diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index 1d778bf..2e6f8da 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -1,54 +1,79 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Chapter 15 – Autoencoders**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_This notebook contains all the sample code and solutions to the exercises in chapter 15._"
-   ]
-  },
-  {
-   "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": [
+    "**15장 – 오토인코더**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_이 노트북은 15장에 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",
     "import sys\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",
@@ -56,13 +81,16 @@
     "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 = \"autoencoders\"\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)"
@@ -72,12 +100,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A couple utility functions to plot grayscale 28x28 image:"
+    "28x28 흑백 이미지를 그리기 위한 유틸리티 함수:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,12 +116,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "def plot_multiple_images(images, n_rows, n_cols, pad=2):\n",
-    "    images = images - images.min()  # make the minimum == 0, so the padding looks white\n",
+    "    images = images - images.min()  # 최소값을 0으로 만들어 패딩이 하얗게 보이도록 합니다.\n",
     "    w,h = images.shape[1:]\n",
     "    image = np.zeros(((w+pad)*n_rows+pad, (h+pad)*n_cols+pad))\n",
     "    for y in range(n_rows):\n",
@@ -107,19 +135,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PCA with a linear Autoencoder"
+    "# 선형 오토인코더를 사용한 PCA"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Build 3D dataset:"
+    "3D 데이터셋을 만듭니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,12 +169,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Normalize the data:"
+    "데이터를 정규화합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,25 +188,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's build the Autoencoder..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: instead of using the `fully_connected()` function from the `tensorflow.contrib.layers` module (as in the book), we now use the `dense()` function from the `tf.layers` module, which did not exist when this chapter was written. This is preferable because anything in contrib may change or be deleted without notice, while `tf.layers` is part of the official API. As you will see, the code is mostly the same.\n",
-    "\n",
-    "The main differences relevant to this chapter are:\n",
-    "* the `scope` parameter was renamed to `name`, and the `_fn` suffix was removed in all the parameters that had it (for example the `activation_fn` parameter was renamed to `activation`).\n",
-    "* the `weights` parameter was renamed to `kernel` and the weights variable is now named `\"kernel\"` rather than `\"weights\"`,\n",
-    "* the bias variable is now named `\"bias\"` rather than `\"biases\"`,\n",
-    "* the default activation is `None` instead of `tf.nn.relu`"
+    "오토인코더를 만듭니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +202,7 @@
     "reset_graph()\n",
     "\n",
     "n_inputs = 3\n",
-    "n_hidden = 2  # codings\n",
+    "n_hidden = 2  # 코딩 유닛\n",
     "n_outputs = n_inputs\n",
     "\n",
     "learning_rate = 0.01\n",
@@ -206,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,21 +237,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure linear_autoencoder_pca_plot\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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xaW3PsmVw660Lw6JGA+64o//1MFm4rFkTejN1qMFMAq8Szp30CnAl8LCZXezuLydsl4yh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\nBYyIRKOAEZFoFDAiEs3/AQcsVH7eWduMAAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x116547be0>"
+       "<Figure size 288x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -256,19 +264,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Stacked Autoencoders"
+    "# 적층 오토인코더"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's use MNIST:"
+    "MNIST 데이터셋을 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -283,34 +291,29 @@
     }
    ],
    "source": [
+    "tf.logging.set_verbosity(tf.logging.ERROR)  # deprecated 경고 메세지를 출력하지 않기 위해 \n",
     "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\")"
+    "mnist = input_data.read_data_sets(\"/tmp/data/\")\n",
+    "tf.logging.set_verbosity(tf.logging.INFO)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Train all layers at once"
+    "## 한 번에 모든 층을 훈련하기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's build a stacked Autoencoder with 3 hidden layers and 1 output layer (ie. 2 stacked Autoencoders). We will use ELU activation, He initialization and L2 regularization."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: since the `tf.layers.dense()` function is incompatible with `tf.contrib.layers.arg_scope()` (which is used in the book), we now use python's `functools.partial()` function instead. It makes it easy to create a `my_dense_layer()` function that just calls `tf.layers.dense()` with the desired parameters automatically set (unless they are overridden when calling `my_dense_layer()`)."
+    "3개의 은닉층과 1개의 출력층(즉, 두 개를 적층)을 가진 적층 오토인코더를 만들어 보겠습니다. ELU 활성화 함수와 He 초기화, L2 정규화를 사용하겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,7 +323,7 @@
     "\n",
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 300\n",
-    "n_hidden2 = 150  # codings\n",
+    "n_hidden2 = 150  # 코딩 유닛\n",
     "n_hidden3 = n_hidden1\n",
     "n_outputs = n_inputs\n",
     "\n",
@@ -329,8 +332,8 @@
     "\n",
     "X = tf.placeholder(tf.float32, shape=[None, n_inputs])\n",
     "\n",
-    "he_init = tf.contrib.layers.variance_scaling_initializer() # He initialization\n",
-    "#Equivalent to:\n",
+    "he_init = tf.contrib.layers.variance_scaling_initializer() # He 초기화\n",
+    "#아래와 동일합니다:\n",
     "#he_init = lambda shape, dtype=tf.float32: tf.truncated_normal(shape, 0., stddev=np.sqrt(2/shape[0]))\n",
     "l2_regularizer = tf.contrib.layers.l2_regularizer(l2_reg)\n",
     "my_dense_layer = partial(tf.layers.dense,\n",
@@ -352,30 +355,30 @@
     "training_op = optimizer.minimize(loss)\n",
     "\n",
     "init = tf.global_variables_initializer()\n",
-    "saver = tf.train.Saver() # not shown in the book"
+    "saver = tf.train.Saver() # 책에는 없음"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's train it! Note that we don't feed target values (`y_batch` is not used). This is unsupervised training."
