0401-tf-embedding-output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd7bbc42",
   "metadata": {},
   "source": [
    "🌱 인프런 📚 모두의 한국어 텍스트 분석과 자연어처리 with 파이썬 🐍 https://inf.run/FX4TP\n",
    "\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/corazzon/python-text-analysis/blob/main/0401-tf-embedding-output.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "928c58d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install tensorflow\n",
    "# !pip install tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0353b1",
   "metadata": {},
   "source": [
    "## Word Embeddings\n",
    "\n",
    "* https://www.tensorflow.org/text/guide/word_embeddings?hl=ko\n",
    "\n",
    "* 임베딩 프로젝터 : [링크](https://projector.tensorflow.org/?hl=ko&_gl=1*qcvijm*_ga*ODk4NzAxOTgzLjE3MDExNjQyNzM.*_ga_W0YLR4190T*MTcwMTgzNzYzMy42LjEuMTcwMTgzODU2Mi4wLjAuMA..)\n",
    "\n",
    "[tensorflow/text: Making text a first-class citizen in TensorFlow.](https://github.com/tensorflow/text/tree/master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfa5e9aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1384120c",
   "metadata": {},
   "source": [
    "## 예시 코퍼스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "500d69fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 음식 리뷰 예제 텍스트 데이터\n",
    "corpus = [\n",
    "    \"Salad 맛이 깔끔해서 좋아요.\",\n",
    "    \"주문한 음식이 늦게 와서 배고팠어요.\",\n",
    "    \"Pizza가 맛있어요.\",\n",
    "    \"라면이 너무 짜게 나왔어요.\",\n",
    "    \"배달된 치킨이 맛있어요.\",\n",
    "    \"메뉴 설명보다 양이 적어요.\",\n",
    "    \"주문한 음식이 다른 것으로 와서 다시 주문했어요.\",\n",
    "    \"맛은 좋은데 양이 좀 적은 편이에요.\",\n",
    "    \"떡볶이의 매운맛이 적절해요.\",\n",
    "    \"초밥이 신선해서 좋았어요.\",\n",
    "    \"빵이 부드럽고 맛있네요!\",\n",
    "    \"스테이크가 너무 질겼어요.\",\n",
    "    \"샐러드의 드레싱이 너무 강했어요.\",\n",
    "    \"나초의 치즈소스가 맛있었어요.\",\n",
    "    \"파스타가 약간 덜 익은 것 같아요.\",\n",
    "    \"커피가 너무 진해서 물을 더 추가했어요.\",\n",
    "    \"디저트는 달지 않아서 좋았습니다.\",\n",
    "    \"음료가 너무 달아서 다음엔 당도 조절할게요.\",\n",
    "    \"피자 도우가 너무 두꺼워서 먹기 힘들었어요.\",\n",
    "    \"피자가 맛 없어요.\",\n",
    "    \"햄버거의 빵이 너무 딱딱해요.\",\n",
    "    \"감자튀김이 너무 기름져서 느끼했어요.\",\n",
    "    \"치즈케이크가 아주 부드럽고 맛있어요.\"\n",
    "]\n",
    "labels = [1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1]\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d35d7729",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>문서</th>\n",
       "      <th>긍정부정</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Salad 맛이 깔끔해서 좋아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>주문한 음식이 늦게 와서 배고팠어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pizza가 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>라면이 너무 짜게 나왔어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>배달된 치킨이 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>메뉴 설명보다 양이 적어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>주문한 음식이 다른 것으로 와서 다시 주문했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>맛은 좋은데 양이 좀 적은 편이에요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>떡볶이의 매운맛이 적절해요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>초밥이 신선해서 좋았어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>빵이 부드럽고 맛있네요!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>스테이크가 너무 질겼어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>샐러드의 드레싱이 너무 강했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>나초의 치즈소스가 맛있었어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>파스타가 약간 덜 익은 것 같아요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>커피가 너무 진해서 물을 더 추가했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>디저트는 달지 않아서 좋았습니다.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>음료가 너무 달아서 다음엔 당도 조절할게요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>피자 도우가 너무 두꺼워서 먹기 힘들었어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>피자가 맛 없어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>햄버거의 빵이 너무 딱딱해요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>감자튀김이 너무 기름져서 느끼했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>치즈케이크가 아주 부드럽고 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                             문서  긍정부정\n",
       "0            Salad 맛이 깔끔해서 좋아요.     1\n",
       "1          주문한 음식이 늦게 와서 배고팠어요.     0\n",
       "2                  Pizza가 맛있어요.     1\n",
       "3               라면이 너무 짜게 나왔어요.     0\n",
       "4                 배달된 치킨이 맛있어요.     1\n",
       "5               메뉴 설명보다 양이 적어요.     0\n",
       "6   주문한 음식이 다른 것으로 와서 다시 주문했어요.     0\n",
       "7          맛은 좋은데 양이 좀 적은 편이에요.     0\n",
       "8               떡볶이의 매운맛이 적절해요.     1\n",
       "9                초밥이 신선해서 좋았어요.     1\n",
       "10                빵이 부드럽고 맛있네요!     1\n",
       "11               스테이크가 너무 질겼어요.     0\n",
       "12           샐러드의 드레싱이 너무 강했어요.     0\n",
       "13             나초의 치즈소스가 맛있었어요.     1\n",
       "14          파스타가 약간 덜 익은 것 같아요.     0\n",
       "15       커피가 너무 진해서 물을 더 추가했어요.     0\n",
       "16           디저트는 달지 않아서 좋았습니다.     1\n",
       "17     음료가 너무 달아서 다음엔 당도 조절할게요.     0\n",
       "18     피자 도우가 너무 두꺼워서 먹기 힘들었어요.     0\n",
       "19                   피자가 맛 없어요.     0\n",
       "20             햄버거의 빵이 너무 딱딱해요.     0\n",
       "21         감자튀김이 너무 기름져서 느끼했어요.     0\n",
       "22         치즈케이크가 아주 부드럽고 맛있어요.     1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame\n",
    "df = pd.DataFrame(corpus, columns=[\"문서\"])\n",
    "df[\"긍정부정\"] = labels\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884223a7",
   "metadata": {},
   "source": [
    "## TextVectorization\n",
    "* https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2891ea98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-09 22:21:48.673554: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# tensorflow\n",
    "# TextVectorization, Embedding, GlobalAveragePooling1D\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import TextVectorization, Embedding, GlobalAvgPool1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1043d27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import string\n",
    "string.punctuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bab346a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 소문자 변환: 입력된 텍스트를 모두 소문자로 변경합니다.\n",
    "# HTML 태그 제거: HTML 태그(<br />)를 공백으로 대체합니다.\n",
    "# 구두점 제거: 모든 구두점을 제거합니다.\n",
    "\n",
    "def custom_standardization(input_data):\n",
    "    lowercase = tf.strings.lower(input_data)\n",
    "    stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')\n",
    "    return tf.strings.regex_replace(stripped_html,\n",
    "                                  '[%s]' % re.escape(string.punctuation), '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3db121",
   "metadata": {},
   "source": [
    "* max_tokens: 텍스트에서 고려할 최대 토큰 수를 지정합니다. 가장 빈도가 높은 max_tokens 개의 토큰만 사용됩니다.\n",
    "* standardize: 텍스트를 표준화하는 방법을 선택합니다. 'lower_and_strip_punctuation'로 설정하면 소문자로 변환하고 문장부호를 제거합니다.\n",
    "* split: 텍스트를 토큰화하는 방법을 선택합니다. 'whitespace'로 설정하면 공백을 기준으로 텍스트가 토큰화됩니다.\n",
    "* ngrams: 단일 토큰 이상의 n-그램을 생성하도록 지정합니다. 예를 들어, ngrams=2로 설정하면 바이그램이 생성됩니다.\n",
    "* output_mode: 출력 모드를 지정합니다. 'int'로 설정하면 정수로 변환된 토큰이 출력됩니다.\n",
    "* output_sequence_length: 출력 시퀀스의 길이를 지정합니다. 이 매개변수를 사용하여 모든 시퀀스를 고정된 길이로 패딩하거나 자를 수 있습니다.\n",
    "* pad_to_max_tokens: True로 설정하면 max_tokens으로 시퀀스를 패딩합니다.\n",
    "* vocabulary: 사용할 어휘 사전을 지정합니다. 사전이 제공되면 모델은 이 사전에 있는 토큰만 고려합니다.\n",
    "* sparse: True로 설정하면 희소 텐서를 출력합니다.\n",
    "* ragged: True로 설정하면 불규칙한 길이의 토큰을 나타내는 tf.RaggedTensor를 출력합니다.\n",
    "* encoding: 텍스트의 인코딩을 지정합니다.\n",
    "* kwargs: 기타 설정을 위한 추가적인 키워드 인수를 받습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4773bb2",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>문서</th>\n",
       "      <th>긍정부정</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Salad 맛이 깔끔해서 좋아요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>주문한 음식이 늦게 와서 배고팠어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pizza가 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>라면이 너무 짜게 나왔어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>배달된 치킨이 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>메뉴 설명보다 양이 적어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>주문한 음식이 다른 것으로 와서 다시 주문했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>맛은 좋은데 양이 좀 적은 편이에요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>떡볶이의 매운맛이 적절해요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>초밥이 신선해서 좋았어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>빵이 부드럽고 맛있네요!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>스테이크가 너무 질겼어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>샐러드의 드레싱이 너무 강했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>나초의 치즈소스가 맛있었어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>파스타가 약간 덜 익은 것 같아요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>커피가 너무 진해서 물을 더 추가했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>디저트는 달지 않아서 좋았습니다.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>음료가 너무 달아서 다음엔 당도 조절할게요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>피자 도우가 너무 두꺼워서 먹기 힘들었어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>피자가 맛 없어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>햄버거의 빵이 너무 딱딱해요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>감자튀김이 너무 기름져서 느끼했어요.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>치즈케이크가 아주 부드럽고 맛있어요.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             문서  긍정부정\n",
