.5258\n",
            "Epoch 2/10\n",
            "3125/3125 [==============================] - 81s 26ms/step - loss: 1.8394 - accuracy: 0.5767 - val_loss: 1.6493 - val_accuracy: 0.6116\n",
            "Epoch 3/10\n",
            "3125/3125 [==============================] - 85s 27ms/step - loss: 1.4269 - accuracy: 0.6509 - val_loss: 1.4209 - val_accuracy: 0.6552\n",
            "Epoch 4/10\n",
            "3125/3125 [==============================] - 80s 26ms/step - loss: 1.1942 - accuracy: 0.6966 - val_loss: 1.3315 - val_accuracy: 0.6733\n",
            "Epoch 5/10\n",
            "3125/3125 [==============================] - 78s 25ms/step - loss: 1.0315 - accuracy: 0.7301 - val_loss: 1.2893 - val_accuracy: 0.6836\n",
            "Epoch 6/10\n",
            "3125/3125 [==============================] - 78s 25ms/step - loss: 0.8994 - accuracy: 0.7586 - val_loss: 1.2818 - val_accuracy: 0.6863\n",
            "Epoch 7/10\n",
            "3125/3125 [==============================] - 80s 26ms/step - loss: 0.7863 - accuracy: 0.7828 - val_loss: 1.2974 - val_accuracy: 0.6876\n",
            "Epoch 8/10\n",
            "3125/3125 [==============================] - 85s 27ms/step - loss: 0.6869 - accuracy: 0.8069 - val_loss: 1.3266 - val_accuracy: 0.6859\n",
            "Epoch 9/10\n",
            "3125/3125 [==============================] - 81s 26ms/step - loss: 0.6011 - accuracy: 0.8273 - val_loss: 1.3606 - val_accuracy: 0.6834\n",
            "Epoch 10/10\n",
            "3125/3125 [==============================] - 80s 26ms/step - loss: 0.5269 - accuracy: 0.8459 - val_loss: 1.4077 - val_accuracy: 0.6809\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe4ee70efb0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "model = tf.keras.Model(inputs=[encoder_inputs, decoder_inputs],\n",
        "                       outputs=[Y_proba])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit((X_train, X_train_dec), Y_train, epochs=10,\n",
        "          validation_data=((X_valid, X_valid_dec), Y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "45lY6t8-C454"
      },
      "outputs": [],
      "source": [
        "def translate(sentence_en):\n",
        "    translation = \"\"\n",
        "    for word_idx in range(max_length):\n",
        "        X = np.array([sentence_en])  # encoder input \n",
        "        X_dec = np.array([\"startofseq \" + translation])  # decoder input\n",
        "        y_proba = model.predict((X, X_dec))[0, word_idx]  # last token's probas\n",
        "        predicted_word_id = np.argmax(y_proba)\n",
        "        predicted_word = text_vec_layer_es.get_vocabulary()[predicted_word_id]\n",
        "        if predicted_word == \"endofseq\":\n",
        "            break\n",
        "        translation += \" \" + predicted_word\n",
        "    return translation.strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "3c4MuFBIC454",
        "outputId": "a1ea7c5c-dd2b-4440-b478-e14ac8871f1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta el fútbol'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "translate(\"I like soccer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqIJLD6AC454"
      },
      "source": [
        "멋지네요! 그러나 이 모델은 긴 문장을 처리하는 데 어려움을 겪습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "qzHziRN2C454",
        "outputId": "6f25bac2-524c-4698-98bf-088257b52e7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta la [UNK] y [UNK] como los domingos'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "translate(\"I like soccer and also going to the beach\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZ7XzRlPC454"
      },
      "source": [
        "## 양방향 RNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1s_45zNC455"
      },
      "source": [
        "양방향 순환 층을 만들려면 일반 순환 층을 `Bidirectional` 층으로 감싸면 됩니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "OTGo3rMJC455"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "encoder = tf.keras.layers.Bidirectional(\n",
        "    tf.keras.layers.LSTM(256, return_state=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "0Fdt9Wt2C455"
      },
      "outputs": [],
      "source": [
        "encoder_outputs, *encoder_state = encoder(encoder_embeddings)\n",
        "encoder_state = [tf.concat(encoder_state[::2], axis=-1),  # 단기 상태 (0 & 2)\n",
        "                 tf.concat(encoder_state[1::2], axis=-1)]  # 장기 상태 (1 & 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6USBcH8DC455"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 몇 시간이 걸릴 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "C5kKFcAuC455",
        "outputId": "476a877a-fa54-4027-b137-39a7a0f8bb47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "3125/3125 [==============================] - 152s 44ms/step - loss: 2.0742 - accuracy: 0.5544 - val_loss: 1.4790 - val_accuracy: 0.6438\n",
            "Epoch 2/10\n",
            "3125/3125 [==============================] - 111s 35ms/step - loss: 1.2850 - accuracy: 0.6812 - val_loss: 1.2740 - val_accuracy: 0.6826\n",
            "Epoch 3/10\n",
            "3125/3125 [==============================] - 109s 35ms/step - loss: 1.0723 - accuracy: 0.7217 - val_loss: 1.1966 - val_accuracy: 0.7001\n",
            "Epoch 4/10\n",
            "3125/3125 [==============================] - 107s 34ms/step - loss: 0.9283 - accuracy: 0.7522 - val_loss: 1.1794 - val_accuracy: 0.7038\n",
            "Epoch 5/10\n",
            "3125/3125 [==============================] - 103s 33ms/step - loss: 0.8088 - accuracy: 0.7784 - val_loss: 1.1780 - val_accuracy: 0.7063\n",
            "Epoch 6/10\n",
            "3125/3125 [==============================] - 107s 34ms/step - loss: 0.7075 - accuracy: 0.8009 - val_loss: 1.2041 - val_accuracy: 0.7032\n",
            "Epoch 7/10\n",
            "3125/3125 [==============================] - 104s 33ms/step - loss: 0.6200 - accuracy: 0.8223 - val_loss: 1.2445 - val_accuracy: 0.7008\n",
            "Epoch 8/10\n",
            "3125/3125 [==============================] - 102s 33ms/step - loss: 0.5439 - accuracy: 0.8413 - val_loss: 1.2835 - val_accuracy: 0.6986\n",
            "Epoch 9/10\n",
            "3125/3125 [==============================] - 107s 34ms/step - loss: 0.4804 - accuracy: 0.8568 - val_loss: 1.3320 - val_accuracy: 0.6973\n",
            "Epoch 10/10\n",
            "3125/3125 [==============================] - 108s 35ms/step - loss: 0.4283 - accuracy: 0.8709 - val_loss: 1.3812 - val_accuracy: 0.6931\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe4e9f0b100>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "# 추가 코드 - 모델을 완성하고 학습시킵니다.\n",
        "decoder = tf.keras.layers.LSTM(512, return_sequences=True)\n",
        "decoder_outputs = decoder(decoder_embeddings, initial_state=encoder_state)\n",
        "output_layer = tf.keras.layers.Dense(vocab_size, activation=\"softmax\")\n",
        "Y_proba = output_layer(decoder_outputs)\n",
        "model = tf.keras.Model(inputs=[encoder_inputs, decoder_inputs],\n",
        "                       outputs=[Y_proba])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit((X_train, X_train_dec), Y_train, epochs=10,\n",
        "          validation_data=((X_valid, X_valid_dec), Y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "wyS-yKphC455",
        "outputId": "3ebd9f16-7510-4385-d0db-5872503c0562",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 4s 4s/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta el fútbol'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "translate(\"I like soccer\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZX7PZXKDC455"
      },
      "source": [
        "## 빔 검색"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yjh98h5gC455"
      },
      "source": [
        "이것은 빔 검색의 매우 기본적인 구현입니다. 읽기 쉽고 이해하기 쉽게 만들려고 노력했지만 속도에 최적화되지는 않았습니다! 이 함수는 먼저 모델을 사용하여 번역을 시작하기 위해 상위 _k_ 단어를 찾습니다(여기서 _k_는 빔 너비). 상위 _k_ 번역 각각에 대해 해당 번역에 추가할 수 있는 모든 가능한 단어의 조건부 확률을 평가합니다. 이러한 확장 번역과 해당 확률이 후보 목록에 추가됩니다. 모든 상위 _k_ 번역과 번역을 완성할 수 있는 모든 단어를 검토한 후에는 확률이 가장 높은 상위 _k_ 후보만 유지하고 모든 번역이 EOS 토큰으로 완료될 때까지 반복해서 반복합니다. 그런 다음 상위 번역이 반환됩니다(해당 EOS 토큰을 제거한 후).\n",
        "\n",
        "* 참고: p(S)가 문장 S의 확률이고 p(W|S)가 번역이 S로 시작한다는 가정 하에 단어 W의 조건부 확률인 경우, 문장 S' = concat(S, W)의 확률은 p(S') = p(S) * p(W|S)입니다. 단어를 더 추가할수록 확률은 점점 더 작아집니다. 너무 작아져 부동 소수점 정밀도 오류가 발생할 수 있는 위험을 피하기 위해 이 함수는 확률 대신 로그 확률을 추적합니다. log(a\\*b) = log(a) + log(b), 따라서 log(p(S')) = log(p(S)) + log(p(W|S))."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "RyX3_XjhC455"
      },
      "outputs": [],
      "source": [
        "# 추가 코드 - 빔 검색의 기본 구현\n",
        "\n",
        "def beam_search(sentence_en, beam_width, verbose=False):\n",
        "    X = np.array([sentence_en])  # 인코더 입력\n",
        "    X_dec = np.array([\"startofseq\"])  # 디코더 입력\n",
        "    y_proba = model.predict((X, X_dec))[0, 0]  # 첫 번째 토큰의 확률\n",
        "    top_k = tf.math.top_k(y_proba, k=beam_width)\n",
        "    top_translations = [  # 촤상의 (log_proba, translation) 리스트 \n",
        "        (np.log(word_proba), text_vec_layer_es.get_vocabulary()[word_id])\n",
        "        for word_proba, word_id in zip(top_k.values, top_k.indices)\n",
        "    ]\n",
        "    \n",
        "    # 추가 코드 - verbose 모드에서 상위 첫 단어를 표시합니다.\n",
        "    if verbose:\n",
        "        print(\"상위 첫 단어:\", top_translations)\n",
        "\n",
        "    for idx in range(1, max_length):\n",
        "        candidates = []\n",
        "        for log_proba, translation in top_translations:\n",
        "            if translation.endswith(\"endofseq\"):\n",
        "                candidates.append((log_proba, translation))\n",
        "                continue  # 번역이 완료되었으므로 번역을 이어가지 않습니다.\n",
        "            X = np.array([sentence_en])  # 인코더 입력\n",
        "            X_dec = np.array([\"startofseq \" + translation])  # 디코더 입력\n",
        "            y_proba = model.predict((X, X_dec))[0, idx]  # 마지막 토큰의 확률\n",
        "            for word_id, word_proba in enumerate(y_proba):\n",
        "                word = text_vec_layer_es.get_vocabulary()[word_id]\n",
        "                candidates.append((log_proba + np.log(word_proba),\n",
        "                                   f\"{translation} {word}\"))\n",
        "        top_translations = sorted(candidates, reverse=True)[:beam_width]\n",
        "\n",
        "        # 추가 코드 - verbose 모드의 경우 지금까지의 최상의 번역을 출력합니다.\n",
        "        if verbose:\n",
        "            print(\"지금까지 최상의 번역:\", top_translations)\n",
        "\n",
        "        if all([tr.endswith(\"endofseq\") for _, tr in top_translations]):\n",
        "            return top_translations[0][1].replace(\"endofseq\", \"\").strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "9LirxQMpC456",
        "outputId": "601b2038-1f13-4941-c55c-43ca4fdafd47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me [UNK] los gatos y los perros'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "# 추가 코드 - 모델이 어떻게 오류를 발생시키는지 보여줍니다.\n",
