From 59b05dd0d34d0c1f81f97616038872cc8f85a8b2 Mon Sep 17 00:00:00 2001
From: Haesun Park <haesunrpark@gmail.com>
Date: Fri, 20 Apr 2018 14:59:43 +0900
Subject: [PATCH] =?UTF-8?q?14=EC=9E=A5=20=EC=8B=A4=ED=96=89?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 14_recurrent_neural_networks.ipynb | 1784 ++++++++++++++--------------
 1 file changed, 884 insertions(+), 900 deletions(-)

diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index 94212be..0c88d05 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -1,53 +1,78 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Chapter 14 – Recurrent Neural Networks**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_This notebook contains all the sample code and solutions to the exercises in chapter 14._"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPython 3.5.5\n",
+      "IPython 6.3.0\n",
+      "\n",
+      "numpy 1.14.2\n",
+      "sklearn 0.19.1\n",
+      "scipy 1.0.1\n",
+      "matplotlib 2.2.2\n",
+      "tensorflow 1.7.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**14장 – 순환 신경망**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_이 노트북은 14장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 설정"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# To support both python 2 and python 3\n",
+    "# 파이썬 2와 파이썬 3 지원\n",
     "from __future__ import division, print_function, unicode_literals\n",
     "\n",
-    "# Common imports\n",
+    "# 공통\n",
     "import numpy as np\n",
     "import os\n",
     "\n",
-    "# to make this notebook's output stable across runs\n",
+    "# 일관된 출력을 위해 유사난수 초기화\n",
     "def reset_graph(seed=42):\n",
     "    tf.reset_default_graph()\n",
     "    tf.set_random_seed(seed)\n",
     "    np.random.seed(seed)\n",
     "\n",
-    "# To plot pretty figures\n",
+    "# 맷플롯립 설정\n",
     "%matplotlib inline\n",
     "import matplotlib\n",
     "import matplotlib.pyplot as plt\n",
@@ -55,13 +80,16 @@
     "plt.rcParams['xtick.labelsize'] = 12\n",
     "plt.rcParams['ytick.labelsize'] = 12\n",
     "\n",
-    "# Where to save the figures\n",
+    "# 한글출력\n",
+    "plt.rcParams['font.family'] = 'NanumBarunGothic'\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "\n",
+    "# 그림을 저장할 폴더\n",
     "PROJECT_ROOT_DIR = \".\"\n",
     "CHAPTER_ID = \"rnn\"\n",
     "\n",
     "def save_fig(fig_id, tight_layout=True):\n",
     "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
-    "    print(\"Saving figure\", fig_id)\n",
     "    if tight_layout:\n",
     "        plt.tight_layout()\n",
     "    plt.savefig(path, format='png', dpi=300)"
@@ -71,12 +99,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then of course we will need TensorFlow:"
+    "텐서플로를 임포트합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,19 +115,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Basic RNNs"
+    "# 기본 RNN"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Manual RNN"
+    "## 수동으로 RNN 만들기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,17 +167,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.0664006   0.96257669  0.68105793  0.70918542 -0.89821601]\n",
-      " [ 0.9977755  -0.71978903 -0.99657607  0.96739239 -0.99989718]\n",
-      " [ 0.99999774 -0.99898803 -0.99999893  0.99677622 -0.99999988]\n",
-      " [ 1.         -1.         -1.         -0.99818915  0.99950868]]\n"
+      "[[-0.06640061  0.9625767   0.6810579   0.7091854  -0.89821595]\n",
+      " [ 0.99777555 -0.7197888  -0.99657613  0.96739244 -0.99989706]\n",
+      " [ 0.99999785 -0.9989881  -0.99999887  0.9967763  -0.9999999 ]\n",
+      " [ 1.         -1.         -1.         -0.9981892   0.9995087 ]]\n"
      ]
     }
    ],
@@ -159,17 +187,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.         -1.         -1.          0.40200251 -0.99999982]\n",
-      " [-0.12210421  0.62805271  0.96718431 -0.99371219 -0.25839362]\n",
-      " [ 0.99999827 -0.9999994  -0.9999975  -0.85943311 -0.99998808]\n",
-      " [ 0.99928284 -0.99999815 -0.99990582  0.98579627 -0.92205757]]\n"
+      "[[ 1.         -1.         -1.          0.40200272 -0.99999994]\n",
+      " [-0.12210429  0.62805295  0.96718436 -0.9937122  -0.2583933 ]\n",
+      " [ 0.99999815 -0.9999994  -0.99999744 -0.8594331  -0.99998796]\n",
+      " [ 0.99928296 -0.9999981  -0.9999059   0.98579615 -0.9220575 ]]\n"
      ]
     }
    ],
@@ -181,12 +209,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using `static_rnn()`"
+    "## `static_rnn()`을 사용하여 만들기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,9 +224,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use the retry module or similar alternatives.\n"
+     ]
+    }
+   ],
    "source": [
     "reset_graph()\n",
     "\n",
@@ -213,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,19 +274,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.30741334, -0.32884315, -0.65428472, -0.93850589,  0.52089024],\n",
-       "       [ 0.99122757, -0.95425421, -0.75180793, -0.99952078,  0.98202348],\n",
-       "       [ 0.99992681, -0.99783254, -0.82473528, -0.9999963 ,  0.99947774],\n",
-       "       [ 0.99677098, -0.68750614,  0.84199691,  0.93039107,  0.8120684 ]], dtype=float32)"
+       "array([[ 0.30741337, -0.32884312, -0.6542847 , -0.9385059 ,  0.52089024],\n",
+       "       [ 0.9912275 , -0.95425415, -0.7518078 , -0.9995208 ,  0.98202336],\n",
+       "       [ 0.99992675, -0.99783254, -0.82473516, -0.99999636,  0.99947786],\n",
+       "       [ 0.9967709 , -0.6875061 ,  0.8419969 ,  0.93039113,  0.81206834]],\n",
+       "      dtype=float32)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -259,19 +298,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.99998885, -0.99976051, -0.06679298, -0.99998039,  0.99982214],\n",
-       "       [-0.65249437, -0.51520866, -0.37968954, -0.59225935, -0.08968385],\n",
-       "       [ 0.99862403, -0.99715197, -0.03308626, -0.99915648,  0.99329019],\n",
-       "       [ 0.99681675, -0.95981938,  0.39660636, -0.83076048,  0.79671967]], dtype=float32)"
+       "array([[ 0.99998885, -0.9997606 , -0.06679297, -0.9999803 ,  0.99982214],\n",
+       "       [-0.65249425, -0.5152086 , -0.37968948, -0.5922594 , -0.08968376],\n",
+       "       [ 0.998624  , -0.99715203, -0.03308632, -0.9991566 ,  0.9932902 ],\n",
+       "       [ 0.99681675, -0.9598194 ,  0.39660627, -0.8307605 ,  0.7967197 ]],\n",
+       "      dtype=float32)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
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@@ -336,7 +376,7 @@
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        "          function load() {\n",
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&quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;init&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^rnn/basic_rnn_cell/kernel/Assign&quot;\\n  input: &quot;^rnn/basic_rnn_cell/bias/Assign&quot;\\n}\\n';\n",
        "          }\n",
        "        </script>\n",
        "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
@@ -362,12 +402,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Packing sequences"
+    "## 시퀀스 패딩"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,16 +444,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_batch = np.array([\n",
     "        # t = 0      t = 1 \n",
-    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
-    "        [[3, 4, 5], [0, 0, 0]], # instance 2\n",
-    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
-    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
+    "        [[0, 1, 2], [9, 8, 7]], # 샘플 1\n",
+    "        [[3, 4, 5], [0, 0, 0]], # 샘플 2\n",
+    "        [[6, 7, 8], [6, 5, 4]], # 샘플 3\n",
+    "        [[9, 0, 1], [3, 2, 1]], # 샘플 4\n",
     "    ])\n",
     "\n",
     "with tf.Session() as sess:\n",
@@ -421,33 +461,6 @@
     "    outputs_val = outputs.eval(feed_dict={X: X_batch})"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[-0.45652324 -0.68064123  0.40938237  0.63104504 -0.45732826]\n",
-      "  [-0.94288003 -0.99988687  0.94055814  0.99999851 -0.9999997 ]]\n",
-      "\n",
-      " [[-0.80015349 -0.99218267  0.78177971  0.9971031  -0.99646091]\n",
-      "  [-0.63711601  0.11300932  0.5798437   0.43105593 -0.63716984]]\n",
-      "\n",
-      " [[-0.93605185 -0.99983788  0.93088669  0.99998152 -0.99998295]\n",
-      "  [-0.9165386  -0.99456042  0.89605415  0.99987197 -0.99997509]]\n",
-      "\n",
-      " [[ 0.99273688 -0.99819332 -0.55543643  0.9989031  -0.9953323 ]\n",
-      "  [-0.02746334 -0.73191994  0.7827872   0.95256817 -0.97817713]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(outputs_val)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -457,10 +470,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.94288003 -0.99988687  0.94055814  0.99999851 -0.9999997 ]\n",
-      " [-0.63711601  0.11300932  0.5798437   0.43105593 -0.63716984]\n",
-      " [-0.9165386  -0.99456042  0.89605415  0.99987197 -0.99997509]\n",
-      " [-0.02746334 -0.73191994  0.7827872   0.95256817 -0.97817713]]\n"
+      "[[[-0.4565232  -0.6806412   0.40938237  0.631045   -0.45732823]\n",
+      "  [-0.94288003 -0.9998869   0.9405581   0.99999845 -0.99999976]]\n",
+      "\n",
+      " [[-0.80015343 -0.99218273  0.78177965  0.9971032  -0.9964609 ]\n",
+      "  [-0.637116    0.11300934  0.5798437   0.43105593 -0.6371699 ]]\n",
+      "\n",
+      " [[-0.93605185 -0.99983793  0.9308867   0.9999814  -0.99998313]\n",
+      "  [-0.9165386  -0.99456036  0.89605415  0.9998719  -0.9999751 ]]\n",
+      "\n",
+      " [[ 0.99273676 -0.9981933  -0.5554365   0.99890316 -0.9953323 ]\n",
+      "  [-0.02746333 -0.7319198   0.7827872   0.9525682  -0.9781772 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(outputs_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.94288003 -0.9998869   0.9405581   0.99999845 -0.99999976]\n",
+      " [-0.637116    0.11300934  0.5798437   0.43105593 -0.6371699 ]\n",
+      " [-0.9165386  -0.99456036  0.89605415  0.9998719  -0.9999751 ]\n",
+      " [-0.02746333 -0.7319198   0.7827872   0.9525682  -0.9781772 ]]\n"
      ]
     }
    ],
@@ -477,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,24 +569,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[-0.0948875  -0.78635412 -0.75976855  0.81456447 -0.86596668]\n",
-      "  [-0.99999809 -0.99989897 -0.99999422  0.99959809 -0.4085584 ]]\n",
+      "[[[-0.85115266  0.8735834   0.5802911   0.8954789  -0.0557505 ]\n",
+      "  [-0.99999595  0.9999957   0.9981816   1.          0.37679613]]\n",
       "\n",
-      " [[-0.98823678 -0.99379838 -0.99848306  0.99407357 -0.9553898 ]\n",
-      "  [ 0.21658944 -0.20340651  0.02591269  0.44528607 -0.40116802]]\n",
+      " [[-0.99832934  0.9992038   0.98071444  0.99998486  0.25192654]\n",
+      "  [-0.7081804  -0.07723369 -0.8522789   0.5845347  -0.7878094 ]]\n",
       "\n",
-      " [[-0.99991536 -0.99983823 -0.99999166  0.99982721 -0.98561245]\n",
-      "  [-0.99981058 -0.99268609 -0.99952865  0.99385983 -0.25559649]]\n",
+      " [[-0.9999826   0.9999953   0.99928635  1.          0.51590705]\n",
+      "  [-0.9993956   0.9984095   0.83422637  0.9999998  -0.47325197]]\n",
       "\n",
-      " [[-0.99883717  0.84198505 -0.97267133 -0.86984301  0.99912703]\n",
-      "  [-0.97511095 -0.34840977 -0.92128128  0.39821982  0.27953893]]]\n"
+      " [[ 0.87888587  0.07356028  0.9721692   0.9998546  -0.7351168 ]\n",
+      "  [-0.9134514   0.3600957   0.7624865   0.99817705  0.80142   ]]]\n"
      ]
     }
    ],
@@ -556,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -566,7 +606,7 @@
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{\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n        }\\n        int_val: 1\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/range_1&quot;\\n  op: &quot;Range&quot;\\n  input: &quot;rnn/range_1/start&quot;\\n  input: &quot;rnn/Rank_1&quot;\\n  input: &quot;rnn/range_1/delta&quot;\\n  attr {\\n    key: &quot;Tidx&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/concat_2/values_0&quot;\\n  op: &quot;Const&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_INT32\\n        tensor_shape {\\n          dim {\\n            size: 2\\n          }\\n        }\\n        tensor_content: &quot;\\\\001\\\\000\\\\000\\\\000\\\\000\\\\000\\\\000\\\\000&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/concat_2/axis&quot;\\n  op: 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}\\n  }\\n  attr {\\n    key: &quot;Tperm&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;init&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^rnn/basic_rnn_cell/kernel/Assign&quot;\\n  input: &quot;^rnn/basic_rnn_cell/bias/Assign&quot;\\n}\\n';\n",
        "          }\n",
        "        </script>\n",
        "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
@@ -592,12 +632,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setting the sequence lengths"
+    "## 시퀀스 길이 지정"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -613,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,23 +673,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_batch = np.array([\n",
-    "        # step 0     step 1\n",
-    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
-    "        [[3, 4, 5], [0, 0, 0]], # instance 2 (padded with zero vectors)\n",
-    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
-    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
+    "        # 스텝 0     스텝 1\n",
+    "        [[0, 1, 2], [9, 8, 7]], # 샘플 1\n",
+    "        [[3, 4, 5], [0, 0, 0]], # 샘플 2 (0 벡터로 패딩)\n",
+    "        [[6, 7, 8], [6, 5, 4]], # 샘플 3\n",
+    "        [[9, 0, 1], [3, 2, 1]], # 샘플 4\n",
     "    ])\n",
     "seq_length_batch = np.array([2, 1, 2, 2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -661,24 +701,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.73155707  0.34835717  0.50581998 -0.22882833 -0.44022718]\n",
-      "  [-0.61832315  0.99999994  0.86584806  0.97933769  0.9902246 ]]\n",
+      "[[[-0.91231877  0.16516447  0.5548655  -0.3915935   0.20846416]\n",
+      "  [-1.          0.95672596  0.9983169   0.9997017   0.96518576]]\n",
       "\n",
-      " [[ 0.8438704   0.99697548  0.84789461  0.4303219   0.09275495]\n",
+      " [[-0.99986124  0.67022896  0.9723652   0.66310453  0.74457586]\n",
       "  [ 0.          0.          0.          0.          0.        ]]\n",
       "\n",
-      " [[ 0.91159219  0.99999052  0.95954454  0.81892216  0.57740247]\n",
-      "  [-0.77545464  0.99987745  0.97847283  0.73172009  0.888776  ]]\n",
+      " [[-0.99999976  0.89679974  0.9986295   0.96475154  0.93662006]\n",
+      "  [-0.99995255  0.96819544  0.9600286   0.9870626   0.8545923 ]]\n",
       "\n",
-      " [[-0.99952269  0.99977839  0.59744656  0.99309695  0.99847412]\n",
-      "  [-0.62120003  0.54000849  0.96938425  0.19893228  0.1944804 ]]]\n"
+      " [[-0.9643544   0.9950159  -0.361507    0.99833775  0.99949706]\n",
+      "  [-0.9613586   0.9568762   0.71322876  0.9772921  -0.09582992]]]\n"
      ]
     }
    ],
@@ -688,17 +728,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.61832315  0.99999994  0.86584806  0.97933769  0.9902246 ]\n",
-      " [ 0.8438704   0.99697548  0.84789461  0.4303219   0.09275495]\n",
-      " [-0.77545464  0.99987745  0.97847283  0.73172009  0.888776  ]\n",
-      " [-0.62120003  0.54000849  0.96938425  0.19893228  0.1944804 ]]\n"
+      "[[-1.          0.95672596  0.9983169   0.9997017   0.96518576]\n",
+      " [-0.99986124  0.67022896  0.9723652   0.66310453  0.74457586]\n",
+      " [-0.99995255  0.96819544  0.9600286   0.9870626   0.8545923 ]\n",
+      " [-0.9613586   0.9568762   0.71322876  0.9772921  -0.09582992]]\n"
      ]
     }
    ],
@@ -710,21 +750,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training a sequence classifier"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: the book uses `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function. The main differences relevant to this chapter are:\n",
-    "* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\n",
-    "* the default `activation` is now `None` rather than `tf.nn.relu`."