+    "이제 훈련시켜 보죠! 여기에서는 타깃 값을 주입하지 않습니다(`y_batch`가 사용되지 않습니다). 이는 비지도 학습입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0204011\n",
-      "19% Train MSE: 0.0114192\n",
-      "2 Train MSE: 0.0102221\n",
-      "3 Train MSE: 0.00989991\n",
-      "4 Train MSE: 0.0103724\n"
+      "0 훈련 MSE: 0.022107124\n",
+      "1 훈련 MSE: 0.012154639\n",
+      "2 훈련 MSE: 0.010653543\n",
+      "3 훈련 MSE: 0.010217447\n",
+      "4 훈련 MSE: 0.010719749\n"
      ]
     }
    ],
@@ -388,25 +391,25 @@
     "    for epoch in range(n_epochs):\n",
     "        n_batches = mnist.train.num_examples // batch_size\n",
     "        for iteration in range(n_batches):\n",
-    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n",
-    "            sys.stdout.flush()                                          # not shown\n",
+    "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # 책에는 없음\n",
+    "            sys.stdout.flush()                                          # 책에는 없음\n",
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
-    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})   # not shown\n",
-    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)           # not shown\n",
-    "        saver.save(sess, \"./my_model_all_layers.ckpt\")                  # not shown"
+    "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})   # 책에는 없음\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)           # 책에는 없음\n",
+    "        saver.save(sess, \"./my_model_all_layers.ckpt\")                  # 책에는 없음"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This function loads the model, evaluates it on the test set (it measures the reconstruction error), then it displays the original image and its reconstruction:"
+    "이 함수는 모델을 로드하고 테스트 세트에서 이를 평가합니다(재구성 오차를 측정합니다). 그런 다음 원본 이미지와 재구성 이미지를 그립니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,22 +430,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_all_layers.ckpt\n",
-      "Saving figure reconstruction_plot\n"
+      "INFO:tensorflow:Restoring parameters from ./my_model_all_layers.ckpt\n"
      ]
     },
     {
      "data": {
-      "image/png": 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EMgAkIJABIAGBDAAJCGQASEAgA0ACAhkAEhDIAJCAQAaABAQyACQgkAEgAYEMAAkIZABI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\ncXeHAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAGoCAYAAAB16I2XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHMNJREFUeJzt3V2MXWX1P/Cnb9OZ6cuUlpbSBixvQiVFkCDFl2rE1BB8iRgukAsVTSQmkkiiBhIT9UbvtDcGiQYUNTEhEWJSMECDAQNGkFZTpBYKVChDgZbSdtppp/C78cJ//s96yj4znTVtP5/Ldbqfvc850/PNTtbaz7R33nmnAAB5pmdfAACc7IQxACQTxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAMmEMAMlmJpzTI784EUzLvoCTwY4dO/xecNxbtmzZUX8v3BkDQDJhDADJhDEAJBPGAJAso4EL4LgwbVq972aytp7tev7o37f0slbX9z+Ra2U7Vu/FnTEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkMxoE0Cg69hPa7RlIseOehmhmT69fu/19ttvd16rq8kak5qoc0zWWv/LnTEAJBPGAJBMGANAMmEMAMmEMQAk000NEOjaudv69710YM+cWf+JjtY6cuRIuFZ0nqiburVWdEx/f394TGRsbKxaj97jjBkzOp/jeNiowp0xACQTxgCQTBgDQDJhDADJhDEAJNNNDRDo5TnIkagLOHpmdOuYqAO4r68vXGt0dLRaHxkZqdb37dsXrjVr1qxqPerAbnVAR2tFneQtvXSsTxXujAEgmTAGgGTCGACSCWMASCaMASCZMAaAZEabADqKNkpojdD0siFDNKoTbcjQGsWKRoUWLFjQ6RyllHLw4MFO54/O0bquXj7jw4cPV+vRZhSlxNcc1aPrGi93xgCQTBgDQDJhDADJhDEAJBPGAJBMNzVwUoi6cFvdsVHXcFRvde3u37+/cXV1c+fOrdbnz59frbc6oIeGhjod03ov0aYT0WfZ2iiia5f5RG7eUUr8dzHR5zkad8YAkEwYA0AyYQwAyYQxACQTxgCQTDf1fz3++OPV+rp168Jjli9fXq0PDAyEx3zpS1+q1hcuXNipDnQTde0eOnQoPGbXrl3V+quvvlqt/+c//wnXeuWVVzqfP3qmc1SPuqxLKeXMM8+s1hcvXlytt7q/9+3bV61Hz4Zu/Sa2ns1dc/rpp4evzZkzp1rv5Tnbx+oZ1BF3xgCQTBgDQDJhDADJhDEAJBPGAJBMGANAsmnRQ7KPoUk/4btx/vnnV+tbt26dlPNHD3FfvXr1pJx/MqxYsaJav+WWW8JjonGMKWBynyJ/ktqxY0fn34tovCYaIRoZGQnX2rlzZ7X+zDPPVOv/+Mc/Oq914MCB8JhojCcaO2qNVi1durTT+Xfv3h2uFY0DRf/HTznllHCtKIOi8a1PfepT4VqXXHJJtd4areo62jR9evd72GXLlh3198KdMQAkE8YAkEwYA0AyYQwAyYQxACSzUcR/3XPPPdX6xo0bw2MuvPDCan3z5s3hMX/961+r9Xvvvbda/9Of/hSuddZZZ1Xrzz//fHhMVzNn1v9EWg9rb3V01kQdmKWU8t3vfrfTWtC123XGjBnha9Hff9SdG3Ust9aKNjcopZSxsbFqPeoMj85RStwdvGfPnmo92gyjdZ6oa3p4eDhca9u2bdV69Pt23nnnhWtFr7W6qVvf/2RyZwwAyYQxACQTxgCQTBgDQDJhDADJhDEAJDPa9F8rV67sVG+56KKLwteuu+66av3HP/5xtf7CCy+Ea0Wt/9GoQC/6+vqq9dZoU3Rdr732WrV+wQUXdL8wTmrTpsXP3Y9GVfr7+zv9+1LiUaW5c+dW660xvWgDi4ULF4bHRBtCbNmypVpvjTYtXry4Wn/zzTer9e3bt4drLV++vFpftmxZtb5+/fpwrX/+85/VevR70csoWi9af2OR8Wy85M4YAJIJYwBIJowBIJkwBoBkwhgAkummniKiTs9eOo176QDvKtrwopRSXn/99Wr98ssvr9bXrl07IdfEyaPVtRp1wc6aNatab3XnRscsWLCgWo+6jEsp5ciRI53PPzo6Wq1HXcuLFi0K14rey86dO6v1K664IlwrOs/u3bur9db3FW16sWTJkmr9zDPPDNeKNt1onT/aQCMyno7pFnfGAJBMGANAMmEMAMmEMQAkE8YAkEw3NU3Rs3E///nPh8dE3Yk//elPq/WBgYHuFwYd9dLNHL0WdWxPnx7f30Rrtbpzow7owcHBaj2ayiillLGxsWp93rx5nc5RSimzZ8+u1p9++ulqPXqWdilx1/Rll11Wrbe6qaPran3G0XcW/b20nlnt2dQAcBwTxgCQTBgDQDJhDADJhDEAJBPGAJDMaBNNd955Z7U+PDwcHhM9RP4973nPRFwS9KQ1dhSJRlWiEaLWaMvBgwer9dZGBdEmCtGYVNdND0qJr7k1wrN9+/Zq/e67767WN2/eHK61Zs2aav3KK6+s1s8444xwrei99DJyFL1/G0UAwAlKGANAMmEMAMmEMQAkE8YAkEw3NaWUUp577rlq/eabb+681mOPPVatL126tPNa0NVEdrtGHdhRl3O0uUAppYyMjFTrrQ7ovr6+an3mzPpP9+HDh8O1os9laGioWh8dHQ3X2rBhQ7W+fv36ar3Vmf3BD36wWo82hGht7BF9Ly3Rd9xLZ/p4uDMGgGTCGACSCWMASCaMASCZMAaAZLqpKaWU8sc//rFaj7ozr7322nCts88+e0KuCSZD65nV0WtR1/T+/fvDtaL/S7Nnzw6PGRgYqNajjuKoY7uUuAM76hreu3dvuNamTZuq9Z07d1brn/70p8O1Vq9eXa3PnTu3Wo+e8V1KKbNmzQpf6yr6jj2bGgBOUMIYAJIJYwBIJowBIJkwBoBkwhgAkhltOom0HiL/hz/8oVqPxi5+9KMfhWu1HuQOU01rVKXrxgOtv/1oTKk1jhNtsLBnz55qvTX2s2jRomo9eo8bN24M1/r73/9era9atapaX7NmTbjWihUrqvXovbc2sIi0NqqIXrNRBACcZIQxACQTxgCQTBgDQDJhDADJdFOfRH75y1+Grz3yyCPV+he/+MVq3WYQTFVRd2zUNd3qmo3WijYRaHVGR53WrU7faOOJsbGxan3+/PnhWnPmzKnWX3zxxWr9wQcfDNd65ZVXqvWrr766Wo82gyillMHBwWo96ppudb/3shlHdEx0ntb3NR7ujAEgmTAGgGTCGACSCWMASCaMASCZbuoTUPRM2W9+85vhMQsWLKjWf/jDH07INUG26dPr9x69PIM46oxuPZs66rRuPTM+EnUgR/VSSjlw4EC1/sADD1Tr99xzT7hWf39/tX7RRRdV69FzsUuJP7OuXc6lxN9x65iuWt3U4zmPO2MASCaMASCZMAaAZMIYAJIJYwBIJowBIJnRpuNYNKpw3XXXVevRw+1LKeX666+v1m0IwfGm64YQrdGmaISn9X8pEm360Forem1gYKBab43dbN68uVq//fbbq/VoM4hSSvnKV75SrV988cXVenS9pcQjTNHIU+v7mjmzHmnRyFNL1w1HxsudMQAkE8YAkEwYA0AyYQwAyYQxACTTTT3FtToHr7766mp9y5Yt1frKlSvDtX7wgx90uzA4zvSyIUQk6s4dGxsLjxkdHe18zOzZs6v1aNOJbdu2hWutW7euWt+6dWu1fvnll4drRRMbZ5xxRrXe6hiPuqmjz2VoaChcKzpP6zOOuqOjei+d2e+GO2MASCaMASCZMAaAZMIYAJIJYwBIppt6itu1a1f42sMPP9xprbvuuit8beHChZ3WgqkqeqZw9KzjVnds1OkbHdN6NvTcuXOr9V6ejR11B993333hWk888US1fv7551frN910U7jWpZde2um6oufolxJ/lnPmzAmPiUSff+szjo5pfZfHgjtjAEgmjAEgmTAGgGTCGACSCWMASCaMASCZ0aYpYs+ePdX66tWrO6/1m9/8plq/5JJLOq8FmaLxkugh/q3Xonpr7CUa1YnWijZwKCUe4WkdE/nXv/5VrW/YsCE8ZufOndX6NddcU623fnuiax4ZGanWoxGxUkoZHBwMX6tpbToRvTbZY0q9cGcMAMmEMQAkE8YAkEwYA0AyYQwAyXRTTxF33HFHtb5t27bOa33kIx+p1o+HjkL4X62u6a562RAg2lwi6rJuibqpW93B0UYx69evr9b//Oc/h2tF72XFihXV+tDQULhW1IEevZdWx3h0Xa0O7EjXTvqpxJ0xACQTxgCQTBgDQDJhDADJhDEAJBPGAJDMaNMk27p1a7X+/e9/f3IvBDiqaOypr6+v078vJR7h2bdvX3jMo48+Wq1v3Lix0zlKiUcezznnnGq9Nb4Vvc9o5Cka62rpZRSstenHVOfOGACSCWMASCaMASCZMAaAZMIYAJLppp5kjzzySLX+1ltvdV5r5cqV1frAwEDnteB40upa7ropQC+bCPSy6cqhQ4eq9db//ZGRkWp90aJF1fpVV10VrnXBBRdU6/PmzavWo+s92ms1rS7vqDs66ow+HjZ96IU7YwBIJowBIJkwBoBkwhgAkgljAEimm3qK+9CHPhS+9sADD1Truqk50U3VjtrWs5EPHz5crbfey1lnndXp/NE5SinlzDPPrNaXLl1arffSsR4d08v3NVW/42PFnTEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkGxaQvv4ydWvzomq+04BdLZjx47j6veil3GgaKOE1jG9/PvoPH19fdX69Ond79W6jjy1XjuRRpuWLVt21N8Ld8YAkEwYA0AyYQwAyYQxACQTxgCQLKObGgD4H+6MASCZMAaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTCGACSCWMASCaMASCZMAaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTCGACSCWMASCaMASCZMAaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEg2M+Gc7yScEybatOwLOBkMDw/7veC4t3Tp0qP+XrgzBoBkwhgAkgljAEgmjAEgmTAGgGQZ3dQAJ51p0+oNte+8EzeMdz0m+vctrfNn6uXzOp65MwaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkhltApgE06d3v/c5cuRItf722293XisaCZoxY0a13hqTitbqZeQqei8TOb7Vy2c/2ab+FQLACU4YA0AyYQwAyYQxACQTxgCQTDf1JPvtb39bre/fv79af/LJJ8O1br/99k7n/t73vhe+9olPfKJa//jHP97pHHAyiDqAW13Oo6Oj1fqhQ4fCY6LfhWitqF5K3IUcdWy3upb7+/ur9VmzZlXrg4OD4Vp9fX3VetTl3eqMjo45HrgzBoBkwhgAkgljAEgmjAEgmTAGgGTToud/HkOTfsLJ9o1vfCN87ec///kkXsm79773va9af/TRR6v1oaGhY3k5x4PuD8ils+Hh4WP+e9H6DRwbG6vWo67lgwcPhmu98cYb1fqzzz4bHrNx48Zq/fnnn6/WX3/99XCt3bt3V+tz5syp1ufOnRuudfHFF1fra9asqdZXrVoVrrVkyZJqPeqMbnWM9/Is78l4bvXSpUuP+nvhzhgAkgljAEgmjAEgmTAGgGTCGACSCWMASGajiHGIRpgmcnzpkksuCV/7whe+UK1v3bq1Wv/Vr34VrvX0009X63fffXe1/tWvfjVcC44n0ThMKfFIzMjISLW+a9eucK1oTCkaHyyllKeeeqpaj8Z73vOe94RrReOL0WjPSy+9FK4VjVDt3LmzWo82kCillIGBgWo9+uxbo2jR93L48OHwmOjaotGqYzUO7M4YAJIJYwBIJowBIJkwBoBkwhgAkummPort27eHr/3iF7/ovN5ll11Wrd9///3V+uDgYLhWX19ftR51h7YeSP+Xv/ylWm89eB6OJ1EXbKs7NurCjTp9W5sYvPXWW9X6zJnxz/DatWur9SuuuKJaf//73x+utXDhwmo96vL+3e9+F64VfS579+6t1lsbaOzfv79anzatvrdCa63o8++lY3727Nmdrutorx2NO2MASCaMASCZMAaAZMIYAJIJYwBIppv6KFrdxFEXZtQxXUopDz74YLU+d+7cbhfWcOedd1brf/vb3zqv9bnPfW6cVwNTQ/QM5ugZxKXE3bFRB26ra/fUU0+t1lvPn//oRz9ara9atapanzdvXrjWnj17qvXoGdTPPPNMuNbY2Fi1Hv32tZ5NfejQoWo9+r727dsXrhV9/q2plKg7O/ruoymW1jHvhjtjAEgmjAEgmTAGgGTCGACSCWMASCaMASCZ0aaj+MAHPhC+Fo09tVrfBwYGxn1NRxNtYBGNEMCJpOsIU2ujiGizgGjThdb//Wit/v7+8JiVK1dW69HvSGvsJxpVuvfee6v1zZs3h2t98pOfrNbPOuusan3JkiXhWtHnEm0gEf37UnobX+tqPONLLe6MASCZMAaAZMIYAJIJYwBIJowBIJlu6nEYGhpKPf9dd91VrW/atKnzWmvXrq3WzznnnM5rwVQUdcG2unOjruVoo4Q5c+aEa7U2cegq6ppubWzzxBNPVOvPPvtstb58+fJwrUsvvbRaP++886r1mTPjqBkdHa3Wo00fWptxROdpdblH3fTRZiDHijtjAEgmjAEgmTAGgGTCGACSCWMASCaMASCZ0aYp7qmnngpf+/rXv16tR6MCp59+erjWunXrqvVZs2Y1rg5yTOTD+lubCETniY5prRWNULVGaKIxngMHDlTrO3bsCNd67rnnqvXomt/73veGa3UdeXzppZfC16LPOBora/0mRe+llzGl6LpaG4uM5+/SnTEAJBPGAJBMGANAMmEMAMmEMQAk0009xT322GPha1HXdOTGG28MX2t1TsJU0+po7XpMLxsP9NKdG52/tYlCZPv27dX6hg0bwmPuv//+an1wcLBajzaDKKWU1atXV+vR53Lw4MFwrQULFlTr0aYb0SYdpZQyfXr9/rL19xJtIhH9XfTyt/duuDMGgGTCGACSCWMASCaMASCZMAaAZLqpp4gbbrihWv/973/fea1vfetb1fp3vvOdzmvBiSLqwm11M0cTC9EziFud2dGzllvHDA8PV+sPPfRQtX7PPfeEa73xxhvV+mc+85lqfc2aNeFay5cvr9b37NlTrbfeY9RNHX3Ge/fuDdeaN29etd7qfo86sCOt9zIe7owBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGRGmybZvn37qvX77ruvWm89YP20006r1m+99dZqPXogOpxIogf5R6MyrQ1XomOicajo37deGxkZCY/ZsmVLtf7UU09V69HvSymlXHnlldX6tddeW62vWrUqXGvGjBnVevS5tMbHou/rwIED1Xrrdyz6vezv7w+PiUSjcK3veDzcGQNAMmEMAMmEMQAkE8YAkEwYA0Ay3dSTLOpc3LlzZ+e1brrppmp94cKFndeCE13Utdt68H/XbupZs2aFa0XnaU1MbNu2rVrfvHlztb5s2bJwrbVr11brF154YbUebeBQSrxZw+HDh6v1Vsd61LV86NChar31GUffV/TdlxJvItE6puv53w13xgCQTBgDQDJhDADJhDEAJBPGAJBMN/Ux8OSTT4avPfzww53Wuuaaa8LXbr755k5rAf+/Xjqge3nO+5tvvlmtb9q0KTxmw4YN1frw8HC1fumll4ZrnXvuudV69B6jc5QSv5eom7jVMT4wMBC+VjM0NBS+Njg42Om6euHZ1ABwghLGAJBMGANAMmEMAMmEMQAkE8YAkMxo0zgcOHCgWr/lllvCY6KHn0daowq9jFcA7170f2zGjBnVeuv/dzQO9Mgjj4THPP7449X6kiVLqvWPfexj4VqLFy+u1qdPr9+TtTZKmDt3brU+e/bsan3Xrl3hWtFoUzSmFG3sUEq86cS+ffvCY6JRpeg7jj6v8XJnDADJhDEAJBPGAJBMGANAMmEMAMl0U4/DbbfdVq0/9NBDnde64YYbqnWbQcDEiLpgW925rU0kalpdu0888US1/vTTT4fHLFiwoFq/9tprq/WrrroqXGvRokXV+ujoaLXe+lyi64q0Jj+iru3omGiKpZRSXnvttU7X1TrPvHnzOq81Hu6MASCZMAaAZMIYAJIJYwBIJowBIJlu6nG49dZbJ2ytn/zkJ9W650/DxOj6DOKWqKP31VdfDY/ZvHlztf7iiy+Gx0TPgF69enW13rXLuZT4edp79+4Nj5k/f361HnVgt57ZHX0vBw8erNZfeumlcK2XX365Wj/jjDPCY0499dTwtcnkzhgAkgljAEgmjAEgmTAGgGTCGACSCWMASGa0aYqIHjAfPdx+os2ePbtaj8Y+jhw5Eq4VPXg+0nrw+7p16zqt1RK9l9aIWteNApi6orGb1nccjRZG/1937NgRrrVly5ZqfevWreExp512WrW+cePGan1oaChc680336zW9+zZU623/o9Ho03RCNOcOXPCtaLPP/q8nnvuuXCtaBRs8eLF4TFThTtjAEgmjAEgmTAGgGTCGACSCWMASKabeopYvnx56vlvvPHGan3ZsmXV+vDwcLjWz372swm5psnS+uy/9rWvTeKVcCy988471Xq0UUEpcQd2NOXQ2nRi5sz6z21rc4exsbFqff369dX6hg0bOq8VbaKwa9eucK1oc4XDhw9X661u6p07d1brr732WrW+YsWKztcVdb+XEn/HUTd5a8Kl9bd0NO6MASCZMAaAZMIYAJIJYwBIJowBIJlu6nG4/vrrq/U77rhjkq9k/G677bZjfo6om7TVgRr58pe/HL52xRVXdFrrwx/+cOfzM3V17Wht/f1FHdiRJUuWhK9deOGF1frLL78cHhN1QL/++uvVevT86VLi7uDoWc+tzzH6zKL3Hz3ju2VwcLBajyY8SinlvPPOq9Zbz6aOuqOj36vxdEy3uDMGgGTCGACSCWMASCaMASCZMAaAZMIYAJJN69q6PwEm/YST7de//nX42qFDhybsPJs2barWJ3Kjhm9/+9vV+rnnntt5rc9+9rPVemscZAo7NvMN/D+Gh4c7/15Eoye9bO4QjeQcPHiwWn/rrbfCtf79739X6y+88EJ4TLReNA7V2twh+q2PxqT27t0brnXKKadU69FoUTSmVEr8vQwNDVXrZ599drjW6aefXq0vXLgwPKa/v79aj777Xkabli5detSD3BkDQDJhDADJhDEAJBPGAJBMGANAMt3U0Bvd1JOgl27qyER2U0cdta3f09HR0Wq91bXcdROVN954o/P5d+/eXa0fOHAgXGvRokXVejQZEXWflxJ/L9FnPGvWrHCtgYGBar31Ofbyd9GVbmoAOA4IYwBIJowBIJkwBoBkwhgAkgljAEg2M/sCACZDNHbUGkcaGxur1qOxl9Za0ZjU/Pnzw2Oi9aIRnmijhFLiaz5y5Ei1Hr331jHR9e7fvz9ca+bMegy9/fbbna8rOn9rc4dotGmyTY2rAICTmDAGgGTCGACSCWMASCaMASCZbmrgpBB12k5k13BL1B3c0rUDutW13HVzi9bmDtFa0fW2Nl3o2uXe6n5O2PhowrgzBoBkwhgAkgljAEgmjAEgmTAGgGS6qQECXbtzW/++l2cgRx3Yo6Oj1XrUZd16LTpHq/t79uzZnY5pfS4T2QHdegb1VOfOGACSCWMASCaMASCZMAaAZMIYAJIJYwBIZrQJYIL0MlrTyzHRCFFrrWi0quumD704njdwmCzujAEgmTAGgGTCGACSCWMASCaMASDZNF1uAJDLnTEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkEwYA0AyYQwAyYQxACQTxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkEwYA0AyYQwAyYQxACQTxgCQTBgDQDJhDADJhDEAJPs/UVHx/YTzJ7kAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x12271fd68>"