       "0            Salad 맛이 깔끔해서 좋아요.     1\n",
       "1          주문한 음식이 늦게 와서 배고팠어요.     0\n",
       "2                  Pizza가 맛있어요.     1\n",
       "3               라면이 너무 짜게 나왔어요.     0\n",
       "4                 배달된 치킨이 맛있어요.     1\n",
       "5               메뉴 설명보다 양이 적어요.     0\n",
       "6   주문한 음식이 다른 것으로 와서 다시 주문했어요.     0\n",
       "7          맛은 좋은데 양이 좀 적은 편이에요.     0\n",
       "8               떡볶이의 매운맛이 적절해요.     1\n",
       "9                초밥이 신선해서 좋았어요.     1\n",
       "10                빵이 부드럽고 맛있네요!     1\n",
       "11               스테이크가 너무 질겼어요.     0\n",
       "12           샐러드의 드레싱이 너무 강했어요.     0\n",
       "13             나초의 치즈소스가 맛있었어요.     1\n",
       "14          파스타가 약간 덜 익은 것 같아요.     0\n",
       "15       커피가 너무 진해서 물을 더 추가했어요.     0\n",
       "16           디저트는 달지 않아서 좋았습니다.     1\n",
       "17     음료가 너무 달아서 다음엔 당도 조절할게요.     0\n",
       "18     피자 도우가 너무 두꺼워서 먹기 힘들었어요.     0\n",
       "19                   피자가 맛 없어요.     0\n",
       "20             햄버거의 빵이 너무 딱딱해요.     0\n",
       "21         감자튀김이 너무 기름져서 느끼했어요.     0\n",
       "22         치즈케이크가 아주 부드럽고 맛있어요.     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9cf09ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TextVectorization 레이어 설정\n",
    "# standardize, max_tokens, output_mode\n",
    "VOCAB_SIZE = 100\n",
    "MAX_SEQUENCE_LENGTH = 10\n",
    "\n",
    "vectorizer = TextVectorization(standardize=custom_standardization, \n",
    "                              max_tokens=VOCAB_SIZE, \n",
    "                              output_sequence_length=MAX_SEQUENCE_LENGTH)\n",
    "vectorizer.adapt(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "460b9397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['', '[UNK]', '너무', '맛있어요', '주문한', '음식이', '와서', '양이', '빵이', '부드럽고']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = vectorizer.get_vocabulary()[:10]\n",
    "vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88cc352f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Salad 맛이 깔끔해서 좋아요.',\n",
       " '주문한 음식이 늦게 와서 배고팠어요.',\n",
       " 'Pizza가 맛있어요.',\n",
       " '라면이 너무 짜게 나왔어요.']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1512160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('주문한', '음식이')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab[4], vocab[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a50bd867",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
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       "      <td>6</td>\n",
       "      <td>69</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>0</td>\n",
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       "      <th>17</th>\n",
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       "      <td>58</td>\n",
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       "      <td>9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1   2   3   4   5   6  7  8  9\n",
       "0   82  53  75  29   0   0   0  0  0  0\n",
       "1    4   5  71   6  46   0   0  0  0  0\n",
       "2   83   3   0   0   0   0   0  0  0  0\n",
       "3   56   2  22  74   0   0   0  0  0  0\n",
       "4   45  17   3   0   0   0   0  0  0  0\n",
       "5   48  43   7  34   0   0   0  0  0  0\n",
       "6    4   5  70  77   6  69  25  0  0  0\n",
       "7   54  26   7  30  33  14   0  0  0  0\n",
       "8   57  50  32   0   0   0   0  0  0  0\n",
       "9   21  41  27   0   0   0   0  0  0  0\n",
       "10   8   9  52   0   0   0   0  0  0  0\n",
       "11  42   2  23   0   0   0   0  0  0  0\n",
       "12  44  60   2  80   0   0   0  0  0  0\n",
       "13  73  19  51   0   0   0   0  0  0  0\n",
       "14  15  38  63  35  78  79   0  0  0  0\n",
       "15  16   2  24  47  64  20   0  0  0  0\n",
       "16  59  66  39  28   0   0   0  0  0  0\n",
       "17  36   2  67  68  65  31   0  0  0  0\n",
       "18  13  62   2  61  49  10   0  0  0  0\n",
       "19  12  55  37   0   0   0   0  0  0  0\n",
       "20  11   8   2  58   0   0   0  0  0  0\n",
       "21  81   2  76  72   0   0   0  0  0  0\n",
       "22  18  40   9   3   0   0   0  0  0  0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_vect\n",
    "vectorizer_corpus = vectorizer(corpus)\n",
    "df_vect = pd.DataFrame(vectorizer_corpus)\n",
    "df_vect"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5371660c",
   "metadata": {},
   "source": [
    "## 벡터화 결과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e7fe961",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_vect.replace(0, np.nan).isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7693c9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.heatmap\n",
    "sns.heatmap(df_vect.replace(0, np.nan).isnull(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b9295ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.heatmap\n",
    "sns.heatmap(df_vect, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df83f534",
   "metadata": {},
   "source": [
    "## 원래 텍스트로 복원하기\n",
    "\n",
    "encoder.get_vocabulary() 메서드를 사용할 때 TextVectorization 레이어는 일반적으로 어휘 목록을 빈도 순으로 정렬합니다. 즉, 코퍼스에서 가장 자주 나타나는 단어가 목록의 상위에 위치하게 됩니다. 이렇게 하는 이유는 빈도가 높은 단어들이 데이터에서 더 중요하거나 유용할 가능성이 높기 때문입니다.\n",
    "\n",
    "그러나 이 목록에는 일반적으로 몇 가지 예약된 토큰들이 포함되어 있습니다. 예를 들어, [UNK] (알 수 없는 단어를 나타내는 토큰), [PAD] (패딩을 위한 토큰) 등이 이에 해당합니다. 이러한 예약된 토큰들은 목록의 최상위에 위치합니다.\n",
    "\n",
    "어휘 목록의 정렬 순서는 TextVectorization 레이어의 설정에 따라 달라질 수 있으며, 필요에 따라 사용자가 이를 커스터마이징할 수도 있습니다. 예를 들어, 토큰화 방식, 텍스트 표준화 방법, 최대 토큰 수 등의 설정을 변경하여 어휘 목록의 생성 방식을 조정할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e424b0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['', '[UNK]', '너무', '맛있어요', '주문한', '음식이', '와서', '양이', '빵이', '부드럽고']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulary = vectorizer.get_vocabulary()\n",
    "vocabulary[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f628e6f4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salad 맛이 깔끔해서 좋아요       "
     ]
    }
   ],
   "source": [
    "# 반복문으로 어휘의 짝을 찾아보기 \n",
    "for num in df_vect.iloc[0]:\n",
    "    print(vocabulary[num], end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce05014b",
   "metadata": {},
   "source": [
    "## 다른 텍스트에 적용하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c62f4282",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_corpus = [\n",
    "    \"샌드위치가 신선한 재료로 만들어져서 좋았어요.\",\n",
    "    \"햄버거의 패티가 매우 맛있어요.\",\n",
    "    \"주문한 스시가 빨리 도착해서 만족스러웠어요.\",\n",
    "    \"라멘의 국물이 너무 매워서 못 먹었어요.\",\n",
    "    \"배달된 파스타가 따뜻해서 좋았어요.\",\n",
    "    \"주문한 샐러드가 늦게 와서 아쉬웠어요.\",\n",
    "    \"케이크의 크림이 너무 달지 않아서 좋았어요.\",\n",
    "    \"샌드위치의 빵이 너무 딱딱해서 먹기 어려웠어요.\",\n",
    "    \"피자의 토핑이 푸짐해서 좋았어요.\",\n",
    "    \"불고기가 너무 질겨서 실망했어요.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f516164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int64, numpy=\n",
       "array([[ 1,  1,  1,  1, 27,  0,  0,  0,  0,  0],\n",
       "       [11,  1,  1,  3,  0,  0,  0,  0,  0,  0],\n",
       "       [ 4,  1,  1,  1,  1,  0,  0,  0,  0,  0],\n",
       "       [ 1,  1,  2,  1,  1,  1,  0,  0,  0,  0],\n",
       "       [45, 15,  1, 27,  0,  0,  0,  0,  0,  0],\n",
       "       [ 4,  1, 71,  6,  1,  0,  0,  0,  0,  0],\n",
       "       [ 1,  1,  2, 66, 39, 27,  0,  0,  0,  0],\n",
       "       [ 1,  8,  2,  1, 49,  1,  0,  0,  0,  0],\n",
       "       [ 1,  1,  1, 27,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  2,  1,  1,  0,  0,  0,  0,  0,  0]])>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer(new_corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211d23a5",
   "metadata": {},
   "source": [
    "## 임베딩하고 시각화 하기\n",
    "* https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding\n",
    "* 시각화 했을 때 너무 겹쳐 보이지 않게 하기 위해 적은양의 텍스트로 실습합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c85a35ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.layers.core.embedding.Embedding at 0x13aa74890>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 임베딩 레이어 생성\n",
    "embedding_dim = 4\n",
    "embedding_layer = Embedding(input_dim=100, output_dim=embedding_dim)\n",
    "embedding_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95a953e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "short_corpus = [\"피자가 맛있어요.\", \n",
    "              \"햄버거가 맛있어요.\", \n",
    "              \"치킨이 맛있어요\", \n",
    "              \"피자가 맛없어요.\",\n",
    "              \"햄버거가 맛없어요.\", \n",
    "              \"치킨이 맛없어요\", ]\n",
    "\n",
    "# 텍스트를 벡터화하고 임베딩 적용\n",
    "vectorized_texts = vectorizer(short_corpus)\n",