+    "## 시퀀스 분류기 훈련하기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -757,17 +788,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:From <ipython-input-36-9aff0e2313ad>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please write your own downloading logic.\n",
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use tf.data to implement this functionality.\n",
       "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use tf.data to implement this functionality.\n",
       "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
       "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
+      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n",
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
      ]
     }
    ],
@@ -780,113 +826,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.946667 Test accuracy: 0.9366\n",
-      "1 Train accuracy: 0.966667 Test accuracy: 0.9488\n",
-      "2 Train accuracy: 0.96 Test accuracy: 0.9614\n",
-      "3 Train accuracy: 0.966667 Test accuracy: 0.9572\n",
-      "4 Train accuracy: 0.946667 Test accuracy: 0.9624\n",
-      "5 Train accuracy: 0.96 Test accuracy: 0.9634\n",
-      "6 Train accuracy: 0.973333 Test accuracy: 0.9714\n",
-      "7 Train accuracy: 0.98 Test accuracy: 0.9686\n",
-      "8 Train accuracy: 0.953333 Test accuracy: 0.9647\n",
-      "9 Train accuracy: 0.973333 Test accuracy: 0.9706\n",
-      "10 Train accuracy: 0.973333 Test accuracy: 0.9697\n",
-      "11 Train accuracy: 0.966667 Test accuracy: 0.9692\n",
-      "12 Train accuracy: 0.986667 Test accuracy: 0.9751\n",
-      "13 Train accuracy: 0.993333 Test accuracy: 0.9738\n",
-      "14 Train accuracy: 0.986667 Test accuracy: 0.9721\n",
-      "15 Train accuracy: 0.98 Test accuracy: 0.9752\n",
-      "16 Train accuracy: 0.993333 Test accuracy: 0.9779\n",
-      "17 Train accuracy: 0.98 Test accuracy: 0.976\n",
-      "18 Train accuracy: 0.986667 Test accuracy: 0.976\n",
-      "19 Train accuracy: 0.98 Test accuracy: 0.9709\n",
-      "20 Train accuracy: 0.986667 Test accuracy: 0.9758\n",
-      "21 Train accuracy: 0.98 Test accuracy: 0.9751\n",
-      "22 Train accuracy: 0.986667 Test accuracy: 0.9748\n",
-      "23 Train accuracy: 0.98 Test accuracy: 0.9767\n",
-      "24 Train accuracy: 0.98 Test accuracy: 0.9696\n",
-      "25 Train accuracy: 1.0 Test accuracy: 0.9779\n",
-      "26 Train accuracy: 0.98 Test accuracy: 0.979\n",
-      "27 Train accuracy: 0.98 Test accuracy: 0.9783\n",
-      "28 Train accuracy: 0.966667 Test accuracy: 0.9793\n",
-      "29 Train accuracy: 0.986667 Test accuracy: 0.9756\n",
-      "30 Train accuracy: 0.98 Test accuracy: 0.9765\n",
-      "31 Train accuracy: 0.986667 Test accuracy: 0.9784\n",
-      "32 Train accuracy: 0.986667 Test accuracy: 0.9753\n",
-      "33 Train accuracy: 0.98 Test accuracy: 0.9769\n",
-      "34 Train accuracy: 0.993333 Test accuracy: 0.9785\n",
-      "35 Train accuracy: 1.0 Test accuracy: 0.9787\n",
-      "36 Train accuracy: 1.0 Test accuracy: 0.971\n",
-      "37 Train accuracy: 0.993333 Test accuracy: 0.9782\n",
-      "38 Train accuracy: 0.993333 Test accuracy: 0.9755\n",
-      "39 Train accuracy: 0.986667 Test accuracy: 0.9732\n",
-      "40 Train accuracy: 0.986667 Test accuracy: 0.9695\n",
-      "41 Train accuracy: 0.986667 Test accuracy: 0.9812\n",
-      "42 Train accuracy: 0.993333 Test accuracy: 0.9785\n",
-      "43 Train accuracy: 0.993333 Test accuracy: 0.9768\n",
-      "44 Train accuracy: 0.98 Test accuracy: 0.979\n",
-      "45 Train accuracy: 0.986667 Test accuracy: 0.981\n",
-      "46 Train accuracy: 0.993333 Test accuracy: 0.9792\n",
-      "47 Train accuracy: 1.0 Test accuracy: 0.9812\n",
-      "48 Train accuracy: 0.993333 Test accuracy: 0.9775\n",
-      "49 Train accuracy: 1.0 Test accuracy: 0.9747\n",
-      "50 Train accuracy: 1.0 Test accuracy: 0.9815\n",
-      "51 Train accuracy: 0.986667 Test accuracy: 0.9805\n",
-      "52 Train accuracy: 0.986667 Test accuracy: 0.9798\n",
-      "53 Train accuracy: 0.986667 Test accuracy: 0.9791\n",
-      "54 Train accuracy: 1.0 Test accuracy: 0.9771\n",
-      "55 Train accuracy: 0.986667 Test accuracy: 0.98\n",
-      "56 Train accuracy: 0.993333 Test accuracy: 0.978\n",
-      "57 Train accuracy: 0.986667 Test accuracy: 0.9794\n",
-      "58 Train accuracy: 0.993333 Test accuracy: 0.9784\n",
-      "59 Train accuracy: 0.993333 Test accuracy: 0.9826\n",
-      "60 Train accuracy: 0.986667 Test accuracy: 0.9746\n",
-      "61 Train accuracy: 1.0 Test accuracy: 0.978\n",
-      "62 Train accuracy: 1.0 Test accuracy: 0.9757\n",
-      "63 Train accuracy: 1.0 Test accuracy: 0.9792\n",
-      "64 Train accuracy: 0.986667 Test accuracy: 0.9758\n",
-      "65 Train accuracy: 1.0 Test accuracy: 0.9823\n",
-      "66 Train accuracy: 0.986667 Test accuracy: 0.9752\n",
-      "67 Train accuracy: 0.986667 Test accuracy: 0.9794\n",
-      "68 Train accuracy: 0.993333 Test accuracy: 0.9798\n",
-      "69 Train accuracy: 0.993333 Test accuracy: 0.9762\n",
-      "70 Train accuracy: 0.993333 Test accuracy: 0.9799\n",
-      "71 Train accuracy: 1.0 Test accuracy: 0.9804\n",
-      "72 Train accuracy: 1.0 Test accuracy: 0.9777\n",
-      "73 Train accuracy: 0.993333 Test accuracy: 0.9771\n",
-      "74 Train accuracy: 0.986667 Test accuracy: 0.9764\n",
-      "75 Train accuracy: 0.993333 Test accuracy: 0.9749\n",
-      "76 Train accuracy: 0.993333 Test accuracy: 0.9804\n",
-      "77 Train accuracy: 0.993333 Test accuracy: 0.9776\n",
-      "78 Train accuracy: 0.986667 Test accuracy: 0.9749\n",
-      "79 Train accuracy: 0.993333 Test accuracy: 0.976\n",
-      "80 Train accuracy: 0.986667 Test accuracy: 0.9754\n",
-      "81 Train accuracy: 0.98 Test accuracy: 0.9783\n",
-      "82 Train accuracy: 1.0 Test accuracy: 0.9787\n",
-      "83 Train accuracy: 0.993333 Test accuracy: 0.9793\n",
-      "84 Train accuracy: 0.986667 Test accuracy: 0.9739\n",
-      "85 Train accuracy: 1.0 Test accuracy: 0.9776\n",
-      "86 Train accuracy: 0.986667 Test accuracy: 0.9761\n",
-      "87 Train accuracy: 0.98 Test accuracy: 0.9781\n",
-      "88 Train accuracy: 0.98 Test accuracy: 0.9549\n",
-      "89 Train accuracy: 0.98 Test accuracy: 0.9767\n",
-      "90 Train accuracy: 1.0 Test accuracy: 0.9809\n",
-      "91 Train accuracy: 0.98 Test accuracy: 0.9809\n",
-      "92 Train accuracy: 0.986667 Test accuracy: 0.9754\n",
-      "93 Train accuracy: 0.993333 Test accuracy: 0.979\n",
-      "94 Train accuracy: 1.0 Test accuracy: 0.9777\n",
-      "95 Train accuracy: 1.0 Test accuracy: 0.9771\n",
-      "96 Train accuracy: 1.0 Test accuracy: 0.9814\n",
-      "97 Train accuracy: 0.993333 Test accuracy: 0.9673\n",
-      "98 Train accuracy: 0.993333 Test accuracy: 0.9788\n",
-      "99 Train accuracy: 1.0 Test accuracy: 0.9792\n"
+      "0 훈련 정확도: 0.92 테스트 정확도: 0.9272\n",
+      "1 훈련 정확도: 0.97333336 테스트 정확도: 0.9524\n",
+      "2 훈련 정확도: 0.9266667 테스트 정확도: 0.9593\n",
+      "3 훈련 정확도: 0.97333336 테스트 정확도: 0.9604\n",
+      "4 훈련 정확도: 0.96666664 테스트 정확도: 0.9627\n",
+      "5 훈련 정확도: 0.94666666 테스트 정확도: 0.9626\n",
+      "6 훈련 정확도: 0.97333336 테스트 정확도: 0.9698\n",
+      "7 훈련 정확도: 0.96666664 테스트 정확도: 0.9719\n",
+      "8 훈련 정확도: 0.9533333 테스트 정확도: 0.9696\n",
+      "9 훈련 정확도: 0.96666664 테스트 정확도: 0.9725\n",
+      "10 훈련 정확도: 0.97333336 테스트 정확도: 0.9561\n",
+      "11 훈련 정확도: 0.94666666 테스트 정확도: 0.9703\n",
+      "12 훈련 정확도: 0.9866667 테스트 정확도: 0.9712\n",
+      "13 훈련 정확도: 0.9866667 테스트 정확도: 0.974\n",
+      "14 훈련 정확도: 0.96 테스트 정확도: 0.9664\n",
+      "15 훈련 정확도: 0.99333334 테스트 정확도: 0.9743\n",
+      "16 훈련 정확도: 0.9866667 테스트 정확도: 0.975\n",
+      "17 훈련 정확도: 0.98 테스트 정확도: 0.9674\n",
+      "18 훈련 정확도: 0.96666664 테스트 정확도: 0.974\n",
+      "19 훈련 정확도: 0.97333336 테스트 정확도: 0.9766\n",
+      "20 훈련 정확도: 0.98 테스트 정확도: 0.9719\n",
+      "21 훈련 정확도: 0.9866667 테스트 정확도: 0.9701\n",
+      "22 훈련 정확도: 0.97333336 테스트 정확도: 0.9736\n",
+      "23 훈련 정확도: 0.97333336 테스트 정확도: 0.976\n",
+      "24 훈련 정확도: 0.9866667 테스트 정확도: 0.9773\n",
+      "25 훈련 정확도: 1.0 테스트 정확도: 0.9733\n",
+      "26 훈련 정확도: 0.96666664 테스트 정확도: 0.9741\n",
+      "27 훈련 정확도: 0.99333334 테스트 정확도: 0.9796\n",
+      "28 훈련 정확도: 0.96666664 테스트 정확도: 0.9712\n",
+      "29 훈련 정확도: 0.99333334 테스트 정확도: 0.9756\n",
+      "30 훈련 정확도: 0.98 테스트 정확도: 0.9767\n",
+      "31 훈련 정확도: 0.99333334 테스트 정확도: 0.9756\n",
+      "32 훈련 정확도: 0.9866667 테스트 정확도: 0.9754\n",
+      "33 훈련 정확도: 0.99333334 테스트 정확도: 0.9769\n",
+      "34 훈련 정확도: 0.99333334 테스트 정확도: 0.9787\n",
+      "35 훈련 정확도: 1.0 테스트 정확도: 0.9738\n",
+      "36 훈련 정확도: 1.0 테스트 정확도: 0.9781\n",
+      "37 훈련 정확도: 1.0 테스트 정확도: 0.9743\n",
+      "38 훈련 정확도: 0.9866667 테스트 정확도: 0.9763\n",
+      "39 훈련 정확도: 1.0 테스트 정확도: 0.9745\n",
+      "40 훈련 정확도: 1.0 테스트 정확도: 0.976\n",
+      "41 훈련 정확도: 0.9866667 테스트 정확도: 0.9739\n",
+      "42 훈련 정확도: 0.99333334 테스트 정확도: 0.9743\n",
+      "43 훈련 정확도: 0.99333334 테스트 정확도: 0.9782\n",
+      "44 훈련 정확도: 0.97333336 테스트 정확도: 0.9773\n",
+      "45 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
+      "46 훈련 정확도: 0.99333334 테스트 정확도: 0.9733\n",
+      "47 훈련 정확도: 1.0 테스트 정확도: 0.9797\n",
+      "48 훈련 정확도: 0.9866667 테스트 정확도: 0.9776\n",
+      "49 훈련 정확도: 0.99333334 테스트 정확도: 0.9785\n",
+      "50 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
+      "51 훈련 정확도: 0.99333334 테스트 정확도: 0.9802\n",
+      "52 훈련 정확도: 1.0 테스트 정확도: 0.9758\n",
+      "53 훈련 정확도: 0.99333334 테스트 정확도: 0.9785\n",
+      "54 훈련 정확도: 1.0 테스트 정확도: 0.9793\n",
+      "55 훈련 정확도: 0.98 테스트 정확도: 0.979\n",
+      "56 훈련 정확도: 1.0 테스트 정확도: 0.976\n",
+      "57 훈련 정확도: 1.0 테스트 정확도: 0.9799\n",
+      "58 훈련 정확도: 0.9866667 테스트 정확도: 0.9761\n",
+      "59 훈련 정확도: 1.0 테스트 정확도: 0.9758\n",
+      "60 훈련 정확도: 1.0 테스트 정확도: 0.9782\n",
+      "61 훈련 정확도: 1.0 테스트 정확도: 0.9792\n",
+      "62 훈련 정확도: 0.99333334 테스트 정확도: 0.98\n",
+      "63 훈련 정확도: 0.99333334 테스트 정확도: 0.9723\n",
+      "64 훈련 정확도: 1.0 테스트 정확도: 0.9718\n",
+      "65 훈련 정확도: 0.99333334 테스트 정확도: 0.9819\n",
+      "66 훈련 정확도: 0.99333334 테스트 정확도: 0.9777\n",
+      "67 훈련 정확도: 0.99333334 테스트 정확도: 0.977\n",
+      "68 훈련 정확도: 0.9866667 테스트 정확도: 0.9786\n",
+      "69 훈련 정확도: 1.0 테스트 정확도: 0.9786\n",
+      "70 훈련 정확도: 0.9866667 테스트 정확도: 0.9749\n",
+      "71 훈련 정확도: 1.0 테스트 정확도: 0.975\n",