+       "<Figure size 576x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -458,19 +462,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Tying weights"
+    "## 가중치 묶기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is common to tie the weights of the encoder and the decoder (`weights_decoder = tf.transpose(weights_encoder)`). Unfortunately this makes it impossible (or very tricky) to use the `tf.layers.dense()` function, so we need to build the Autoencoder manually:"
+    "인코더와 디코더의 가중치를 묶는 일은 자주 있습니다(`weights_decoder = tf.transpose(weights_encoder)`). 안타깝지만 `tf.layers.dense()` 함수를 사용해서 이렇게 하기는 불가능합니다(또는 매우 어렵습니다). 수동으로 직접 오토인코더를 만들어야 합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +482,7 @@
     "\n",
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 300\n",
-    "n_hidden2 = 150  # codings\n",
+    "n_hidden2 = 150  # 코딩 유닛\n",
     "n_hidden3 = n_hidden1\n",
     "n_outputs = n_inputs\n",
     "\n",
@@ -488,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,8 +507,8 @@
     "\n",
     "weights1 = tf.Variable(weights1_init, dtype=tf.float32, name=\"weights1\")\n",
     "weights2 = tf.Variable(weights2_init, dtype=tf.float32, name=\"weights2\")\n",
-    "weights3 = tf.transpose(weights2, name=\"weights3\")  # tied weights\n",
-    "weights4 = tf.transpose(weights1, name=\"weights4\")  # tied weights\n",
+    "weights3 = tf.transpose(weights2, name=\"weights3\")  # 가중치 묶기\n",
+    "weights4 = tf.transpose(weights1, name=\"weights4\")  # 가중치 묶기\n",
     "\n",
     "biases1 = tf.Variable(tf.zeros(n_hidden1), name=\"biases1\")\n",
     "biases2 = tf.Variable(tf.zeros(n_hidden2), name=\"biases2\")\n",
@@ -528,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,18 +541,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0150667\n",
-      "19% Train MSE: 0.0164884\n",
-      "2 Train MSE: 0.0173757\n",
-      "3 Train MSE: 0.0168781\n",
-      "4 Train MSE: 0.0155875\n"
+      "0 훈련 MSE: 0.01506695\n",
+      "1 훈련 MSE: 0.016488748\n",
+      "2 훈련 MSE: 0.017375939\n",
+      "3 훈련 MSE: 0.016878333\n",
+      "4 훈련 MSE: 0.015587721\n"
      ]
     }
    ],
@@ -566,16 +570,14 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "        saver.save(sess, \"./my_model_tying_weights.ckpt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -586,9 +588,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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yrLqu0XgWWe5ncdxxAQBCoXEBAEKhcQEAQqFxAQBCoXEBAEIhVQhgzBRJsOUm84ok+H7++efs9T/99JOpTZyY99/+x44dk/W6ujpTU+/f+0yqq6uzXku9z5T09ecmDT2VmObBHRcAIBQaFwAgFBoXACAUGhcAIBTCGWXw1ltvyfrRo0dN7fPPPze1l156Kfu1Hn/8cVP7wx/+YGrXXHNN9jmBSsgdhVRk5JIKHXjhAHXs8PBw1nEppXTo0CFZP5kKcdTW1spj+/r6TO3EiROm5r0nFe5QtUmT9E+9eq2qqipT8z6TsdqWhTsuAEAoNC4AQCg0LgBAKDQuAEAoE0rdJ2YEKv6C5XTvvfea2osvvjgGV/L/Fi1aZGqffPKJPHbq1KmjfTljYfT/V31IXV1d2d/n3N+aIpMzVBBicHBQrh8aGjI1FY744Ycf5Pqvv/7a1A4fPpx1nd57V5MvZs+ebWrt7e1yvTpWhTO8770KbahrLTJ5wwuCKOq15syZc9rvM3dcAIBQaFwAgFBoXACAUGhcAIBQaFwAgFAY+XQKo5EgXLJkiandeOONprZt2za5/rXXXjO1zZs3m9qf//xnuf7OO+883SUCY8pL4KnxRKp25MgRuX737t2mtn37dlP76quv5Ppdu3aZmkoFqto555wjz6lSkarmpQI7OzuzjvVeX33WKino7Sem0ob19fXyWIWRTwCAcYHGBQAIhcYFAAiFxgUACIVwRkqpq6tL1l9++eWs9VdccYWsv/fee6amxrGo/W/UA9qU9MPkTz/91NQOHDgg1wOjrdQxct6//ePHj5uaGrnU09Mj16twhQpyTJkyRa6/7rrrTK2jo8PUVOBgz5498pxe/WTeZ6p+O6ZNm2ZqKnCRkt5jTH3O3vrc8Vbe+pHijgsAEAqNCwAQCo0LABAKjQsAEArhjOQHGdRDRhXE+OCDD+T6hoaGEV/Tq6++KuufffZZ1vo//vGPI35toFLU5AVvyoOqF5ny0NbWZmoqXOHtfTVr1ixTmz59uqnt37/f1P7973/Lc6opHb29vabm/ZYsWLDA1FpaWkxt8uTJcr36/FQ4w1uvfiPVNBNvj66Rhja44wIAhELjAgCEQuMCAIRC4wIAhELjAgCEQqowpbR06VJZV2lDNWKltra27NfkjZv68ccfy/5aQDkV2WNJpdW8pJkaBaXSaueff75crxJwKhU4Z84cub6xsdHUhoaGTK27u9vUvD2+tm7dampNTU2mptKXHpUK9KgEoEpveqlC9bdWfyfvb8p+XACAcYHGBQAIhcYFAAiFxgUACIVwxilMnTq1Iq/zxhtvmNqmTZuy16t9gjo7O0u6JqASSn2Qr8JSas87T3Nzs6l5Yavh4WFT27Jli6n94x//MLUvvvhCnlNd65IlS0zN2/NPBVHU56eu3TtWhTO8EIUKbRQJXIx07zbuuAAAodC4AACh0LgAAKHQuAAAoRDOqLANGzaY2t13321qRfYUWr16tal5/6c7UE4jfbj+K/XvtMg51Xo1DSKllGpqarLWq8BISint3bvX1P75z3+amtozzzvnokWLTG3FihWmtnz5crlehUuOHDliat7EHRVuUYGNIn+Tke6xVQR3XACAUGhcAIBQaFwAgFBoXACAUGhcAIBQSBVW2Nq1a03NSxAq99xzj6ldeOGFJV0TUAm5ezd5+0mpUURqvfd9UvtpqbTc4cOH5Xo1tumbb74xtYGBAVPz9vhasGCBqV1wwQWm1tLSIter61cJQi9lrBKA6u9UZD+wIqlC9uMCAIwLNC4AQCg0LgBAKDQuAEAohDNG0R133GFqb7/9dtbaBx54QNYffvjhkq4JKKdS915SD/K9IIEKbai9s7z1qq72qdqzZ49c/7///c/UDh06ZGoNDQ2mNmvWLHnO6dOnm5oKkXj7aQ0ODpqaCod4ewvmBl48uXt3eSOj2I8LADAu0LgAAKHQuAAAodC4AAChEM4oA/UwNKWU3n33XVNTD1lnzpxpao899pg8p9o/Bxgr6uG6F9jIfWjvhQPURAgVOlCBAe/1u7u7Te3LL7+U63ft2mVqud9nFSJJKaW6ujpTU9ep9thKKaXe3l5TmzTJ/qx7IQj1Walr8l5fUb9R3uQNwhkAgHGBxgUACIXGBQAIhcYFAAiFcEYZ3HTTTbK+b9++rPX333+/qTU3N5d0TUAlFJmSUKqampqsWnV1tVyvghQHDx40ta1bt8r1PT09pqYmYrS2tpra3Llz5TkvuugiU1PhBvXaKektXGbMmGFq6nNKKaUTJ07Iei4VmPHCMeXEHRcAIBQaFwAgFBoXACAUGhcAIBQaFwAgFFKFBX3++eem9tFHH2Wv/9Of/mRqDz74YCmXBJxRiuzRpY71xpqptKCXllMOHz5sajt37jS1b7/9Vq5XCbwpU6aYWmdnp6ktXbpUnnP27NmmViSVqT4TNbLJ+0xV0lLt8eWN4VLnLTIGbKS44wIAhELjAgCEQuMCAIRC4wIAhEI44xSGhoZM7dFHHzU1NfbEs2zZMlNjjy2MB+qhvaqp/aRS0kGMiRPtf3t7e0epUU5r1641ta6uLrn+/PPPN7UlS5aY2uLFi01NjYFKSe9TpQITat8xb31uYCKllPr6+rLO6YUzVDhFvX65x0BxxwUACIXGBQAIhcYFAAiFxgUACIVwxim88MILpvbhhx9mr7/jjjtMjSkZONt5QYDcvbu8cIY6Vu1HpaZhpJTSp59+amrr1683taNHj8r1K1euNDU1EUOFOLwAV29vr6mp9zl58mS5XoUeVLiiv79frj9w4ICpHT9+3NS8cEjuRAwv3DHS0AZ3XACAUGhcAIBQaFwAgFBoXACAUGhcAIBQSBWewmOPPVbS+meffdbUGO+Es0mRvaMUtXeUSrWlpEc+qfFI3d3dcv2uXbtMTaX6Wlpa5Pq5c+eamvo+q2vyxlANDAyYWm1trak1NjbK9erzV+/fG2OlPhP1+gsXLpTr1Vg8NYbLS0WOFHdcAIBQaFwAgFBoXACAUGhcAIBQCGeMIvXgVT24LFV1dbWpeaNU1OgVNTbHox7Grl69Onu9oq7VC8aU+yEvSpM7xsk7Vo1X8sYLqfOqUUpq5JFXP3HiRPb6/fv3m9qePXtMrUg4Qn136+vrTc0bg6XCJWrk1d69e+V69d3r7Ow0NW+0k7rW3DFUKfnv63S44wIAhELjAgCEQuMCAIRC4wIAhEI4YxSdd955FXmde+65x9RmzZolj+3p6TG1559/vuzXVCrvs7vrrrsqfCUoF28ixsm8cIdar8IV3t5XasqFCgccOnRIrlf7eW3fvj3rmrwAlAqiqACSOmdKekqHCmx44Qg1EUNdU1NTk1yvwl4qgFbuUBp3XACAUGhcAIBQaFwAgFBoXACAUGhcAIBQSBWewq233mpqa9asGYMrObUXXnih7Of0RrF4o6ROdvvtt8v6lVdembX+6quvzjoOY6vU/bgULxWoEmzq32NbW5tcr/bTUmPZNm/eLNd/8cUXptbf329qaiyat5+WutaZM2eampdKVAnEadOmmVpHR4dcv3LlSlNTiV412ikl/TtRJFU40n8/3HEBAEKhcQEAQqFxAQBCoXEBAEKZMBoPV0+j4i9YTq+//rqpeQ+Tc23atMnUSh3D9NBDD8n6/Pnzs9bfcMMNsn7uueeO+JpGkd4sCKOuq6sr+/usfmvUKCM1miml/GCQNx7pu+++MzUVxNi9e7dcv2PHDlNTI58GBwdNrbW1VZ6zpaXF1GpqarJqKelwhgpieOGMOXPmmJoa+eTtx6X+JiqI4f3t1L+J9vb2036fueMCAIRC4wIAhELjAgCEQuMCAIRCOANnA8IZY6TUcIbaJ8r7TVJBBBXkUMelpPfzUtM4VC0lvU9XV1eXqalpHN7kCRV6KPKbrKZkqCkd3iScuro6U1Ofn3dNudfvhTsUwhkAgLMOjQsAEAqNCwAQCo0LABAKjQsAEAr7cQEYM7kjg1LSyTSVAKyurpbrVbJPndNLwM2YMcPULrzwwqxr8lJ5ajzV8PCwqRXZo8z7/BSVylR7fxU5Z5EEIftxAQDGBRoXACAUGhcAIBQaFwAgFMIZACoidzyQ98Be1dUoIzXaqQgVjkhJByHUyCrF249KfSYqsOGNbFLUyCYvMKE+q9EIV3jnLPJav8UdFwAgFBoXACAUGhcAIBQaFwAgFMIZAMZMkf2oVF2FC7z16lg1JaKmpkauV9Mj1EQLNblD7XuVkt67SwU5igROiuxxpupFpmTknrPcuOMCAIRC4wIAhELjAgCEQuMCAIRC4wIAhEKqEEBFlDoeKPecajSTJzcpWGS9GtnU398v16sEoRo5VeoeZUVUIhVYKu64AACh0LgAAKHQuAAAodC4AAChTIjwIA4AgF9xxwUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACIXGBQAIhcYFAAiFxgUACOX/AGoIgo6hmZ7yAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1173a7f60>"
+       "<Figure size 576x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -603,26 +605,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training one Autoencoder at a time in multiple graphs"
+    "## 여러 개의 그래프에서 오토인토더를 따로따로 훈련하기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are many ways to train one Autoencoder at a time. The first approach is to train each Autoencoder using a different graph, then we create the Stacked Autoencoder by simply initializing it with the weights and biases copied from these Autoencoders."