        "sentence_en = \"I love cats and dogs\"\n",
        "translate(sentence_en)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "lPXxMOXdC456",
        "outputId": "d64b5037-a407-4ad4-83f8-675ed3469e9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 40ms/step\n",
            "상위 첫 단어: [(-0.081242576, 'me'), (-3.3500824, 'odio'), (-3.5662193, 'yo')]\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "지금까지 최상의 번역: [(-0.79621536, 'me [UNK]'), (-1.42585, 'me gustan'), (-1.9397042, 'me encanta')]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "지금까지 최상의 번역: [(-0.86510205, 'me [UNK] los'), (-1.4312103, 'me gustan los'), (-2.4591732, 'me encanta los')]\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "지금까지 최상의 번역: [(-1.2447193, 'me [UNK] los gatos'), (-2.0246096, 'me [UNK] los perros'), (-2.1221235, 'me gustan los perros')]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-1.2883906, 'me [UNK] los gatos y'), (-2.1663733, 'me gustan los perros y'), (-2.2151074, 'me [UNK] los perros y')]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "지금까지 최상의 번역: [(-1.3628391, 'me [UNK] los gatos y los'), (-2.1718218, 'me gustan los perros y los'), (-2.2216043, 'me [UNK] los perros y los')]\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-1.3677791, 'me [UNK] los gatos y los perros'), (-2.39374, 'me [UNK] los perros y los gatos'), (-2.5055, 'me gustan los perros y los gatos')]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-1.3678507, 'me [UNK] los gatos y los perros endofseq'), (-2.3938456, 'me [UNK] los perros y los gatos endofseq'), (-2.50606, 'me gustan los perros y los gatos endofseq')]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me [UNK] los gatos y los perros'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "# 추가 코드 - 빔 검색이 어떻게 도움이 되는지 보여줍니다.\n",
        "beam_search(sentence_en, beam_width=3, verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFqpL8BqC456"
      },
      "source": [
        "빔 검색에서 찾은 상위 3개 문장에 올바른 번역이 있지만 첫 번째 번역은 아닙니다. 작은 어휘를 사용하기 때문에 \\[UNK] 토큰이 꽤 자주 사용되므로 페널티를 줄 수 있습니다(예를 들어, 빔 검색 함수에서 이 토큰의 확률을 2로 나눕니다.): 이렇게 하면 빔 검색이 이 토큰을 너무 많이 사용하지 않게 됩니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gUCUlOe-C456"
      },
      "source": [
        "# 어텐션 메커니즘"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIsLqkm8C456"
      },
      "source": [
        "모든 인코더의 출력을 `Attention` 층에 공급해야 하므로 인코더에 `return_sequences=True`를 추가해야 합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "6-rNRZIkC456"
      },
      "outputs": [],
      "source": [
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "encoder = tf.keras.layers.Bidirectional(\n",
        "    tf.keras.layers.LSTM(256, return_sequences=True, return_state=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "JhIdaM3xC456"
      },
      "outputs": [],
      "source": [
        "# 추가 코드 - 모델의 이 부분은 이전과 완전히 동일합니다.\n",
        "encoder_outputs, *encoder_state = encoder(encoder_embeddings)\n",
        "encoder_state = [tf.concat(encoder_state[::2], axis=-1),  # 단기 (0 & 2)\n",
        "                 tf.concat(encoder_state[1::2], axis=-1)]  # 장기 (1 & 3)\n",
        "decoder = tf.keras.layers.LSTM(512, return_sequences=True)\n",
        "decoder_outputs = decoder(decoder_embeddings, initial_state=encoder_state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygB3hXHcC456"
      },
      "source": [
        "마지막으로 'Attention' 층과 출력 층을 추가해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "Q5HwprzWC456"
      },
      "outputs": [],
      "source": [
        "attention_layer = tf.keras.layers.Attention()\n",
        "attention_outputs = attention_layer([decoder_outputs, encoder_outputs])\n",
        "output_layer = tf.keras.layers.Dense(vocab_size, activation=\"softmax\")\n",
        "Y_proba = output_layer(attention_outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrI_qChjC456"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 몇 시간이 걸릴 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "okXEBXhUC456",
        "outputId": "94223b0b-b030-4610-ebb3-55d496e028c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "3125/3125 [==============================] - 145s 42ms/step - loss: 2.0050 - accuracy: 0.5656 - val_loss: 1.4373 - val_accuracy: 0.6527\n",
            "Epoch 2/10\n",
            "3125/3125 [==============================] - 109s 35ms/step - loss: 1.2995 - accuracy: 0.6808 - val_loss: 1.2947 - val_accuracy: 0.6847\n",
            "Epoch 3/10\n",
            "3125/3125 [==============================] - 108s 35ms/step - loss: 1.1325 - accuracy: 0.7156 - val_loss: 1.2282 - val_accuracy: 0.7002\n",
            "Epoch 4/10\n",
            "3125/3125 [==============================] - 110s 35ms/step - loss: 1.0151 - accuracy: 0.7402 - val_loss: 1.2098 - val_accuracy: 0.7082\n",
            "Epoch 5/10\n",
            "3125/3125 [==============================] - 105s 33ms/step - loss: 0.9227 - accuracy: 0.7591 - val_loss: 1.2087 - val_accuracy: 0.7080\n",
            "Epoch 6/10\n",
            "3125/3125 [==============================] - 104s 33ms/step - loss: 0.8454 - accuracy: 0.7756 - val_loss: 1.2267 - val_accuracy: 0.7098\n",
            "Epoch 7/10\n",
            "3125/3125 [==============================] - 106s 34ms/step - loss: 0.7786 - accuracy: 0.7901 - val_loss: 1.2497 - val_accuracy: 0.7111\n",
            "Epoch 8/10\n",
            "3125/3125 [==============================] - 104s 33ms/step - loss: 0.7217 - accuracy: 0.8027 - val_loss: 1.2779 - val_accuracy: 0.7102\n",
            "Epoch 9/10\n",
            "3125/3125 [==============================] - 110s 35ms/step - loss: 0.6728 - accuracy: 0.8135 - val_loss: 1.3100 - val_accuracy: 0.7083\n",
            "Epoch 10/10\n",
            "3125/3125 [==============================] - 111s 35ms/step - loss: 0.6300 - accuracy: 0.8237 - val_loss: 1.3368 - val_accuracy: 0.7080\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe4a5ac3850>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "model = tf.keras.Model(inputs=[encoder_inputs, decoder_inputs],\n",
        "                       outputs=[Y_proba])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit((X_train, X_train_dec), Y_train, epochs=10,\n",
        "          validation_data=((X_valid, X_valid_dec), Y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "vXvP6O28C457",
        "outputId": "e31e3b9f-63c5-456d-a875-3ebf65e410f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 4s 4s/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta el fútbol también también a la playa'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "translate(\"I like soccer and also going to the beach\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "-ARGoK9JC457",
        "outputId": "29675432-c838-4a79-d15b-5de85399f878",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 949
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 29ms/step\n",
            "상위 첫 단어: [(-0.13340783, 'me'), (-2.6013386, 'prefiero'), (-4.113326, 'a')]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-0.21696502, 'me gusta'), (-2.7019665, 'me el'), (-3.2995813, 'prefiero el')]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "지금까지 최상의 번역: [(-0.9973064, 'me gusta el'), (-1.8115215, 'me gusta [UNK]'), (-2.4537234, 'me gusta ir')]\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "지금까지 최상의 번역: [(-1.1455431, 'me gusta el fútbol'), (-2.384302, 'me gusta [UNK] y'), (-2.7248979, 'me gusta ir a')]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "지금까지 최상의 번역: [(-2.2578285, 'me gusta el fútbol también'), (-2.4209971, 'me gusta el fútbol y'), (-2.8621109, 'me gusta el fútbol a')]\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "지금까지 최상의 번역: [(-2.55618, 'me gusta el fútbol y también'), (-3.0544732, 'me gusta el fútbol también también'), (-3.4438112, 'me gusta el fútbol a la')]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "지금까지 최상의 번역: [(-3.2134438, 'me gusta el fútbol también también a'), (-3.2902212, 'me gusta el fútbol y también también'), (-3.7078638, 'me gusta el fútbol a la playa')]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "지금까지 최상의 번역: [(-3.2200549, 'me gusta el fútbol también también a la'), (-4.029897, 'me gusta el fútbol y también también ir'), (-4.345028, 'me gusta el fútbol a la playa y')]\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-3.2227175, 'me gusta el fútbol también también a la playa'), (-4.042217, 'me gusta el fútbol y también también ir a'), (-4.931091, 'me gusta el fútbol a la playa y también')]\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.048686, 'me gusta el fútbol y también también ir a la'), (-6.1043167, 'me gusta el fútbol a la playa y también me')]\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.049686, 'me gusta el fútbol y también también ir a la playa'), (-6.403825, 'me gusta el fútbol a la playa y también me ir')]\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.0501018, 'me gusta el fútbol y también también ir a la playa endofseq'), (-6.4167256, 'me gusta el fútbol a la playa y también me ir a')]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.0501018, 'me gusta el fútbol y también también ir a la playa endofseq'), (-6.4352946, 'me gusta el fútbol a la playa y también me ir a la')]\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.0501018, 'me gusta el fútbol y también también ir a la playa endofseq'), (-6.4363847, 'me gusta el fútbol a la playa y también me ir a la playa')]\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "지금까지 최상의 번역: [(-3.2230828, 'me gusta el fútbol también también a la playa endofseq'), (-4.0501018, 'me gusta el fútbol y también también ir a la playa endofseq'), (-6.4367766, 'me gusta el fútbol a la playa y también me ir a la playa endofseq')]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta el fútbol también también a la playa'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "beam_search(\"I like soccer and also going to the beach\", beam_width=3,\n",
        "            verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJIqmnc6C457"
      },
      "source": [
        "## 트랜스포머 구조: 어텐션이 필요한 전부다\n",
        "### 위치 인코딩"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "LAnQQ7o5C457"
      },
      "outputs": [],
      "source": [
        "max_length = 50  # 전체 훈련 세트에 있는 최대 길이\n",
        "embed_size = 128\n",
        "tf.random.set_seed(42)  # 추가 코드 - CPU에서 재현성 보장\n",
        "pos_embed_layer = tf.keras.layers.Embedding(max_length, embed_size)\n",
        "batch_max_len_enc = tf.shape(encoder_embeddings)[1]\n",
        "encoder_in = encoder_embeddings + pos_embed_layer(tf.range(batch_max_len_enc))\n",
        "batch_max_len_dec = tf.shape(decoder_embeddings)[1]\n",
        "decoder_in = decoder_embeddings + pos_embed_layer(tf.range(batch_max_len_dec))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjQlUHd5C457"
      },
      "source": [
        "또는 훈련하지 않는 고정 위치 인코딩을 사용할 수도 있습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "yElNnWh8C457"
      },