    "embeddings = embedding_layer(vectorized_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0393a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([6, 10]), TensorShape([6, 10, 4]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape (문장의 수, 각 문장의 최대 길이, 임베딩 차원)\n",
    "vectorized_texts.shape, embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f9e1541",
   "metadata": {},
   "source": [
    "## 차원축소로 시각화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "080f4459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6, 4), dtype=float32, numpy=\n",
       "array([[ 0.01675782,  0.027034  , -0.02147168, -0.03378123],\n",
       "       [ 0.01101944,  0.02282492, -0.01759885, -0.0271598 ],\n",
       "       [ 0.01157174,  0.02430947, -0.02246631, -0.02824588],\n",
       "       [ 0.01471905,  0.01867659, -0.02163013, -0.03356628],\n",
       "       [ 0.00898067,  0.01446751, -0.01775729, -0.02694485],\n",
       "       [ 0.00953297,  0.01595205, -0.02262475, -0.02803093]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.layers import GlobalAveragePooling1D\n",
    "# 차원축소를 위해 dim 3 => dim => 2 로 줄이기\n",
    "# GlobalAveragePooling1D 레이어 추가\n",
    "pooling_layer = GlobalAveragePooling1D()\n",
    "\n",
    "# 각 문장에 대한 임베딩의 평균 계산\n",
    "average_embeddings = pooling_layer(embeddings)\n",
    "average_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e8e148f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -56.48057   ,   75.48894   ],\n",
       "       [  75.050545  ,   20.996138  ],\n",
       "       [  25.346807  ,   49.34595   ],\n",
       "       [ -56.171974  ,  -18.02028   ],\n",
       "       [  55.58583   , -106.00941   ],\n",
       "       [   0.86041415,  -82.85081   ]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# t-SNE를 사용한 차원 축소\n",
    "tsne = TSNE(n_components=2, perplexity=3, random_state=42)\n",
    "reduced_embeddings = tsne.fit_transform(average_embeddings)\n",
    "reduced_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "340634c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAdi2bRtatWqFbdu2FXkn8ku6uroYNWoUnJ2d0bp1a4wbNw6Ojo4wNjZWe6f1jRs3xMsd7t69iy1btiAuLg6tWrUS6zx79kyrfb9q8uTJaNmyJUaOHCkpr1GjBgwMDJCbm1vk/7W6yysuXbpU5L47d+6MjIwMXLx4EQcPHoSXl5e4rmfPnjh8+DCSk5NhZ2en0kv4Ork/R2+7PQMDA3h4eODPP/8sdB/BwcEYOHCg2stbiKj4OCxO5VaXLl2QnJyM3377rdB6O3bswNWrVxEQEIBvv/0WV69eVblGz8jISG1vYVlZtWqVpFfn8ePHiIyMRJcuXQBod+zVq1eHi4uLSqJ18OBByfWQANCrVy+VeleuXEFMTMzbHgqAF0Ov//3vfyVl6enpCA8P19jDaGpqCnt7e9y+fbvI7fv4+GDHjh2IiopCpUqViv30FAcHB1SuXFncl5WVlXin8kvHjh2TXG7x4MEDKBQKNG/eXCzLycnBoUOHirXvQ4cO4bfffsPatWtV1unr68PV1bXIHtVevXrh119/lfRUZmVlqdztr46BgQG6d++Ow4cPi9dbvvQyuTx48CB69+5d5Lbk/hy9vL7zbbZ54sQJybyX6ri5uaFDhw5qp2UiouJjcknlVoMGDTBmzBj4+vpi48aN+Pfff3Hz5k2sW7dO7LF59uwZJk+ejCVLlqBy5cqoUqUKAgICMGXKFMm0PvXr10dMTAyuXbuGixcvqgx/vio/Px9paWmS19OnT2U9NoVCgVGjRuHu3bu4ePEivLy84OTkJP7wa3PsADBz5kx89913+O9//4sHDx4gIiICo0aNUrkR6Ntvv8VPP/2E77//HklJSTh16hT69OmD9u3by3I806dPR3x8PMaNGyceU48ePVCpUiXxmtC8vDyEhobi+vXrSE5OxoYNG3Do0CH06tWryO13794d2dnZmDJlCoYNG1Zkj1dCQgJ++eUX3L17F7dv38ZXX30FHR0d8Xi9vb0RExODdevW4dmzZzhz5gzGjBkjuUmrefPmqFq1KmbMmIH79+/j3LlzGDhwIGrXrq31eUlNTcXo0aMREhICXV1dyXvq5fWmgYGB2LdvH8aOHYuEhAQkJycjKipKkqyPGDECqampGDlyJG7cuIG///4bAwcOhKWlpVZx9O7dG6GhoSgoKICdnZ1Y3q1bN5w5cwZ79+7V6nrL+vXrY/fu3bh//z4uXLig9XnQpHr16qhUqRI2btyIO3fuFPqHRk5ODiZOnIjTp0/jwYMHiI+Px5gxY3Dz5k3MnDmzyH0tXrwYoaGhKtMvAS/m6Xz9M1/UHKJEFRmTSyo3jIyMVO5UXbVqFaZMmYJ58+bBysoKbdu2xfbt28U7shcvXowmTZrgk08+Edt8/vnnqFy5suQmnP79+6NNmzZo06YNPvzwQzx+/FhtDPr6+oiKikK1atVUXi97tfT09NT2xqmL38DAQKXM0NAQ69evh46ODhwcHNC5c2c0adIEv/32myRpKurYXx7X6tWrsWjRIlhbW+Pbb7/Fxo0b0apVK8l+27Rpgz179mDTpk1o1KgRPv30U8ybNw9eXl6Senp6etDT09O4rOlY69Wrh//973/4559/YGdnBy8vL/Tu3RtNmjQR503Mzs7G6tWrYW9vD2tra4SEhGDDhg3i+sLo6uri448/xpUrVyQTp7/q1VgfPnyIKVOmwNraGi1btsTFixcRGRkpJoZNmjTB9u3b8cMPP6BmzZoYM2YMVqxYAVtbW3EblStXxpEjR/D777+jWbNm+PDDD9GlSxdMmzZNco3p6+fi1ThOnTqF5ORkDB06VOX9tGrVKgAvhq0jIyPx999/w9HREY0bN8bEiRMlx2ZiYoJjx47h33//hYODAzp16gQnJycsWLBAq7u7vby8cOfOHQwcOFBSXrVqVbRr1w4AVP4gUfd/P2XKFDx48ABNmjQR57lU974vrPz1fSxZsgSzZs1Cq1atJHevv06pVCIxMRGDBg1CgwYN0Lt3bwiCgN9//12S8Gvar729PXx8fJCamiq5NlpfXx8bNmxQ+f+pXbu2bNckE71vFEJJXlFNRIQXPYUHDhxAjx49UL16dSQmJmLFihVISEjA8ePHZX1OPBERlS32XBJRiTMxMcHRo0fh5uYGS0tL9O/fH2ZmZjh48CATSyKi9wx7LomIiIhINuy5JCIiIiLZMLkkIiIiItkwuSQiIiIi2fAJPa8oKChAUlISqlSp8s49sYWIiIjUEwQBGRkZqFu3LnR02G9W1phcviIpKUmc0JmIiIjKl3v37mn98AAqOUwuX1GlShUAL96cpqamZRwNERERaSM9PR1WVlbi7ziVLSaXr3g5FG5qasrkkoiIqJzhJW3vBl6YQERERESyYXJJRERERLJhcklEREREsmFy+Q5q3bo1EhMTta5/8uRJDBw4sFj7eJM2rwsPD8fXX39d7HY2NjbIyMgoVhtbW1ukpaUVe19ERERUuphclqKLFy/C3t4eDRs2RJMmTWBvb48WLVqgXr16cHBwwJMnTwAAubm5yMvLE9vNnj0b9vb24qtZs2ZwcHBAVlYWACAvL09SHwC++OILSZvmzZujc+fOePkoeXVtXrdgwQLUqVMHdnZ24svc3BynT58Wt5GbmytpIwgClixZAltbW1hZWaFt27Y4cOCApE52djaUSqWkbM2aNWjUqBFq166NHj164Pbt25L1OTk5yM/PV4nx119/hZeXF6ytrWFlZYXGjRtj5MiRuHDhQqHHRkRERCWDd4uXolatWiE+Ph5+fn744IMPMGzYMDx48ACurq64dOmSxnbz5s3DvHnzxOW///4bPXr0gJGRkcY2q1evliwXFBTAzMysWPEmJCRg2bJl8Pb2FstGjBiB+/fva2wTEhKCqKgonDhxAjVr1sT169cxePBgVKpUCV27dlXbJiIiAkuWLEFsbCwsLS2xYsUK9O/fH3FxcYXe+Td79mxER0cjJCQELi4uUCgUyMrKwv79+zFo0CCsWrUKnp6exTpmIiIiejvsuSyHfvrpJ4wePbpYUy5cv34dNjY2xZ6m4fX6RbXfuHEjgoODUbNmTQAvhrNnzpyJ0NBQjW1Wr16NoKAgceJbPz8/mJqa4tChQxrb5ObmIjg4GAcOHEDbtm3FuIyNjeHt7Y2lS5ciODhYq2MkIiIi+TC5LGdu3bqFHTt2YMKECcVqt2fPHnh5eckeT3h4OOzs7DBx4kQAgL6+vjhc/1JmZib09DR3kp87dw5dunSRlPXs2VMcfldHR0cHurq6KsPyL+Xk5MDAwEDbwyAiIiKZMLksR/Ly8jBq1Ci0bt0aVatWlaw7fvw47OzsMGjQIJV2mZmZWLt2LcaMGaN1G+BFL+Xr10YqlUpJ76WPjw+uXbuGH3/8EQDw1Vdfwd/fH1evXkVubi6io6Mxb948+Pv7azyu1NRUVKtWTVJWs2ZNLF++XLzW8/UbnPT09BAcHIzu3btj3759SE1NRX5+Pu7du4cVK1Zg5syZ+O677zTuk4iIiEoGr7ksBcoCAWdvPUFKRjZqVTESb6opjoKCAvj6+sLR0RFxcXHi0PhLbm5u2L9/v9q2M2bMwKBBg9CwYUNJeWFtAMDJyQkzZsxAYGCgJI6pU6dqbDN8+HAYGxtj/PjxSEpKgp2dHbZu3Yq2bdtqbFO9enU8efIEtWvXFssSExMxZcoUzJw5EwBgbW2t0s7Pzw/Ozs4ICwvDrFmzkJaWBgsLC7i7uyM6OhoNGjTQuE8iIiIqGeUquVQqlejQoQNycnIk5f/88w+2b9+OXr16QU9PD/b29pL1CxcuLJEhYW1ExCdjzr6rSH6aLZZln0+EiWWapF5qaipSUlJga2urso3nz59j5MiRMDU1RUhICNLS0tC9e3cYGRnBx8en0P2vWbMGZ86cQUxMTLFj9/f3V+lxzMjIgImJCYAXvYfqhrsHDx6MwYMHIzs7Gzo6OpLh6UePHmHbtm2Sx2u6urri8OHD8PX1Fcv27duHFStWFBljTEwMRo8ejZ9++kksW7lyJa5fv87kkoiIqAyUq+RSV1cXZ86ckZRlZ2ejcePGaN++PYAXCeiff/5Z6DV+pSUiPhlfbD6Pl/2UWbcu4OmJcOQ//Rf/+es0tq79AYa6L2Lu168fOnbsiKCgIMk2kpOT0atXLwwcOBABAQFQKBSoVq0ajhw5gkGDBsHR0VHtvvPy8hAQEIDo6GgcOnRItusPPTw8MG/ePHh4eMDHxwcff/yxxrpTp05Fy5YtMXbsWLGsWbNmSEhIgI7O/12RMWnSJHh7e8Pe3h62traYN28eLCws0LFjxyLj+euvv1QS8oSEBDEBJiIiotJV9hnYW9q+fTu6deumcg1iWVMWCJiz7ypeHQA3auAAg9qNAAAKHV3UqW6GUzN7QldH8x3YpqamWLZsGTp37iwpNzc3R3R0NIAX106+6tmzZ2jbti3c3Nxw/PjxQqcsKo7o6GgkJSVh/vz5cHNzg76+PnR1dTXWv3XrFqpXry4uJyUl4dGjR7h3757k/8vFxQVhYWGYMGECUlJS4O7ujt27d8sSMxEREZWucn9DT2hoqKRn7F1x9tYTyVA48CKh1K1kBt1KZtAxqox/nytx9taTQrdjYmKiklgWpXLlyjh06BBWrVolW2IZGRmJzz//HJGRkejWrRsGDBiA1NRUjfWvXbuGK1euYM+ePeJlDKGhoXB1dVU73O3h4YGTJ08iISEBoaGhMDY21rhtf39/8Uaf06dPY9q0aZKJ3iMiIhAUFCQuh4WFvf0JICIiIq2U657Ly5cvIzU1FZ06dXqj9jk5OZLrN9PT0+UKDSkZ2UVXKka94pLresPY2FiEhobi2rVr2LdvH2xtbREQEID169fD2dkZw4cPx7Bhw2BjYyO2OXv2LHx9fbF+/XrcvHkTvr6+8Pb2RlRUFI4dO4bBgwdj1qxZCAgIeKPh+mXLlr3RsWzbtg3379/HlClT3qg9ERERFa1c91yuWbNGZXodAPD09ETLli3Rrl07/PDDDygoKFDbftGiRTAzMxNfVlZWssVWq4p2PYbq6unr6xfrmlF9fX3o6+trXV/bNo8fP0ZQUBB69eqFs2fPSq5tHDVqFM6cOYNKlSohPDxcLE9PT8dXX32FTZs2wc3NDaNHj0afPn2wc+dOHDhwAAYGBti1axdycnKwY8cOreM1NDQsdAheG0OHDmViSUREVMIUwpvMi/MOeP78ORo2bIirV6+iRo0aYvmDBw9gYWEBALh9+zZ8fX3h6emJGTNmqGxDXc+llZUVnj59Krmb+U0oCwS4Lj6GB0+zoe4EKwBYmBnhxLfuhV5zSURERIVLT0+HmZmZLL/f9PbKbc/ltm3b4O7uLkksAYiJJfBibsSFCxfil19+UbsNQ0NDmJqaSl5y0dVRILBvcwAvEslXvVwO7NuciSURERG9V8ptcrlmzRqtbuRRKpVlNi2Rp30drB7WGhZm0qFvCzMjrB7WGp72dcokLiIiIqKSUi5v6Dl//jyePHkCd3d3SXlmZiaeP3+OmjVrAngxLD5lyhTJk2xKm6d9HXg0t5A8oadtQ3P2WBIREdF7qVwml+vWrcMXX3whecY18OIpN3369EFeXh709PRgbGyMiRMnYtiwYWUU6Qu6Ogq0b1y96IpERERE5Vy5vaGnJPCCYCIiovKHv9/vlnJ7zSURERERvXuYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSEVUgrVu3RmJiYrHaeHh44OLFiyW+n1eFh4fj66+/LnY7GxsbZGRkFKuNra0t0tLSir0vIlKPySUR0XskPDwcdnZ24isyMlKyPjc3F3l5eeLyH3/8IalvZ2eHWrVq4dtvvxXr5OXlSdoAwP/+9z+0bdsWLVq0QJs2bYrcz+sWLFiAOnXqSPZrbm6O06dPi/vMzc2VtBEEAUuWLIGtrS2srKzQtm1bHDhwQFInOzsbSqVSUrZmzRo0atQItWvXRo8ePXD79m3J+pycHOTn56vE+Ouvv8LLywvW1tawsrJC48aNMXLkSFy4cEHjcRERoFfWARARkXx8fHzg4+OjdX0XFxdcu3ZNUvb999/jwYMHGtukpKRgyJAh+O233+Ds7IyrV6+iR48eiI2NRaNGjbTab0JCApYtWwZvb2+xbMSIEbh//77GNiEhIYiKisKJEydQs2ZNXL9+HYMHD0alSpXQtWtXtW0iIiKwZMkSxMbGwtLSEitWrED//v0RFxcHhUKhcV+zZ89GdHQ0QkJC4OLiAoVCgaysLOzfvx+DBg3CqlWr4OnpqdWxElU07LkkInpP/Pjjj7C3txdfLVq0gJmZGe7cuaNV+7y8PKSmpuLAgQPo3bu3xnrh4eHw9vaGs7MzAKB58+b48ssvsXr16mLF+3pyV1iyBwAbN25EcHAwatasCeDFcPbMmTMRGhqqsc3q1asRFBQES0tLAICfnx9MTU1x6NAhjW1yc3MRHByMAwcOoG3btmJcxsbG8Pb2xtKlSxEcHKzVMRJVROUqudy8eTPMzc3h6Ogovtq1aycOgSQnJ6N3795o1aoVWrZsiTVr1pRxxEREpWfixImIj48XX7t27UK1atVQt25djW327dsHJycnODk5oWPHjnByckJiYiI6deqksc25c+dU1nfr1k0c0pbLyyH+iRMnAgD09fWRlZUlqZOZmQk9Pc2DcOfOnUOXLl0kZT179iw0Vh0dHejq6qoMy7+Uk5MDAwMDbQ+DqMIpV8Pi+fn58PLywubNm9WuHzRoEMaPHw8fHx9kZGTAw8MD9evXh5eXVylHSkRUOpQFAs7eeoKUjGzUqmKEtg3NoaujgCAIGD9+PAICAqCvr6+xfd++fdG3b18AwI0bN9CtWzeEhYVBV1dXY5vU