+      "72 훈련 정확도: 1.0 테스트 정확도: 0.9745\n",
+      "73 훈련 정확도: 1.0 테스트 정확도: 0.9782\n",
+      "74 훈련 정확도: 0.99333334 테스트 정확도: 0.9769\n",
+      "75 훈련 정확도: 0.9866667 테스트 정확도: 0.9811\n",
+      "76 훈련 정확도: 0.99333334 테스트 정확도: 0.9757\n",
+      "77 훈련 정확도: 1.0 테스트 정확도: 0.9795\n",
+      "78 훈련 정확도: 0.9866667 테스트 정확도: 0.9741\n",
+      "79 훈련 정확도: 0.98 테스트 정확도: 0.9747\n",
+      "80 훈련 정확도: 1.0 테스트 정확도: 0.9783\n",
+      "81 훈련 정확도: 0.98 테스트 정확도: 0.9773\n",
+      "82 훈련 정확도: 0.98 테스트 정확도: 0.9764\n",
+      "83 훈련 정확도: 1.0 테스트 정확도: 0.9763\n",
+      "84 훈련 정확도: 1.0 테스트 정확도: 0.9801\n",
+      "85 훈련 정확도: 1.0 테스트 정확도: 0.9744\n",
+      "86 훈련 정확도: 0.99333334 테스트 정확도: 0.9785\n",
+      "87 훈련 정확도: 0.98 테스트 정확도: 0.9765\n",
+      "88 훈련 정확도: 0.99333334 테스트 정확도: 0.9731\n",
+      "89 훈련 정확도: 0.99333334 테스트 정확도: 0.9707\n",
+      "90 훈련 정확도: 1.0 테스트 정확도: 0.9766\n",
+      "91 훈련 정확도: 0.99333334 테스트 정확도: 0.9765\n",
+      "92 훈련 정확도: 0.99333334 테스트 정확도: 0.9737\n",
+      "93 훈련 정확도: 1.0 테스트 정확도: 0.9805\n",
+      "94 훈련 정확도: 1.0 테스트 정확도: 0.9753\n",
+      "95 훈련 정확도: 1.0 테스트 정확도: 0.977\n",
+      "96 훈련 정확도: 0.99333334 테스트 정확도: 0.9754\n",
+      "97 훈련 정확도: 1.0 테스트 정확도: 0.9759\n",
+      "98 훈련 정확도: 0.9866667 테스트 정확도: 0.9771\n",
+      "99 훈련 정확도: 0.99333334 테스트 정확도: 0.9797\n"
      ]
     }
    ],
@@ -903,19 +949,19 @@
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
+    "        print(epoch, \"훈련 정확도:\", acc_train, \"테스트 정확도:\", acc_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi-layer RNN"
+    "# 다층 RNN"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -933,7 +979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -949,7 +995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -967,23 +1013,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.946667 Test accuracy: 0.9495\n",
-      "1 Train accuracy: 0.973333 Test accuracy: 0.9625\n",
-      "2 Train accuracy: 0.953333 Test accuracy: 0.9648\n",
-      "3 Train accuracy: 0.986667 Test accuracy: 0.9761\n",
-      "4 Train accuracy: 0.993333 Test accuracy: 0.9719\n",
-      "5 Train accuracy: 0.993333 Test accuracy: 0.9739\n",
-      "6 Train accuracy: 0.98 Test accuracy: 0.9746\n",
-      "7 Train accuracy: 0.98 Test accuracy: 0.9782\n",
-      "8 Train accuracy: 0.986667 Test accuracy: 0.9768\n",
-      "9 Train accuracy: 0.986667 Test accuracy: 0.9769\n"
+      "0 훈련 정확도: 0.9533333 테스트 정확도: 0.9376\n",
+      "1 훈련 정확도: 0.97333336 테스트 정확도: 0.9602\n",
+      "2 훈련 정확도: 0.96666664 테스트 정확도: 0.9594\n",
+      "3 훈련 정확도: 0.97333336 테스트 정확도: 0.9688\n",
+      "4 훈련 정확도: 0.99333334 테스트 정확도: 0.9763\n",
+      "5 훈련 정확도: 0.98 테스트 정확도: 0.9742\n",
+      "6 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
+      "7 훈련 정확도: 0.97333336 테스트 정확도: 0.9773\n",
+      "8 훈련 정확도: 0.9866667 테스트 정확도: 0.9729\n",
+      "9 훈련 정확도: 0.9866667 테스트 정확도: 0.9822\n"
      ]
     }
    ],
@@ -1000,19 +1046,19 @@
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)"
+    "        print(epoch, \"훈련 정확도:\", acc_train, \"테스트 정확도:\", acc_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Time series"
+    "# 시계열"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1031,21 +1077,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
+   "execution_count": 43,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure time_series_plot\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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U1bbjSYwlx53Qv2xhnodndpygo8dLcnz0XY4qGjtDNonLT00kLkbUAzFG4uLi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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x129c87cf8>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1060,20 +1101,20 @@
     "\n",
     "plt.figure(figsize=(11,4))\n",
     "plt.subplot(121)\n",
-    "plt.title(\"A time series (generated)\", fontsize=14)\n",
+    "plt.title(\"시계열 데이터 (인공 생성)\", fontsize=14)\n",
     "plt.plot(t, time_series(t), label=r\"$t . \\sin(t) / 3 + 2 . \\sin(5t)$\")\n",
-    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"b-\", linewidth=3, label=\"A training instance\")\n",
+    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"b-\", linewidth=3, label=\"훈련 샘플\")\n",
     "plt.legend(loc=\"lower left\", fontsize=14)\n",
     "plt.axis([0, 30, -17, 13])\n",
-    "plt.xlabel(\"Time\")\n",
-    "plt.ylabel(\"Value\")\n",
+    "plt.xlabel(\"시간\")\n",
+    "plt.ylabel(\"값\", rotation=0)\n",
     "\n",
     "plt.subplot(122)\n",
-    "plt.title(\"A training instance\", fontsize=14)\n",
-    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
-    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
+    "plt.title(\"훈련 샘플\", fontsize=14)\n",
+    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=12, label=\"샘플\")\n",
+    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markeredgewidth=0.5, markeredgecolor=\"b\", markersize=14, label=\"타깃\")\n",
     "plt.legend(loc=\"upper left\")\n",
-    "plt.xlabel(\"Time\")\n",
+    "plt.xlabel(\"시간\")\n",
     "\n",
     "\n",
     "save_fig(\"time_series_plot\")\n",
@@ -1082,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1119,7 +1160,7 @@
        "       [-2.54795941, -3.28097239]])"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1132,19 +1173,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using an `OuputProjectionWrapper`"
+    "## `OuputProjectionWrapper` 사용하기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create the RNN. It will contain 100 recurrent neurons and we will unroll it over 20 time steps since each traiing instance will be 20 inputs long. Each input will contain only one feature (the value at that time). The targets are also sequences of 20 inputs, each containing a sigle value:"
+    "RNN 하나를 만들어 보겠습니다. 이 신경망은 100개의 순환 뉴런을 가지고 있고 각 훈련 샘플은 20개의 입력 길이로 구성되므로 20개의 타임 스텝에 펼칠 것입니다. 각 입력은 하나의 특성을 가집니다(각 시간에서의 값 하나). 타깃도 20개의 입력 시퀀스이고 하나의 값을 가집니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1166,12 +1207,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At each time step we now have an output vector of size 100. But what we actually want is a single output value at each time step. The simplest solution is to wrap the cell in an `OutputProjectionWrapper`."
+    "각 타임 스텝에서 크기가 100인 출력 벡터가 만들어 집니다. 하지만 각 타임 스텝에서 하나의 출력 값을 원합니다. 간단한 방법은 `OutputProjectionWrapper`로 셀을 감싸는 것입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1199,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1208,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1223,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1232,28 +1273,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 \tMSE: 18.9177\n",
-      "100 \tMSE: 0.762551\n",
-      "200 \tMSE: 0.290907\n",
-      "300 \tMSE: 0.149525\n",
-      "400 \tMSE: 0.0772763\n",
-      "500 \tMSE: 0.0665644\n",
-      "600 \tMSE: 0.0636215\n",
-      "700 \tMSE: 0.0559812\n",
-      "800 \tMSE: 0.0554487\n",
-      "900 \tMSE: 0.0529518\n",
-      "1000 \tMSE: 0.0534466\n",
-      "1100 \tMSE: 0.051972\n",
-      "1200 \tMSE: 0.0442769\n",
-      "1300 \tMSE: 0.0547834\n",
-      "1400 \tMSE: 0.0466028\n"
+      "0 \tMSE: 11.234554\n",
+      "100 \tMSE: 0.38021186\n",
+      "200 \tMSE: 0.12111329\n",
+      "300 \tMSE: 0.06443737\n",
+      "400 \tMSE: 0.05846016\n",
+      "500 \tMSE: 0.056370605\n",
+      "600 \tMSE: 0.05040648\n",
+      "700 \tMSE: 0.045476887\n",
+      "800 \tMSE: 0.04885428\n",
+      "900 \tMSE: 0.046516392\n",
+      "1000 \tMSE: 0.046919473\n",
+      "1100 \tMSE: 0.04594408\n",
+      "1200 \tMSE: 0.03982782\n",
+      "1300 \tMSE: 0.046534885\n",
+      "1400 \tMSE: 0.04091823\n"
      ]
     }
    ],
@@ -1275,7 +1316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1287,8 +1328,8 @@
     }
    ],
    "source": [
-    "with tf.Session() as sess:                          # not shown in the book\n",
-    "    saver.restore(sess, \"./my_time_series_model\")   # not shown\n",
+    "with tf.Session() as sess:                          # 책에는 없음\n",
+    "    saver.restore(sess, \"./my_time_series_model\")   # 책에는 없음\n",
     "\n",
     "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
     "    y_pred = sess.run(outputs, feed_dict={X: X_new})"
@@ -1296,35 +1337,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
+   "execution_count": 54,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[-3.42637658],\n",
-       "        [-2.46367216],\n",
-       "        [-1.15637207],\n",
-       "        [ 0.76617211],\n",
-       "        [ 2.29781222],\n",
-       "        [ 3.12525225],\n",
-       "        [ 3.4899745 ],\n",
-       "        [ 3.33646941],\n",
-       "        [ 2.82800984],\n",
-       "        [ 2.24254656],\n",
-       "        [ 1.71146703],\n",
-       "        [ 1.62021911],\n",
-       "        [ 2.00660276],\n",
-       "        [ 2.79940104],\n",
-       "        [ 3.91245008],\n",
-       "        [ 5.18602514],\n",
-       "        [ 6.16892099],\n",
-       "        [ 6.69519091],\n",
-       "        [ 6.66508675],\n",
-       "        [ 6.10952806]]], dtype=float32)"
+       "array([[[-3.3843877 ],\n",
+       "        [-2.442578  ],\n",
+       "        [-1.1406771 ],\n",
+       "        [ 0.70999134],\n",
+       "        [ 2.169664  ],\n",
+       "        [ 3.0662253 ],\n",
+       "        [ 3.4722242 ],\n",
+       "        [ 3.3540928 ],\n",
+       "        [ 2.83155   ],\n",
+       "        [ 2.201286  ],\n",
+       "        [ 1.7171311 ],\n",
+       "        [ 1.5864581 ],\n",
+       "        [ 1.9358639 ],\n",
+       "        [ 2.733486  ],\n",
+       "        [ 3.8900287 ],\n",
+       "        [ 5.1092954 ],\n",
+       "        [ 6.122177  ],\n",
+       "        [ 6.665222  ],\n",
+       "        [ 6.6429915 ],\n",