      "outputs": [],
      "source": [
        "class PositionalEncoding(tf.keras.layers.Layer):\n",
        "    def __init__(self, max_length, embed_size, dtype=tf.float32, **kwargs):\n",
        "        super().__init__(dtype=dtype, **kwargs)\n",
        "        assert embed_size % 2 == 0, \"embed_size must be even\"\n",
        "        p, i = np.meshgrid(np.arange(max_length),\n",
        "                           2 * np.arange(embed_size // 2))\n",
        "        pos_emb = np.empty((1, max_length, embed_size))\n",
        "        pos_emb[0, :, ::2] = np.sin(p / 10_000 ** (i / embed_size)).T\n",
        "        pos_emb[0, :, 1::2] = np.cos(p / 10_000 ** (i / embed_size)).T\n",
        "        self.pos_encodings = tf.constant(pos_emb.astype(self.dtype))\n",
        "        self.supports_masking = True\n",
        "\n",
        "    def call(self, inputs):\n",
        "        batch_max_length = tf.shape(inputs)[1]\n",
        "        return inputs + self.pos_encodings[:, :batch_max_length]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "a7dOkPc5C457"
      },
      "outputs": [],
      "source": [
        "pos_embed_layer = PositionalEncoding(max_length, embed_size)\n",
        "encoder_in = pos_embed_layer(encoder_embeddings)\n",
        "decoder_in = pos_embed_layer(decoder_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "YvbES7RFC457",
        "outputId": "079b2744-b728-48cd-d9be-71b07b79575a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 추가 코드 - 이 셀은 그림 16-9를 생성하고 저장합니다.\n",
        "figure_max_length = 201\n",
        "figure_embed_size = 512\n",
        "pos_emb = PositionalEncoding(figure_max_length, figure_embed_size)\n",
        "zeros = np.zeros((1, figure_max_length, figure_embed_size), np.float32)\n",
        "P = pos_emb(zeros)[0].numpy()\n",
        "i1, i2, crop_i = 100, 101, 150\n",
        "p1, p2, p3 = 22, 60, 35\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(9, 5))\n",
        "ax1.plot([p1, p1], [-1, 1], \"k--\", label=\"$p = {}$\".format(p1))\n",
        "ax1.plot([p2, p2], [-1, 1], \"k--\", label=\"$p = {}$\".format(p2), alpha=0.5)\n",
        "ax1.plot(p3, P[p3, i1], \"bx\", label=\"$p = {}$\".format(p3))\n",
        "ax1.plot(P[:,i1], \"b-\", label=\"$i = {}$\".format(i1))\n",
        "ax1.plot(P[:,i2], \"r-\", label=\"$i = {}$\".format(i2))\n",
        "ax1.plot([p1, p2], [P[p1, i1], P[p2, i1]], \"bo\")\n",
        "ax1.plot([p1, p2], [P[p1, i2], P[p2, i2]], \"ro\")\n",
        "ax1.legend(loc=\"center right\", fontsize=14, framealpha=0.95)\n",
        "ax1.set_ylabel(\"$P_{(p,i)}$\", rotation=0, fontsize=16)\n",
        "ax1.grid(True, alpha=0.3)\n",
        "ax1.hlines(0, 0, figure_max_length - 1, color=\"k\", linewidth=1, alpha=0.3)\n",
        "ax1.axis([0, figure_max_length - 1, -1, 1])\n",
        "ax2.imshow(P.T[:crop_i], cmap=\"gray\", interpolation=\"bilinear\", aspect=\"auto\")\n",
        "ax2.hlines(i1, 0, figure_max_length - 1, color=\"b\", linewidth=3)\n",
        "cheat = 2  # need to raise the red line a bit, or else it hides the blue one\n",
        "ax2.hlines(i2+cheat, 0, figure_max_length - 1, color=\"r\", linewidth=3)\n",
        "ax2.plot([p1, p1], [0, crop_i], \"k--\")\n",
        "ax2.plot([p2, p2], [0, crop_i], \"k--\", alpha=0.5)\n",
        "ax2.plot([p1, p2], [i2+cheat, i2+cheat], \"ro\")\n",
        "ax2.plot([p1, p2], [i1, i1], \"bo\")\n",
        "ax2.axis([0, figure_max_length - 1, 0, crop_i])\n",
        "ax2.set_xlabel(\"$p$\", fontsize=16)\n",
        "ax2.set_ylabel(\"$i$\", rotation=0, fontsize=16)\n",
        "save_fig(\"positional_embedding_plot\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "korxcX1rC458"
      },
      "source": [
        "### 멀티 헤드 어텐션"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "9lSdmUzyC458"
      },
      "outputs": [],
      "source": [
        "N = 2  # 원본 구조는 6\n",
        "num_heads = 8\n",
        "dropout_rate = 0.1\n",
        "n_units = 128  # 피드 포워드 블록의 첫 번째 Dense 층의 유닛 개수\n",
        "encoder_pad_mask = tf.math.not_equal(encoder_input_ids, 0)[:, tf.newaxis]\n",
        "Z = encoder_in\n",
        "for _ in range(N):\n",
        "    skip = Z\n",
        "    attn_layer = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_heads, key_dim=embed_size, dropout=dropout_rate)\n",
        "    Z = attn_layer(Z, value=Z, attention_mask=encoder_pad_mask)\n",
        "    Z = tf.keras.layers.LayerNormalization()(tf.keras.layers.Add()([Z, skip]))\n",
        "    skip = Z\n",
        "    Z = tf.keras.layers.Dense(n_units, activation=\"relu\")(Z)\n",
        "    Z = tf.keras.layers.Dense(embed_size)(Z)\n",
        "    Z = tf.keras.layers.Dropout(dropout_rate)(Z)\n",
        "    Z = tf.keras.layers.LayerNormalization()(tf.keras.layers.Add()([Z, skip]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "3phVF7mZC458"
      },
      "outputs": [],
      "source": [
        "decoder_pad_mask = tf.math.not_equal(decoder_input_ids, 0)[:, tf.newaxis]\n",
        "causal_mask = tf.linalg.band_part(  # 하삼각행렬을 생성합니다.\n",
        "    tf.ones((batch_max_len_dec, batch_max_len_dec), tf.bool), -1, 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "57Z0AJeqC458"
      },
      "outputs": [],
      "source": [
        "encoder_outputs = Z  # 인코더의 최종 출력을 저장해 보겠습니다.\n",
        "Z = decoder_in  # 디코더는 자체 입력으로 시작합니다.\n",
        "for _ in range(N):\n",
        "    skip = Z\n",
        "    attn_layer = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_heads, key_dim=embed_size, dropout=dropout_rate)\n",
        "    Z = attn_layer(Z, value=Z, attention_mask=causal_mask & decoder_pad_mask)\n",
        "    Z = tf.keras.layers.LayerNormalization()(tf.keras.layers.Add()([Z, skip]))\n",
        "    skip = Z\n",
        "    attn_layer = tf.keras.layers.MultiHeadAttention(\n",
        "        num_heads=num_heads, key_dim=embed_size, dropout=dropout_rate)\n",
        "    Z = attn_layer(Z, value=encoder_outputs, attention_mask=encoder_pad_mask)\n",
        "    Z = tf.keras.layers.LayerNormalization()(tf.keras.layers.Add()([Z, skip]))\n",
        "    skip = Z\n",
        "    Z = tf.keras.layers.Dense(n_units, activation=\"relu\")(Z)\n",
        "    Z = tf.keras.layers.Dense(embed_size)(Z)\n",
        "    Z = tf.keras.layers.LayerNormalization()(tf.keras.layers.Add()([Z, skip]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycKiICgIC458"
      },
      "source": [
        "**경고**: 다음 셀을 실행하는 데 시간이 걸릴 수 있습니다(GPU를 사용하지 않는 경우 2~3시간 정도 소요될 수 있습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "__MZCUCBC458",
        "outputId": "0525e8ad-0b43-4e5e-e3fd-e0106efa5266",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "3125/3125 [==============================] - 225s 63ms/step - loss: 1.8388 - accuracy: 0.5988 - val_loss: 1.4028 - val_accuracy: 0.6636\n",
            "Epoch 2/10\n",
            "3125/3125 [==============================] - 187s 60ms/step - loss: 1.3411 - accuracy: 0.6735 - val_loss: 1.2936 - val_accuracy: 0.6854\n",
            "Epoch 3/10\n",
            "3125/3125 [==============================] - 175s 56ms/step - loss: 1.2371 - accuracy: 0.6932 - val_loss: 1.2046 - val_accuracy: 0.7057\n",
            "Epoch 4/10\n",
            "3125/3125 [==============================] - 176s 56ms/step - loss: 1.1725 - accuracy: 0.7051 - val_loss: 1.1706 - val_accuracy: 0.7106\n",
            "Epoch 5/10\n",
            "3125/3125 [==============================] - 175s 56ms/step - loss: 1.1248 - accuracy: 0.7144 - val_loss: 1.1387 - val_accuracy: 0.7181\n",
            "Epoch 6/10\n",
            "3125/3125 [==============================] - 175s 56ms/step - loss: 1.0870 - accuracy: 0.7210 - val_loss: 1.1092 - val_accuracy: 0.7222\n",
            "Epoch 7/10\n",
            "3125/3125 [==============================] - 185s 59ms/step - loss: 1.0567 - accuracy: 0.7272 - val_loss: 1.1045 - val_accuracy: 0.7241\n",
            "Epoch 8/10\n",
            "3125/3125 [==============================] - 175s 56ms/step - loss: 1.0298 - accuracy: 0.7326 - val_loss: 1.0816 - val_accuracy: 0.7298\n",
            "Epoch 9/10\n",
            "3125/3125 [==============================] - 175s 56ms/step - loss: 1.0073 - accuracy: 0.7368 - val_loss: 1.0500 - val_accuracy: 0.7365\n",
            "Epoch 10/10\n",
            "3125/3125 [==============================] - 174s 56ms/step - loss: 0.9883 - accuracy: 0.7404 - val_loss: 1.0440 - val_accuracy: 0.7383\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe4a5e93670>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "Y_proba = tf.keras.layers.Dense(vocab_size, activation=\"softmax\")(Z)\n",
        "model = tf.keras.Model(inputs=[encoder_inputs, decoder_inputs],\n",
        "                       outputs=[Y_proba])\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit((X_train, X_train_dec), Y_train, epochs=10,\n",
        "          validation_data=((X_valid, X_valid_dec), Y_valid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "-clYUdADC458",
        "outputId": "ab861629-7e21-4878-baf8-d9df383c7b72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'me gusta el fútbol y voy a la playa'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "translate(\"I like soccer and also going to the beach\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tD7fD4YBC458"
      },
      "source": [
        "# 허깅 페이스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra-US0kjC458"
      },
      "source": [
        "코랩에서 실행하는 경우 트랜스포머스 및 데이터셋 라이브러리를 설치합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "rCyu5LjqC459",
        "outputId": "1dc920c7-a205-43a0-993e-25933a64b648",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m76.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m105.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m474.6/474.6 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m25.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m56.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "if \"google.colab\" in sys.modules:\n",
        "    %pip install -q -U transformers\n",
        "    %pip install -q -U datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "ojXkbEn6C459",
        "outputId": "b097066d-e1df-42ad-86e8-7ed9edeaca33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201,
          "referenced_widgets": [
            "237c7c31d1964223bb5fb876f9d8f668",
            "257df5b2836548fe9becf248ae9fe515",
            "265fac771c32497c89b4be06b5611a2d",
            "52d7bff106904afab5b8bf4d70e55bd0",
            "745b1642e17f4b8dbdeee6dc5f6191b8",
            "b7d822796e364b5ea8c26049c19fa808",
            "c2543f32986846649891afa4622e390f",
            "86e2c148f77a4476b5c53ebc65d91f4e",
            "02624ea3c4a74f80ad7a2702ce68cb31",
            "1e3d7a6158cd473680e0230c5c124995",
            "64b668b2280a44839071263808877625",
            "bb4c9a860ae042fe9ef12b6c5fbd8e9d",
            "5280ddc77f804788a823e5dcca291961",
            "6cc6fd95e5ba4bb9bc129613118476eb",
            "49920a99ace44cb0a3d8fdfcdd570173",
