Lab12/Lab 12-07 younchanchoi.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab-12-7-bonus: sequence to sequence with attention chatbot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### simple neural machine translation chatbot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- sequence to sequence|\n",
    "- variable input sequence length\n",
    "- variable output sequence length\n",
    "- Luong attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sequence to Sequence Learning with Neural Networks\n",
    "- Effective Approaches to Attention-based Neural Machine Translation\n",
    "- Neural Machine Translation with Attention from Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### matplotlib 한글 폰트 설정 #############################\n",
    "from matplotlib import font_manager, rc\n",
    "## for window #####\n",
    "font_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\").get_name()\n",
    "rc('font', family=font_name)\n",
    "## for mac #####\n",
    "#rc('font', family='AppleGothic') #for mac\n",
    "## linux는 맨 아래 code 참고 #####\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import unicodedata\n",
    "import re\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chat data load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## konlpy 사전 설치가 필요함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'konlpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-c35b7f2085d4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkonlpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTwitter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0menum\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'konlpy'"
     ]
    }
   ],
   "source": [
    "from konlpy.tag import Twitter\n",
    "import pandas as pd\n",
    "import enum\n",
    "import os\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### konlpy 다운로드에 실패해서.. 어쩔 수 없이 여기까지 했습니다.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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