+       "        [ 6.080037  ]]], dtype=float32)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1335,21 +1378,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure time_series_pred_plot\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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rwD/Y4lQsrdazvQFOAUp6RX+aWVoDfntH87O9ie33+KSoSM/2BrhBma6ASNi1\nHfC75bEYE6jmhYYiRheVMrWbA34lkBqSUPFgKQUlJcGEvvF4EJxKSohPK2X2bHjiiUzXVDJFAUqk\nC0pLg2chlZXBgN/1tWVEo/C5vcFtSwWn7ks92zv3GxFWra3k0A0xqK6GoiLqZ5Ry7tcjbNwIQ4Zk\nuqaSKQpQIl2Uum25fHmma9I/zJ8fzMQxdCg0eYSmM8rYd0oZH/sYNNUH2/Vsb2DTMygRyYjUs71L\nLoH8fHjhBTj77GCdnx/M1KFnewObApRIH9KEsx/qj0MSJL0UoKR9iQRUBFP6UFERvJeDoglnP6q/\nDUmQ9LJgUG/mFBcX+6ZNmzJaB2mtZmuCxlNmMeytKgqIE6eANz5dQu5jlYw6JpLp6mWlmpogCNXV\nQQ4JNv9zjMLEZp7Pmcjx15ayn0jzbS61GKS/M7Pn3L24s/3USUJaicXgZ3Ni3Lu3iii1AESp5TNv\nVnFhYYxL15TpV20PpAalpubz83+pgro4hfkFJE4tgcpKiETYsiXTNRUJD93ik2apqWfG7t1MPq1H\n9ucTZ8zeak0900PNg1JjMaiqwuLBtD4Wrw3G/8RiGpQq0oYClDRL/cpfum4ikWjrkf2RaAE3rCvS\n1DM9lBqUuq+q/USO+56t7vM8WSJhl7YAZWaHmNndZrbdzPaYWbWZ6WZQFmn+lV8aTOnTMpcTJSVQ\nWqpf+T2UGpR648MT8Tbz+Xl+ATfGirJywlmlypDelM4W1CDgdWA6cBhwLXC/mY1I4zGkF6V+5ccb\nIsEzkZUr4frrg3VlJfGGiH7l91BqwtlXjw4SOXpBFDfDC4JEjq8eXZp1g1JjMSgan2DunbP46e55\njP2P67hj9zzm3jmLovGJAdkrUdKsKzk5eroALwBzO9pH+aDCY/DgIMXR7NnudXWtP6urC7aD+5Ah\nmalfNmuVJ2tvk//3Lev8559f6v99yzpv2tuUdXmyUuczm3W+m9Y5snYT9dmsy6rzkb5FpvNBmdmn\ngGOAl9r57DIz22Rmm3bs2NFbVZBuSv3KHzoUmpqCpa7uw9eaeqbnWg1KvTrCMVeWcfH/XMuxC8tY\nck2kW4NSwzDYV6kypC/0SoAys1xgBfALd/9Ix1l3v9Pdi929+Mgjj+yNKkgPaOqZ3pWOQalhGeyr\nVBnSF9I+UNfMcoBfA0OAs929w99QGqgbLrFY0FrauBGWLQvu2eTkwJVXwrRpMGiQRvdnSsvBvgCP\nPRYEqcceg1NPDbb11WDfnByYPh0qHkxQ8NVZH02V8UAls78c4YknNAmJfFRGBuqamQF3A58Czuws\nOEn4fJhWIviVX1vb+le+ZjkuAf85AAAQWUlEQVTInObBvgCJBI0PxWDpZqaOm4g3lUIkmOWjLwb7\nKlWG9IV0zyTx78AY4DR3r09z2dJHlFYinBYtgvvvh4K8YDaK3GSrJTfZakn1tFy4EH73u87Lq6mB\nZTclqF0VY/GXNvPD308kek4pV14V6fSHiFJlSJ/oSk+KrizAcMCBBqC2xXJeR99TLz6RrjFznzHD\nvX7VOvdo655zHo16/ap1Pn26e05O52WtX+8ePbTJH7VTvX5Q1N3M6wZF/VE71aOHNvn69R1/v1Wv\nxCb3555zP+20YN3U5FnXK1H6Fn3di8/dt7u7uXueu0dbLCvSdQyRgSx1W231NZvxNrNReDzOqmuq\nuzTYNzWl1fT6GJO9irymYNqlQ5tqmexVTK+PdTqllVJlSF/QVEciWSI1DOCdoyZCm9koyC/gr0cV\ndem2Wrq6iCtVhvQ2BSiRLJEaBjDp2mAqqsShUfZjJA4NpqI6/prSLg0DSGcX8dTzyl27gt56u3YF\n79VyknRQug2RLNF8W60+wj9MqGTLYzEmUM0LDUWMLiplakOkS7fVUlNaVTxYSkFJyUe7iE8rZfZs\neOKJvjkvkQNRgBLJIh8OA4jwhyFlrK8tIxqFz+3t+jAAdRGXbKEAJZJlDnYYgLqIS7bQMyiRAUZT\nWkm2UIASGWDURVyyhQKUyACkLuKSDdI+WWx3abJYEZGBpauTxaoFJSIioaQAJSIioaQAJSIioaQA\n1c+EIR24iEg6KED1I2FJBy4ikg4KUP1EKoVCXR0kGhNcNaYCli7lqjEVJBoT1NXRaQoFEZEw0VRH\n/URzOvBEkG3V/6UK6uIU5heQODXItkok0ifpwEVE0kEtqH6iOYVCLAZVVVg8SEJn8dpgtupYrMsp\nFEREwkABqp9IpVDYV7U5GalaiMfZ92w1s2fDww9npn4iIt2lANVPpFIo3PjwRLxNtlXPL+DGWFGX\n0oGLiISFAlQ/kUoH/urRpSSKS/CCKG6GF0RJFJfw6tGlSqEgIllFAaqfSKVQuPibEez3ldTcsJJ7\nR15PzQ0rsd9XctGlEaVQEJGsosli+5FYDJqaYONGWLYM3INZqq+8EqZNg0GDNEu1iGSeJosdgJRC\nQUT6E7WgRESkT6kFJSIiWU0BSkREQkkBSkREQkkBSkREQimtAcrMDjezNWYWN7PtZvaNdJYvIiID\nR7pnM78d2Ad8CigCfmdmz7v7S2k+joiI9HNpa0GZWQEwF/hHd6919yeBhwBNriMiIt2Wzlt8xwBN\n7r61xbbngePa7mhml5nZJjPbtGPHjjRWQURE+ot0BqgosLvNtl3A4LY7uvud7l7s7sVHHnlkGqsg\nIiL9RToDVC0wpM22IcCeNB5DREQGiHQGqK3AIDM7usW2CYA6SIiISLelLUC5exx4ALjezArMbApw\nNnBfuo4hIiIDR7oH6i4ADgX+H7AS+La6mIuISE+kdRyUu78HfCWdZYqIyMCkqY5ERCSUFKBERCSU\nFKBCoqYGFiyAYcNgzZpgvWBBsF1EZCBSgAqBWAwKC+FnP4OZM2HOHJgxI3hfWBh8LiIy0ChAZVhN\nDZSXQ10dNDbCxRcH2y++OHhfVxd8rpaUiAw06Z7NXLpp3z6Ixz98v3dvsJ4yBdw/3L5lS9/WS0Qk\n09SCyrBFi1oHqEMOab2G4POFC/u2XiIimaYAlWGxGJSVtQ5SLcXjMHs2PPxw39ZLRCTTFKAyLBqF\nDRvg3HOhvr71Z/X1wfaNG4P9REQGEgWoDJs/H3JzYehQaGoKlrq6D18PHRp8fr7SPorIAKMAlWEL\nFwYB6JJLID8fXngBzj47WOfnB735cnPhiisyXVMRkb6lAJVho0bB6tVQWwuLF0NxMTzyCEyeDEuW\nBM+gVq8O9hMRGUjUzTwESkuDcU6/jyUoz4sxumEzWw6ZSEO8lLFjIwpOIjIgKUCFxKgRCW7bOgsi\nVUAcIgWwtQRGVAKRTFdPRKTP6RZfWMRiUFUV3OtzD9ZVVZrnSEQGLAWosNi8+aODoeJxqK7OTH1E\nRDJMASosJk6EgoLW2woKoKgoM/UREckwBaiwKC2FkpJgRK5ZsC4pCbaLiAxA6iQRFpEIVFYGz5yq\nq4OWU2lpsF1EZABSgAqTSCSYmK+sLNM1ERHJON3iExGRUFKAEhGRUFKAEhGRUFKAEhGRUFKAEhGR\nUFKAEhGRUFKAEhGRUFKASoOaGliwAIYNgzVrgvWCBcF2ERHpmYMOUGZ2iJndbWbbzWyPmVWb2YCZ\nnycWg8JC+NnPYOZMmDMHZswI3hcWajJyEZGeSkcLahDwOjAdOAy4FrjfzEakoexQq6mB8nKoq4PG\nxiA9OwTrxsZge3m5WlIiIj1x0FMduXsc+EGLTRVm9howCdh2sOWH2b59rTNk7N0brKdMCVI6pWzZ\n0rf1EhHpD9L+DMrMPgUcA7zUwT6XmdkmM9u0Y8eOdFehzyxa1DpAHXJI6zUEny9c2Lf1EhHpD9Ia\noMwsF1gB/MLdD9hucPc73b3Y3YuPPPLIdFahT8ViwbyubfMMpsTjMHs2PPxw39ZLRKQ/6DRAmdkG\nM/MDLE+22C8HuA/YB3y3F+scGtEobNgA554L9fWtP6uvD7Zv3BjsJyIi3dNpgHL3Ge5uB1hOBjAz\nA+4GPgXMdffGXq53KMyfD7m5MHQoNDUFS13dh6+HDg0+P//8TNdURCT7pOsW378DY4Cz3L2+s537\ni4ULgwB0ySWQnw8vvABnnx2s8/OD3ny5uXDFFZmuqYhI9knHOKjhwOVAEfCOmdUml/MOunYhN2oU\nrF4NtbWweDEUF8Mjj8DkybBkSfAMavXqYD8REemedHQz3w5YGuqSlUpLg3FOv48lKM+LMbphM1sO\nmUhDvJSxYyMKTiIiPaSU72kwakSC27bOgkgVEIdIAWwtgRGVQCTT1RMRyUqaiy8dYjGoqgru9bkH\n66oqzXMkInIQFKDSYfPmjw6Gisehujoz9RER6QcUoNJh4kQoKGi9raAAiooyUx8RkX5AASodSkuh\npCQYkWsWrEtKgu0iItIj6iSRDpEIVFYGz5yqq4OWU2lpsF1ERHpEASpdIpFgYr6yskzXRESkXxjw\nt/iUDVdEJJwGdIBSNlwRkfAasAFK2XBFRMJtwD6DUjZcEZFwG7AtKGXDFREJtwEboJQNV0Qk3AZs\ngFI2XBGRcMvaAJXqHj5kCOTkBOvudA9XNlwRkXDLygDVsnv4nj1Bp4Y9e7rXPVzZcEVEws28ZZe1\nDCguLvZNmzZ1ef+amiAI1dUdeJ9UwOksWWAsFrSWNm6EZcuCQJeTA1deCdOmwaBBmk5PRCTdzOw5\ndy/ubL+sa0HdfHMwTqkjjY1wyy2dl1VaCmPHwt66BOV5FfyjLWXuIRU0xBOMHavgJCKSSVnXghoy\nJLid15X9du3qQoGJBMyaFSQYjMeDNBklJcHkr5rsVUQk7fptC6q2Nr37KRuuiEg4ZV2A6mq37y53\nD1c2XBGRUMq6AJXqHt6RbnUPVzZcEZFQyroAleoe3pFudQ9XNlwRkVDKusliR42C1auDmcYbG1v3\n6MvNDZbVqzvvYt5M2XBFREIp63rxpdTUBF3J77sv6NcQjQa39a64ohvBSURE+lxXe/FlbYASEZHs\n1G+7mYuIyMCgACUiIqGkACUiIqGkACUiIqGU8U4SZrYD2J7RSoTHJ4B3M12JfkLXMj10HdNH1/JD\nw939yM52yniAkg+Z2aau9GyRzulapoeuY/roWnafbvGJiEgoKUCJiEgoKUCFy52ZrkA/omuZHrqO\n6aNr2U16BiUiIqGkFpSIiISSApSIiISSApSIiISSAlQvMrPvmtkmM9trZve22H6imf1fM3vPzHaY\n2SozO6qDcjaYWYOZ1SaX/+qTEwiRDq7l2OT295PLI2Y2toNyDjezNWYWN7PtZvaNPjmBkEjjddTf\n5AGuZZt9vm9mbmandVDOCDN73MzqzGxLR/sONApQvest4Abg5222f5ygR88IYDiwB7ink7K+6+7R\n5HJsuiuaBQ50Ld8CyoHDCUbqPwT8poNybgf2AZ8CzgP+3cyOS3ttwytd1xH0N3mgawmAmY0CzgHe\n7qSclcBm4AjgGmC1mXU6y8JAoADVi9z9AXdfC+xssz3m7qvcfbe71wHLgSkZqWSW6OBafuDu2zzo\njmpAAvhCe2WYWQEwF/hHd6919ycJ/iM+v3drHx7puI4SONC1bOF2YDHBD6J2mdkxwPHAde5e7+6/\nBf5M8Hc64ClAhcM04KVO9rnRzN41sz+a2Yw+qFNWMbMPgAbgNuBfDrDbMUCTu29tse15YCC1oDrU\nxeuYor/JAzCzc4C97r6+k12PA/7H3fe02Ka/yaRBma7AQGdmhcD3gbM72G0x8DLBL7GvA+vMrMjd\na/qgilnB3YcmW0gXcODJh6PA7jbbdgGDe7Nu2aSL1xH0N3lAZjaYILif3oXdowR/gy3tAj6T7npl\nI7WgMsjMvgDEgL9z9ycOtJ+7V7n7Hnff6+6/AP4InNlX9cwW7h4H7gB+aWafbGeXWmBIm21DCJ4B\nSlIXrqP+Jjv2A+A+d9/WhX31N9kBBagMMbPhwCPAUne/r5tfTz0nkI/KAfJp/xfoVmCQmR3dYtsE\nOr+9OhB1dB3bo7/JD50KfM/M3jGzd4DPAveb2eJ29n0J+Hyy1ZWiv8kkBaheZGaDzCwPiAARM8tL\nbvsM8Biw3N3v6KSMoWY2q8V3zyN4ZvVw759BeHRwLU83s4lmFjGzIcAy4H3glbZlJFsGDwDXm1mB\nmU0huLXa3R8IWSsd11F/k4EDXUuCADUOKEoubwGXE3SaaCX5PLQauC75/TlAIfDbPjqNcHN3Lb20\nEDT1vc3yA+C65OvalkuL710NxJKvjwT+k6DJ/wHwDHB6ps8tRNfyHGBL8hruAH4HFLZ3LZPvDwfW\nAnHgL8A3Mn1u2XYd9TfZ8bVsZ79twGkt3t8B3NHi/QhgA1AP/FfLfQf6osliRUQklHSLT0REQkkB\nSkREQkkBSkREQkkBSkREQkkBSkREQkkBSkREQkkBSqQXJXMBlWe6HiLZSAFKpAeSgaej5d7krkcB\n6zJYVZGspYG6Ij1gZv+rxdsy4C6CYJRS7+5tZ6kWkW5QC0qkB9z9ndRCMN1Pq22p4NTyFl8ytbeb\n2dfNbKOZ1ZvZZjMrNLNxZvZUMhX9k2Y2suXxzOwsM3sumWb9NTP7ZzP7WJ+fuEgfUoAS6Xv/BPwQ\nmEgQ3FYSJAi8BjgByANuTe1sZrOAFQSZl48DLiZIz95ZQkGRrKYAJdL3lrn7enffAtwMjAVuc/fH\n3f0lgkA0s8X+1wA3ufs97l7j7o8TJAz8lpkpxYX0W8qoK9L3Xmjx+q/J9Z/bbCsws3x3rwMmASe0\nySeUAxwK/C/g7d6srEimKECJ9L3GFq+9g205Ldb/BKxqp6wd6a2aSHgoQImE35+A0e7+aqYrItKX\nFKBEwu96oMLMtgP3A00EGVtPcPd/yGjNRHqROkmIhJy7VwKzCTpOPJtclhBkBBbptzRQV0REQkkt\nKBERCSUFKBERCSUFKBERCSUFKBERCSUFKBERCSUFKBERCSUFKBERCSUFKBERCaX/D9QXvuia6JOx\nAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117c04780>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1357,12 +1393,12 @@
     }
    ],
    "source": [
-    "plt.title(\"Testing the model\", fontsize=14)\n",
-    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
-    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
-    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
+    "plt.title(\"모델 테스트\", fontsize=14)\n",
+    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=12, label=\"샘플\")\n",
+    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markeredgewidth=0.5, markeredgecolor=\"b\", markersize=14, label=\"타깃\")\n",
+    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"예측\")\n",
     "plt.legend(loc=\"upper left\")\n",
-    "plt.xlabel(\"Time\")\n",
+    "plt.xlabel(\"시간\")\n",
     "\n",
     "save_fig(\"time_series_pred_plot\")\n",
     "plt.show()"
@@ -1372,12 +1408,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Without using an `OutputProjectionWrapper`"
+    "## `OutputProjectionWrapper` 사용하지 않기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1393,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1403,7 +1439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1413,7 +1449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1424,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1438,28 +1474,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 \tMSE: 10.1622\n",
-      "100 \tMSE: 0.406083\n",
-      "200 \tMSE: 0.116547\n",
-      "300 \tMSE: 0.0686967\n",
-      "400 \tMSE: 0.0689586\n",
-      "500 \tMSE: 0.0625274\n",
-      "600 \tMSE: 0.056175\n",
-      "700 \tMSE: 0.0507173\n",
-      "800 \tMSE: 0.0516515\n",
-      "900 \tMSE: 0.0502068\n",
-      "1000 \tMSE: 0.0497242\n",
-      "1100 \tMSE: 0.0506494\n",
-      "1200 \tMSE: 0.0413749\n",
-      "1300 \tMSE: 0.0501782\n",
-      "1400 \tMSE: 0.0440728\n"
+      "0 \tMSE: 13.798526\n",
+      "100 \tMSE: 0.51873666\n",
+      "200 \tMSE: 0.18096465\n",
+      "300 \tMSE: 0.08363678\n",
+      "400 \tMSE: 0.06251121\n",
+      "500 \tMSE: 0.060582373\n",
+      "600 \tMSE: 0.055472486\n",
+      "700 \tMSE: 0.051638845\n",
+      "800 \tMSE: 0.05216893\n",
+      "900 \tMSE: 0.04946163\n",
+      "1000 \tMSE: 0.049049225\n",
+      "1100 \tMSE: 0.051213406\n",
+      "1200 \tMSE: 0.04204171\n",
+      "1300 \tMSE: 0.049295332\n",
+      "1400 \tMSE: 0.043647002\n"
      ]
     }
    ],
@@ -1484,35 +1520,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[-3.40363002],\n",
-       "        [-2.45480704],\n",
-       "        [-1.10760546],\n",
-       "        [ 0.85981268],\n",
-       "        [ 2.19396615],\n",
-       "        [ 3.03818107],\n",
-       "        [ 3.44206738],\n",
-       "        [ 3.40469313],\n",
-       "        [ 2.89831972],\n",
-       "        [ 2.2327323 ],\n",
-       "        [ 1.62960398],\n",
-       "        [ 1.48093259],\n",
-       "        [ 1.91334391],\n",
-       "        [ 2.81092381],\n",
-       "        [ 3.99130607],\n",
-       "        [ 5.17190695],\n",
-       "        [ 6.16106033],\n",
-       "        [ 6.65612125],\n",
-       "        [ 6.57139349],\n",
-       "        [ 5.97566175]]], dtype=float32)"
+       "array([[[-3.4256656],\n",
+       "        [-2.4964879],\n",