            "9b95fb4bf05d4ad8bd355f21aec37f5c",
            "ca93dd56333a4fc59fcdd30b240781e9",
            "c0e18abc672842fd960cc5d48dc83ef9",
            "d04a16a9eed8400bb6c7b842ef08be70",
            "69441245fcac4da7bb742e87bef76614",
            "5dc4f51749c24a988b6ba57997fa4730",
            "4fe88838411d49eab268802e77aa9920",
            "6ddcfae6a2594c299b0a353f866dc5ab",
            "b3309f96d6e74031bc7450e0acbb032d",
            "49999e4c5bc448ccab8d607305f2a8d3",
            "7bde1392ba2844c1b241158cb083067b",
            "92da1b8527984a6a85a8d5df7421e3b9",
            "0257cd94d9ad4c55850201c2b476af1f",
            "114b62c37a4c498bbc2de1c0d7ed018c",
            "41bbb3c538fb402c87b6367593054665",
            "d5e4e85c498542e39f40fad43d7344b7",
            "a2aee52d8a744c5985339bc544d107ea",
            "e2e16346ccdf473b8c4dfccb5f2b32bd",
            "88658c0cb52c4351a1f4c3682dcc65c2",
            "ab246c47762e4ff5acb392f16fd81cb9",
            "83f26e20117e4a468563e945ed316ab4",
            "022726835a774d6f86eaaabc3047fc3b",
            "f61bcb221d3743dd9eeef956a265da84",
            "e7e19c8d63ee4621a9d93bfd4630a4a8",
            "4e6e757a3316433cb4baf41a2f3e2635",
            "6025805759b24446981b2007e3220304",
            "2cc50f22a37844ad9a95167046e15df6",
            "65cd939648434872a275bd1eefd5d756",
            "4ed0aadede5344cf9110a564713ed30b"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "237c7c31d1964223bb5fb876f9d8f668"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb4c9a860ae042fe9ef12b6c5fbd8e9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ddcfae6a2594c299b0a353f866dc5ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88658c0cb52c4351a1f4c3682dcc65c2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")  # 다른 많은 작업을 사용할 수 있습니다.\n",
        "result = classifier(\"The actors were very convincing.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PDCKXay4C459"
      },
      "source": [
        "모델은 매우 편향적일 수 있습니다. 예를 들어, 훈련 데이터와 사용 방법에 따라 일부 국가를 좋아하거나 싫어할 수 있으므로 신중하게 사용하세요:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "CCg5-SHUC459",
        "outputId": "2e396a72-063c-4332-c2c8-06e7ea46ee96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9896161556243896},\n",
              " {'label': 'NEGATIVE', 'score': 0.9811071157455444}]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "classifier([\"I am from India.\", \"I am from Iraq.\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "uUx8IbKuC459",
        "outputId": "d5360500-0f3f-4704-e6ba-ba5922370f77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227,
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            "42da820310d74a0cb8dc075f4d4a46dc"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/729 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "00001e82b0be4072968f82d3500c61df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8584e1c412924ba99a90c414b28e5e97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/58.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ffbaa884beb04f7d86274aad91920aed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed78dd4e0fcf48fda5f53e5d59f82576"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a0cf4a429ec4b2995786176db3ebf66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c39e89f92414f12bbb1735effbdf51b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'contradiction', 'score': 0.9790191650390625}]"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "model_name = \"huggingface/distilbert-base-uncased-finetuned-mnli\"\n",
        "classifier_mnli = pipeline(\"text-classification\", model=model_name)\n",
        "classifier_mnli(\"She loves me. [SEP] She loves me not.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "LI8riqr5C459",
        "outputId": "5923aa74-4dd2-455f-9974-20dceb172ada",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47c37f22280742c5ae07b6d211663f72"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at huggingface/distilbert-base-uncased-finetuned-mnli were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at huggingface/distilbert-base-uncased-finetuned-mnli and are newly initialized: ['dropout_21']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = TFAutoModelForSequenceClassification.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "my6XeY67C459",
        "outputId": "8c67f6a9-3224-41ea-fb39-bc8138171ab8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(2, 15), dtype=int32, numpy=\n",
              "array([[ 101, 1045, 2066, 4715, 1012,  102, 2057, 2035, 2293, 4715,  999,\n",
              "         102,    0,    0,    0],\n",
              "       [ 101, 3533, 2973, 2005, 1037, 2200, 2146, 2051, 1012,  102, 3533,\n",
              "        2003, 2214, 1012,  102]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 15), dtype=int32, numpy=\n",
              "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
              "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "token_ids = tokenizer([\"I like soccer. [SEP] We all love soccer!\",\n",
        "                       \"Joe lived for a very long time. [SEP] Joe is old.\"],\n",
        "                      padding=True, return_tensors=\"tf\")\n",
        "token_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "DbJtA8zFC459",
        "outputId": "cd319211-d2e7-4206-d5eb-2e338550170a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': <tf.Tensor: shape=(2, 15), dtype=int32, numpy=\n",
              "array([[ 101, 1045, 2066, 4715, 1012,  102, 2057, 2035, 2293, 4715,  999,\n",
              "         102,    0,    0,    0],\n",
              "       [ 101, 3533, 2973, 2005, 1037, 2200, 2146, 2051, 1012,  102, 3533,\n",
              "        2003, 2214, 1012,  102]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 15), dtype=int32, numpy=\n",
              "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],\n",
              "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)>}"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "token_ids = tokenizer([(\"I like soccer.\", \"We all love soccer!\"),\n",
        "                       (\"Joe lived for a very long time.\", \"Joe is old.\")],\n",
        "                      padding=True, return_tensors=\"tf\")\n",
        "token_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "A9ug694NC459",
        "outputId": "361fcc33-b94e-4082-af64-965a32659578",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
              "array([[-2.1123812 ,  1.178679  ,  1.4101001 ],\n",
              "       [-0.01478288,  1.0962466 , -0.9919953 ]], dtype=float32)>, hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "outputs = model(token_ids)\n",
        "outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "HIlbEUh0C45-",
        "outputId": "c934d77a-c447-4517-e3a2-b30b3334eda4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n",
              "array([[0.01619703, 0.43523598, 0.54856706],\n",
              "       [0.22655995, 0.6881722 , 0.08526783]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "Y_probas = tf.keras.activations.softmax(outputs.logits)\n",
        "Y_probas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "SpVg2ouSC45-",
        "outputId": "e1f89bc4-b84c-48b1-e81c-f2c9a2cab408",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2,), dtype=int64, numpy=array([2, 1])>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "Y_pred = tf.argmax(Y_probas, axis=1)\n",
        "Y_pred  # 0 = contradiction, 1 = entailment, 2 = neutral"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "lbmfSRt6C45-",
        "outputId": "10089796-57b5-4631-e02c-3cdc9d93acc0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "1/1 [==============================] - 48s 48s/step - loss: 0.6666 - accuracy: 0.5000\n",
            "Epoch 2/2\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.3430 - accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "sentences = [(\"Sky is blue\", \"Sky is red\"), (\"I love her\", \"She loves me\")]\n",
        "X_train = tokenizer(sentences, padding=True, return_tensors=\"tf\").data\n",
        "y_train = tf.constant([0, 2])  # contradiction, neutral\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(loss=loss, optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, y_train, epochs=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xc3RWZHC45-"
      },
      "source": [
        "# 연습문제 해답"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLDEEspbC45-"
      },
      "source": [
        "## 1. to 7."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2Q2vKIWC45-"
      },
      "source": [
        "부록 A 참조."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPu2UpX_C45-"
      },
      "source": [
        "## 8.\n",
        "_연습문제: 호크라이터와 슈미트후버는 LSTM에 관한 [논문](https://homl.info/93)에서 임베딩된 레버 문법을 사용했습니다. 이는 ‘BPBTSXXVPSEPE’와 같은 문자열을 만드는 인공 문법입니다. 이 주제에 대한 제니 오어의 훌륭한 소개(https://homl.info/108)를 확인해보세요. 특정 임베딩된 레버 문법 하나를 선택하고(제니 오어의 페이지에 있는 것과 같은), 그다음에 문자열이 이 문법을 따르는지 아닌지 구별하는 RNN을 훈련해보세요. 먼저 문법에 맞는 문자열 50%와 그렇지 않은 문자열 50%를 담은 훈련 배치를 생성하는 함수를 만들어야 합니다._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8oV6k3yC45-"
      },
      "source": [
        "먼저 문법에 맞는 문자열을 생성하는 함수가 필요합니다. 이 문법은 각 상태에서 가능한 전이 상태의 리스트입니다. 하나의 전이는 출력할 문자열(또는 생성할 문법)과 다음 상태를 지정합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "nx0sOrZXC45_"
      },
      "outputs": [],
      "source": [
        "default_reber_grammar = [\n",
        "    [(\"B\", 1)],           # (state 0) =B=>(state 1)\n",
        "    [(\"T\", 2), (\"P\", 3)], # (state 1) =T=>(state 2) or =P=>(state 3)\n",
        "    [(\"S\", 2), (\"X\", 4)], # (state 2) =S=>(state 2) or =X=>(state 4)\n",
        "    [(\"T\", 3), (\"V\", 5)], # 등등 ...\n",
        "    [(\"X\", 3), (\"S\", 6)],\n",
        "    [(\"P\", 4), (\"V\", 6)],\n",
        "    [(\"E\", None)]]        # (state 6) =E=>(terminal state)\n",
        "\n",
        "embedded_reber_grammar = [\n",
        "    [(\"B\", 1)],\n",
        "    [(\"T\", 2), (\"P\", 3)],\n",
        "    [(default_reber_grammar, 4)],\n",
        "    [(default_reber_grammar, 5)],\n",
        "    [(\"T\", 6)],\n",
        "    [(\"P\", 6)],\n",
        "    [(\"E\", None)]]\n",
        "\n",
        "def generate_string(grammar):\n",
        "    state = 0\n",
        "    output = []\n",
        "    while state is not None:\n",
        "        index = np.random.randint(len(grammar[state]))\n",
        "        production, state = grammar[state][index]\n",
        "        if isinstance(production, list):\n",
        "            production = generate_string(grammar=production)\n",
        "        output.append(production)\n",
        "    return \"\".join(output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI0N2i3WC45_"
      },
      "source": [
        "기본 레버 문법을 기반으로 몇 가지 문자열을 생성해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "oSNDdGzxC45_",