1FRUrVpVUlajRg08fPhQ67gVCoXKtZFKpVLSe+nj44MVK1aIy1999RX8/f2xYcMG2NjY4OTJk5g3bx62b99eaKzVqlWTlNWsWRPTpk3Djh07AEDlxiM9PT0EBweje/fumD9/PlxdXVGlShUkJydj7969CAkJwZYtW7Q+VqKKplwll4W5dOkSlEqleK1RlSpVMHfuXKxatYrJJRG9lyLikzFn31UkP80Wy+qYGSGwb3Mc2bAUV65c0fr6y507d2LKlClYvnw5OnfuXGjdGjVq4PHjx5KypKQk1KlTR+vYnZycMGPGDAQGBoplBQUFmDp1qsY2w4cPh7GxMcaPH4+kpCTY2dlh69ataNu2rcY21atXx5MnT1C7dm2xLDExEVOmTMHMmTMBANbW1irt/Pz84OzsjLCwMMyaNQtpaWmwsLCAu7s7oqOj0aBBA62PlaiiKVfD4oU5evSoytBHp06dcOzYMQiCUEZRERGVjIj4ZHyx+bwksQSA5NTn+GjEZ/j9whX4+fnB29tbZSj5VSdPnkSnTp2wZs0aREZGol+/fkXuu1OnTjh69KikbP/+/ejevbvW8fv7++PmzZu4du2a+Dp37hxatGgB4EXvobrh7sGDByM6OhoXL17Ezp078cEHH4jrHj16hG3btsHU1FQsc3V1xeHDhyXb2LdvH9zc3IqMMSYmBqNHj8bFixdx584dnDlzBpaWlrh+/brWx0lUEb03yWVSUhKsrKwkZcbGxjAyMkJKSoraNjk5OUhPT5e8iIjedcoCAXP2XcXrfzbnJF5D8uapUOjooqDbZEyfMRMeHh7o2LEjzp8/r3ZbOTk5mDt3LqKiomBra6vV/ocMGYLY2Fjs3bsXAHDkyBGEh4dj7Nixb3NY8PDwQFRUFIAXQ+Lff/+9xrpTp07Fxo0bJWXNmjWDvb09dHT+76dt0qRJCAgIwPnz5/H8+XNMmzYNFhYW6NixY5Hx/PXXXyq/HwkJCUhKSirOYRFVOOUquVQoFIiNjYWrqyuaNWuGvn374vfffwcApKWlwcjISKWNkZERMjMz1W5v0aJFMDMzE1+vJ6dERO+is7eeqPRYCsp8PD3zC8w9Pke1bmPxICMXZ289wYQJE7By5UqNN6e4u7uja9eumDhxIg4ePKi2zowZM9CkSRNxuUqVKoiMjMTGjRthb2+P5cuX4/Dhw6hVq9YbH1N0dDSSkpIwf/585OXlQaFQFHrd561btyRJXlJSEh49eoR79+5J6rm4uCAsLAwTJkyAo6MjUlNTsXv37jeOk4iKVq6uuRw8eDAGDhwIU1NTCIKAgwcPol+/fjh16hQMDQ2RnZ2t0iYrKwvGxsZqtzd9+nTJEyDS09OZYBLROy8lQ/W7TqGrh1ofzlJbr3379kVu8+nTpxr/EO/Ro4dKWYMGDWRL0iIjI+Hn54fIyEjs2LEDAwYMwObNm1VuxHnp2rVruHLlCu7fv49p06bB0NAQoaGhcHV1xYoVK1SmJvLw8ICHh4dWsfj7+0smhD99+jSmTZsmqRMREYGgoCAAwOTJk9+6x5bofVOukksTExPx3wqFAr1790b//v1x6NAhWFpa4u7du5L6WVlZePbsmca/pg0NDWFoaFiiMRMRya1WFdVRmrepBxQ9x2RJiI2NRWhoKK5du4Z9+/bB1tYWAQEBWL9+PZydnTF8+HAMGzYMNjY2YpuzZ8/C19cX69evx82bN+Hr6wtvb29ERUXh2LFjGDx4MGbNmoWAgIA3mi5o2bJlch4iUYVUrobF1VEqldDT00OHDh0QExMjWRcbGwsXFxfJ9TdEROVd24bmqGNmBE3poAIv7hpv29BcZZ2+vr7aG2VsbW0xadIkySTsr74CAgKKFaOm/bz0+PFjBAUFoVevXjh79qzkes9Ro0bhzJkzqFSpEsLDw8Xy9PR0fPXVV9i0aRPc3NwwevRo9OnTBzt37sSBAwdgYGCAXbt2IScnR5xmSBuGhoaFDsETUfEohHJ0K3ViYiJq164tfmHt2rULfn5+OH/+PCwsLNC6dWtMmTJFMs/l5MmTJY8XK0x6ejrMzMzw9OlTyd2GRETvmpd3iwOQ3NjzMuFcPaw1PO21nxqIqDzj7/e7pVx16UVERMDe3h6tWrVCq1atsH37dhw7dgx16tSBQqHAnj178PPPP6Nly5Zo164dPvroI60TSyKi8sTTvg5WD2sNCzPp0LeFmRETSyIqU+Wq57Kk8S8fIipvND2hh6gi4e/3u6Vc3dBDRERSujoKtG9cvazDICISlathcSIiIiJ6tzG5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2ZS75PLgwYPo1q0bHBwcYG9vj3HjxiEzM1Ncr6enB0dHR8nr4MGDZRgxERERUcVR7pLLypUr4+eff8alS5cQFxeHjIwMBAQEiOuVSiX+/PNPxMXFiS8vL68yjJiIiIheN3XqVNjY2MDGxgZfffWVWF6/fn0AwKVLlzBw4MBibbNTp05ITEwsVhsPDw9cvHhR6/onT54sdlyvCw8Px9dff13sdjY2NsjIyChWG1tbW6SlpRV7X2+j3CWXnTt3Rr169QC86KX85ptvEBkZWcZRERERUXEEBwfjxo0buHHjBn744Qex/OVoZG5uLvLy8sTy0aNHY9++fZJtzJs3D8uXLxeX8/LyJG3Wr18Pe3t7ycvW1hanT5/W2OaLL76Q1G/evDk6d+4MQRDU1ldnwYIFqFOnDuzs7MSXubm5uN+8vDzk5uZK2giCgCVLlsDW1hZWVlZo27YtDhw4IKmTnZ0NpVIpKVuzZg0aNWqE2rVro0ePHrh9+7ZkfU5ODvLz81Vi/PXXX+Hl5QVra2tYWVmhcePGGDlyJC5cuFDosWlD7623UMaePHkCIyOjN2qbk5ODnJwccTk9PV2usIiIiEiNhw8fwt3dXUzWXtLV1cWJEyc0tlMqlSqJlbqyV40aNQqjRo2SlA0fPhx3797FBx98oLbN6tWrJcsFBQUwMzPTuA91EhISsGzZMnh7e4tlI0aMwP379zW2CQkJQVRUFE6cOIGaNWvi+vXrGDx4MCpVqoSuXbuqbRMREYElS5YgNjYWlpaWWLFiBfr374+4uDgoFAqN+5o9ezaio6MREhICFxcXKBQKZGVlYf/+/Rg0aBBWrVoFT0/PYh3zq8p9crlmzRr4+vq+UdtFixZhzpw5MkdEREREmtSsWROXL1/Gw4cPcfToURQUFKB79+6oXbt2qez/ypUrmDZtmtb1r1+/Dhsbm0KTNXVer19U+40bN2Lz5s2oWbMmgBfD2TNnzkRoaKjG5HL16tUICgqCpaUlAMDPzw/bt2/HoUOHNF4SmJubi+DgYKSkpEiSZmNjY3h7e0NXVxfBwcFvlVyWu2HxVx0+fBhxcXEYO3aspNzT0xMtW7ZEu3bt8MMPP6CgoEBt++nTp+Pp06fi6969e6URNhERUYV25coVuLq64saNG7hz5w7c3d0lw7F2dnYYPHiwSru9e/di6dKl4uvUqVPF2u+dO3eQmpqKFi1aaN1mz549JXLvRnh4OOzs7DBx4kQAgL6+PrKysiR1MjMzoaenuR/w3Llz6NKli6SsZ8+ekmH/1+no6EBXV1dlWP6lnJwcGBgYaHsYapXbnst79+7hs88+w65du2BoaCiWJycnw8LCAgBw+/Zt+Pr6IjMzEzNmzFDZhqGhoaQtERERlbz//ve/mD17NoYNGwYAaNmyJdauXSsOSV+7dg1//vknvvvuO0k7ExMTVK1aVVw2MjLCokWLsGrVKgDAzZs3C93v0qVLMW7cOIwYMQJnzpwBANy9e1dj/czMTKxduxbHjh2TlB8/fhx2dnZo0aIFdu3apdJOoVCoHcJ/tffSx8cHK1asEJe/+uor+Pv7Y8OGDbCxscHJkycxb948bN++XWN8qampqFatmqSsZs2amDZtGnbs2AEAKjc46enpITg4GN27d8f8+fPh6uqKKlWqIDk5GXv37kVISAi2bNmicZ/aKJfJ5fPnzzFgwADMnz8fzs7OknUvE0sAsLa2xsKFC+Hv7682uSQiIqLSoSwQcPbWE6RkZEO3al0cO3YMQ4cOhY6ODo4dOwZbW9sit9G9e3cMGDBAXL5//z7c3d0xatQomJmZoXHjxhrbnj59GocPH8bFixdhbGwslru5uWlsM2PGDAwaNAgNGzaUlLu5uWH//v0a2zk5OWHGjBkIDAwUywoKCjB16lSNbYYPHw5jY2OMHz8eSUlJsLOzw9atW9G2bVuNbapXr44nT55ILilITEzElClTMHPmTAAvcqHX+fn5wdnZGWFhYZg1axbS0tJgYWEBd3d3REdHo0GDBhr3qY1yl1wqlUoMHToUvXr1wvDhw7WqX1iXMhEREZWsiPhkzNl3FclPswEAgmAD5Z0LaO7oDDNjffTo0QMTJkwA8OLGF+DF8K2OjjxX712/fh3Dhg3D9u3bJYllYdasWYMzZ84gJiam2Pvz9/eHv7+/pCwjIwMmJiYAXvQeqstNBg8ejMGDByM7Oxs6OjqS4elHjx5h27ZtMDU1FctcXV1x+PBhyb0n+/btk/SIahITE4PRo0fjp59+EstWrlyJ69evV7zkcuLEiTA2Nsa8efNU1mVmZuL58+fixbC3b9/GlClTMHr06NIOk4iIiPAisfxi83m8em+4QqEDfZchyMMQzBvWGp72dcR133//PQCgdevW4tAuADRo0AATJkzArFmzxLKUlJQiE6mIiAiMHz8eYWFhaNOmTZHx5uXlISAgANHR0Th06NBbX3/4koeHB+bNmwcPDw/4+Pjg448/1lh36tSpaNmypeSekmbNmiEhIUGScE+aNAne3t7iFEvz5s2DhYUFOnbsWGQ8f/31l0pvcUJCgpgAv41ylVympqZi5cqVsLW1hZOTk1iuUCgQERGB/Px89OnTB3l5edDT04OxsTEmTpwoXtNBREREpUdZIGDOvqsQ1KwTAKRGhaHf6hNoVKeGyvr09HR89NFH4hyYc+bM0TjDi6apBKdOnYpjx47hwIEDsLOzKzLeZ8+eoW3btnBzc8Px48ffeKrD10VHRyMpKQnz58+Hm5sb9PX1oaurq7H+rVu3UL16dXE5KSkJjx49wr179yTXnLq4uCAsLAwTJkxASkoK3N3dsXv3bllifhvlKrmsVq2ayrxYr5Nj8k8iIiJ6e2dvPRGHwtXJS01CNc+vsGHhF2jfuLpkXXR0tNiL+ab8/f2xaNGiQhO5V1WuXBmHDh1662HhV0VGRsLPzw+RkZHYsWMHBgwYgM2bN6vciPPStWvXcOXKFdy/fx/Tpk2DoaEhQkND4erqihUrViA0NFRS38PDAx4eHlrF4u/vL3nwzOnTp1WmZYqIiEBQUBAAYPLkySoz8mijXCWXREREVH6kZGhOLN+kXnG9nP+xOORKLGNjYxEaGopr165h3759sLW1RUBAANavXw9nZ2cMHz4cw4YNg42Njdjm7Nmz8PX1xfr163Hz5k34+vrC29sbUVFROHbsGAYPHoxZs2YhICDgjYbrly1b9kbHsm3bNty/fx9TpkzRqn65nueSiIiI3l21qhQ+rKxnVhuPD/2ISR91V3lMo6+vL5o2bVqs/Wm6UaYw+vr60NfXl7X+48ePERQUhF69euHs2bOSaxtHjRqFM2fOoFKlSggPDxfL09PT8dVXX2HTpk1wc3PD6NGj0adPH+zcuRMHDhyAgYEBdu3ahZycHMm1qEUxNDTUuudWk6FDh2qdWAKAQihqnLkCSU9Ph5mZGZ4+fSq5G4uIiIiKT1kgwHXxMTx4mq32uksFAAszI5z41h26OsV7As6r+Pv9bmHPJREREZUIXR0FAvs2B/AikXzVy+XAvs3fKrGkdw+TSyIiIioxnvZ1sHpYa1iYSYfILcyMsPq1aYjo/cAbeoiIiKhEedrXgUdzC/EJPbWqGKFtQ3P2WL6nmFwSERFRidPVUahMN0TvJw6LExEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWz0tKl09+5d5OfnF1lPX18fVlZWbx0UEREREZVPWiWXTZs2hbm5OQwNDQutZ2hoiGvXrskSGBERERGVP1oll6tWrcIvv/yCAwcOQKFQlHRMRERERFROaXXN5ahRo+Ds7IzQ0NCSjoeIiIiIyjGFIAiCtpUFQXivey7T09NhZmaGp0+fwtTUtKzDISIiIi3w9/vdUqy7xd/nxJKIiIiI3h6nIiIiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2Wg1z+XrUlNTsX//fqSnp2P8+PFyx0RERERE5VSxey6joqLg5OSEkydP4scffxTL79y5A19fX1mDIyIiIqLypdg9l99++y2OHDmCJk2awMnJSSxv0KABrly5ImtwRERERFS+FLvnMjMzE02aNAGgOu9lbm6uPFG9pbCwMLRs2RKtWrVCr169kJiYWNYhEREREVUIxU4ujYyMcOfOHZXyU6dOwcTERJag3sbhw4exdu1anDhxAhcvXsTIkSPx4YcflnVYRERERBVCsZPLJUuWwMvLC6tXr0ZGRgZ++eUXTJ06FX379sXChQtLIsZiCQ0Nxdy5c2FmZgYA+Oijj6Crq4u4uLiyDYyIiIioAih2ctmtWzdERkYiMTERLVu2xE8//YTs7GwcOXIE7u7uJRFjsURFRaFz586Ssi5duuDIkSNlFBERERFRxVHsG3p2796NDz/8EPPnzy+JeN7Ks2fPoKenpzI8b2VlhcuXL6vUz8nJQU5Ojricnp5e4jESERERvc+K3XP5Lgx9a5KWlgYjIyOVciMjI2RmZqqUL1q0CGZmZuLLysqqNMIkIiIiem8VO7ns2LEjDh48WBKxvDVDQ0NkZ2erlGdlZcHY2FilfPr06Xj69Kn4unfvXmmESURERPTeKvawuK6uLsaNG4cqVaqgWbNmqFSpkrjO0NAQYWFhsgZYHDVq1EBWVhaePXuGypUri+X37t2DpaWlSn1DQ0MYGhqWZohERERE77ViJ5cuLi5o1aqV2nVlnagpFAq0a9cOsbGx8PLyEstjYmKwYMGCMoyMiIiIqGIodnL58ccfl0QcsvH390dAQABcXV1hamqKHTt24Pnz53Bzcyvr0IiIiIjee8VOLk+fPq3xSTwGBgb44IMP3jqotzFw4EDcu3cP7du3h46ODiwsLLB3717o6BT78lIiIiIiKiaFIAhCcRp0795dMn1PZmYm/v77b1StWhWdOnXCli1bZA+ytKSnp8PMzAxPnz6FqalpWYdDREREWuDv97ul2D2XR48eVSl7/vw5Zs+ejbp168oSFBERERGVT7KMFZuYmCAkJARbt26VY3NEREREVE7JdiFiQUGBZLiciIiIiCqeYg+LnzhxQuWGnkePHmHz5s1wcnKSLTAiIiIiKn+KnVzOmzdPJbmsWrUqOnToAH9/f9kCIyIiIqLyp9jJ5eHDh0siDiIiIiJ6DxT7msvNmzdrXDd69Oi3CoaIiIiIyrdiJ5chISFqywsKCnD69Om3DoiIiIiIyi+th8U3bdqEtWvX4saNG+jcubPK+vv378PFxUXW4IiIiIiofNE6uezWrRusrKwwduxYzJs3T7JOV1cX1atXR7NmzWQPkIiIiIjKD62Ty7p166Ju3bro3bs3unTpUpIxEREREVE5VexrLn/44YcSCIOIiIiI3gfFnoooKSkJ3377LS5cuIDMzEzJOh0dHdy4cUO24IiIiIiofCl2z+WYMWPQvHlzbN++HYaGhti/fz+CgoLQoEEDbNy4sSRiJCIiIqJyotg9l4mJiZg+fTqAFz2VzZs3R/PmzdGpUyd4e3vjxIkTsgdJREREROVDsXsulUql+O9atWrh2rVrAIA6derg+fPn8kVGREREROVOsZNLS0tLMaHs1asXAgICkJaWhujoaBgYGMgeYEXVunVrJCYmal3/5MmTGDhwYLH28SZtXhceHo6vv/5apXzr1q1o2rQp6tevD3d3d8TFxUnWW1tbIyMjo1j7srW1RVpa2ltES0RERCWt2MnlmjVrYGRkBACYOHEiDA0NYWVlhXHjxmHlypWyB/i+uXjxIuzt7dGwYUM0adIE9vb2aNGiBerVqwcHBwc8efIEAJCbm4u8vDyx3ezZs2Fvby++mjVrBgcHB2RlZQEA8vLyJPUB4IsvvpC0ad68OTp37gxBEDS2ed2CBQtQp04d2NnZiS9zc3PxaUx5eXnIzc2VtNm/fz+WLl2KY8eO4e7du1iwYAEGDx4sSZazs7MlveDAi/dWo0aNULt2bfTo0QO3b9+WrM/JyUF+fr5KjL/++iu8vLxgbW0NKysrNG7cGCNHjsSFCxcKPTYiIiKSX7GvubS2thb/bWhoiE2bNskZz3uvVatWiI+Ph5+fHz744AMMGzYMDx48gKurKy5duqSx3bx58yST1//999/o0aOHmOirs3r1aslyQUEBzMzMihVvQkICli1bBm9vb7FsxIgRuH//vsY2S5YsQXBwMCwtLQEA7du3x/Dhw7Fp0yZMmzZNbZuIiAgsWbIEsbGxsLS0xIoVK9C/f3/ExcVBoVBo3Nfs2bMRHR2NkJAQuLi4QKFQICsrC/v378egQYOwatUqeHp6FuuYiYiI6M0VO7kEXiQ227dvx/PnzxEUFCSWC4JQaCJA8vnpp58wevToYp3v69evw8bGptj/R6/XL6p9cnIyGjZsKCmztbXFuHHjsHnzZgDA48ePJetXr16NoKAgMSH18/PD9u3bcejQIXh5eandT25uLoKDg5GSkiJJmo2NjeHt7Q1dXV0EBwczuSQiIipFxR4W37ZtG/r16wd9fX3s3btXLL979y5/xEvJrVu3sGPHDkyYMKFY7fbs2aMxUXsb4eHhsLOzw8SJEwEATZo0URmSPnv2LL755hvEx8cjPj4e1atXl6w/d+6cypOfevbsKQ6/q6OjowNdXV2VYfmXcnJyeB0wERFRKSt2z+XChQsRGxuLWrVqYfv27WJ5/fr18fDhQ1mDe58oCwScvfUEKRnZqFXFSLzusbjy8vIwatQotG7dGlWrVpWsO378OOzs7NCiRQvs2rVLsi4zMxNr167FsWPHtG4DvOilfP3aSKVSKem99PHxwYoVK8TlOXPmYMiQIahduzYcHBywe/du/Prrrzhz5ozG40pNTUW1atUkZTVr1sS0adOwY8cOAFC5wUlPTw/BwcHo3r075s+fD1dXV1SpUgXJycnYu3cvQkJCsGXLFo37JCIiIvkVO7nMz89HrVq1AKgOj+bk5MgT1XsmIj4Zc/ZdRfLTbLEs+3wiTCzTirWdgoIC+Pr6wtHREXFxceLQ+Etubm7Yv3+/2rYzZszAoEGDVIarC2sDAE5OTpgxYwYCAwMlcUydOlVjG2dnZ2zbtg0BAQFISkqCs7Mzjh8/Lr5v1KlevTqePHmC2rVri2WJiYmYMmUKZs6cCUB6ve9Lfn5+cHZ2RlhYGGbNmoW0tDRYWFjA3d0d0dHRaNCggcZ9EhERkfyKnVyam5vj4sWLaNWqlaR8z549hSYPFVVEfDK+2Hwer/dTZubmY8Op23CPT4ZjjRdJempqKlJSUmBra6uynefPn2PkyJEwNTVFSEgI0tLS0L17dxgZGcHHx6fQGNasWYMzZ84gJiam2PH7+/vD399fUpaRkQETExMAL3oP9fRU30bNmzfH/v37oaurC0A6P+q9e/ewc+dOmJqaimWurq44fPgwfH19xbJ9+/ZJekQ1iYmJwejRo/HTTz+JZStXrsT169eZXBIREZWyYl9zuWLFCgwdOhTffvstHj16hKVLl+Kjjz7CyJEj8Z///KckYiy3lAUC5uy7Kkkss25dwINNU5B5/RTS/rcZA7u2g6urK5RKJfr164cNGzaobCc5ORkdO3ZEixYtEBYWBoVCgWrVquHIkSNYt24drly5onb/eXl5mD59Ov773//i4MGDsl1/6OHhgaioKAAvhsS///57lTrjxo0Tn9YUGxuLkSNHiuucnJzQpk0b6Oj839tv0qRJCAgIwPnz5/H8+XNMmzYNFhYW6NixY5Hx/PXXX0hJSZGUJSQkICkp6Y2Oj4iIiN5csXsuHR0dce7cOWzZsgWDBw/GzZs38cEHH+A///kP6tWrVxIxlltnbz2RDIUDgFEDBxjUbvRiQUcXOvqG2DSuE9o3rq5mCy+Ymppi2bJl6Ny5s6Tc3Nwc0dHRAF5cO/mqZ8+eoW3btnBzc8Px48cLnbKoOKKjo5GUlIT58+fDzc0N+vr6Yu/kqwRBEK8rLSgoQEFBAYAX81umpaVJEksAcHFxQVhYGCZMmICUlBS4u7tj9+7dssRMREREpUer5HLPnj0YMGAAAOCff/5Bo0aNMGbMmJKM672QkpGtUqbQ0YVuJbMi673KxMREJbEsSuXKlXHo0CFZh4UjIyPh5+eHyMhI7NixAwMGDMDmzZtVbsR5ycfHB8bGxsjKykLXrl0BAAcOHIBSqcSePXswdOhQSX0PDw94eHhoFYu/vz8iIyPF5dOnT6vMoRkRESFOlTV58mSMHTtW62MlIiKiN6NVcvndd9+JyeXgwYNx/vz5kozpvVGrina9hdrWKy65EsvY2FiEhobi2rVr2LdvH2xtbREQEID169fD2dkZw4cPx7Bhw2BjYyNpFx4eLvacrlu3DhkZGZg5cya2bt2K7777Ds7OzipttLVs2bI3ardt2zbcv38fU6ZMeaP2REREVDitksuGDRtixIgRaNSoEZKTkzF37ly19QwMDDQ+gaUiatvQHHXMjPDgabbKDT0AoABgYWaEtg3NVdbp6+urvVFGE319fejr6xcrPm3aPH78GEFBQfjkk0/w888/S4bAR40ahX79+mH9+vUIDw+X3FFubW2NYcOGoXLlynj27Bk+/PBDdO3aFWPHjsXQoUNhbm6O7t2747fffoODg4NW8RoaGqodgi+O13tLiYiISF4KQYsJF3NychATE4OUlBTMnDmz0OTy448/lj3I0pKeng4zMzM8ffpUcifz23h5tzgASYL5chKn1cNaw9O+jiz7epcplUrExsaKw+PAi6mGatasyYnOiYjorZTE7ze9Oa2Sy1f16dOn0HkRy7OSenOqm+eyjpkRAvs2rxCJJRERUUlicvluKXZy+T4ryTfn60/oadvQHLo6fA47ERHR22Jy+W4p9lRE9GZ0dRSFTjdERERE9D4o9iTqRERERESalKvk8uDBg+jWrRscHBxgb2+PcePGITMzU1JHT08Pjo6OktfBgwfLKGIiIiKiiqVcXXMZGxuLxo0bo169esjPz8eIESNQp04dLF26VKyjUCiQl5dXrGl8XuI1G0REROUPf7/fLVr1XG7atEml7OLFiyplmqYokkvnzp3FR0zq6enhm2++kTylhYiIiIjKllbJ5X/+8x+VspEjR6qU7dmz560DKo4nT57I9sxsIiIiInp7Wo0dqxs517asJK1Zswa+vr5v3D4nJwc5OTnicnp6uhxhEREREVVYWvVcKhSq8zFqW/am4uPjJTfl7Nq1S7L+8OHDiIuLw9ixY1Xaenp6omXLlmjXrh1++OEHFBQUqN3HokWLYGZmJr6srKxki5+IiIioItKq5zI3Nxe3bt2S9ExqKpOLvb094uLi1K67d+8ePvvsM+zatQuGhoaSdcnJybCwsAAA3L59G76+vsjMzMSMGTNUtjN9+nR8/fXX4nJ6ejoTTCIiIqK3oNXd4k2bNkV+fn6Rw976+vr4+++/ZQtOnefPn6Nz58746quvMHz48CLrnzhxAv7+/jh//nyRdXm3GRERUfnD3+93i1Y9lyWdMGpLqVRi6NCh6NWrl1aJ5cs2bzItEREREREVX7maRH3ixIkwNjbGvHnz1K7PzMzEw4cPxeXbt29jypQpGDVqVGmFSERERFShaTUsPn/+fK2upzQwMMCsWbNkCex1qampMDc3h62trWT6IYVCgYiICNSuXRuJiYno06ePOIm6sbExxo8fj2HDhmm1D3arExERlT/8/X63aDVerKurC11dXY3rBUHA7t27ce3atRJLLqtVq1bkNZ/16tXDhQsXSmT/RERERFQ0rZLL6dOna1wXGxuLqVOnok6dOvj5559lC4yIiIiIyp83vtPl6tWr+Pbbb/HgwQMEBQWhW7ducsZFREREROVQsW/oSU5OxujRozFw4ED4+Pjgjz/+YGJJRERERACKkVxmZGRgxowZaN++PRwdHREfH4+hQ4eWZGxEREREVM5olVyuWLECrVq1gr6+Pq5cuYIJEyZAX1+/pGMjIiIionJGq6mIdHR0YGpqiipVqmh8frggCDAyMkJCQoLsQZYWTmVARERU/vD3+92i1Q09d+/ehVKpLLIeezOJiIiIKjatkktLS8uSjoOIiIiI3gNaXXMZFRWl9gk9W7duRbt27eDk5IQNGzbIHhwRERERlS9a9VxOmjQJcXFxkrKtW7di4sSJ2Lx5M8zMzPDdd98BAEaOHCl3jERERERUTmiVXOrp6UFH5/86OR8+fIgJEyZg165d6NKlCwBg+/bt8PDwYHJJREREVIFpNSyuUCgkw+LffPMNvLy8xMQSAExNTZGXlyd/hERERERUbmjVczlkyBB8/vnnmDBhAnbs2IHjx4/jwoULkjoFBQXIysoqkSCJiIiIqHzQqudy6tSpaNKkCXx9fXHx4kVER0ejWrVqkjpXr17lYyCJiIiIKjitJlGvKDgJKxERUfnD3+93i9bPFn/dypUr5YyDiIiIiN4Db5xc/vTTT3LGQURERETvgTdOLomIiIiIXsfkkoiIiIhk88bJZVRUlJxxEBEREdF7oNjJZXBwMACoTEUEAF5eXm8fERERERGVW8VOLrdt26a2PDMzEzdu3HjrgIiIiIio/NLqCT0AsGLFCixevBgPHz5E/fr1VdanpaVhzJgxsgZHREREROWL1pOoP3/+HA8fPkTv3r1x8OBByTpdXV1Uq1YNJiYmJRJkaeEkrEREROUPf7/fLVr3XJqYmMDExAQjRoxAgwYNSjImIiIiIiqn+PjHV/AvHyIiovKHv9/vFs5zSURERESyYXJJRERERLJhcklEREREsmFySURERESyYXJJRERERLJhcklEREREsmFySURERESyYXJJRERERLIpV8nl5s2bYW5uDkdHR/HVrl07KJVKsU5ycjJ69+6NVq1aoWXLllizZk0ZRkxERERUsWj9+Md3QX5+Pry8vLB582aNdQYNGoTx48fDx8cHGRkZ8PDwQP369eHl5VWKkRIRERFVTOWq57Ioly5dglKphI+PDwCgSpUqmDt3LtauXVvGkRERERFVDO9Vcnn06FF06dJFUtapUyccO3YMfIQ6ERERUckrV8PiRUlKSkKDBg0kZcbGxjAyMkJKSgpq164tWZeTk4OcnBxxOT09vVTiJCIiInpfvbM9l/Hx8ZIbd3bt2gWFQoHY2Fi4urqiWbNm6Nu3L37//XexTVpaGoyMjFS2ZWRkhMzMTJXyRYsWwczMTHxZWVmV6DERERERve/e2Z5Le3t7xMXFScqeP3+OgQMHwtTUFIIg4ODBg+jXrx9OnTqFJk2awNDQENnZ2SrbysrKgrGxsUr59OnT8fXXX4vL6enpTDCJiIiI3sI7m1yqY2JiIv5boVCgd+/e6N+/Pw4dOoQmTZrA0tISd+/elbTJysrCs2fPUKtWLZXtGRoawtDQsMTjJiIiIqoo3tlhcW0plUro6b3IkTt06ICYmBjJ+tjYWLi4uEBHp9wfKhEREdE7r1xlXImJicjPzxeXd+3ahYiICAwcOBAA0LlzZ+Tl5SE8PBwAkJGRgcDAQEyYMKFM4iUiIiKqaMrVsHhERASWLFkiDmXb2tri2LFjqFOnDoAXQ+V79uzBZ599hqCgICiVSowZMwbe3t5lGTYRERFRhaEQOAGkKD09HWZmZnj69ClMTU3LOhwiIiLSAn+/3y3laliciIiIiN5tTC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZMLomIiIhINkwuiYiIiEg2TC6JiIiISDZ6ZR2AtpRKJTp06ICcnBxJ+T///IPt27ejV69eAAA9PT3Y29tL6ixcuBBeXl6lFisRERFRRVVukktdXV2cOXNGUpadnY3GjRujffv2YplSqcSff/4JPb1yc2hERERE741yPSy+fft2dOvWDVWrVi3rUIiIiIgI5Ty5DA0NxdixY8s6DCIiIiL6/8rt2PHly5eRmpqKTp06vfE2cnJyJNdwpqenyxEaERERUYX1zvZcxsfHw9HRUXzt2rVLsn7NmjUYM2aM2raenp5o2bIl2rVrhx9++AEFBQVq6y1atAhmZmbiy8rKSvbjICIiIqpIFIIgCGUdRHE9f/4cDRs2xNWrV1GjRg3JugcPHsDCwgIAcPv2bfj6+sLT0xMzZsxQ2Y66nksrKys8ffoUpqamJXsQREREJIv09HSYmZnx9/sd8c72XBZm27ZtcHd3V0ksAYiJJQBYW1tj4cKF+OWXX9Rux9DQEKamppIXEREREb25cplcrlmzRusbeZRKJaclIiIiIiol5S65PH/+PJ48eQJ3d3eVdZmZmXj48KG4fPv2bUyZMgWjRo0qzRCJSGatW7dGYmJisdp4eHjg4sWLJb6fV4WHh+Prr79WKd+6dSuaNm2K+vXrw93dHXFxcZL11tbWyMjIKNa+bG1tkZaW9saxEhGVlHKXXK5btw5ffPEFFAqFyrrU1FT06NED9vb2cHR0xMcff4yJEydi3LhxZRApEWkrPDwcdnZ24isyMlKyPjc3F3l5eeLyH3/8IalvZ2eHWrVq4dtvvxXr5OXlSdoAwP/+9z+0bdsWLVq0QJs2bYrcz+sWLFiAOnXqSPZrbm6O06dPi/vMzc2VtNm/fz+WLl2KY8eO4e7du1iwYAEGDx4sSWKzs7OhVCol7dasWYNGjRqhdu3a6NGjB27fvi1Zn5OTg/z8fJUYf/31V3h5ecHa2hpWVlZo3LgxRo4ciQsXLmg8LiIiOZW78eJVq1ZpXFevXj1+gRKVQz4+PvDx8dG6vouLC65duyYp+/777/HgwQONbVJSUjBkyBD89ttvcHZ2xtWrV9GjRw/ExsaiUaNGWu03ISEBy5Ytg7e3t1g2YsQI3L9/X2ObJUuWIDg4GJaWlgCA9u3bY/jw4di0aROmTZumtk1ERASWLFmC2NhYWFpaYsWKFejfvz/i4uLU/mH90uzZsxEdHY2QkBC4uLhAoVAgKysL+/fvx6BBg7Bq1Sp4enpqdaxERG+q3PVcEtH75ccff4S9vb34atGiBczMzHDnzh2t2ufl5SE1NRUHDhxA7969NdYLDw+Ht7c3nJ2dAQDNmzfHl19+idWrVxcr3teTu8KSPQBITk5Gw4YNJWW2trYICgoSj/nx48eS9atXr0ZQUJCYkPr5+cHU1BSHDh3SuJ/c3FwEBwfjwIEDaNu2rRiXsbExvL29sXTpUgQHB2t9nEREb4rJJRGVqYkTJyI+Pl587dq1C9WqVUPdunU1ttm3bx+cnJzg5OSEjh07wsnJCYmJiYU+VOHcuXMq67t16yYOacvl5RD/xIkTAQBNmjRRGVE5e/YsvvnmG/GYq1evrhJrly5dJGU9e/YsNFYdHR3o6uqqDMu/lJOTAwMDgzc5JCKiYil3w+JEVP4pCwScvfUEKRnZqFXFCG0bmkNXRwFBEDB+/HgEBARAX19fY/u+ffuib9++AIAbN26gW7duCAsLg66ursY2qampqFq1qqSsRo0akpsAi6JQKFSujVQqlZLeSx8fH6xYsUJcnjNnDoYMGYLatWvDwcEBu3fvxq+//oozZ84UGmu1atUkZTVr1sS0adOwY8cOAFC58UhPTw/BwcHo3r075s+fD1dXV1SpUgXJycnYu3cvQkJCsGXLFq2PlYjoTTG5JKJSFRGfjDn7riL5abZYVsfMCIF9m+PIhqW4cuWK1tdf7ty5E1OmTMHy5cvRuXPnQuvWqFFDZfg5KSkJderU0Tp2JycnzJgxA4GBgWJZQUEBpk6dqrGNs7Mztm3bhoCAACQlJcHZ2RnHjx9HrVq1NLapXr06njx5gtq1a4tliYmJmDJlCmbOnAngxR3mr/Pz84OzszPCwsIwa9YspKWlwcLCAu7u7oiOjkaDBg20PlYiojfF5JKISk1EfDK+2Hwerz8WLDn1OT4a8RnszfLg5+cHb29vbN++HcbGxmq3c/LkSUybNg0GBgaIjIyEra1tkfvu1KkTjh49iiFDhohl+/fvR/fu3bWO39/fH/7+/pKyjIwMmJiYAHjRe6huXt3mzZtj//79Ys/qq72f9+7dw86dOyUPcXB1dcXhw4fh6+srlu3bt0/SI6pJTEwMRo8ejZ9++kksW7lyJa5fv87kkohKBa+5JKJSoSwQMGffVZXEMifxGpI3T4VCRxcF3SZj+oyZ8PDwQMeOHXH+/Hm128rJycHcuXMRFRWlVWIJAEOGDEFsbCz27t0LADhy5AjCw8O1fiCDJh4eHoiKigLwYkj8+++/V6kzbtw4nDhxAgAQGxuLkSNHiuucnJzQpk0b6Oj839fxpEmTEBAQgPPnz+P58+eYNm0aLCws0LFjxyLj+euvv5CSkiIpS0hIQFJS0hsdHxFRcTG5JKJScfbWE8lQOAAIynw8PfMLzD0+R7VuY/EgIxdnbz3BhAkTsHLlSo03p7i7u6Nr166YOHEiDh48qLbOjBkz0KRJE3G5SpUqiIyMxMaNG2Fvb4/ly5fj8OHDhQ5PFyU6OhpJSUmYP38+8vLyoFAo1F73KQgCBOFFWl1QUICCggIAL+a3TEtLkySWwIuplsLCwjBhwgQ4OjoiNTUVu3fvfuM4iYhKE4fFiahUpGRkq5QpdPVQ68NZauu1b9++yG0+ffoUmZmZatf16NFDpaxBgwayJWmRkZHw8/NDZGQkduzYgQEDBmDz5s0qN+K85OPjA2NjY2RlZaFr164AgAMHDkCpVGLPnj0YOnSopL6Hhwc8PDy0isXf318yIfzp06dV5tCMiIhAUFAQAGDy5Mlv3WNLRKQJk0siKhW1qhjJWg8oeo7JkhAbG4vQ0FBcu3YN+/btg62tLQICArB+/Xo4Oztj+PDhGDZsGGxsbCTtwsPD4ebmhuPHj2PdunXIyMjAzJkzsXXrVnz33XdwdnZWaaOtZcuWyXFoRESy4LA4EZWKtg3NUcfMCJrSQQVe3DXetqG5yjp9fX21N8rY2tpi0qRJkknYX30FBAQUK0ZN+3np8ePHCAoKQq9evXD27FnJ9Z6jRo3CmTNnUKlSJYSHh0vaWVtbY9iwYbCzs8OwYcNgbm6Orl27YuzYsRg6dCiWLVuG7t2749KlS1rHamhoWOjUS0REZUUhvLwQiJCeng4zMzM8ffpUcucmEcnj5d3iACQ39rxMOFcPaw1Pe+2nBiqvlEolYmNjxeFx4MVUQzVr1uRE50RvgL/f7xb2XBJRqfG0r4PVw1rDwkw69G1hZlRhEksA0NXVlSSWAFCvXj0mlkT0XuA1l0RUqjzt68CjuYXaJ/QQEVH5x+SSiEqdro4C7RtXL7oiERGVOxwWJyIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItm8c8nl+vXrYWhoiNu3b6us++uvv9ClSxc4OjrCyckJu3fvlqzPy8vDxIkT0aJFC7Ro0QITJkxAbm5uKUVORERERO9Ucjl79mzs3LkT1apVQ35+vmRddnY2+vfvj7lz5yIuLg6HDh3C9OnTcenSJbFOQEAAcnJycPnyZVy+fBmCIGDWrFmlfRhEREREFdY7k1wWFBSgTp062L9/P4yMjFTWR0ZGwsnJCV26dAEAWFhYYPLkyVi/fr3YftOmTQgKCoKOjg50dHSwcOFChIeHQ6lUluqxEBEREVVU70