+       "        [-1.1188097],\n",
+       "        [ 0.6010748],\n",
+       "        [ 2.0974448],\n",
+       "        [ 3.2044346],\n",
+       "        [ 3.585753 ],\n",
+       "        [ 3.3138356],\n",
+       "        [ 2.8268244],\n",
+       "        [ 2.2785282],\n",
+       "        [ 1.7047077],\n",
+       "        [ 1.5082613],\n",
+       "        [ 1.8684278],\n",
+       "        [ 2.742042 ],\n",
+       "        [ 3.9028168],\n",
+       "        [ 5.073093 ],\n",
+       "        [ 6.147061 ],\n",
+       "        [ 6.7127986],\n",
+       "        [ 6.645899 ],\n",
+       "        [ 6.041297 ]]], dtype=float32)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,14 +1559,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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lg8sACvLSqSPp4ZJKvUmnkI1a7mFM7DhPECUluoeRAfQwlGSU3j5IlU1ycmDa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\nt36W6gqkOZ2frun8dE/nqB2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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118083278>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1538,12 +1574,12 @@
     }
    ],
    "source": [
-    "plt.title(\"Testing the model\", fontsize=14)\n",
+    "plt.title(\"모델 테스트\", fontsize=14)\n",
     "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
     "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
     "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
     "plt.legend(loc=\"upper left\")\n",
-    "plt.xlabel(\"Time\")\n",
+    "plt.xlabel(\"시간\")\n",
     "\n",
     "plt.show()"
    ]
@@ -1552,12 +1588,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generating a creative new sequence"
+    "## 새로운 시퀀스 생성하기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1569,8 +1605,8 @@
     }
    ],
    "source": [
-    "with tf.Session() as sess:                        # not shown in the book\n",
-    "    saver.restore(sess, \"./my_time_series_model\") # not shown\n",
+    "with tf.Session() as sess:                        # 책에는 없음\n",
+    "    saver.restore(sess, \"./my_time_series_model\") # 책에는 없음\n",
     "\n",
     "    sequence = [0.] * n_steps\n",
     "    for iteration in range(300):\n",
@@ -1581,14 +1617,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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XoH37eAs+yQL3cdG3bw907Bj9HkkWfJx73rUH/LwZLU1qgReR3iJhaf4JIYS0Fl59FVi9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\ngu/Uyaz07HNUV/uXKw1z0a9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JJxvvnzPH9v/0py3a5byxbVuyG3z8+OQ51Oy5b1UTh/33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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115b73b00>"
+       "<Figure size 576x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1599,29 +1635,30 @@
     "plt.figure(figsize=(8,4))\n",
     "plt.plot(np.arange(len(sequence)), sequence, \"b-\")\n",
     "plt.plot(t[:n_steps], sequence[:n_steps], \"b-\", linewidth=3)\n",
-    "plt.xlabel(\"Time\")\n",
-    "plt.ylabel(\"Value\")\n",
+    "plt.xlabel(\"시간\")\n",
+    "plt.ylabel(\"값\")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 66,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n",
-      "Saving figure creative_sequence_plot\n"
+      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n"
      ]
     },
     {
      "data": {
-      "image/png": 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In4BwORCAeeFTagJCoGThhQujkJJlyyiUxVWhCwC+8Q36GRhwH1coBgb8Mf8M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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115bba2e8>"
+       "<Figure size 792x288 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1648,13 +1685,13 @@
     "plt.subplot(121)\n",
     "plt.plot(t, sequence1, \"b-\")\n",
     "plt.plot(t[:n_steps], sequence1[:n_steps], \"b-\", linewidth=3)\n",
-    "plt.xlabel(\"Time\")\n",
-    "plt.ylabel(\"Value\")\n",
+    "plt.xlabel(\"시간\")\n",
+    "plt.ylabel(\"값\", rotation=0)\n",
     "\n",
     "plt.subplot(122)\n",
     "plt.plot(t, sequence2, \"b-\")\n",
     "plt.plot(t[:n_steps], sequence2[:n_steps], \"b-\", linewidth=3)\n",
-    "plt.xlabel(\"Time\")\n",
+    "plt.xlabel(\"시간\")\n",
     "save_fig(\"creative_sequence_plot\")\n",
     "plt.show()"
    ]
@@ -1663,7 +1700,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deep RNN"
+    "# 심층 RNN"
    ]
   },
   {
@@ -1675,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1689,7 +1726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1704,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1713,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1722,7 +1759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1733,7 +1770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1742,7 +1779,7 @@
        "(2, 5, 100)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1755,26 +1792,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Distributing a Deep RNN Across Multiple GPUs"
+    "## 여러 GPU에 심층 RNN 분산하기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Do **NOT** do this:"
+    "이렇게 사용해서는 **안됩니다**:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with tf.device(\"/gpu:0\"):  # BAD! This is ignored.\n",
+    "with tf.device(\"/gpu:0\"):  # 이 할당은 무시됩니다\n",
     "    layer1 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
     "\n",
-    "with tf.device(\"/gpu:1\"):  # BAD! Ignored again.\n",
+    "with tf.device(\"/gpu:1\"):  # 이 할당은 무시됩니다\n",
     "    layer2 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
    ]
   },
@@ -1782,12 +1819,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Instead, you need a `DeviceCellWrapper`:"
+    "대신 `DeviceCellWrapper`를 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1813,7 +1850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,11 +1865,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
-    "devices = [\"/cpu:0\", \"/cpu:0\", \"/cpu:0\"] # replace with [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"] if you have 3 GPUs\n",
+    "devices = [\"/cpu:0\", \"/cpu:0\", \"/cpu:0\"] # 만약 GPU가 세 개 있다면 [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"]로 바꿉니다\n",
     "cells = [DeviceCellWrapper(dev,tf.contrib.rnn.BasicRNNCell(num_units=n_neurons))\n",
     "         for dev in devices]\n",
     "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells)\n",
@@ -1843,12 +1880,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Alternatively, since TensorFlow 1.1, you can use the `tf.contrib.rnn.DeviceWrapper` class (alias `tf.nn.rnn_cell.DeviceWrapper` since TF 1.2)."
+    "또 다른 방법으로 텐서플로 1.1부터 `tf.contrib.rnn.DeviceWrapper` 클래스를 사용할 수 있습니다(텐서플로 1.2부터는 `tf.nn.rnn_cell.DeviceWrapper`가 되었습니다)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1857,42 +1894,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 78,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.06828325 -0.11375451  0.06424566 ..., -0.24244198 -0.04821674\n",
-      "   -0.12077257]\n",
-      "  [ 0.07453813 -0.2251049   0.20471548 ..., -0.14811224 -0.0922538\n",
-      "   -0.04429063]\n",
-      "  [ 0.13780868 -0.14680631 -0.0095655  ..., -0.08136044  0.07381541\n",
-      "   -0.03125775]\n",
-      "  ..., \n",
-      "  [-0.25400278 -0.32078549  0.39923593 ..., -0.26669216  0.33505771\n",
-      "   -0.03757669]\n",
-      "  [ 0.22596692 -0.09880773 -0.27422303 ..., -0.13385999 -0.25443044\n",
-      "   -0.36498186]\n",
-      "  [ 0.1655937  -0.33435836  0.34313348 ..., -0.36904442  0.06908746\n",
-      "    0.46574104]]\n",
+      "[[[-0.033671    0.00615814 -0.0224805  ... -0.08477943  0.07812577\n",
+      "    0.0409406 ]\n",
+      "  [ 0.2202788   0.06180531  0.22256151 ... -0.23308094  0.06236795\n",
+      "   -0.01974874]\n",
+      "  [-0.13161832  0.00174079  0.15934998 ... -0.14575946 -0.29136425\n",
+      "   -0.22982824]\n",
+      "  ...\n",
+      "  [ 0.40135226 -0.0683148   0.19030328 ... -0.07745527  0.35425738\n",
+      "    0.15830204]\n",
+      "  [ 0.7006527  -0.0154526  -0.06332135 ... -0.20901318 -0.0539793\n",
+      "    0.12965451]\n",
+      "  [ 0.3396977  -0.3388407  -0.1747916  ... -0.31547976 -0.10045247\n",
+      "   -0.00143633]]\n",
       "\n",
-      " [[ 0.00489879 -0.03151967  0.02628033 ..., -0.19341362 -0.0730375\n",
-      "    0.00451888]\n",
-      "  [ 0.03073939 -0.0579551   0.17785911 ..., -0.20945786  0.05200011\n",
-      "   -0.07436937]\n",
-      "  [ 0.00192378 -0.25690764  0.12488247 ...,  0.02644884 -0.25046453\n",
-      "   -0.12239399]\n",
-      "  ..., \n",
-      "  [-0.13501379 -0.06209698  0.1595035  ..., -0.20012119 -0.3338365\n",
-      "   -0.09281697]\n",
-      "  [-0.44347292  0.20323271  0.12526961 ..., -0.07962411  0.27046496\n",
-      "    0.31884009]\n",
-      "  [ 0.23965777 -0.22903362  0.07749593 ..., -0.02653922  0.084024\n",
-      "    0.02313657]]]\n"
+      " [[ 0.04636343  0.05075924 -0.01460598 ... -0.01488171  0.12059579\n",
+      "    0.03546331]\n",
+      "  [ 0.10273134  0.02954917  0.14052095 ... -0.09312268 -0.1330954\n",
+      "   -0.07913667]\n",
+      "  [-0.03998944 -0.23300202  0.10142665 ... -0.27028072  0.0087689\n",
+      "    0.07616787]\n",
+      "  ...\n",
+      "  [ 0.3201678   0.17390148  0.06568783 ... -0.32528323  0.00921553\n",
+      "    0.37827423]\n",
+      "  [ 0.6501674  -0.10477125 -0.183512   ... -0.5114695   0.00990305\n",
+      "    0.2610496 ]\n",
+      "  [ 0.50272596 -0.12907378  0.07752537 ... -0.2155963   0.02334055\n",
+      "   -0.1325899 ]]]\n"
      ]
     }
    ],
@@ -1906,12 +1941,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dropout"
+    "## 드롭아웃"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1926,7 +1961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1938,12 +1973,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: the `input_keep_prob` parameter can be a placeholder, making it possible to set it to any value you want during training, and to 1.0 during testing (effectively turning dropout off). This is a much more elegant solution than what was recommended in earlier versions of the book (i.e., writing your own wrapper class or having a separate model for training and testing). Thanks to Shen Cheng for bringing this to my attention."