        "outputId": "fbd62f64-2d0f-4e4d-9f0c-b96d18e642af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BTXXTTVPXTVPXTTVPSE BPVPSE BTXSE BPVVE BPVVE BTSXSE BPTVPXTTTVVE BPVVE BTXSE BTXXVPSE BPTTTTTTTTVVE BTXSE BPVPSE BTXSE BPTVPSE BTXXTVPSE BPVVE BPVVE BPVVE BPTTVVE BPVVE BPVVE BTXXVVE BTXXVVE BTXXVPXVVE "
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "for _ in range(25):\n",
        "    print(generate_string(default_reber_grammar), end=\" \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkez1mYGC45_"
      },
      "source": [
        "좋아 보이네요. 이제 임베딩된 레버 문법을 기반으로 몇 가지 문자열을 생성해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "aO2VwxHQC45_",
        "outputId": "f47b316f-a878-41ba-e9dc-c8d6e24839df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BTBPTTTVPXTVPXTTVPSETE BPBPTVPSEPE BPBPVVEPE BPBPVPXVVEPE BPBTXXTTTTVVEPE BPBPVPSEPE BPBTXXVPSEPE BPBTSSSSSSSXSEPE BTBPVVETE BPBTXXVVEPE BPBTXXVPSEPE BTBTXXVVETE BPBPVVEPE BPBPVVEPE BPBTSXSEPE BPBPVVEPE BPBPTVPSEPE BPBTXXVVEPE BTBPTVPXVVETE BTBPVVETE BTBTSSSSSSSXXVVETE BPBTSSSXXTTTTVPSEPE BTBPTTVVETE BPBTXXTVVEPE BTBTXSETE "
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "for _ in range(25):\n",
        "    print(generate_string(embedded_reber_grammar), end=\" \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flmVfAElC45_"
      },
      "source": [
        "좋네요, 이제 이 문법을 따르지 않는 문자열을 생성할 함수를 만듭니다. 무작위하게 문자열을 만들 수 있지만 그렇게 하면 너무 문제가 쉬워지므로 대신 문법을 따르는 문자열을 만든 후 하나의 문자만 바꾸어 놓도록 하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "8ZY6tDtOC45_"
      },
      "outputs": [],
      "source": [
        "POSSIBLE_CHARS = \"BEPSTVX\"\n",
        "\n",
        "def generate_corrupted_string(grammar, chars=POSSIBLE_CHARS):\n",
        "    good_string = generate_string(grammar)\n",
        "    index = np.random.randint(len(good_string))\n",
        "    good_char = good_string[index]\n",
        "    bad_char = np.random.choice(sorted(set(chars) - set(good_char)))\n",
        "    return good_string[:index] + bad_char + good_string[index + 1:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0_BvQc5C46A"
      },
      "source": [
        "잘못된 문자열 몇 개를 만들어 보죠:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "KHESmc2RC46A",
        "outputId": "e54da45c-2e0e-43ec-a2b8-93dff780b445",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BTBPTTTPPXTVPXTTVPSETE BPBTXEEPE BPBPTVVVEPE BPBTSSSSXSETE BPTTXSEPE BTBPVPXTTTTTTEVETE BPBTXXSVEPE BSBPTTVPSETE BPBXVVEPE BEBTXSETE BPBPVPSXPE BTBPVVVETE BPBTSXSETE BPBPTTTPTTTTTVPSEPE BTBTXXTTSTVPSETE BBBTXSETE BPBTPXSEPE BPBPVPXTTTTVPXTVPXVPXTTTVVEVE BTBXXXTVPSETE BEBTSSSSSXXVPXTVVETE BTBXTTVVETE BPBTXSTPE BTBTXXTTTVPSBTE BTBTXSETX BTBTSXSSTE "
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "for _ in range(25):\n",
        "    print(generate_corrupted_string(embedded_reber_grammar), end=\" \")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDZ-yEe1C46A"
      },
      "source": [
        "문자열을 바로 RNN에 주입할 수는 없기 때문에 어떤 식으로든 인코딩해야 합니다. 한 가지 방법은 각 문자를 원-핫 인코딩하는 것입니다. 또 다른 방식은 임베딩을 사용하는 것입니다. 두 번째 방법을 사용해 보겠습니다(문자 개수가 작다면 원-핫 인코딩도 좋은 선택일 것입니다). 임베딩을 위해 각 문자열을 문자 ID의 시퀀스로 바꾸어야 합니다. 가능한 문자 \"BEPSTVX\"의 문자열 인덱스를 사용해 이런 작업을 수행하는 함수를 만들어 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "IkokV-4gC46A"
      },
      "outputs": [],
      "source": [
        "def string_to_ids(s, chars=POSSIBLE_CHARS):\n",
        "    return [chars.index(c) for c in s]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "-5HUdUSKC46A",
        "outputId": "3bf744a8-05e7-4649-cff0-af1cca689192",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 4, 4, 4, 6, 6, 5, 5, 1, 4, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "string_to_ids(\"BTTTXXVVETE\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdx_-rYUC46A"
      },
      "source": [
        "이제 50%는 올바른 문자열 50%는 잘못된 문자열로 이루어진 데이터셋을 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "IVZOpmlHC46A"
      },
      "outputs": [],
      "source": [
        "def generate_dataset(size):\n",
        "    good_strings = [\n",
        "        string_to_ids(generate_string(embedded_reber_grammar))\n",
        "        for _ in range(size // 2)\n",
        "    ]\n",
        "    bad_strings = [\n",
        "        string_to_ids(generate_corrupted_string(embedded_reber_grammar))\n",
        "        for _ in range(size - size // 2)\n",
        "    ]\n",
        "    all_strings = good_strings + bad_strings\n",
        "    X = tf.ragged.constant(all_strings, ragged_rank=1)\n",
        "    y = np.array([[1.] for _ in range(len(good_strings))] +\n",
        "                 [[0.] for _ in range(len(bad_strings))])\n",
        "    return X, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "f2fdot5NC46A"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "X_train, y_train = generate_dataset(10000)\n",
        "X_valid, y_valid = generate_dataset(2000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RTI9Yh-_C46A"
      },
      "source": [
        "첫 번째 훈련 샘플을 확인해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "UviLZYNjC46B",
        "outputId": "10e25256-ec31-49e4-bb86-1dec3af8f727",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(22,), dtype=int32, numpy=\n",
              "array([0, 4, 0, 2, 4, 4, 4, 5, 2, 6, 4, 5, 2, 6, 4, 4, 5, 2, 3, 1, 4, 1],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "X_train[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RqgWR5ZC46B"
      },
      "source": [
        "어떤 클래스에 속할까요?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "6fAFbAGpC46B",
        "outputId": "cdb29546-70c3-4270-e8da-740a82a110a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "y_train[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-fRBiyoKC46B"
      },
      "source": [
        "완벽합니다! 이제 올바른 문자열을 구분할 RNN을 만들 준비가 되었습니다. 간단한 시퀀스 이진 분류기를 만듭니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "zd6OcjicC46B",
        "outputId": "97125496-e653-4111-b53c-7d54e2936cba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "313/313 [==============================] - 6s 10ms/step - loss: 0.6888 - accuracy: 0.5351 - val_loss: 0.6774 - val_accuracy: 0.4805\n",
            "Epoch 2/20\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 0.6630 - accuracy: 0.5665 - val_loss: 0.6589 - val_accuracy: 0.4960\n",
            "Epoch 3/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.6472 - accuracy: 0.5904 - val_loss: 0.6473 - val_accuracy: 0.5615\n",
            "Epoch 4/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.6301 - accuracy: 0.6083 - val_loss: 0.6375 - val_accuracy: 0.6190\n",
            "Epoch 5/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.5689 - accuracy: 0.6828 - val_loss: 0.5264 - val_accuracy: 0.7450\n",
            "Epoch 6/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.4899 - accuracy: 0.7544 - val_loss: 0.4457 - val_accuracy: 0.8165\n",
            "Epoch 7/20\n",
            "313/313 [==============================] - 4s 12ms/step - loss: 0.3436 - accuracy: 0.8618 - val_loss: 0.3240 - val_accuracy: 0.8750\n",
            "Epoch 8/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.2299 - accuracy: 0.9186 - val_loss: 0.1771 - val_accuracy: 0.9450\n",
            "Epoch 9/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.1588 - accuracy: 0.9498 - val_loss: 0.1582 - val_accuracy: 0.9565\n",
            "Epoch 10/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.0891 - accuracy: 0.9769 - val_loss: 0.0152 - val_accuracy: 0.9955\n",
            "Epoch 11/20\n",
            "313/313 [==============================] - 3s 9ms/step - loss: 0.0451 - accuracy: 0.9868 - val_loss: 0.0261 - val_accuracy: 0.9915\n",
            "Epoch 12/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 0.0098 - accuracy: 0.9978 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "Epoch 13/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 9.9437e-04 - accuracy: 0.9999 - val_loss: 8.0492e-04 - val_accuracy: 1.0000\n",
            "Epoch 14/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 5.1818e-04 - accuracy: 1.0000 - val_loss: 4.1737e-04 - val_accuracy: 1.0000\n",
            "Epoch 15/20\n",
            "313/313 [==============================] - 3s 9ms/step - loss: 3.6918e-04 - accuracy: 1.0000 - val_loss: 3.1813e-04 - val_accuracy: 1.0000\n",
            "Epoch 16/20\n",
            "313/313 [==============================] - 5s 14ms/step - loss: 2.9639e-04 - accuracy: 1.0000 - val_loss: 2.6576e-04 - val_accuracy: 1.0000\n",
            "Epoch 17/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 2.5316e-04 - accuracy: 1.0000 - val_loss: 2.2870e-04 - val_accuracy: 1.0000\n",
            "Epoch 18/20\n",
            "313/313 [==============================] - 2s 7ms/step - loss: 2.1909e-04 - accuracy: 1.0000 - val_loss: 2.0328e-04 - val_accuracy: 1.0000\n",
            "Epoch 19/20\n",
            "313/313 [==============================] - 2s 8ms/step - loss: 1.9400e-04 - accuracy: 1.0000 - val_loss: 1.8130e-04 - val_accuracy: 1.0000\n",
            "Epoch 20/20\n",
            "313/313 [==============================] - 3s 9ms/step - loss: 1.7483e-04 - accuracy: 1.0000 - val_loss: 1.6414e-04 - val_accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "embedding_size = 5\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.InputLayer(input_shape=[None], dtype=tf.int32, ragged=True),\n",
        "    tf.keras.layers.Embedding(input_dim=len(POSSIBLE_CHARS),\n",
        "                              output_dim=embedding_size),\n",
        "    tf.keras.layers.GRU(30),\n",
        "    tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.02, momentum = 0.95,\n",
        "                                    nesterov=True)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=optimizer,\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, y_train, epochs=20,\n",
        "                    validation_data=(X_valid, y_valid))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpW6UeU4C46B"
      },
      "source": [
        "이제 두 개의 까다로운 문자열로 이 RNN을 테스트해 보죠: 첫 번째는 잘못된 것이고 두 번째는 올바른 것입니다. 이 문자열은 마지막에서 두 번째 글자만 다릅니다. RNN이 이를 맞춘다면 두 번째 문자가 항상 끝에서 두 번째 문자와 같아야 한다는 패턴을 알게 됐다는 것을 의미합니다. 이렇게 하려면 꽤 긴 단기 기억(long short-term memory)이 필요합니다(그래서 GRU 셀을 사용했습니다)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "wgJZ6rr0C46B",
        "outputId": "9faecb21-05c6-411f-b463-fed7bbc78a4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 486ms/step\n",
            "\n",
            "레버 문자열일 추정 확률:\n",
            "BPBTSSSSSSSXXTTVPXVPXTTTTTVVETE: 0.03%\n",
            "BPBTSSSSSSSXXTTVPXVPXTTTTTVVEPE: 99.69%\n"
          ]
        }
      ],
      "source": [
        "test_strings = [\"BPBTSSSSSSSXXTTVPXVPXTTTTTVVETE\",\n",
        "                \"BPBTSSSSSSSXXTTVPXVPXTTTTTVVEPE\"]\n",
        "X_test = tf.ragged.constant([string_to_ids(s) for s in test_strings], ragged_rank=1)\n",
        "\n",
        "y_proba = model.predict(X_test)\n",
        "print()\n",
        "print(\"레버 문자열일 추정 확률:\")\n",
        "for index, string in enumerate(test_strings):\n",