+    "하나의 오토인코더를 따로따로 훈련하는 방법이 많이 있습니다. 첫 번째 방법은 각 오토인코더를 다른 그래프를 사용하여 훈련하는 것입니다. 그런 다음 이런 오토인코더의 가중치와 편향을 복사해 초깃값으로 지정해서 적층 오토인코더를 만듭니다."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a function that will train one autoencoder and return the transformed training set (i.e., the output of the hidden layer) and the model parameters."
+    "하나의 오토인코더를 훈련하고 변환된 훈련 세트(즉, 은닉층의 출력)와 모델 파라미터를 반환하는 함수를 만들겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -671,7 +673,7 @@
     "                X_batch = X_train[indices]\n",
     "                sess.run(training_op, feed_dict={X: X_batch})\n",
     "            loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
-    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "            print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "        params = dict([(var.name, var.eval()) for var in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)])\n",
     "        hidden_val = hidden.eval(feed_dict={X: X_train})\n",
     "        return hidden_val, params[\"hidden/kernel:0\"], params[\"hidden/bias:0\"], params[\"outputs/kernel:0\"], params[\"outputs/bias:0\"]"
@@ -681,26 +683,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's train two Autoencoders. The first one is trained on the training data, and the second is trained on the previous Autoencoder's hidden layer output:"
+    "이제 두 개의 오토인코더를 훈련시켜 보죠. 첫 번째는 훈련 데이터를 사용하고 두 번째는 첫 번째 오토인코더의 은닉층 출력을 사용해 훈련시킵니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0185175\n",
-      "1 Train MSE: 0.0186825\n",
-      "2 Train MSE: 0.0184675\n",
-      "3 Train MSE: 0.0192315\n",
-      "0 Train MSE: 0.00423611\n",
-      "1 Train MSE: 0.00483268\n",
-      "2 Train MSE: 0.00466874\n",
-      "3 Train MSE: 0.0044039\n"
+      "0 훈련 MSE: 0.018564083\n",
+      "1 훈련 MSE: 0.018759882\n",
+      "2 훈련 MSE: 0.018457456\n",
+      "3 훈련 MSE: 0.019239973\n",
+      "0 훈련 MSE: 0.0042280294\n",
+      "1 훈련 MSE: 0.004805386\n",
+      "2 훈련 MSE: 0.004675172\n",
+      "3 훈련 MSE: 0.004376106\n"
      ]
     }
    ],
@@ -714,12 +716,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we can create a Stacked Autoencoder by simply reusing the weights and biases from the Autoencoders we just trained:"
+    "마지막으로 방금전 훈련한 오토인코더의 가중치와 편향을 재사용하여 적층 오토인코더를 만듭니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,14 +738,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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MOCpBZzYxU3TqXmfPnu1qJ06cyF5/8uTJrFrK9On+uWTmTP3nPmpakScvAEA4NC8AQDg0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\nODQvAEA4NC8AQDj/A27TzGrTpYm2AAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x105ba2f60>"
+       "<Figure size 576x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -758,29 +760,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Training one Autoencoder at a time in a single graph"
+    "## 하나의 그래프에서 오토인코더를 따로따로 훈련하기"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another approach is to use a single graph. To do this, we create the graph for the full Stacked Autoencoder, but then we also add operations to train each Autoencoder independently: phase 1 trains the bottom and top layer (ie. the first Autoencoder) and phase 2 trains the two middle layers (ie. the second Autoencoder)."
+    "하나의 그래프를 사용하는 방법도 있습니다. 이 방법은 전체 적층 오토인코더를 위한 그래프를 만들지만 각 오토인코더를 독립적으로 훈련하기 위한 연산도 추가합니다. 단계 1은 맨 아래층과 맨 윗층을 훈련하고(즉, 첫 번째 오토인코더), 단계 2는 두 개의 가운데 층을 훈련합니다(즉, 두 번째 오토인코더)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 300\n",
-    "n_hidden2 = 150  # codings\n",
+    "n_hidden2 = 150  # 코딩 유닛\n",
     "n_hidden3 = n_hidden1\n",
     "n_outputs = n_inputs\n",
     "\n",
@@ -818,14 +818,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
     "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
     "\n",
     "with tf.name_scope(\"phase1\"):\n",
-    "    phase1_outputs = tf.matmul(hidden1, weights4) + biases4  # bypass hidden2 and hidden3\n",
+    "    phase1_outputs = tf.matmul(hidden1, weights4) + biases4  # hidden2와 hidden3 통과합니다\n",
     "    phase1_reconstruction_loss = tf.reduce_mean(tf.square(phase1_outputs - X))\n",
     "    phase1_reg_loss = regularizer(weights1) + regularizer(weights4)\n",
     "    phase1_loss = phase1_reconstruction_loss + phase1_reg_loss\n",
@@ -836,15 +836,13 @@
     "    phase2_reg_loss = regularizer(weights2) + regularizer(weights3)\n",
     "    phase2_loss = phase2_reconstruction_loss + phase2_reg_loss\n",
     "    train_vars = [weights2, biases2, weights3, biases3]\n",
-    "    phase2_training_op = optimizer.minimize(phase2_loss, var_list=train_vars) # freeze hidden1"
+    "    phase2_training_op = optimizer.minimize(phase2_loss, var_list=train_vars) # hidden1 동결"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [],
    "source": [
     "init = tf.global_variables_initializer()\n",
@@ -853,24 +851,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training phase #1\n",
-      "0 Train MSE: 0.00740679\n",
-      "1 Train MSE: 0.00782866\n",
-      "2 Train MSE: 0.00772802\n",
-      "3 Train MSE: 0.00740893\n",
-      "Training phase #2\n",
-      "0 Train MSE: 0.295499\n",
-      "1 Train MSE: 0.00594454\n",
-      "2 Train MSE: 0.00310264\n",
-      "3 Train MSE: 0.00249803\n",
-      "Test MSE: 0.00979144\n"
+      "훈련 단계 #1\n",
+      "0 훈련 MSE: 0.007406866\n",
+      "1 훈련 MSE: 0.0078288205\n",
+      "2 훈련 MSE: 0.007728097\n",
+      "3 훈련 MSE: 0.0074090064\n",
+      "훈련 단계 #2\n",
+      "0 훈련 MSE: 0.35758996\n",
+      "1 훈련 MSE: 0.00593613\n",
+      "2 훈련 MSE: 0.0030089526\n",
+      "3 훈련 MSE: 0.0024365915\n",
+      "테스트 MSE: 0.009815396\n"
      ]
     }
    ],
@@ -883,7 +881,7 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for phase in range(2):\n",
-    "        print(\"Training phase #{}\".format(phase + 1))\n",
+    "        print(\"훈련 단계 #{}\".format(phase + 1))\n",
     "        for epoch in range(n_epochs[phase]):\n",
     "            n_batches = mnist.train.num_examples // batch_sizes[phase]\n",
     "            for iteration in range(n_batches):\n",
@@ -892,22 +890,22 @@
     "                X_batch, y_batch = mnist.train.next_batch(batch_sizes[phase])\n",
     "                sess.run(training_ops[phase], feed_dict={X: X_batch})\n",
     "            loss_train = reconstruction_losses[phase].eval(feed_dict={X: X_batch})\n",
-    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "            print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "            saver.save(sess, \"./my_model_one_at_a_time.ckpt\")\n",