+    "노트: `input_keep_prob` 매개변수는 플레이스홀더로 훈련하는 동안에는 어느 값이나 가능하고 테스트할 때는 1.0으로 지정합니다(드롭아웃을 끕니다)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1958,7 +1993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1978,28 +2013,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Training MSE: 14.9647\n",
-      "100 Training MSE: 4.7444\n",
-      "200 Training MSE: 3.65731\n",
-      "300 Training MSE: 3.9575\n",
-      "400 Training MSE: 3.08749\n",
-      "500 Training MSE: 2.63093\n",
-      "600 Training MSE: 3.60809\n",
-      "700 Training MSE: 4.01957\n",
-      "800 Training MSE: 4.08699\n",
-      "900 Training MSE: 3.31099\n",
-      "1000 Training MSE: 2.95242\n",
-      "1100 Training MSE: 3.30796\n",
-      "1200 Training MSE: 3.28226\n",
-      "1300 Training MSE: 2.87491\n",
-      "1400 Training MSE: 3.31036\n"
+      "0 훈련 MSE: 14.73364\n",
+      "100 훈련 MSE: 4.686095\n",
+      "200 훈련 MSE: 3.6556115\n",
+      "300 훈련 MSE: 3.8748214\n",
+      "400 훈련 MSE: 2.3531404\n",
+      "500 훈련 MSE: 3.7559667\n",
+      "600 훈련 MSE: 4.06035\n",
+      "700 훈련 MSE: 4.9739947\n",
+      "800 훈련 MSE: 4.0471015\n",
+      "900 훈련 MSE: 3.8106322\n",
+      "1000 훈련 MSE: 4.920709\n",
+      "1100 훈련 MSE: 3.858466\n",
+      "1200 훈련 MSE: 3.3147843\n",
+      "1300 훈련 MSE: 3.8227804\n",
+      "1400 훈련 MSE: 4.623097\n"
      ]
     }
    ],
@@ -2016,14 +2051,14 @@
     "                          feed_dict={X: X_batch, y: y_batch,\n",
     "                                     keep_prob: train_keep_prob})\n",
     "        if iteration % 100 == 0:                   # not shown in the book\n",
-    "            print(iteration, \"Training MSE:\", mse) # not shown\n",
+    "            print(iteration, \"훈련 MSE:\", mse) # not shown\n",
     "    \n",
     "    saver.save(sess, \"./my_dropout_time_series_model\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2044,14 +2079,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
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ppSAvkqRkq0yi6J2sdy/SEQV5kSQlW2WiFjKSSgryIhFIpspELWQklRTkRSLS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\nvRQbgm+9mf2Lme2X9gsXaUN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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12a5314a8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2059,12 +2094,12 @@
     }
    ],
    "source": [
-    "plt.title(\"Testing the model\", fontsize=14)\n",
+    "plt.title(\"모델 테스트\", fontsize=14)\n",
     "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
     "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
     "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
     "plt.legend(loc=\"upper left\")\n",
-    "plt.xlabel(\"Time\")\n",
+    "plt.xlabel(\"시간\")\n",
     "\n",
     "plt.show()"
    ]
@@ -2073,7 +2108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Oops, it seems that Dropout does not help at all in this particular case. :/"
+    "이런 드롭아웃이 이 경우엔 크게 도움이 안되네요. :/"
    ]
   },
   {
@@ -2085,10 +2120,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 86,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
@@ -2098,10 +2131,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 87,
+   "metadata": {},
    "outputs": [],
    "source": [
     "n_steps = 28\n",
@@ -2133,18 +2164,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_2:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>),\n",
-       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>),\n",
-       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>))"
+       "(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>),\n",
+       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>),\n",
+       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_8:0' shape=(?, 150) dtype=float32>))"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2155,16 +2186,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>"
+       "<tf.Tensor 'rnn/while/Exit_8:0' shape=(?, 150) dtype=float32>"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2175,7 +2206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "scrolled": true
    },
@@ -2184,16 +2215,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0 Train accuracy = 0.946667 Test accuracy = 0.9506\n",
-      "Epoch 1 Train accuracy = 0.98 Test accuracy = 0.9691\n",
-      "Epoch 2 Train accuracy = 0.993333 Test accuracy = 0.9745\n",
-      "Epoch 3 Train accuracy = 0.993333 Test accuracy = 0.982\n",
-      "Epoch 4 Train accuracy = 0.973333 Test accuracy = 0.9819\n",
-      "Epoch 5 Train accuracy = 0.993333 Test accuracy = 0.9838\n",
-      "Epoch 6 Train accuracy = 0.993333 Test accuracy = 0.9799\n",
-      "Epoch 7 Train accuracy = 0.993333 Test accuracy = 0.9873\n",
-      "Epoch 8 Train accuracy = 1.0 Test accuracy = 0.9862\n",
-      "Epoch 9 Train accuracy = 1.0 Test accuracy = 0.9873\n"
+      "에포크 0 훈련 정확도 = 0.9533333 테스트 정확도 = 0.9506\n",
+      "에포크 1 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9661\n",
+      "에포크 2 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9796\n",
+      "에포크 3 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9777\n",
+      "에포크 4 훈련 정확도 = 0.9866667 테스트 정확도 = 0.9833\n",
+      "에포크 5 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9857\n",
+      "에포크 6 훈련 정확도 = 1.0 테스트 정확도 = 0.9863\n",
+      "에포크 7 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9875\n",
+      "에포크 8 훈련 정확도 = 1.0 테스트 정확도 = 0.9879\n",
+      "에포크 9 훈련 정확도 = 1.0 테스트 정확도 = 0.9868\n"
      ]
     }
    ],
@@ -2210,15 +2241,13 @@
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
     "        acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
-    "        print(\"Epoch\", epoch, \"Train accuracy =\", acc_train, \"Test accuracy =\", acc_test)"
+    "        print(\"에포크\", epoch, \"훈련 정확도 =\", acc_train, \"테스트 정확도 =\", acc_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 91,
+   "metadata": {},
    "outputs": [],
    "source": [
     "lstm_cell = tf.contrib.rnn.LSTMCell(num_units=n_neurons, use_peepholes=True)"
@@ -2226,10 +2255,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 92,
+   "metadata": {},
    "outputs": [],
    "source": [
     "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
@@ -2239,29 +2266,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Embeddings"
+    "# 임베딩"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This section is based on TensorFlow's [Word2Vec tutorial](https://www.tensorflow.org/versions/r0.11/tutorials/word2vec/index.html)."
+    "이 섹션은 텐서플로의 [Word2Vec 튜토리얼](https://www.tensorflow.org/versions/r0.11/tutorials/word2vec/index.html)을 기반으로 합니다."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Fetch the data"
+    "## 데이터 추출"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 93,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from six.moves import urllib\n",
@@ -2274,9 +2299,10 @@
     "WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\n",
     "\n",
     "def mkdir_p(path):\n",
-    "    \"\"\"Create directories, ok if they already exist.\n",
+    "    \"\"\"디렉토리 생성, 이미 있다면 그냥 통과\n",
     "    \n",
-    "    This is for python 2 support. In python >=3.2, simply use:\n",
+    "    이 함수는 파이썬 2 버전을 지원하기 위해서입니다.\n",
+    "    파이썬 3.2 이상이면 다음과 같이 쓸 수 있습니다:\n",
     "    >>> os.makedirs(path, exist_ok=True)\n",
     "    \"\"\"\n",
     "    try:\n",
@@ -2299,10 +2325,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 94,
+   "metadata": {},
    "outputs": [],
    "source": [
     "words = fetch_words_data()"
@@ -2310,7 +2334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -2319,7 +2343,7 @@
        "['anarchism', 'originated', 'as', 'a', 'term']"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2332,15 +2356,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build the dictionary"
+    "## 사전 구축"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 96,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from collections import Counter\n",
@@ -2355,17 +2377,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "('anarchism originated as a term of abuse first used',\n",
-       " array([5242, 3081,   12,    6,  195,    2, 3136,   46,   59]))"
+       " array([5243, 3084,   12,    6,  195,    2, 3135,   46,   59]))"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2376,16 +2398,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'default originated as a term of presidency first used'"
+       "'guided matrix as a term of recipient first used'"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2396,7 +2418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -2405,7 +2427,7 @@
        "('culottes', 0)"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2418,15 +2440,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generate batches"
+    "## 배치 생성"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 100,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import random\n",
@@ -2444,7 +2464,7 @@
     "        buffer.append(data[data_index])\n",
     "        data_index = (data_index + 1) % len(data)\n",
     "    for i in range(batch_size // num_skips):\n",
-    "        target = skip_window  # target label at the center of the buffer\n",
+    "        target = skip_window  # buffer 중간에 타깃 레이블을 둡니다\n",
     "        targets_to_avoid = [ skip_window ]\n",
     "        for j in range(num_skips):\n",
     "            while target in targets_to_avoid:\n",
@@ -2459,37 +2479,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 101,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_index=0\n",
     "batch, labels = generate_batch(8, 2, 1)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([3081, 3081,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
-       " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "batch, [vocabulary[word] for word in batch]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 102,
@@ -2498,15 +2495,8 @@
     {
      "data": {
       "text/plain": [
-       "(array([[  12],\n",
-       "        [5242],\n",
-       "        [   6],\n",
-       "        [3081],\n",
-       "        [  12],\n",
-       "        [ 195],\n",
-       "        [   2],\n",
-       "        [   6]], dtype=int32),\n",
-       " ['as', 'anarchism', 'a', 'originated', 'as', 'term', 'of', 'a'])"
+       "(array([3084, 3084,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
+       " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])"
       ]
      },
      "execution_count": 102,
@@ -2514,6 +2504,34 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "batch, [vocabulary[word] for word in batch]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[  12],\n",
+       "        [5243],\n",
+       "        [3084],\n",
+       "        [   6],\n",
+       "        [ 195],\n",
+       "        [  12],\n",
+       "        [   6],\n",
+       "        [   2]], dtype=int32),\n",
+       " ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'a', 'of'])"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "labels, [vocabulary[word] for word in labels[:, 0]]"
    ]
@@ -2522,118 +2540,67 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build the model"
+    "## 모델 구성"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 104,
+   "metadata": {},
    "outputs": [],
    "source": [
     "batch_size = 128\n",
-    "embedding_size = 128  # Dimension of the embedding vector.\n",
-    "skip_window = 1       # How many words to consider left and right.\n",
-    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
+    "embedding_size = 128  # 임베딩 벡터 차원\n",
+    "skip_window = 1       # 고려할 왼쪽과 오른쪽 단어의 개수\n",
+    "num_skips = 2         # 레이블을 생성하기 위한 입력의 재사용 횟수\n",
     "\n",
-    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
-    "# validation samples to the words that have a low numeric ID, which by\n",
-    "# construction are also the most frequent.\n",
-    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
-    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
+    "# 가까운 이웃을 샘플링하기 위해 랜덤한 검증 세트를 만듭니다.\n",
+    "# 검증 샘플은 가장 흔한 단어인 낮은 ID 번호를 가진 것으로 제한합니다.\n",
+    "valid_size = 16     # 유사도를 평가하기 위해 랜덤하게 구성할 단어 세트 크기\n",
+    "valid_window = 100  # 검증 샘플을 전체 샘플의 앞 부분에서만 선택합니다\n",
     "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
-    "num_sampled = 64    # Number of negative examples to sample.\n",
+    "num_sampled = 64    # 부정 샘플링(negative sampling)의 수\n",
     "\n",
     "learning_rate = 0.01"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 105,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
-    "# Input data.\n",
+    "# 입력 데이터\n",
     "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
     "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 106,
+   "metadata": {},
    "outputs": [],
    "source": [
     "vocabulary_size = 50000\n",
     "embedding_size = 150\n",
     "\n",
-    "# Look up embeddings for inputs.\n",
+    "# 입력을 위해 임베딩을 조회합니다\n",
     "init_embeds = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
     "embeddings = tf.Variable(init_embeds)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 107,
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_inputs = tf.placeholder(tf.int32, shape=[None])\n",
     "embed = tf.nn.embedding_lookup(embeddings, train_inputs)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Construct the variables for the NCE loss\n",
-    "nce_weights = tf.Variable(\n",
-    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
-    "                        stddev=1.0 / np.sqrt(embedding_size)))\n",
-    "nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
-    "\n",
-    "# Compute the average NCE loss for the batch.\n",
-    "# tf.nce_loss automatically draws a new sample of the negative labels each\n",
-    "# time we evaluate the loss.\n",
-    "loss = tf.reduce_mean(\n",
-    "    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n",
-    "                   num_sampled, vocabulary_size))\n",
-    "\n",
-    "# Construct the Adam optimizer\n",
-    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
-    "training_op = optimizer.minimize(loss)\n",
-    "\n",
-    "# Compute the cosine similarity between minibatch examples and all embeddings.\n",
-    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\n",
-    "normalized_embeddings = embeddings / norm\n",
-    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
-    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
-    "\n",
-    "# Add variable initializer.\n",
-    "init = tf.global_variables_initializer()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train the model"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 108,
@@ -2643,44 +2610,93 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration: 0\tAverage loss at step  0 :  285.895568848\n",
-      "Nearest to would: stems, secessionist, coronary, provers, surpasses, subscriber, losing, excommunicated,\n",
-      "Nearest to on: enumerative, camus, biochemical, skeptic, idealised, sooty, grossman, flashing,\n",
-      "Nearest to four: maj, glue, doubt, emil, civilis, generative, bested, necrosis,\n",
-      "Nearest to his: exerted, talmudic, switzerland, particles, somehow, standstill, value, hau,\n",
-      "Nearest to often: billed, shrine, lems, aalborg, prescriptions, winger, manichaeism, sox,\n",
-      "Nearest to in: charlestown, cobe, moody, appeals, dauphin, buenos, liar, inuit,\n",
-      "Nearest to an: rota, haihowak, melatonin, graham, guaranteeing, catalyze, pcd, gobind,\n",
-      "Nearest to eight: onstage, usn, brl, guevara, arbitrate, wild, radiocarbon, manning,\n",
-      "Nearest to these: thames, natufian, lasted, genghis, amitabh, dodo, bain, thorne,\n",
-      "Nearest to nine: mindstorms, blocks, proceed, tn, nihilist, elucidated, aspects, intermediate,\n",
-      "Nearest to called: humanoid, dealing, writ, gracefully, superfluous, wooing, confessed, diputados,\n",
-      "Nearest to about: smile, mirrored, affaires, ducktales, threatening, eureka, hiller, woke,\n",
-      "Nearest to up: subgenre, proposal, straps, orchestral, constants, equinox, gmc, livejournal,\n",
-      "Nearest to one: tubulin, hijack, alum, decentralisation, sombre, lords, ts, drumming,\n",
-      "Nearest to and: soft, flap, costumed, heldenplatz, paleo, kiel, monotheistic, fermion,\n",
-      "Nearest to been: navigable, mansi, wards, ud, hideous, yong, linear, josh,\n",
-      "Iteration: 2000\tAverage loss at step  2000 :  130.956728756\n",
-      "Iteration: 4000\tAverage loss at step  4000 :  62.7161855488\n",
-      "Iteration: 6000\tAverage loss at step  6000 :  42.0940016119\n",
-      "Iteration: 8000\tAverage loss at step  8000 :  31.4492927847\n",
-      "Iteration: 10000\tAverage loss at step  10000 :  25.6146161559\n",