        "    print(\"{}: {:.2f}%\".format(string, 100 * y_proba[index][0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdffdfhhC46B"
      },
      "source": [
        "쨘! 잘 작동하네요. 이 RNN이 매우 높은 신뢰도로 정확한 답을 냈습니다. :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCmWD4tnC46B"
      },
      "source": [
        "## 9.\n",
        "_연습문제: 날짜 문자열 포맷을 변환하는 인코더-디코더 모델을 훈련하세요(예를 들어, ‘April 22, 2019’에서 ‘2019-04-22’로 바꿉니다)._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ge3AVnYmC46C"
      },
      "source": [
        "먼저 데이터셋을 만들어 보죠. 1000-01-01 ~ 9999-12-31 사이의 랜덤한 날짜를 사용하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "w-f5t_67C46C"
      },
      "outputs": [],
      "source": [
        "from datetime import date\n",
        "\n",
        "# strftime()의 %B 포맷은 로케일에 의존하기 때문에 사용할 수 있습니다.\n",
        "MONTHS = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n",
        "          \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
        "\n",
        "def random_dates(n_dates):\n",
        "    min_date = date(1000, 1, 1).toordinal()\n",
        "    max_date = date(9999, 12, 31).toordinal()\n",
        "\n",
        "    ordinals = np.random.randint(max_date - min_date, size=n_dates) + min_date\n",
        "    dates = [date.fromordinal(ordinal) for ordinal in ordinals]\n",
        "\n",
        "    x = [MONTHS[dt.month - 1] + \" \" + dt.strftime(\"%d, %Y\") for dt in dates]\n",
        "    y = [dt.isoformat() for dt in dates]\n",
        "    return x, y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaEpKS1oC46C"
      },
      "source": [
        "다음은 입력과 출력 형식에 맞춘 랜덤한 몇 개의 날짜입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "21czij8VC46C",
        "outputId": "87e1bb53-b59b-47ad-fbf9-3ddab43f1613",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input                    Target                   \n",
            "--------------------------------------------------\n",
            "September 20, 7075       7075-09-20               \n",
            "May 15, 8579             8579-05-15               \n",
            "January 11, 7103         7103-01-11               \n"
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "n_dates = 3\n",
        "x_example, y_example = random_dates(n_dates)\n",
        "print(\"{:25s}{:25s}\".format(\"Input\", \"Target\"))\n",
        "print(\"-\" * 50)\n",
        "for idx in range(n_dates):\n",
        "    print(\"{:25s}{:25s}\".format(x_example[idx], y_example[idx]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nszUXihGC46C"
      },
      "source": [
        "입력 가능한 전체 문자를 나열해 보죠:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "CY7Knl58C46C",
        "outputId": "433ea02a-b32e-4bb3-de3a-f2560e8011e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' ,0123456789ADFJMNOSabceghilmnoprstuvy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "INPUT_CHARS = \"\".join(sorted(set(\"\".join(MONTHS) + \"0123456789, \")))\n",
        "INPUT_CHARS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZPlepcgC46C"
      },
      "source": [
        "그리고 다음은 출력 가능한 전체 문자입니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "RG7nVJDLC46C"
      },
      "outputs": [],
      "source": [
        "OUTPUT_CHARS = \"0123456789-\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "al_1vGceC46C"
      },
      "source": [
        "이전 연습문제에서처럼 문자열을 문자 ID 리스트로 바꾸는 함수를 작성해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "RMHco9w7C46C"
      },
      "outputs": [],
      "source": [
        "def date_str_to_ids(date_str, chars=INPUT_CHARS):\n",
        "    return [chars.index(c) for c in date_str]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "W1ar8s8dC46C",
        "outputId": "07f5ffa6-37e2-41d6-ad68-c5de51f65b5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[19, 23, 31, 34, 23, 28, 21, 23, 32, 0, 4, 2, 1, 0, 9, 2, 9, 7]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ],
      "source": [
        "date_str_to_ids(x_example[0], INPUT_CHARS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "NFrnenuaC46D",
        "outputId": "94fbbcca-6551-4dbf-b1b5-a915f3bb19a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 0, 7, 5, 10, 0, 9, 10, 2, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "date_str_to_ids(y_example[0], OUTPUT_CHARS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "VikEI1hmC46D"
      },
      "outputs": [],
      "source": [
        "def prepare_date_strs(date_strs, chars=INPUT_CHARS):\n",
        "    X_ids = [date_str_to_ids(dt, chars) for dt in date_strs]\n",
        "    X = tf.ragged.constant(X_ids, ragged_rank=1)\n",
        "    return (X + 1).to_tensor() # 0을 패딩 토큰 ID로 사용\n",
        "\n",
        "def create_dataset(n_dates):\n",
        "    x, y = random_dates(n_dates)\n",
        "    return prepare_date_strs(x, INPUT_CHARS), prepare_date_strs(y, OUTPUT_CHARS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "Rcz_6S8dC46D"
      },
      "outputs": [],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "X_train, Y_train = create_dataset(10000)\n",
        "X_valid, Y_valid = create_dataset(2000)\n",
        "X_test, Y_test = create_dataset(2000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "RQiuiL8YC46D",
        "outputId": "cf5f37d6-20d6-4c45-a5a2-a5b8b9a03ad3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 8,  1,  8,  6, 11,  1, 10, 11,  3,  1], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ],
      "source": [
        "Y_train[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiZ7vky7C46D"
      },
      "source": [
        "### 첫 번째 버전: 기본적인 seq2seq 모델"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HtoNr-rC46D"
      },
      "source": [
        "먼저 가장 간단한 모델을 시도해 보겠습니다: 입력 시퀀스가 먼저 (임베딩 층 뒤에 하나의 LSTM 층으로 구성된) 인코더를 통과하여 벡터로 출력됩니다. 그 다음 이 벡터가 (하나의 LSTM 층 뒤에 밀집 층으로 구성된) 디코더로 들어가 벡터의 시퀀스를 출력합니다. 각 벡터는 가능한 모든 출력 문자에 대한 추정 확률입니다.\n",
        "\n",
        "디코더는 시퀀스를 입력으로 기대하기 때문에 가능한 가장 긴 출력 시퀀스만큼 (인코더의 출력) 벡터를 반복합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "pjsQfMLvC46D",
        "outputId": "8af3e4d9-7cb7-4e18-946b-0671c6fb46ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "313/313 [==============================] - 11s 20ms/step - loss: 1.6892 - accuracy: 0.3992 - val_loss: 1.2189 - val_accuracy: 0.5632\n",
            "Epoch 2/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 1.2325 - accuracy: 0.5697 - val_loss: 1.0699 - val_accuracy: 0.6237\n",
            "Epoch 3/20\n",
            "313/313 [==============================] - 3s 11ms/step - loss: 0.8406 - accuracy: 0.6900 - val_loss: 0.7009 - val_accuracy: 0.7292\n",
            "Epoch 4/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.5980 - accuracy: 0.7603 - val_loss: 0.5092 - val_accuracy: 0.7904\n",
            "Epoch 5/20\n",
            "313/313 [==============================] - 4s 14ms/step - loss: 0.4135 - accuracy: 0.8348 - val_loss: 0.3419 - val_accuracy: 0.8657\n",
            "Epoch 6/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.2778 - accuracy: 0.8982 - val_loss: 0.2149 - val_accuracy: 0.9294\n",
            "Epoch 7/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.1545 - accuracy: 0.9578 - val_loss: 0.1125 - val_accuracy: 0.9754\n",
            "Epoch 8/20\n",
            "313/313 [==============================] - 4s 12ms/step - loss: 0.3370 - accuracy: 0.9126 - val_loss: 0.1480 - val_accuracy: 0.9690\n",
            "Epoch 9/20\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 0.0892 - accuracy: 0.9841 - val_loss: 0.0617 - val_accuracy: 0.9909\n",
            "Epoch 10/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0422 - accuracy: 0.9952 - val_loss: 0.0334 - val_accuracy: 0.9966\n",
            "Epoch 11/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0239 - accuracy: 0.9984 - val_loss: 0.0208 - val_accuracy: 0.9990\n",
            "Epoch 12/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0149 - accuracy: 0.9995 - val_loss: 0.0130 - val_accuracy: 0.9996\n",
            "Epoch 13/20\n",
            "313/313 [==============================] - 4s 14ms/step - loss: 0.0097 - accuracy: 0.9998 - val_loss: 0.0094 - val_accuracy: 0.9998\n",
            "Epoch 14/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0067 - accuracy: 0.9999 - val_loss: 0.0066 - val_accuracy: 1.0000\n",
            "Epoch 15/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "Epoch 16/20\n",
            "313/313 [==============================] - 5s 15ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0038 - val_accuracy: 1.0000\n",
            "Epoch 17/20\n",
            "313/313 [==============================] - 6s 18ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "Epoch 18/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "Epoch 19/20\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 20/20\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n"
          ]
        }
      ],
      "source": [
        "embedding_size = 32\n",
        "max_output_length = Y_train.shape[1]\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "encoder = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=len(INPUT_CHARS) + 1,\n",
        "                           output_dim=embedding_size,\n",
        "                           input_shape=[None]),\n",
        "    tf.keras.layers.LSTM(128)\n",
        "])\n",
        "\n",
        "decoder = tf.keras.Sequential([\n",
        "    tf.keras.layers.LSTM(128, return_sequences=True),\n",
        "    tf.keras.layers.Dense(len(OUTPUT_CHARS) + 1, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.RepeatVector(max_output_length),\n",
        "    decoder\n",
        "])\n",
        "\n",
        "optimizer = tf.keras.optimizers.Nadam()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, Y_train, epochs=20,\n",
        "                    validation_data=(X_valid, Y_valid))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJg19_aQC46D"
      },
      "source": [
        "좋아 보이네요, 100% 검증 정확도를 달성했습니다! 이 모델을 사용해 예측을 만들어 보죠. 문자 ID 시퀀스를 문자열로 바꾸는 함수를 작성하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "7gSr1tuoC46D"
      },
      "outputs": [],
      "source": [
        "def ids_to_date_strs(ids, chars=OUTPUT_CHARS):\n",
        "    return [\"\".join([(\"?\" + chars)[index] for index in sequence])\n",
        "            for sequence in ids]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ML55jOcC46D"
      },
      "source": [
        "이제 모델을 사용해 샘플 날짜를 변환합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "AOqEWk75C46D"
      },
      "outputs": [],
      "source": [
        "X_new = prepare_date_strs([\"September 17, 2009\", \"July 14, 1789\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "2w4nPudNC46D",
        "outputId": "12a4c312-7634-4774-cd36-c3a0d80661f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n",
            "2009-09-17\n",
            "1789-07-14\n"
          ]
        }
      ],
      "source": [
        "ids = model.predict(X_new).argmax(axis=-1)\n",
        "for date_str in ids_to_date_strs(ids):\n",