xyqaOjgy+//BK6urpq1x89elRMLF/q0qULjhw5AgCIi4tD3bp1UbVqVXG9qakp6tevj/Pnz5dY3ERERET0f96Z5LIoSUlJsLKykpRZWVnhn3/+0bj+9Tqvy8nJQXp6uuRFRERERG+uTJLL+Ph4ODo6iq9du3YV2SYtLU1luNzIyAjZ2dkQBEHt+pd1MjMz1W5z0aJFMDMzE1/qklMiIiIi0l6ZJJf29vaIi4sTX4MGDSqyjaGhIbKzsyVlWVlZMDQ0hEKhULv+ZR1jY2O125w+fTqePn0qvu7du/dmB0RERKSFqVOnwsbGBjY2Nvjqq6/E8vr16wMALl26hIEDBxZrm61bt0ZiYmKx2nh4eODixYta1z958mSx43pdeHg4vv76a5XyrVu3omnTpqhfvz7c3d0RFxcnWW9tbY2MjIxi7cvW1hZpaWlvES29jXIzLG5paYm7d+9Kyu7duwdLS0uN61+v8zpDQ0OYmppKXkRERCUlODgYN27cwI0bN/DDDz+I5S9H2HJzc5GXlyeWjx49Gvv27ZNsY968eVi+fLm4/Hqb9evXw97eXvKytbXF6dOnxTp5eXmSNl988YWkfvPmzdG5c2cIgqC2vjoLFixAnTp1YGdnJ77Mzc3F/ebl5alMD7h//34sXboUx44dw927d7FgwQIMHjxYkixnZ2er3Ji7Zs0aNGrUCLVr10aPHj1w584dyfqcnByVWWcA4Ndff4WXlxesra1hZWWFxo0bY+TIkbhw4UKhx0bFo1fWAWirQ4cOOHDgAMaPHy+WxcTEoEOHDgAAR0dHJCQkIC0tTbypJz09HdeuXUPr1q3LImQiIiIAwMOHD+Hu7i4may/p6urixIkTGtsplUqVxEpd2atGjRqFUaNGScqGDx+Ou3fv4oMPPlDbZvXq1ZLlgoICmJmZadyHOgkJCVi2bBm8vb3FshEjRuD+/fsa2yxZsgTBwcFiJ1D79u0xfPhwbNq0CdOmTVPbJiIiAkuWLEFsbCwsLS2xYsUKfPLJJ0XGN3v2bERHRyMkJAQuLi5QKBTIysrC/v37MWjQIKxatQqenp7FOmZSr9wkl4MHD0ZAQABiYmLQpUsXPHjwAEuXLsXmzZsBAMbGxhgxYgSmTZuGVatWQaFQYMaMGfDx8UGlSpXKOHoiIqrIatasicuXL+Phw4c4evQoCgoK0L17d9SuXbtU9n/lyhWNyZo6169fh42NDRQKRbH283r9otonJyejYcOGkjJbW1uMGzdO/H1//PixZP3q1asRFBQkJqR+fn7YsmVLofvJzc1FcHAwUlJSJEmzsbExvL29oauri+DgYCaXMnknh8UNDAygr68vKTMxMcFvv/2GGTNmoFWrVvDw8MCcOXPQrl07sc7ixYsBAC1atEDz5s2Rk5ODpUuXlmrsRERE6ly5cgWurq64ceMG7ty5A3d3d8lwrJ2dHQYPHqzSbu/evVi6dKn4OnXqVLH2e+fOHaSmpqJFixZat9mzZw+8vLyKtR9thIeHw87ODhMnTgQANGnSRGVI+uzZs/jmm28QHx+P+Ph4VK9eXbL+3LlzKlMTduvWrdD96ujoQFdXV+NT+3JycmBgYFDcwyEN3smey7///ltteatWrXDy5EmN7YyMjLBmzZqSCouIiKhYlAUCzt56gpSMbOxYuRozZ86Cr+9wAEDLli2xdu1acUj62rVr+PPPP/Hdd99JtmFiYiKZw9nIyAiLFi0Sf+9u3bpVaAxLly7FuHHjMGLECJw5cwYA1N6j8FJmZibWrl2LY8eOScqPHz8OOzs7tGjRQu0sLwqFQu0Q/qu9lz4+PlixYoW4PGfOHAwZMgS1a9eGg4MDdu/ejV9//VWMU53U1FRUq1ZNUvYyAXV2doaOjo7KDU56enoIDg5G9+7dMX/+fLi6uqJKlSpITk7G3r17ERISUmTvJ2nvnUwuiYiIyruI+GTM2XcVyU9fzGSScU+BI8u2oLqDG3o51MOxY8dga2tb5Ha6d++OAQMGiMv379+Hu7u7eLe5vb29xranT5/G4cOHcfHiRcnMKW5ubhrbzJgxA4MGDVIZrnZzc8P+/fs1tnNycsKMGTMQGBgolhUUFGDq1Kka2zg7O2Pbtm0ICAhAUlISnJ2dcfz4cdSqVUtjm+rVq+PJkyeSSwqSk5MBAH/++SdMTU1hbW2t0s7Pzw/Ozs4ICwvDrFmzkJaWBgsLC7i7uyM6OhoNGjTQuE8qHiaXREREMouIT8YXm8/j1dt3KrfqgacnH+PDXt3QoHolePfvjQkTJgB4ceMLAPHxxXK4fv06hg0bhu3bt2ucku91a9aswZkzZxATE1Ps/fn7+8Pf319SlpGRARMTEwAveg/19FTTjubNm2P//v3iE/pe7f28d+8edu7cKZnNxdXVFYcPH4avr69YdujQIa1ijImJwejRo/HTTz+JZStXrsT169eZXMronbzmkoiIqLxSFgiYs+8qhNfKFQodVHX1Qd0R/0F1n+8xd958MaH6/vvvAbyYs3LHjh1imwYNGmDChAmSaYJWrVqFunXrFhpDREQEvLy8sHbtWrRp06bImPPy8jB9+nT897//xcGDB2W7/tDDwwNRUVEAXgyJvzzOV40bN068Yz42NhYjR44U1zk5OaFNmzaShHvSpEkICAjA+fPn8fz5c0ybNk3rG6P++usvpKSkSMoSEhKQlJRU7GMjzdhzSUREJKOzt56IQ+HqPI4Kw71r/0OjdeYw1teVrEtPT8dHH30kzoE5Z84czJkzp1j7nzp1Ko4dO4YDBw7Azs6uyPrPnj1D27Zt4ebmhuPHj6t92t2biI6ORlJSEubPnw83Nzfo6+uLyfSrBEEQp2gqKChAQUEBgBfzW6alpan05Lq4uCAsLAwTJkxASkoK3N3dsXnzZlhYWMgSN709JpdEREQySsnQnFgCQH5qEqp7TcKy6SPR37GeZF10dLTa3r3i8Pf3x6JFi9QmcupUrlwZhw4dknVYODIyEn5+foiMjMSOHTswYMAAbN68WeVGnJd8fHxgbGyMrKwsdO3aFQBw4MABKJVK7NmzB0OHDpXU9/DwgIeHh7icnp6uMRZ/f39ERkaKy6dPn1aZlikiIgJBQUEAgMmTJ2Ps2LHFO2CSYHJJREQko1pVtOv507ZecWl6Kl1h5EosY2NjERoaimvXrmHfvn2wtbVFQEAA1q9fD2dnZwwfPhzDhg2DjY2NpF14eLjYc7pu3TpkZGRg5syZ2Lp1K7777js4OzurtNHWsmXL3qjdtm3bcP/+fUyZMuWN2ldkTC6JiIhk1LahOeqYGeHB02yV6y4BQM+sNtIOL8PYK9tU1j19+lTyhBtt6Ovrq71Rpqg2r88n/bb1Hz9+jKCgIHzyySf4+eefJT2no0aNQr9+/bB+/XqEh4dL7ii3trbGsGHDULlyZTx79gwffvghunbtirFjx2Lo0KEwNzdH9+7d8dtvv8HBwUGreA0NDbXuudXk9d5S0p5CeP1ZVBVYeno6zMzM8PTpUz5nnIiI3tjLu8UBSBLMlzM+rh7WGp72dUo9rvJAqVQiNjZWHB4HgMTERNSsWVPjjUb8/X638G5xIiIimXna18HqYa1hYSYd+rYwM2JiWQRdXV1JYgkA9erV4xN0yhEOixMREZUAT/s68GhuIT6hp1YVI7RtaA5dneI9r5uovGFySUREVEJ0dRRo37h60RWJ3iMcFiciIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItkwuSQiIiIi2TC5JCIiIiLZMLkkIiIiItnwCT2vEAQBAJCenl7GkRAREZG2Xv5uv/wdp7LF5PIVGRkZAAArK6syjoSIiIiKKyMjA2ZmZmUdRoWnEJjmiwoKCpCUlIQqVapAoVAUWjc9PR1WVla4d+8eTE1NSynCdxfPhyqeE1U8J1I8H6p4TqR4PlSpOyeCICAjIwN169aFjg6v+Ctr7Ll8hY6ODiwtLYvVxtTUlB/4V/B8qOI5UcVzIsXzoYrnRIrnQ9Xr54Q9lu8OpvdEREREJBsml0REREQkGyaXb8jQ0BCBgYEwNDQs61DeCTwfqnhOVPGcSPF8qOI5keL5UMVz8u7jDT1EREREJBv2XBIRERGRbJhcEhEREZFsmFwSERERkWyYXBZDVlYWAgMD0aZNGzg5OaFZs2Y4duyYuD45ORm9e/dGq1at0LJlS6xZs6YMoy09165dg6GhIebMmSMpr2jn4+DBg+jWrRscHBxgb2+PcePGITMzU1Lnr7/+QpcuXeDo6AgnJyfs3r27jKItfWFhYWjZsiVatWqFXr16ITExsaxDKlVFvT8q8ntD3XdIRfv+APgb87r09HT4+/ujVatWcHR0RMeOHXH06FFxfUX+zLzzBNJKXl6e0KVLF+G7774TsrOzBUEQhIKCAiEvL0+s0759e2Hz5s2CIAhCenq60K5dO+HAgQNlEm9p6tGjh+Dp6SnMnDlTUl7RzkdMTIxw//59QRBevF8++eQTYfLkyeL6rKwsoUmTJsLx48cFQRCE5ORkoWnTpsLFixfLJN7SFBERITg7OwtpaWmCIAjC9u3bhbZt25ZxVKWrsPdHRX5vCIL675CK9v3B3xhVPXv2FBYuXCgolUpBEAThzz//FOrUqSPcvn27wn9m3nVMLrW0fv16oV+/fhrXX7x4UeXH8vDhw0L//v1LOLKy9csvvwjDhw8XAgMDJT8MFfV8vOrChQtCy5YtxeW9e/cKH330kaROaGioMHHixFKOrPQNHDhQOHjwoKSsffv2woULF8omoHfAq++PivzeUPcdUhG/P/gbo0pfX1/8g/Sl3r17C7t27arQn5nygMPiWtq2bRs+//xzjeuPHj2KLl26SMo6deqEY8eOQXhPZ3vKzMxEQEAAgoKCVNZVxPPxuidPnsDIyEhcVndOunTpgiNHjpR2aKUuKioKnTt3lpRVlGPX5NX3R0V9b2j6DqmI3x/8jVH1wQcfICQkRFyOjY3FqVOn0LZt2wr7mSkvmFxq6eLFizA2NsagQYPg4OAAd3d3REREiOuTkpJgZWUlaWNsbAwjIyOkpKSUdrilYuHChfDx8UHdunVV1lXE8/G6NWvWwNfXV1xWd06srKzwzz//lHZoperZs2fQ09ODiYmJpLwiHHthXn1/VNT3hqbvkIr4/cHfGFUbN27E9u3b0bNnT/j7++PDDz/E5s2bYWlpWWE/M+UFk0s14uPj4ejoKL527dqFx48fY/78+ViwYAEuXbqEH374AZ999hmOHz8OAEhLS5P0Ur1kZGSkclNHeaPufNy8eRO7du3C119/rbbN+3w+APXn5FWHDx9GXFwcxo4dK5apOydGRkbIzs5+b3segPf/vfAmXn9/VMT3RmHfIRXxPVORf2M0adCgAcaPH4+oqCgsX74cPXr0gIuLC4CK+ZkpT/TKOoB3kb29PeLi4iRlOjo6mDp1Kuzs7AAADg4OmDRpEtavXw83NzcYGhoiOztbZVtZWVkwNjYujbBLjLrz0adPH8yfP1/tlx2A9/p8AOrPyUv37t3DZ599hl27dkkeT6bunGRlZcHQ0BAKhaIkwy1T7/t7objUvT8q4ntj4sSJGr9DKuJ7piL/xmgybNgwJCQk4OjRo6hfvz7mzJkDBwcH/PHHHxXyM1OesOdSS7Vq1ULTpk0lZTY2Nnj48CEAwNLSEnfv3pWsz8rKwrNnz1CrVq1Si7M0REREIDMzE4MGDdJYpyKdj1c9f/4cAwYMwPz58+Hs7CxZp+6c3Lt3D5aWlqUZYqmrUaOG+H//qopw7K/T9P6oaO+Nor5DKuL3B39jpG7cuIGDBw/i6NGjcHNzQ6NGjbBx40b07NkTq1atqnCfmfKGyaWWXFxccPnyZUlZQkICbGxsAAAdOnRATEyMZH1sbCxcXFygo/N+neZbt27h/v37kmHhNWvWYN26dXB2dkZmZmaFOh8vKZVKDB06FL169cLw4cNV1qs7JzExMejQoUNphVgmFAoF2rVrh9jYWEl5RTj2VxX2/qho742ivkMq4vcHf2Ok0tPTUbduXZiZmUnKW7ZsidTU1Ar3mSl3yvBO9XLlyJEjQosWLYTk5GRBEATh6tWrQoMGDYS//vpLEIQX85E5OjqqzEG2Y8eOMou5NL0+FVFFPB/jx48XvL29hYKCArXrnz17JtSvX18yL5uNjY1w+vTp0gyzTOzevVto06aN8PTpU0EQXsxz2bJlS3H+uoqgsPdHRX5vvPTqd0hF/P7gb4xUfn6+0LZtWyEkJET8nrhx44Zga2srnDhxgp+Zd5xCEHjlq7bWrVuH4OBg6OjowMTEBAsWLICnp6e4/s6dO/jss8+QlJQEpVKJMWPGaLzh5X2zYMEC5OfnIzAwUCyrSOcjNTUV5ubmsLW1lVxDplAoEBERgdq1awN4cUfol19+iWfPnqGgoADTp0/HJ598UlZhl6ply5YhNDQUOjo6sLCwwNq1a9GwYcOyDqtUaPP+qMjvDUD1O6QifX+8xN8YqUePHmHmzJn4/fffoauri0qVKuHbb79Fv379AFTs79N3HZNLIiIiIpLN+3ehBhERERGVGSaXRERERCQbJpdEREREJBsml0REREQkGyaXRERERCQbJpdEREREJBsml0QkO0EQsHjxYrRu3RpOTk6wt7fHkSNHALx4RJtCocCyZctU2i1cuBBz584Vl5s2bYrmzZtLnuSyceNGjfvNzs7GgAED4ODggN27d8t/YKUoLCwMLVu2RKtWrdCrVy8kJiaWdUhERFrRK+sAiKj8ePbsGTZs2IAJEyYUWm/RokW4fv06Tp06BSMjI+Tn5yM/Px/Ai8cgmpqaIiQkBCNGjJA83i03N1es93L54MGD4iPwihITE4MqVarg0qVLb3B0qhITExEVFQVfX19Ztqetw4cPY+3atThx4gTMzMywY8cOfPjhhzhz5kypxkFE9CbYc0lEWnv06BEWL15cZL3w8HBMmDBBfBqNnp6e5Mk0VapUwfDhwxEUFCRrfCkpKWjUqJFs20tISMDatWtl2562QkNDMXfuXDHx/uijj6Crq4u4uLhSj4WIqLiYXBKRVhYvXgwvLy+kpKTA0dGx0MTQ0tISO3fuLHR7U6dOxZYtW2Qb7u3duzdmzpyJ0NBQODo64saNGwCAuLg4dOzYEc2aNYO9vT22bt0qtrl16xZ69uyJ5s2bw8HBAZ6enrh//z4AYOLEiRgzZgzi4uLg6OiIn3/+GQDQo0cPxMbGSvbdrVs3nDp1CgCwZcsWTJo0Cf7+/mjdujV++eUXAEBUVBScnJzQrFkztG7dGkePHtV4LFFRUejcubOkrEuXLuKlBURE77QyfbI5EZUrt27dEurVq1dkvZs3bwr169cXhg8fLvz7778at/Hjjz8KI0eOFNcFBgYKM2fOFJcbNGggJCQkaB3fhg0bhMDAQHH52bNngq2trXDu3DlBEAQhOTlZaNKkifDXX38JgiAI//zzj3DlyhWx/vz58wUfHx9xOTo6WujYsaNkH126dBGOHDkiKevYsaMQHR0txmBpaSn89ttv4vp79+4JdnZ2ws2bNwVBEIRr164J9evXFx49eqRyDBkZGYK5ublK+cqVK4Vx48ZpcxqIiMoUey6J6I09efIETk5O4s02y5cvBwA0atQI58+fhyAIsLOzw5YtW9S2HzduHP73v/8hPj5e4z769esnuaHnwoULWse3ZcsW9O3bF61btwYAWFhYYOTIkdi+fTsAoGHDhmjevLlYf8CAATh//rzW29ekcuXK6Nu3r7i8evVq+Pn5iUP2tra28PT0xP79+1XapqWlSS4heMnIyAiZmZlvHRsRUUnjDT1E9MbMzc01JnvVq1fHpk2bEB0dDW9vbwiCAB8fH0kdAwMDzJ07F9OmTVObaAHAb7/9pvUNPa+7evUqfvnlF8lwclZWFvr37w/gxd3lP/74Iw4dOoR///0XgiAgOzv7jfb1qmbNmqnEsW3bNoSFhYllz549g729vUpbQ0NDtTFkZWXB2Nj4rWMjIippTC6JqER17doV4eHhmD17tkpyCQBDhw7F0qVLERMTI/u+s7KyMGHCBEydOlXt+rFjxyIrKwurVq1Cs2bNcPXqVfTu3bvY+3m9R7FSpUoqcSxatAgfffRRkduqUaMGsrKy8OzZM1SuXFksv3fvHiwtLYsdGxFRaeOwOBFpTVdX943aVa1aFTk5OWrXKRQKLF68GFOnToUgCG8TnoomTZrgjz/+0Lj+119/RWhoKJo3bw6FQoErV65I1qs73qpVq+LRo0fi8vPnz/H333+/VRyvUigUaNeuncpNQzExMejQoYNW2yAiKktMLolIa9WqVUNaWhqePXtWaL3Lly9DqVQCANLT0xEYGFjo3Jjdu3eHmZmZ7BOfDxkyBJGRkZLt3rlzR0xi69Spg4sXLwIAkpOTsWrVKkn76tWr4/79++KxAEC7du2wefNmsWzWrFmSuTrV+fTTT7Fu3TqcOHFCLLt9+7bG+v7+/ggICEB6ejoAYMeOHXj+/Dnc3NyKPmgiojLGYXEi0lrlypUxZswYODk5oXHjxoiIiFBbb82aNYiKioKRkRH09fXx+eefY8yYMQAAfX19GBoaqrRZvHgxXFxcMHjwYLFMT08P/fr1g4GBgVg2YMAAfPfdd2r3a2BgIKlraWmJ6OhofP3115g5cyaMjIxQo0YNREREQFdXF+Hh4fDz80Nubi4qVaqExYsXY8SIEWL7Zs2awcXFBfb29vjggw+wYcMG+Pv7Iz4+Hg4ODjAwMMDHH3+MTp06ifs1NDRUOb42bdpg586dmDx5Mp49ewYDAwO0aNECmzdvVnscAwcOxL1799C+fXvo6OjAwsICe/fuhY4O+wOI6N2nEOQehyIiIiKiCot/BhMRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFsmFwSERERkWyYXBIRERGRbJhcEhEREZFs/h/9mEIgg9rWTQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시각화\n",