-      "Nearest to would: to, washed, fit, investigators, can, was, repeated, alkanes,\n",
-      "Nearest to on: in, aegean, actinium, methylene, donohue, levitt, of, and,\n",
-      "Nearest to four: nine, six, five, two, one, seven, eight, zero,\n",
-      "Nearest to his: the, and, satisfies, rescue, molyneux, conquer, babylon, bambaataa,\n",
-      "Nearest to often: priests, progressed, sometimes, it, whitfield, participation, horrible, guise,\n",
-      "Nearest to in: and, on, ampere, of, with, conformations, omotic, altaic,\n",
-      "Nearest to an: a, this, induced, submachine, and, imaged, that, the,\n",
-      "Nearest to eight: nine, one, six, four, seven, two, five, three,\n",
-      "Nearest to these: nsu, altaic, neutral, signification, romanus, cosmonaut, and, staggered,\n",
-      "Nearest to nine: one, six, eight, four, seven, five, two, zero,\n",
-      "Nearest to called: motorola, is, absurd, matrices, lubricants, familial, outer, rigor,\n",
-      "Nearest to about: denunciation, fixtures, gide, endeavor, abortions, esp, methanol, unarable,\n",
-      "Nearest to up: spite, delos, lighthouses, possessions, arcade, unhappy, exposure, socialite,\n",
-      "Nearest to one: nine, two, six, eight, seven, four, five, three,\n",
-      "Nearest to and: in, of, a, the, asparagales, altaic, which, UNK,\n",
-      "Nearest to been: have, bani, annealing, are, alhazred, ppp, columbus, guise,\n"
+      "WARNING:tensorflow:From <ipython-input-108-1dfd74cb904f>:18: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NCE 손실을 위한 변수를 만듭니다\n",
+    "nce_weights = tf.Variable(\n",
+    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
+    "                        stddev=1.0 / np.sqrt(embedding_size)))\n",
+    "nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
+    "\n",
+    "# 배치에서 NCE 손실의 평균을 계산합니다.Compute the average NCE loss for the batch.\n",
+    "# tf.nce_loss는 자동으로 손실을 평가할 때마다 음성 레이블에서 새로운 샘플을 뽑습니다.\n",
+    "loss = tf.reduce_mean(\n",
+    "    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n",
+    "                   num_sampled, vocabulary_size))\n",
+    "\n",
+    "# Adam 옵티마이저\n",
+    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
+    "training_op = optimizer.minimize(loss)\n",
+    "\n",
+    "# 미니배치 샘플과 모든 임베딩 사이의 코사인 유사도를 계산합니다\n",
+    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keep_dims=True))\n",
+    "normalized_embeddings = embeddings / norm\n",
+    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
+    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
+    "\n",
+    "# 초기화를 위한 연산\n",
+    "init = tf.global_variables_initializer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 모델 훈련"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "반복: 0\t스텝  0 에서의 평균 손실:  293.06048583984375\n",
+      "would에 가장 가까운 단어: factories, daemon, mayan, webern, leveling, swabia, carter, medicinal,\n",
+      "on에 가장 가까운 단어: roofed, assent, freenet, gilles, dagmar, bland, wagers, redevelopment,\n",
+      "four에 가장 가까운 단어: signifies, overlooked, tank, carboxylic, hustler, tina, disastrously, mammuthus,\n",
+      "his에 가장 가까운 단어: penn, suzuki, switzerland, sector, discharge, capacitive, value, backlight,\n",
+      "often에 가장 가까운 단어: mentor, contrasts, bootlegged, ling, observatories, ubangi, robber, implemented,\n",
+      "in에 가장 가까운 단어: intercommunal, congestion, needlessly, tobacco, necronomicon, displays, pottery, skeptics,\n",
+      "an에 가장 가까운 단어: guzm, pickett, validating, practiced, sandinista, vitis, andries, expanses,\n",
+      "eight에 가장 가까운 단어: rig, nagorno, creasy, paschal, disjointed, processing, seconded, retroflex,\n",
+      "these에 가장 가까운 단어: calculating, vaporized, angel, kandahar, relinquish, fluids, bedtime, puzzling,\n",
+      "nine에 가장 가까운 단어: picchi, luke, atheists, signifying, awt, stables, develop, relating,\n",
+      "called에 가장 가까운 단어: callisto, tends, intends, veterinarians, watkins, matador, phenomenal, chilperic,\n",
+      "about에 가장 가까운 단어: nominees, sorcerer, southey, curtin, filters, lightly, davao, indecisive,\n",
+      "up에 가장 가까운 단어: lyricist, hidden, biggs, staged, declining, pilsener, march, aboriginal,\n",
+      "one에 가장 가까운 단어: inclinations, kolkata, foxx, rodan, lalor, lords, edith, collectivism,\n",
+      "and에 가장 가까운 단어: attached, singly, omnivores, tabular, suppliers, silhouette, maradona, metaphase,\n",
+      "been에 가장 가까운 단어: absinthe, entice, nde, hive, linear, unscr, indicted, determinative,\n",
+      "반복: 2000\t스텝  2000 에서의 평균 손실:  131.21407338047027\n",
+      "반복: 4000\t스텝  4000 에서의 평균 손실:  62.32993085551262\n",
+      "반복: 6000\t스텝  6000 에서의 평균 손실:  41.999602062940596\n",
+      "반복: 8000\t스텝  8000 에서의 평균 손실:  31.167041951179506\n",
+      "반복: 10000\t스텝  10000 에서의 평균 손실:  25.85122103357315\n",
+      "would에 가장 가까운 단어: to, shelter, had, could, cannot, niche, should, topalov,\n",
+      "on에 가장 가까운 단어: cooperstown, in, predominant, anand, var, antidepressants, from, ataxia,\n",
+      "four에 가장 가까운 단어: three, five, nine, two, one, eight, seven, zero,\n",
+      "his에 가장 가까운 단어: ampere, actinium, straczynski, s, staggered, celebrates, the, ataxia,\n",
+      "often에 가장 가까운 단어: heuristics, wanting, hochschule, altaic, hoxha, valley, rationale, commonly,\n",
+      "in에 가장 가까운 단어: and, branched, of, for, operates, the, achill, kickoff,\n",
+      "an에 가장 가까운 단어: spectral, preoccupied, atemi, ankh, a, is, columbus, chancellorsville,\n",
+      "eight에 가장 가까운 단어: nine, seven, one, six, zero, three, five, four,\n",
+      "these에 가장 가까운 단어: joy, provable, preservation, amphibian, fame, vibrating, fallacy, outcome,\n",
+      "nine에 가장 가까운 단어: eight, six, five, zero, three, one, four, seven,\n",
+      "called에 가장 가까운 단어: steppe, foot, switch, satire, told, suppose, researches, leech,\n",
+      "about에 가장 가까운 단어: obscure, parasite, horch, garrett, transcriptase, seo, dollfuss, provable,\n",
+      "up에 가장 가까운 단어: actinium, diacritic, the, kournikova, save, and, starvation, appointing,\n",
+      "one에 가장 가까운 단어: three, nine, eight, six, seven, two, four, five,\n",
+      "and에 가장 가까운 단어: in, the, of, asparagales, amhr, chun, operates, arches,\n",
+      "been에 가장 가까운 단어: has, had, omotic, unofficial, have, chadic, by, investigates,\n"
      ]
     }
    ],
@@ -2692,30 +2708,29 @@
     "\n",
     "    average_loss = 0\n",
     "    for step in range(num_steps):\n",
-    "        print(\"\\rIteration: {}\".format(step), end=\"\\t\")\n",
+    "        print(\"\\r반복: {}\".format(step), end=\"\\t\")\n",
     "        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\n",
     "        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n",
     "\n",
-    "        # We perform one update step by evaluating the training op (including it\n",
-    "        # in the list of returned values for session.run()\n",
+    "        # 훈련 연산을 평가하여 스텝을 한 단계를 업데이트합니다(session.run()에서 반환된 값을 사용합니다)\n",
     "        _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\n",
     "        average_loss += loss_val\n",
     "\n",
     "        if step % 2000 == 0:\n",
     "            if step > 0:\n",
     "                average_loss /= 2000\n",
-    "            # The average loss is an estimate of the loss over the last 2000 batches.\n",
-    "            print(\"Average loss at step \", step, \": \", average_loss)\n",
+    "            # 평균 손실은 2000개 배치에 대한 손실의 추정입니다.\n",
+    "            print(\"스텝 \", step, \"에서의 평균 손실: \", average_loss)\n",
     "            average_loss = 0\n",
     "\n",
-    "        # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
+    "        # 이 코드는 비용이 많이 듭니다 (500 스텝마다 ~20%씩 느려집니다)\n",
     "        if step % 10000 == 0:\n",
     "            sim = similarity.eval()\n",
     "            for i in range(valid_size):\n",
     "                valid_word = vocabulary[valid_examples[i]]\n",
-    "                top_k = 8 # number of nearest neighbors\n",
+    "                top_k = 8 # 가장 가까운 단어의 개수\n",
     "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
-    "                log_str = \"Nearest to %s:\" % valid_word\n",
+    "                log_str = \"%s에 가장 가까운 단어:\" % valid_word\n",
     "                for k in range(top_k):\n",
     "                    close_word = vocabulary[nearest[k]]\n",
     "                    log_str = \"%s %s,\" % (log_str, close_word)\n",
@@ -2728,15 +2743,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's save the final embeddings (of course you can use a TensorFlow `Saver` if you prefer):"
+    "마지막 임베딩을 저장합니다(물론 텐서플로의 `Saver`를 사용해도 됩니다):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 110,
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.save(\"./my_final_embeddings.npy\", final_embeddings)"
@@ -2746,20 +2759,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot the embeddings"
+    "## 임베딩 그래프"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 111,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_with_labels(low_dim_embs, labels):\n",
-    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
-    "    plt.figure(figsize=(18, 18))  #in inches\n",
+    "    assert low_dim_embs.shape[0] >= len(labels), \"임베딩보다 레이블이 많습니다.\"\n",
+    "    plt.figure(figsize=(18, 18))  # 인치 크기\n",
     "    for i, label in enumerate(labels):\n",
     "        x, y = low_dim_embs[i,:]\n",
     "        plt.scatter(x, y)\n",
@@ -2773,14 +2784,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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gQUTkFhQXF8e0adMcXYaIyHUxm818//33jB07lqSkJKxWKy1atMDX1xeA4cOHs379egdX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\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NaiuFkJCoKSZOnIi1tTW2trYMGjTopVZ9bt26xezZszl+/DhHjx5l6NChqKurs2zZMry9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\nzp4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4urpia2tL9+7defDgAQCenp6UJAC/d+8eJiYmAFy4cAFnZ2ekUim2trYkJiYCsGnT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\ne30TKuwvOekYevSYF4WKJKn6BUGwKAzD7IP1c/FGSapk77zzDi+//DIAZ5x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r+iYlJdGl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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f43d408fa20>"
+       "<Figure size 1296x1296 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2801,22 +2812,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Machine Translation"
+    "# 기계 번역"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `basic_rnn_seq2seq()` function creates a simple Encoder/Decoder model: it first runs an RNN to encode `encoder_inputs` into a state vector, then runs a decoder initialized with the last encoder state on `decoder_inputs`. Encoder and decoder use the same RNN cell type but they don't share parameters."
+    "`basic_rnn_seq2seq()` 함수는 간단한 인코더/디코더 모델을 만듭니다. 먼저 `encoder_inputs`를 상태 벡터로 인코딩하는 RNN을 실행하고 그다음 `decoder_inputs`을 마지막 인코더 상태로 초기화시킨 디코더를 실행합니다. 인코더와 디코더는 같은 RNN 셀 타입을 사용하지만 파라미터를 공유하지는 않습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 113,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import tensorflow as tf\n",
@@ -2830,14 +2839,14 @@
     "embedding_size = 150\n",
     "learning_rate = 0.01\n",
     "\n",
-    "X = tf.placeholder(tf.int32, [None, n_steps]) # English sentences\n",
-    "Y = tf.placeholder(tf.int32, [None, n_steps]) # French translations\n",
+    "X = tf.placeholder(tf.int32, [None, n_steps]) # 영어 문장\n",
+    "Y = tf.placeholder(tf.int32, [None, n_steps]) # 프랑스어 번역\n",
     "W = tf.placeholder(tf.float32, [None, n_steps - 1, 1])\n",
     "Y_input = Y[:, :-1]\n",
     "Y_target = Y[:, 1:]\n",
     "\n",
-    "encoder_inputs = tf.unstack(tf.transpose(X)) # list of 1D tensors\n",
-    "decoder_inputs = tf.unstack(tf.transpose(Y_input)) # list of 1D tensors\n",
+    "encoder_inputs = tf.unstack(tf.transpose(X)) # 1D 텐서의 리스트\n",
+    "decoder_inputs = tf.unstack(tf.transpose(Y_input)) # 1D 텐서의 리스트\n",
     "\n",
     "lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)\n",
     "              for layer in range(n_layers)]\n",
@@ -2856,10 +2865,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 114,
+   "metadata": {},
    "outputs": [],
    "source": [
     "logits_flat = tf.reshape(logits, [-1, num_decoder_symbols])\n",
@@ -2879,7 +2886,7 @@
     "collapsed": true
    },
    "source": [
-    "# Exercise solutions"
+    "# 연습문제 해답"
    ]
   },
   {
@@ -2893,45 +2900,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "See Appendix A."
+    "부록 A 참조."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 7. Embedded Reber Grammars"
+    "## 7. 임베딩된 레버(Reber) 문법"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we need to build a function that generates strings based on a grammar. The grammar will be represented as a list of possible transitions for each state. A transition specifies the string to output (or a grammar to generate it) and the next state."
+    "먼저 문법에 맞는 문자열을 생성하는 함수가 필요합니다. 이 문법은 각 상태에서 가능한 전이 상태의 리스트입니다. 하나의 전이는 출력할 문자열(또는 생성할 문법)과 다음 상태를 지정합니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 115,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from random import choice, seed\n",
     "\n",
-    "# to make this notebook's output stable across runs\n",
+    "# 일관된 출력을 위한 유사난수 초기화\n",
     "seed(42)\n",
     "np.random.seed(42)\n",
     "\n",
     "default_reber_grammar = [\n",
-    "    [(\"B\", 1)],           # (state 0) =B=>(state 1)\n",
-    "    [(\"T\", 2), (\"P\", 3)], # (state 1) =T=>(state 2) or =P=>(state 3)\n",
-    "    [(\"S\", 2), (\"X\", 4)], # (state 2) =S=>(state 2) or =X=>(state 4)\n",
-    "    [(\"T\", 3), (\"V\", 5)], # and so on...\n",
+    "    [(\"B\", 1)],           # (상태 0) =B=>(상태 1)\n",
+    "    [(\"T\", 2), (\"P\", 3)], # (상태 1) =T=>(상태 2) or =P=>(상태 3)\n",
+    "    [(\"S\", 2), (\"X\", 4)], # (상태 2) =S=>(상태 2) or =X=>(상태 4)\n",
+    "    [(\"T\", 3), (\"V\", 5)], # 등등..\n",
     "    [(\"X\", 3), (\"S\", 6)],\n",
     "    [(\"P\", 4), (\"V\", 6)],\n",
-    "    [(\"E\", None)]]        # (state 6) =E=>(terminal state)\n",
+    "    [(\"E\", None)]]        # (상태 6) =E=>(종료 상태)\n",
     "\n",
     "embedded_reber_grammar = [\n",
     "    [(\"B\", 1)],\n",
@@ -2957,12 +2962,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's generate a few strings based on the default Reber grammar:"
+    "기본 레버 문법에 맞는 문자열을 몇 개 만들어 보겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -2982,12 +2987,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looks good. Now let's generate a few strings based on the embedded Reber grammar:"
+    "좋습니다. 이제 임베딩된 레버 문법에 맞는 문자열을 몇 개 만들어 보겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -3007,15 +3012,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Okay, now we need a function to generate strings that do not respect the grammar. We could generate a random string, but the task would be a bit too easy, so instead we will generate a string that respects the grammar, and we will corrupt it by changing just one character:"
+    "좋네요, 이제 이 문법을 따르지 않는 문자열을 생성할 함수를 만듭니다. 무작위하게 문자열을 만들 수 있지만 그렇게 하면 너무 문제가 쉬워지므로 대신 문법을 따르는 문자열을 만든 후 하나의 문자만 바꾸어 놓도록 하겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 118,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def generate_corrupted_string(grammar, chars=\"BEPSTVX\"):\n",
@@ -3030,19 +3033,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at a few corrupted strings:"
+    "잘못된 문자열 몇 개를 만들어 보죠:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BPBPVPPEPE BPBPXSEPE BPBPTVVPPE BTBPTPSETE BTBPVVBTE BPBTSSXXTPTVVEPE BPPTSXXTVPSEPE BPBTXSSPE BTBPTTTVPSPTE BPBTSXXTTTSTTVVEPE BPBBXXVPXTVPXTTVVEPE BPBPTTVBEPE BPBPVVETE BPEPTTVVEPE BPBPVTSEPE BPBTXXXVVEPE BXBPTTTVPXVVETE BPBTSSXBEPE BPBPVBEPE BSBTXSEPE BTBPTVPXVPXVVETB TPBTSXXTVPXVPSEPE BPBTSXXTTTVXSEPE BPBPVPXVVSPE BTBTSBSETE "
+      "BPBPVPEEPE BPBSXSEPE BPBPTVVXPE BTBPPPSETE BTBPVVSTE BPBTSSXXTSTVVEPE BPETSXXTVPSEPE BPBTXSTPE BTBPTTTVPSXTE BPBTSXXTTTBTTVVEPE BPBVXXVPXTVPXTTVVEPE BPBPTTVBEPE BPBPVVEBE BPXPTTVVEPE BPBPVBSEPE BPBTVXXVVEPE BEBPTTTVPXVVETE BPBTSSXTEPE BPBPVBEPE BEBTXSEPE BTBPTVPXVPXVVETS PPBTSXXTVPXVPSEPE BPBTSXXTTTVVSEPE BPBPVPXVVTPE BTBTSVSETE "
      ]
     }
    ],
@@ -3055,15 +3058,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It's not possible to feed a string directly to an RNN: we need to convert it to a sequence of vectors, first. Each vector will represent a single letter, using a one-hot encoding. For example, the letter \"B\" will be represented as the vector `[1, 0, 0, 0, 0, 0, 0]`, the letter E will be represented as `[0, 1, 0, 0, 0, 0, 0]` and so on. Let's write a function that converts a string to a sequence of such one-hot vectors. Note that if the string is shorted than `n_steps`, it will be padded with zero vectors (later, we will tell TensorFlow how long each string actually is using the `sequence_length` parameter)."