        "    print(date_str)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87pBdGs_C46E"
      },
      "source": [
        "완벽합니다! :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJGqPiEFC46E"
      },
      "source": [
        "하지만 (가장 긴 날짜에 해당하는) 길이가 18인 입력 문자열에서만 모델이 훈련되었기 때문에 짧은 시퀀스에서는 잘 동작하지 않습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "nsCpmNEzC46E"
      },
      "outputs": [],
      "source": [
        "X_new = prepare_date_strs([\"May 02, 2020\", \"July 14, 1789\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "7yGpS56eC46E",
        "outputId": "f3b186f8-7f1d-4982-b743-27123b69a4e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 737ms/step\n",
            "2020-01-02\n",
            "1789-09-14\n"
          ]
        }
      ],
      "source": [
        "ids = model.predict(X_new).argmax(axis=-1)\n",
        "for date_str in ids_to_date_strs(ids):\n",
        "    print(date_str)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtsE9bM1C46E"
      },
      "source": [
        "이런! 패딩을 사용해 훈련할 때와 동일한 길이의 시퀀스를 전달해야 할 것 같습니다. 이를 위해 헬퍼 함수를 작성해 보죠:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "ntqHdbRsC46E"
      },
      "outputs": [],
      "source": [
        "max_input_length = X_train.shape[1]\n",
        "\n",
        "def prepare_date_strs_padded(date_strs):\n",
        "    X = prepare_date_strs(date_strs)\n",
        "    if X.shape[1] < max_input_length:\n",
        "        X = tf.pad(X, [[0, 0], [0, max_input_length - X.shape[1]]])\n",
        "    return X\n",
        "\n",
        "def convert_date_strs(date_strs):\n",
        "    X = prepare_date_strs_padded(date_strs)\n",
        "    ids = model.predict(X).argmax(axis=-1)\n",
        "    return ids_to_date_strs(ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "s40aAFFxC46E",
        "outputId": "e4ad7754-6d4e-4df6-e150-60b3d88056d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2020-05-02', '1789-07-14']"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ],
      "source": [
        "convert_date_strs([\"May 02, 2020\", \"July 14, 1789\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lLO0D0XC46E"
      },
      "source": [
        "좋네요! 물론 더 쉽게 날짜 변환 도구를 만들 수 있습니다(예를 들면, 정규식이나 더 단순한 문자열 조작). 하지만 신경망을 사용하는 것이 더 멋져 보이네요. ;-)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qg8viC-OC46E"
      },
      "source": [
        "하지만 실제 시퀀스-투-시퀀스 문제는 더 어렵습니다. 완벽함을 추구하기 위해 더 강력한 모델을 만들어 보겠습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixNvBf4eC46E"
      },
      "source": [
        "### 두 번째 버전: 디코더에서 한 타임 스텝 이동된 타깃 주입하기(티처 포싱(teacher forcing))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ftFtVskC46E"
      },
      "source": [
        "디코더에세 인코더 출력 벡터를 단순히 반복한 것을 주입하는 대신 한 타임 스텝 오른쪽으로 이동된 타깃 시퀀스를 주입할 수 있습니다. 이렇게 하면 각 타임 스텝에서 디코더는 이전 타깃 문자가 무엇인지 알게 됩니다. 이는 더 복잡한 시퀀스-투-시퀀스 문제를 다루는데 도움이 됩니다.\n",
        "\n",
        "각 타깃 시퀀스의 첫 번째 출력 문자는 이전 문자가 없기 때문에 시퀀스 시작(start-of-sequence, sos)을 나타내는 새로운 토큰이 필요합니다.\n",
        "\n",
        "추론에서는 타깃을 알지 못하므로 디코더에게 무엇을 주입해야 할까요? sos 토큰을 시작해서 한 번에 하나의 문자를 예측하고 디코더에게 지금까지 예측한 모든 문자를 주입할 수 있습니다(나중에 이 노트북에서 더 자세히 알아 보겠습니다).\n",
        "\n",
        "하지만 디코더의 LSTM이 스텝마다 이전 타깃을 입력으로 기대한다면 인코더의 벡터 출력을 어떻게 전달할까요? 한가지 방법은 출력 벡터를 무시하는 것입니다. 그리고 대신 인코더의 LSTM 상태를 디코더의 LSTM의 초기 상태로 사용합니다(이렇게 하려면 인코더의 LSTM과 디코더의 LSTM 유닛 개수가 같아야 합니다).\n",
        "\n",
        "그럼 (훈련, 검증, 테스트를 위한) 디코더의 입력을 만들어 보죠. sos 토큰은 가능한 출력 문자의 마지막 ID + 1으로 나타냅니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "id": "zZFQ7sLYC46E"
      },
      "outputs": [],
      "source": [
        "sos_id = len(OUTPUT_CHARS) + 1\n",
        "\n",
        "def shifted_output_sequences(Y):\n",
        "    sos_tokens = tf.fill(dims=(len(Y), 1), value=sos_id)\n",
        "    return tf.concat([sos_tokens, Y[:, :-1]], axis=1)\n",
        "\n",
        "X_train_decoder = shifted_output_sequences(Y_train)\n",
        "X_valid_decoder = shifted_output_sequences(Y_valid)\n",
        "X_test_decoder = shifted_output_sequences(Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jufA47OCC46E"
      },
      "source": [
        "디코더의 훈련 입력을 확인해 보죠:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "076Gm8L_C46F",
        "outputId": "97c4ea43-794a-43d6-9ffe-753e3e600bf7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10000, 10), dtype=int32, numpy=\n",
              "array([[12,  8,  1, ..., 10, 11,  3],\n",
              "       [12,  9,  6, ...,  6, 11,  2],\n",
              "       [12,  8,  2, ...,  2, 11,  2],\n",
              "       ...,\n",
              "       [12, 10,  8, ...,  2, 11,  4],\n",
              "       [12,  2,  2, ...,  3, 11,  3],\n",
              "       [12,  8,  9, ...,  8, 11,  3]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ],
      "source": [
        "X_train_decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mGK_DUv4C46F"
      },
      "source": [
        "이제 모델을 만듭니다. 이제 더 이상 간단한 시퀀셜 모델이 아니므로 함수형 API를 사용하겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "Be1_R-AbC46F",
        "outputId": "d4ca18d4-1395-4c69-c329-4e2d403cb1c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "313/313 [==============================] - 11s 18ms/step - loss: 1.6308 - accuracy: 0.3862 - val_loss: 1.3434 - val_accuracy: 0.4890\n",
            "Epoch 2/10\n",
            "313/313 [==============================] - 4s 12ms/step - loss: 1.1673 - accuracy: 0.5669 - val_loss: 0.9584 - val_accuracy: 0.6450\n",
            "Epoch 3/10\n",
            "313/313 [==============================] - 3s 11ms/step - loss: 0.7778 - accuracy: 0.7225 - val_loss: 0.6256 - val_accuracy: 0.7752\n",
            "Epoch 4/10\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.4397 - accuracy: 0.8555 - val_loss: 0.2771 - val_accuracy: 0.9229\n",
            "Epoch 5/10\n",
            "313/313 [==============================] - 4s 14ms/step - loss: 0.1917 - accuracy: 0.9562 - val_loss: 0.1189 - val_accuracy: 0.9815\n",
            "Epoch 6/10\n",
            "313/313 [==============================] - 4s 12ms/step - loss: 0.0807 - accuracy: 0.9912 - val_loss: 0.0603 - val_accuracy: 0.9942\n",
            "Epoch 7/10\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0766 - accuracy: 0.9893 - val_loss: 0.2957 - val_accuracy: 0.9049\n",
            "Epoch 8/10\n",
            "313/313 [==============================] - 3s 11ms/step - loss: 0.0453 - accuracy: 0.9950 - val_loss: 0.0242 - val_accuracy: 0.9991\n",
            "Epoch 9/10\n",
            "313/313 [==============================] - 5s 15ms/step - loss: 0.0187 - accuracy: 0.9997 - val_loss: 0.0166 - val_accuracy: 0.9995\n",
            "Epoch 10/10\n",
            "313/313 [==============================] - 3s 11ms/step - loss: 0.0775 - accuracy: 0.9843 - val_loss: 0.0482 - val_accuracy: 0.9943\n"
          ]
        }
      ],
      "source": [
        "encoder_embedding_size = 32\n",
        "decoder_embedding_size = 32\n",
        "lstm_units = 128\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "encoder_input = tf.keras.layers.Input(shape=[None], dtype=tf.int32)\n",
        "encoder_embedding = tf.keras.layers.Embedding(\n",
        "    input_dim=len(INPUT_CHARS) + 1,\n",
        "    output_dim=encoder_embedding_size)(encoder_input)\n",
        "_, encoder_state_h, encoder_state_c = tf.keras.layers.LSTM(\n",
        "    lstm_units, return_state=True)(encoder_embedding)\n",
        "encoder_state = [encoder_state_h, encoder_state_c]\n",
        "\n",
        "decoder_input = tf.keras.layers.Input(shape=[None], dtype=tf.int32)\n",
        "decoder_embedding = tf.keras.layers.Embedding(\n",
        "    input_dim=len(OUTPUT_CHARS) + 2,\n",
        "    output_dim=decoder_embedding_size)(decoder_input)\n",
        "decoder_lstm_output = tf.keras.layers.LSTM(lstm_units, return_sequences=True)(\n",
        "    decoder_embedding, initial_state=encoder_state)\n",
        "decoder_output = tf.keras.layers.Dense(len(OUTPUT_CHARS) + 1,\n",
        "                                    activation=\"softmax\")(decoder_lstm_output)\n",
        "\n",
        "model = tf.keras.Model(inputs=[encoder_input, decoder_input],\n",
        "                           outputs=[decoder_output])\n",
        "\n",
        "optimizer = tf.keras.optimizers.Nadam()\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit([X_train, X_train_decoder], Y_train, epochs=10,\n",
        "                    validation_data=([X_valid, X_valid_decoder], Y_valid))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71q6Co72C46F"
      },
      "source": [
        "이 모델도 100% 검증 정확도를 달성했지만 더 빠릅니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXTlJuN4C46F"
      },
      "source": [
        "이 모델을 사용해 몇 가지 예측을 수행해 보죠. 이번에는 한 문자씩 예측해야 합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "TWcxPDKEC46F"
      },
      "outputs": [],
      "source": [
        "sos_id = len(OUTPUT_CHARS) + 1\n",
        "\n",
        "def predict_date_strs(date_strs):\n",
        "    X = prepare_date_strs_padded(date_strs)\n",
        "    Y_pred = tf.fill(dims=(len(X), 1), value=sos_id)\n",
        "    for index in range(max_output_length):\n",
        "        pad_size = max_output_length - Y_pred.shape[1]\n",
        "        X_decoder = tf.pad(Y_pred, [[0, 0], [0, pad_size]])\n",
        "        Y_probas_next = model.predict([X, X_decoder])[:, index:index+1]\n",
        "        Y_pred_next = tf.argmax(Y_probas_next, axis=-1, output_type=tf.int32)\n",
        "        Y_pred = tf.concat([Y_pred, Y_pred_next], axis=1)\n",
        "    return ids_to_date_strs(Y_pred[:, 1:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "TIRhYhUpC46F",
        "outputId": "66c3f1a7-5c55-4566-b7b4-c0f310f95a0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 15 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fe45d137400> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 673ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1789-07-14', '2020-05-01']"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ],
      "source": [
        "predict_date_strs([\"July 14, 1789\", \"May 01, 2020\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwL2wFhkC46F"
      },
      "source": [
        "잘 동작하네요! 다음으로 트랜스포머 버전을 만들어 보세요. :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ZKNAfcHC46F"
      },
      "source": [
        "## 10.\n",
        "_문제: 케라스 웹사이트에 있는 \"Natural language image search with a Dual Encoder\"(https://homl.info/dualtuto) 예제를 살펴보세요. 동일한 임베딩 공간 내에서 이미지와 텍스트를 모두 표현할 수 있는 모델을 만드는 방법을 배우게 됩니다. 이렇게 하면 OpenAI의 [CLIP 모델](https://openai.com/blog/clip/)에서와 같이 텍스트 프롬프트를 사용하여 이미지를 검색할 수 있습니다._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-If5_mvC46F"
      },
      "source": [
        "링크를 클릭하고 안내를 따르기만 하면 됩니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROiY3kzkC46F"
      },
      "source": [
        "## 11.\n",
        "_문제: 허깅 페이스의 트랜스포머스 라이브러리를 사용하여 텍스트를 생성할 수 있는 사전 훈련된 언어 모델(예, GPT)을 다운로드하고 보다 설득력 있는 셰익스피어식 텍스트를 생성해 보세요. 모델의 `generate()` 메서드를 사용해야 합니다. 자세한 내용은 허깅 페이스 온라인 문서를 참조하세요._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85Z2Aqq8C46F"