    "plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1])\n",
    "for i, text in enumerate(short_corpus):\n",
    "    plt.annotate(text, (reduced_embeddings[i, 0], reduced_embeddings[i, 1]))\n",
    "plt.xlabel('t-SNE feature 0')\n",
    "plt.ylabel('t-SNE feature 1')\n",
    "plt.title('Text Embeddings Visualized with t-SNE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1728d827",
   "metadata": {},
   "source": [
    "## Sequential\n",
    "\n",
    "* TextVectorization 레이어는 문자열을 어휘 인덱스로 변환합니다.\n",
    "* TextVectorization으로 변환된 문자열을 Embedding 계층에 공급할 수 있습니다.\n",
    "* Embedding 레이어는 정수로 인코딩된 어휘를 취하고 각 단어 인덱스에 대한 임베딩 벡터를 찾습니다. \n",
    "* 이러한 벡터는 모델이 학습될 때 학습됩니다. \n",
    "* 벡터는 출력 배열에 차원을 추가합니다. 결과 차원은 (batch, sequence, embedding) 입니다.\n",
    "* GlobalAveragePooling1D 계층은 시퀀스 차원을 평균화하여 각 예제에 대해 고정 길이 출력 벡터를 반환합니다. 이를 통해 모델은 가능한 가장 간단한 방법으로 가변 길이의 입력을 처리할 수 있습니다.\n",
    "* 고정 길이 출력 벡터는 16개의 은닉 유닛이 있는 완전 연결( Dense ) 레이어를 통해 연결됩니다.\n",
    "* 마지막 레이어는 단일 출력 노드와 조밀하게 연결됩니다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a4823da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.engine.sequential.Sequential at 0x13e051790>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# embedding_dim = 16\n",
    "\n",
    "# Sequential\n",
    "model = Sequential([embedding_layer, \n",
    "                    GlobalAveragePooling1D(), \n",
    "                    Dense(units=16, activation=\"relu\"),\n",
    "                    Dense(1)\n",
    "                   ])\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16f945e",
   "metadata": {},
   "source": [
    "## summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3f4955b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 4)           400       \n",
      "                                                                 \n",
      " global_average_pooling1d_1  (None, 4)                 0         \n",
      "  (GlobalAveragePooling1D)                                       \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                80        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 497 (1.94 KB)\n",
      "Trainable params: 497 (1.94 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b9e27c",
   "metadata": {},
   "source": [
    "## compile\n",
    "\n",
    "* model.compile은 TensorFlow 모델을 컴파일하는 메서드로, 모델의 학습 과정을 설정합니다. \n",
    "* optimizer: 옵티마이저를 지정합니다. 'adam'은 Adam 옵티마이저를 사용하겠다는 것을 의미합니다. Adam은 경사 하강법의 한 종류로, 학습 속도를 동적으로 조절하여 효율적인 최적화를 수행하는 알고리즘입니다.\n",
    "* loss: 손실 함수를 지정합니다. tf.keras.losses.BinaryCrossentropy는 이진 분류 문제를 위한 크로스 엔트로피 손실 함수입니다. from_logits=True는 모델의 출력이 로짓(확률이 아닌 모델의 선형 출력)으로 제공되며, 이를 확률로 변환하여 손실을 계산하겠다는 의미입니다.\n",
    "* metrics: 모델의 성능을 평가할 지표를 지정합니다. 여기서는 'accuracy'만 사용되었으며, 이는 모델의 정확도를 평가하는 지표입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aaeeb0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30d6831f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.compile\n",
    "# y = ax + b\n",
    "model.compile(optimizer=\"adam\", \n",
    "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842f1bf5",
   "metadata": {},
   "source": [
    "## fit\n",
    "\n",
    "* model.fit은 TensorFlow 모델을 학습시키는 메서드입니다.\n",
    "* x: 모델의 입력 데이터를 지정합니다. 시험의 기출문제인 x_train 을 지정합니다.\n",
    "* y: 모델의 정답값(label)을 지정합니다.\n",
    "* epochs: 전체 데이터셋에 대해 학습을 반복하는 횟수를 지정합니다.\n",
    "* callbacks: 학습 중에 호출되는 콜백 함수들의 리스트를 지정합니다. \n",
    "* model.fit을 호출하면 모델은 주어진 데이터셋과 목표값을 사용하여 지정된 에포크 수만큼 학습을 진행합니다. 각 에포크마다 모델의 가중치가 조정되며, 손실 함수를 최소화하는 방향으로 학습이 이루어집니다. \n",
    "* TensorBoard 콜백을 사용하면 학습 과정을 모니터링하고 시각화할 수 있습니다.\n",
    "* https://www.tensorflow.org/tensorboard/get_started?hl=ko"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81c33f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear any logs from previous runs\n",
    "# 아래 명령어가 운영체제 문제로 동작하지 않는다면 폴더위치에 가서 삭제해 주어도 됩니다.\n",
    "# !rm -rf ./logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cbb8700",
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=\"logs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8a0f702",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([23, 10]), (23,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer_corpus.shape, np.array(labels).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2d81bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for tensorboard Clear any logs from previous runs\n",
    "!rm -rf ./logs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8cc8111f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 28ms/step - loss: 0.6037 - accuracy: 0.6087\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.6020 - accuracy: 0.6087\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.6004 - accuracy: 0.6087\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5987 - accuracy: 0.6087\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.5970 - accuracy: 0.6087\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5953 - accuracy: 0.6087\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5935 - accuracy: 0.6087\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5918 - accuracy: 0.6087\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5901 - accuracy: 0.6087\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5883 - accuracy: 0.6087\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5866 - accuracy: 0.6087\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5848 - accuracy: 0.6087\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5831 - accuracy: 0.6087\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.5813 - accuracy: 0.6087\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.5795 - accuracy: 0.6087\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.5777 - accuracy: 0.6087\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5759 - accuracy: 0.6087\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5741 - accuracy: 0.6087\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5722 - accuracy: 0.6087\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.5704 - accuracy: 0.6087\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 20ms/step - loss: 0.5685 - accuracy: 0.6087\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.5666 - accuracy: 0.6087\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5648 - accuracy: 0.6087\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5629 - accuracy: 0.6087\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5609 - accuracy: 0.6087\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5590 - accuracy: 0.6087\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5571 - accuracy: 0.6087\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.5552 - accuracy: 0.6087\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.5532 - accuracy: 0.6087\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5513 - accuracy: 0.6087\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.5493 - accuracy: 0.6087\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5473 - accuracy: 0.6087\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.5454 - accuracy: 0.6087\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5434 - accuracy: 0.6087\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5414 - accuracy: 0.6087\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 17ms/step - loss: 0.5393 - accuracy: 0.6087\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5373 - accuracy: 0.6087\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.5353 - accuracy: 0.6087\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 20ms/step - loss: 0.5332 - accuracy: 0.6087\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5312 - accuracy: 0.6087\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.5291 - accuracy: 0.6087\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.5270 - accuracy: 0.6087\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5249 - accuracy: 0.6087\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.5228 - accuracy: 0.6087\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.5207 - accuracy: 0.6087\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5186 - accuracy: 0.6087\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 0.5165 - accuracy: 0.6087\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 26ms/step - loss: 0.5143 - accuracy: 0.6087\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5122 - accuracy: 0.6087\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 29ms/step - loss: 0.5100 - accuracy: 0.6087\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 16ms/step - loss: 0.5079 - accuracy: 0.6087\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.5057 - accuracy: 0.6087\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.5035 - accuracy: 0.6087\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.5014 - accuracy: 0.6087\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4992 - accuracy: 0.6087\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4970 - accuracy: 0.6087\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4947 - accuracy: 0.6087\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4925 - accuracy: 0.6087\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4903 - accuracy: 0.6087\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4881 - accuracy: 0.6087\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4858 - accuracy: 0.6087\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4836 - accuracy: 0.6087\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4813 - accuracy: 0.6087\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4791 - accuracy: 0.6087\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4768 - accuracy: 0.6087\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.4745 - accuracy: 0.6087\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4723 - accuracy: 0.6087\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4700 - accuracy: 0.6087\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4677 - accuracy: 0.6087\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4654 - accuracy: 0.6087\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.4631 - accuracy: 0.6087\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.4608 - accuracy: 0.6087\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.4585 - accuracy: 0.6087\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.4562 - accuracy: 0.6087\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4539 - accuracy: 0.6087\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.4516 - accuracy: 0.6087\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4493 - accuracy: 0.6087\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4470 - accuracy: 0.6087\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 15ms/step - loss: 0.4447 - accuracy: 0.6087\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 10ms/step - loss: 0.4423 - accuracy: 0.6087\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4400 - accuracy: 0.6087\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4377 - accuracy: 0.6087\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4354 - accuracy: 0.6087\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.4330 - accuracy: 0.6087\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4307 - accuracy: 0.6087\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 14ms/step - loss: 0.4284 - accuracy: 0.6087\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 19ms/step - loss: 0.4260 - accuracy: 0.6087\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4236 - accuracy: 0.6087\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4211 - accuracy: 0.6087\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 0.4186 - accuracy: 0.6087\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.4162 - accuracy: 0.6087\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4137 - accuracy: 0.6087\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4113 - accuracy: 0.6087\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 8ms/step - loss: 0.4088 - accuracy: 0.6087\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 11ms/step - loss: 0.4063 - accuracy: 0.6087\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.4039 - accuracy: 0.6087\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 0.4014 - accuracy: 0.6087\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 18ms/step - loss: 0.3989 - accuracy: 0.6087\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 13ms/step - loss: 0.3965 - accuracy: 0.6087\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 12ms/step - loss: 0.3940 - accuracy: 0.6087\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x13e1fbbd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.fit\n",
    "model.fit(x=vectorizer_corpus, y=np.array(labels), epochs=100, \n",
    "    callbacks=[tensorboard_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43af0a87",
   "metadata": {},
   "source": [
    "## tensorboard\n",
    "* https://www.tensorflow.org/get_started/summaries_and_tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1eb8a85c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-d9b87a4200238eab\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-d9b87a4200238eab\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# docs_infra: no_execute\n",
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac80c046",
   "metadata": {},
   "source": [
    "## visualize_the_embeddings\n",
    "\n",
    "* https://www.tensorflow.org/text/guide/word_embeddings?hl=ko#visualize_the_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "41a94ea8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 4)           400       \n",
      "                                                                 \n",
      " global_average_pooling1d_1  (None, 4)                 0         \n",
      "  (GlobalAveragePooling1D)                                       \n",
      "                                                                 \n",
      " dense (Dense)               (None, 16)                80        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 497 (1.94 KB)\n",
      "Trainable params: 497 (1.94 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7be172da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# No such layer: embedding. Existing layers are: ['embedding_2', 등의 메시지가 나온다면\n",
    "# 컴파일을 여러번 해서 그렇습니다. embedding_2 등으로 변경해 주거나 해당 주피터 노트북의 커널 메뉴에서 재시작을 해주시면 됩니다.\n",
    "# weights\n",
    "# vocab\n",
    "weights = model.get_layer(\"embedding\").get_weights()[0]\n",
    "vocab = vectorizer.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a36f4ca3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>주문한</td>\n",
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       "      <td>음식이</td>\n",
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       "      <td>와서</td>\n",
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       "      <th>80</th>\n",
       "      <td>salad</td>\n",
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       "      <td>pizza가</td>\n",
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       "     [UNK]\n",
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       "3      음식이\n",
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       "78    강했어요\n",
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       "80   salad\n",
       "81  pizza가\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# columns=['word'] 로 데이터프레임을 생성 \n",
    "# 가장 첫 데이터는 패딩으로 이후 데이터 부터 사용\n",
    "# 파일로 저장 : \"metadata.tsv\",  sep=\"\\t\", index=False, header=False\n",
    "df_vocab = pd.DataFrame(vocab[1:], columns=[\"word\"])\n",
    "df_vocab.to_csv(\"metadata.tsv\", sep=\"\\t\", index=False, header=False)\n",
    "pd.read_csv(\"metadata.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ac1a53b9",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.138564</td>\n",
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       "      <td>0.082837</td>\n",
       "      <td>0.091115</td>\n",
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       "<p>82 rows × 4 columns</p>\n",
       "</div>"
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       "    -0.042667903  -0.04853275  0.019147303  0.021409761\n",
       "0       0.141308     0.123149    -0.129947    -0.118388\n",
       "1      -0.144288    -0.082949     0.145169     0.147159\n",
       "2       0.166320     0.097318    -0.087067    -0.080207\n",
       "3       0.072453     0.068829    -0.148653    -0.142139\n",
       "4       0.071517     0.154308    -0.146167    -0.074678\n",
       "..           ...          ...          ...          ...\n",
       "77      0.126403     0.159853    -0.148593    -0.121677\n",
       "78      0.166985     0.104194    -0.140893    -0.086625\n",
       "79      0.073908     0.099187    -0.140462    -0.142638\n",
       "80     -0.107122    -0.091469     0.088074     0.138564\n",
       "81     -0.164181    -0.127142     0.082837     0.091115\n",
       "\n",
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# \"vectors.tsv\"\n",
    "# index=False, header=False 로 저장\n",
    "df_w = pd.DataFrame(weights[1:len(vocab)])\n",
    "df_w.to_csv(\"vectors.tsv\", sep=\"\\t\", index=False, header=False)\n",
    "pd.read_csv(\"vectors.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7618f7b6",
   "metadata": {},
   "source": [
    "* 임베딩 프로젝터 : [링크](https://projector.tensorflow.org/?hl=ko&_gl=1*qcvijm*_ga*ODk4NzAxOTgzLjE3MDExNjQyNzM.*_ga_W0YLR4190T*MTcwMTgzNzYzMy42LjEuMTcwMTgzODU2Mi4wLjAuMA..)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae83a74e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# colab 사용시 파일 다운로드\n",
    "# try:\n",
    "#     from google.colab import files\n",
    "#     files.download('vectors.tsv')\n",
    "#     files.download('metadata.tsv')\n",
    "# except Exception:\n",
    "#     pass"
   ]
  }
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