+    "문자열을 바로 RNN에 주입할 수는 없습니다. 먼저 벡터의 연속으로 바꾸어야 합니다. 각 벡터는 원-핫 인코딩을 사용하여 하나의 문자를 나타냅니다. 예를 들어, 벡터 `[1, 0, 0, 0, 0, 0, 0]`는 문자 \"B\"를 나타내고 벡터 `[0, 1, 0, 0, 0, 0, 0]`는 문자 \"E\"를 나타내는 식입니다. 이런 원-핫 벡터의 연속으로 문자열을 바꾸는 함수를 작성해 보겠습니다. 문자열이 `n_steps`보다 짧으면 0 벡터로 패딩됩니다(나중에, 텐서플로에게 각 문자열의 실제 길이를 `sequence_length` 매개변수로 전달할 것입니다)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 120,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def string_to_one_hot_vectors(string, n_steps, chars=\"BEPSTVX\"):\n",
@@ -3076,7 +3077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -3096,7 +3097,7 @@
        "       [0, 0, 0, 0, 0, 0, 0]], dtype=int32)"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3109,15 +3110,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can now generate the dataset, with 50% good strings, and 50% bad strings:"
+    "이제 50%는 올바른 문자열 50%는 잘못된 문자열로 이루어진 데이터셋을 만듭니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 122,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def generate_dataset(size):\n",
@@ -3138,10 +3137,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 123,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_train, l_train, y_train = generate_dataset(10000)"
@@ -3151,12 +3148,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's take a look at the first training instances:"
+    "첫 번째 훈련 샘플을 확인해 보겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -3177,7 +3174,7 @@
        "       [0, 0, 0, 0, 0, 1, 0],\n",
        "       [0, 0, 1, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 1],\n",
-       "       [0, 0, 1, 0, 0, 0, 0],\n",
+       "       [0, 0, 0, 0, 1, 0, 0],\n",
        "       [0, 0, 1, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 1],\n",
        "       [0, 0, 0, 0, 0, 1, 0],\n",
@@ -3207,7 +3204,7 @@
        "       [0, 0, 0, 0, 0, 0, 0]], dtype=int32)"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3220,12 +3217,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It's padded with a lot of zeros because the longest string in the dataset is that long. How long is this particular string?"
+    "데이터셋에서 가장 긴 문자열 때문에 패딩된 0 벡터가 많습니다. 문자열 길이가 얼마나 될까요?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -3234,7 +3231,7 @@
        "23"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3247,12 +3244,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What class is it?"
+    "타깃 클래스는?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -3261,7 +3258,7 @@
        "array([0])"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3274,17 +3271,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Perfect! We are ready to create the RNN to identify good strings. We build a sequence classifier very similar to the one we built earlier to classify MNIST images, with two main differences:\n",
-    "* First, the input strings have variable length, so we need to specify the `sequence_length` when calling the `dynamic_rnn()` function.\n",
-    "* Second, this is a binary classifier, so we only need one output neuron that will output, for each input string, the estimated log probability that it is a good string. For multiclass classification, we used `sparse_softmax_cross_entropy_with_logits()` but for binary classification we use `sigmoid_cross_entropy_with_logits()`.\n"
+    "아주 좋습니다! 올바른 문자열을 구분할 RNN을 만들 준비가 되었습니다. 앞서 MNIST 이미지를 분류하기 위해 만든 것과 매우 비슷한 시퀀스 분류기를 만듭니다. 차이점은 다음 두 가지입니다:\n",
+    "* 첫째, 입력 문자열이 가변 길이이므로 `dynamic_rnn()` 함수를 호출할 때 `sequence_length`를 지정해야 합니다.\n",
+    "* 둘째, 이진 분류기이므로 출력 뉴런은 하나만 필요합니다. 이 뉴런은 각 문자열에 대해 올바른 문자열일 추정 로그 확률을 출력할 것입니다. 다중 클래스 분류에서는 `sparse_softmax_cross_entropy_with_logits()`를 사용했지만 이진 분류에서는 `sigmoid_cross_entropy_with_logits()`를 사용합니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 127,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
@@ -3327,15 +3322,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's generate a validation set so we can track progress during training:"
+    "훈련하는 동안 진척 상황을 확인할 수 있도록 검증 세트를 만듭니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 128,
+   "metadata": {},
    "outputs": [],
    "source": [
     "X_val, l_val, y_val = generate_dataset(5000)"
@@ -3343,63 +3336,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   0  Train loss: 0.6869, accuracy: 54.00%  Validation accuracy: 58.86%\n",
-      "   1  Train loss: 0.6651, accuracy: 54.00%  Validation accuracy: 59.56%\n",
-      "   2  Train loss: 0.6379, accuracy: 72.00%  Validation accuracy: 71.92%\n",
-      "   3  Train loss: 0.5612, accuracy: 66.00%  Validation accuracy: 72.88%\n",
-      "   4  Train loss: 0.4574, accuracy: 82.00%  Validation accuracy: 78.56%\n",
-      "   5  Train loss: 0.3446, accuracy: 84.00%  Validation accuracy: 85.80%\n",
-      "   6  Train loss: 0.3048, accuracy: 88.00%  Validation accuracy: 87.96%\n",
-      "   7  Train loss: 0.3263, accuracy: 90.00%  Validation accuracy: 91.28%\n",
-      "   8  Train loss: 0.2017, accuracy: 94.00%  Validation accuracy: 94.92%\n",
-      "   9  Train loss: 0.1600, accuracy: 98.00%  Validation accuracy: 95.06%\n",
-      "  10  Train loss: 0.1414, accuracy: 98.00%  Validation accuracy: 96.20%\n",
-      "  11  Train loss: 0.0723, accuracy: 98.00%  Validation accuracy: 98.26%\n",
-      "  12  Train loss: 0.0181, accuracy: 100.00%  Validation accuracy: 98.70%\n",
-      "  13  Train loss: 0.0769, accuracy: 100.00%  Validation accuracy: 97.64%\n",
-      "  14  Train loss: 0.0111, accuracy: 100.00%  Validation accuracy: 99.56%\n",
-      "  15  Train loss: 0.0987, accuracy: 100.00%  Validation accuracy: 97.46%\n",
-      "  16  Train loss: 0.0080, accuracy: 100.00%  Validation accuracy: 99.66%\n",
-      "  17  Train loss: 0.0061, accuracy: 100.00%  Validation accuracy: 99.76%\n",
-      "  18  Train loss: 0.0025, accuracy: 100.00%  Validation accuracy: 99.84%\n",
-      "  19  Train loss: 0.0405, accuracy: 100.00%  Validation accuracy: 98.42%\n",
-      "  20  Train loss: 0.0058, accuracy: 100.00%  Validation accuracy: 99.78%\n",
-      "  21  Train loss: 0.0072, accuracy: 100.00%  Validation accuracy: 99.30%\n",
-      "  22  Train loss: 0.0015, accuracy: 100.00%  Validation accuracy: 99.96%\n",
-      "  23  Train loss: 0.0013, accuracy: 100.00%  Validation accuracy: 99.90%\n",
-      "  24  Train loss: 0.0013, accuracy: 100.00%  Validation accuracy: 99.94%\n",
-      "  25  Train loss: 0.0007, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  26  Train loss: 0.0006, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  27  Train loss: 0.0005, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  28  Train loss: 0.0004, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  29  Train loss: 0.0004, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  30  Train loss: 0.0003, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  31  Train loss: 0.0003, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  32  Train loss: 0.0003, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  33  Train loss: 0.0003, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  34  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  35  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  36  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  37  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  38  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  39  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  40  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  41  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  42  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  43  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  44  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  45  Train loss: 0.0002, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  46  Train loss: 0.0001, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  47  Train loss: 0.0001, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  48  Train loss: 0.0001, accuracy: 100.00%  Validation accuracy: 100.00%\n",
-      "  49  Train loss: 0.0001, accuracy: 100.00%  Validation accuracy: 100.00%\n"
+      "   0  훈련 손실: 0.6798, 정확도: 58.00%  검증 세트 정확도: 58.60%\n",
+      "   1  훈련 손실: 0.6454, 정확도: 60.00%  검증 세트 정확도: 61.82%\n",
+      "   2  훈련 손실: 0.5676, 정확도: 80.00%  검증 세트 정확도: 69.98%\n",
+      "   3  훈련 손실: 0.4996, 정확도: 78.00%  검증 세트 정확도: 79.56%\n",
+      "   4  훈련 손실: 0.4083, 정확도: 86.00%  검증 세트 정확도: 86.26%\n",
+      "   5  훈련 손실: 0.3434, 정확도: 84.00%  검증 세트 정확도: 85.70%\n",
+      "   6  훈련 손실: 0.2667, 정확도: 90.00%  검증 세트 정확도: 89.78%\n",
+      "   7  훈련 손실: 0.2397, 정확도: 90.00%  검증 세트 정확도: 90.44%\n",
+      "   8  훈련 손실: 0.0917, 정확도: 98.00%  검증 세트 정확도: 95.74%\n",
+      "   9  훈련 손실: 0.1826, 정확도: 96.00%  검증 세트 정확도: 94.10%\n",
+      "  10  훈련 손실: 0.0136, 정확도: 100.00%  검증 세트 정확도: 98.52%\n",
+      "  11  훈련 손실: 0.0161, 정확도: 100.00%  검증 세트 정확도: 98.14%\n",
+      "  12  훈련 손실: 0.0113, 정확도: 100.00%  검증 세트 정확도: 98.62%\n",
+      "  13  훈련 손실: 0.0155, 정확도: 100.00%  검증 세트 정확도: 99.26%\n",
+      "  14  훈련 손실: 0.0089, 정확도: 100.00%  검증 세트 정확도: 99.26%\n",
+      "  15  훈련 손실: 0.0072, 정확도: 100.00%  검증 세트 정확도: 99.00%\n",
+      "  16  훈련 손실: 0.0019, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  17  훈련 손실: 0.0009, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  18  훈련 손실: 0.0007, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  19  훈련 손실: 0.0006, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  20  훈련 손실: 0.0005, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  21  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  22  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  23  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  24  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  25  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  26  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  27  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  28  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  29  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  30  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  31  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  32  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  33  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  34  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  35  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  36  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  37  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  38  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  39  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  40  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  41  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  42  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  43  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  44  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  45  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  46  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  47  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  48  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  49  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n"
      ]
     }
    ],
@@ -3419,21 +3412,21 @@
     "                feed_dict={X: X_batch, seq_length: l_batch, y: y_batch})\n",
     "        acc_train = accuracy.eval(feed_dict={X: X_batch, seq_length: l_batch, y: y_batch})\n",
     "        acc_val = accuracy.eval(feed_dict={X: X_val, seq_length: l_val, y: y_val})\n",
-    "        print(\"{:4d}  Train loss: {:.4f}, accuracy: {:.2f}%  Validation accuracy: {:.2f}%\".format(\n",
+    "        print(\"{:4d}  훈련 손실: {:.4f}, 정확도: {:.2f}%  검증 세트 정확도: {:.2f}%\".format(\n",
     "            epoch, loss_val, 100 * acc_train, 100 * acc_val))\n",
-    "        saver.save(sess, \"my_reber_classifier\")"
+    "        saver.save(sess, \"./my_reber_classifier\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's test our RNN on two tricky strings: the first one is bad while the second one is good. They only differ by the second to last character. If the RNN gets this right, it shows that it managed to notice the pattern that the second letter should always be equal to the second to last letter. That requires a fairly long short-term memory (which is the reason why we used a GRU cell)."
+    "이제 두 개의 문자열에 이 RNN을 테스트해 보죠. 첫 번째는 잘못된 것이고 두 번째는 올바른 것입니다. 이 문자열은 마지막에서 두 번째 글자만 다릅니다. RNN이 이를 맞춘다면 두 번째 문자가 항상 끝에서 두 번째 문자와 같아야 한다는 패턴을 알게 됐다는 것을 의미합니다. 이렇게 하려면 꽤 긴 단기 기억(long short-term memory)이 필요합니다(그래서 GRU 셀을 사용했습니다)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -3442,7 +3435,7 @@
      "text": [
       "INFO:tensorflow:Restoring parameters from my_reber_classifier\n",
       "\n",
-      "Estimated probability that these are Reber strings:\n",
+      "레버 문자열일 추정 확률:\n",
       "BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVETE: 0.00%\n",
       "BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVEPE: 1.00%\n"
      ]
@@ -3462,7 +3455,7 @@
     "    y_proba_val = y_proba.eval(feed_dict={X: X_test, seq_length: l_test})\n",
     "\n",
     "print()\n",
-    "print(\"Estimated probability that these are Reber strings:\")\n",
+    "print(\"레버 문자열일 추정 확률:\")\n",
     "for index, string in enumerate(test_strings):\n",
     "    print(\"{}: {:.2f}%\".format(string, y_proba_val[index][0]))"
    ]
@@ -3471,14 +3464,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ta-da! It worked fine. The RNN found the correct answers with absolute confidence. :)"
+    "쨘! 잘 작동하네요. 이 RNN이 완벽한 신뢰도로 정확한 답을 냈습니다. :)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 8. and 9."
+    "## 8. 과 9."
    ]
   },
   {
@@ -3487,15 +3480,6 @@
    "source": [
     "Coming soon..."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -3514,7 +3498,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.5.5"
   },
   "nav_menu": {},
   "toc": {