      },
      "source": [
        "먼저 사전 훈련된 모델을 로드해 보겠습니다. 이 예제에서는 추가 언어 모델(입력 임베딩에 가중치가 연결된 선형 층)을 위에 얹은 OpenAI의 GPT 모델을 사용합니다. 임포트하고 사전 훈련된 가중치를 로드해 보겠습니다(이렇게 하면 약 445MB의 데이터가 `~/.cache/torch/transformers`로 다운로드됩니다):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "DfQngPaTC46G",
        "outputId": "506b47dd-7b8b-46a4-9fd6-0d78198c9407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204,
          "referenced_widgets": [
            "0feb9383e16e47f790c33172a9331dfb",
            "849420ccddb84316a07415acddbab54b",
            "d2cd82bb59d947ab971b4676f23311ee",
            "961be25566a24eb196a2a28d2a691042",
            "77eae93c9f9540c3ae08f1e86fc3ecd1",
            "608308c3230d4e62834db03bd6c36980",
            "397039a05107424b85c0852166e190fa",
            "39722074dab64dc0a73cb942eb2feef8",
            "52ddc4e2ced04e47b3ad292254b298f6",
            "dbddf15ed36440ea934f2fc95aab0b32",
            "c5ecf15571a84cb2a9a29c54a3b654a4",
            "5838fe822acb4e5a8ee3f7d779e8ce65",
            "818d6cf3b41a464e82db2029c3262f5a",
            "8d4251d8695844c5a5354bbd9a3ce839",
            "e7ce54b5e54a4573ba15cb5304b8d5fe",
            "af52cf7964054b8a814df1880b671388",
            "479556eb1e5e43ddb033a3d88653dec8",
            "b4c74a8ee1bf48f699868756e2129bb0",
            "20c2f13213f5407fb6fb20bed14d1c1c",
            "ae0480db3cb646a6a7cbdca8cdbbb1a5",
            "4d271bc91cf5452bbdef7e98539d4840",
            "f604a02248fb4c0dba17ed7d5da9e144",
            "aa8b8e27770949898273a4113dcb7c34",
            "fdb6a07016944f2087482abf04cf9ee6",
            "d4f125d88fd940c0a2cb631c6e3c1fad",
            "975331f180e64048a330afb337b0b522",
            "d4c28a3db482472cabab1b0a557c56d6",
            "c61a07cbe1ed45d69029b6d3ac5c587b",
            "cc475ff3df4741b4bae207232a21e463",
            "a5f979fa86004655b1ec836a9d37c6a1",
            "2e879f9e50794e1e82b5a5666bc20a82",
            "b1b40cc9a49f42eb8702594b83a92eca",
            "4dbe7fdc1cc94ee78c0d1f052d169859"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/656 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0feb9383e16e47f790c33172a9331dfb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/466M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5838fe822acb4e5a8ee3f7d779e8ce65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFOpenAIGPTLMHeadModel.\n",
            "\n",
            "All the layers of TFOpenAIGPTLMHeadModel were initialized from the model checkpoint at openai-gpt.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFOpenAIGPTLMHeadModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/74.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa8b8e27770949898273a4113dcb7c34"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import TFOpenAIGPTLMHeadModel\n",
        "\n",
        "model = TFOpenAIGPTLMHeadModel.from_pretrained(\"openai-gpt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puEpHsoiC46G"
      },
      "source": [
        "다음으로 이 모델에 특화된 토크나이저가 필요합니다. 만약 설치되어 있으면 [spaCy](https://spacy.io/) 및 [ftfy](https://pypi.org/project/ftfy/) 라이브러리를 사용하려고 시도하고, 그렇지 않으면 BERT의 `BasicTokenizer`와 바이트 쌍 인코딩(대부분의 사용 사례에 적합할 것입니다)을 사용합니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "r_8yAG6rC46G",
        "outputId": "8461458c-4d08-422f-8b04-aa2249f1165b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99,
          "referenced_widgets": [
            "07fa12d7956d4f65b6252aacf56a96d3",
            "0497de2d8ff342e7879d7c3706c88acd",
            "b829491c00b24caead4f9f4a1b1167a6",
            "8a4f178a7a3a4d76b064cacc3083737d",
            "4ba50c1829784ca99209f0d9c1b39443",
            "f7e34a1c0aeb437cbd6c738bfe00cf16",
            "24566bf9eb0a46c69b2ecd42b12e4dc1",
            "aef88ab3668f46e392081146cec60ac1",
            "a71b6a9347c04aca8e9d9aa8fddc5f96",
            "560de97ff190486da8d3f99e3320fca4",
            "004825eb517c49b592f47d4653611bd9",
            "49ae449b415342e9a3a33f91179a9ec3",
            "2793ce9c86834478b55ec48abaefba89",
            "a50b057f642f431e908180a4383b1199",
            "44831970d41a4a60936783d9a8e9e937",
            "ac5273e5db9e4f9fb2b5e8ddfbfa4235",
            "472d679aa85b42d6ac4e5a4b8c7bfcd9",
            "a0d818c4e7554d199709f235090a065a",
            "7b5d0a86cb98486ab4cca8fcf700d80f",
            "29c421671cfc4298b83a7bf7510bf369",
            "2c0d895f9d5b4de798ccfc0d23f6aace",
            "6aa41ef32f9640a2a5ac0426a444f988"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/816k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07fa12d7956d4f65b6252aacf56a96d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/458k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.\n"
          ]
        }
      ],
      "source": [
        "from transformers import OpenAIGPTTokenizer\n",
        "\n",
        "tokenizer = OpenAIGPTTokenizer.from_pretrained(\"openai-gpt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlJNj6ufC46G"
      },
      "source": [
        "이제 토크나이저를 사용하여 프롬프트 텍스트를 토큰화 및 인코딩해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "fc3VlTQhC46G",
        "outputId": "b65e99f8-ca62-4d06-a9f6-a9d32777ab59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [3570, 1473], 'attention_mask': [1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ],
      "source": [
        "tokenizer(\"hello everyone\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "id": "kJmW3sIcC46G",
        "outputId": "a502f333-2e55-4e44-b6e5-38f31030057f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 10), dtype=int32, numpy=\n",
              "array([[  616,  5751,  6404,   498,  9606,   240,   616, 26271,  7428,\n",
              "        16187]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ],
      "source": [
        "prompt_text = \"This royal throne of kings, this sceptred isle\"\n",
        "encoded_prompt = tokenizer.encode(prompt_text,\n",
        "                                  add_special_tokens=False,\n",
        "                                  return_tensors=\"tf\")\n",
        "encoded_prompt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uEjYrhZ_C46G"
      },
      "source": [
        "쉬워요! 다음으로 모델을 사용하여 프롬프트 뒤를 이은 텍스트를 생성해 보겠습니다. 프롬프트 텍스트로 시작하여 각각 5개의 다른 문장을 40개의 토큰 안에서 생성합니다. 모든 하이퍼파라미터의 기능에 대한 설명은 패트릭 폰 플라텐(Hugging Face)의 [블로그 게시물](https://huggingface.co/blog/how-to-generate)을 참조하세요. 하이퍼파라미터를 사용해 더 나은 결과를 얻을 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "Hp5KkqQ1C46G",
        "outputId": "4e63fb76-904c-4355-c4ea-fce70b451ef2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 50), dtype=int32, numpy=\n",
              "array([[  616,  5751,  6404,   498,  9606,   240,   616, 26271,  7428,\n",
              "        16187, 40477,   544,   246, 15147,   562,   481,  9606,   498,\n",
              "          481,  2868,   239, 40477,   620,   481,  5908,   498,   481,\n",
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              "       [  616,  5751,  6404,   498,  9606,   240,   616, 26271,  7428,\n",
              "        16187,   246,  1436,   239,   606,  1683,   793,   504,   246,\n",
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              "       [  616,  5751,  6404,   498,  9606,   240,   616, 26271,  7428,\n",
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              "         1767,   240,   487,   636,  1362],\n",
              "       [  616,  5751,  6404,   498,  9606,   240,   616, 26271,  7428,\n",
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              "         1896,   812,   580, 11505,   240]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ],
      "source": [
        "num_sequences = 5\n",
        "length = 40\n",
        "\n",
        "generated_sequences = model.generate(\n",
        "    input_ids=encoded_prompt,\n",
        "    do_sample=True,\n",
        "    max_length=length + len(encoded_prompt[0]),\n",
        "    temperature=1.0,\n",
        "    top_k=0,\n",
        "    top_p=0.9,\n",
        "    repetition_penalty=1.0,\n",
        "    num_return_sequences=num_sequences,\n",
        ")\n",
        "\n",
        "generated_sequences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMmMM7dEC46G"
      },
      "source": [
        "이제 생성된 시퀀스를 디코딩하고 인쇄해 보겠습니다:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "mC0KQG4UC46G",
        "outputId": "9bd319d2-d54d-42d0-84c9-9e9e665e484d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "this royal throne of kings, this sceptred isle \n",
            " is a beacon for the kings of the ages. \n",
            " so the hero of the ages, with an older son and his first mate, of stolen runes, \n",
            " returns to his former kingdom with his\n",
            "--------------------------------------------------------------------------------\n",
            "this royal throne of kings, this sceptred isle a god. we sit here on a throne of blessed harmony. we must sit as ruler of this people. \" \n",
            " no one had dared contradict the king, and no one dared speak out of turn\n",
            "--------------------------------------------------------------------------------\n",
            "this royal throne of kings, this sceptred isle! \n",
            " may the gods of ah - puch's balance still hold the power of this - tent - white flesh upon his body, may you be a light of power in the great heat and heat of\n",
            "--------------------------------------------------------------------------------\n",
            "this royal throne of kings, this sceptred isle. \n",
            " in his choice, the king was given the kingship of house gidedrian, a three - headed dragon, since he was of the royal line. upon his death, he would name\n",
            "--------------------------------------------------------------------------------\n",
            "this royal throne of kings, this sceptred isle of silver stood behind his throne, - a great hall of the kings of earendil and of the king baldor that has been hidden here forever, but ever that whole seat will be vacant,\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "for sequence in generated_sequences:\n",
        "    text = tokenizer.decode(sequence, clean_up_tokenization_spaces=True)\n",
        "    print(text)\n",
        "    print(\"-\" * 80)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eqt-b4c2C46G"
      },
      "source": [
        "언어 모델이 위에 있는 변형을 포함하여 트랜스포머 라이브러리에서 사전 학습된 모델로 사용할 수 있는 GPT-2, CTRL, Transformer-XL 또는 XLNet과 같은 최신(및 더 큰) 모델을 사용해 볼 수 있습니다. 전처리 단계는 모델마다 조금씩 다르므로 트랜스포머 문서에서 이 [생성 예제](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py)를 확인하시기 바랍니다(이 예제에서는 파이토치를 사용하지만 모델 클래스 이름 앞에 `TF`를 추가하고, `.to()` 메서드 호출을 제거하고, `\"pt\"` 대신 `return_tensors=\"tf\"`를 사용하는 등 약간의 조정만 하면 작동합니다)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQCTjohHC46H"
      },
      "source": [
        "이 챕터가 재미있으셨기를 바랍니다! :)"
      ]
    }
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