     "    loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n",
-    "    print(\"Test MSE:\", loss_test)"
+    "    print(\"테스트 MSE:\", loss_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cache the frozen layer outputs"
+    "## 동결 층의 출력을 캐싱하기"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "scrolled": true
    },
@@ -916,17 +914,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training phase #1\n",
-      "0 Train MSE: 0.00753817\n",
-      "1 Train MSE: 0.00775457\n",
-      "2 Train MSE: 0.00734359\n",
-      "3 Train MSE: 0.00783768\n",
-      "Training phase #2\n",
-      "0 Train MSE: 0.200137\n",
-      "1 Train MSE: 0.00520852\n",
-      "2 Train MSE: 0.00259211\n",
-      "3 Train MSE: 0.00210128\n",
-      "Test MSE: 0.0097786\n"
+      "훈련 단계 #1\n",
+      "0 훈련 MSE: 0.0075382586\n",
+      "1 훈련 MSE: 0.007754701\n",
+      "2 훈련 MSE: 0.007343679\n",
+      "3 훈련 MSE: 0.007837775\n",
+      "훈련 단계 #2\n",
+      "0 훈련 MSE: 0.18597357\n",
+      "1 훈련 MSE: 0.0044647465\n",
+      "2 훈련 MSE: 0.002462252\n",
+      "3 훈련 MSE: 0.0020415403\n",
+      "테스트 MSE: 0.009790834\n"
      ]
     }
    ],
@@ -939,7 +937,7 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for phase in range(2):\n",
-    "        print(\"Training phase #{}\".format(phase + 1))\n",
+    "        print(\"훈련 단계 #{}\".format(phase + 1))\n",
     "        if phase == 1:\n",
     "            hidden1_cache = hidden1.eval(feed_dict={X: mnist.train.images})\n",
     "        for epoch in range(n_epochs[phase]):\n",
@@ -957,22 +955,22 @@
     "                    feed_dict = {X: X_batch}\n",
     "                    sess.run(training_ops[phase], feed_dict=feed_dict)\n",
     "            loss_train = reconstruction_losses[phase].eval(feed_dict=feed_dict)\n",
-    "            print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "            print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "            saver.save(sess, \"./my_model_cache_frozen.ckpt\")\n",
     "    loss_test = reconstruction_loss.eval(feed_dict={X: mnist.test.images})\n",
-    "    print(\"Test MSE:\", loss_test)"
+    "    print(\"테스트 MSE:\", loss_test)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualizing the Reconstructions"
+    "## 재구성 시각화"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -984,9 +982,9 @@
     },
     {
      "data": {
-      "image/png": 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111yjYjNmzIhjuXQzhBBuuummOL7uuutUbP/+/XFcX1+vYnV1depYxm0bTO/evePY/luS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\nggBcYxIE4BqTIADXmAQBuMYkCMC1/wBFeVZKJWQkbwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdd169fe6a0>"
+       "<Figure size 432x288 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -1016,27 +1014,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Visualizing the extracted features"
+    "## 특성 시각화"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n",
-      "Saving figure extracted_features_plot\n"
+      "INFO:tensorflow:Restoring parameters from ./my_model_one_at_a_time.ckpt\n"
      ]
     },
     {
      "data": {
-      "image/png": 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VikePHpV7JRtiRdTU1JSwecrxpZdewjPPPAMAkb5pKysr8p3MSw0NDQmj5TGWs2ez2dhX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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdd1cf348d0>"
+       "<Figure size 432x288 with 5 Axes>"
       ]
      },
      "metadata": {},
@@ -1045,34 +1042,34 @@
    ],
    "source": [
     "with tf.Session() as sess:\n",
-    "    saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # not shown in the book\n",
+    "    saver.restore(sess, \"./my_model_one_at_a_time.ckpt\") # 책에는 없음\n",
     "    weights1_val = weights1.eval()\n",
     "\n",
     "for i in range(5):\n",
     "    plt.subplot(1, 5, i + 1)\n",
     "    plot_image(weights1_val.T[i])\n",
     "\n",
-    "save_fig(\"extracted_features_plot\") # not shown\n",
-    "plt.show()                          # not shown"
+    "save_fig(\"extracted_features_plot\") # 책에는 없음\n",
+    "plt.show()                          # 책에는 없음"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Unsupervised pretraining"
+    "# 비지도 사전훈련"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a small neural network for MNIST classification:"
+    "MNIST 분류 문제를 위한 작은 신경망을 만들겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1127,22 +1124,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Regular training (without pretraining):"
+    "(사전훈련 없이)평범하게 훈련시킵니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train accuracy: 0.973333 Test accuracy: 0.9334\n",
-      "1 Train accuracy: 0.98 Test accuracy: 0.936\n",
-      "2 Train accuracy: 0.973333 Test accuracy: 0.9382\n",
-      "3 Train accuracy: 0.986667 Test accuracy: 0.9494\n"
+      "0 훈련 정확도: 0.97333336 테스트 정확도: 0.9334\n",
+      "1 훈련 정확도: 0.98 테스트 정확도: 0.936\n",
+      "2 훈련 정확도: 0.97333336 테스트 정확도: 0.9382\n",
+      "3 훈련 정확도: 0.9866667 테스트 정확도: 0.9489\n"
      ]
     }
    ],
@@ -1162,22 +1159,22 @@
     "            X_batch, y_batch = mnist.train.images[indices], mnist.train.labels[indices]\n",
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
     "        accuracy_val = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train accuracy:\", accuracy_val, end=\" \")\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 정확도:\", accuracy_val, end=\" \")\n",
     "        saver.save(sess, \"./my_model_supervised.ckpt\")\n",
     "        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\n",
-    "        print(\"Test accuracy:\", accuracy_val)"
+    "        print(\"테스트 정확도:\", accuracy_val)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now reusing the first two layers of the autoencoder we pretrained:"
+    "사전 훈련된 오토인코더의 첫 두개의 층을 재사용해 보겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1185,10 +1182,10 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_cache_frozen.ckpt\n",
-      "0 Train accuracy: 0.94\tTest accuracy: 0.9266\n",
-      "1 Train accuracy: 0.98\tTest accuracy: 0.94\n",
-      "2 Train accuracy: 1.0\tTest accuracy: 0.946\n",
-      "3 Train accuracy: 0.98\tTest accuracy: 0.9401\n"
+      "0 훈련 정확도: 0.9533333\t테스트 정확도: 0.9294\n",
+      "1 훈련 정확도: 0.96666664\t테스트 정확도: 0.9342\n",
+      "2 훈련 정확도: 0.98\t테스트 정확도: 0.9449\n",
+      "3 훈련 정확도: 0.9866667\t테스트 정확도: 0.9462\n"
      ]
     }
    ],
@@ -1197,7 +1194,7 @@
     "batch_size = 150\n",
     "n_labeled_instances = 20000\n",
     "\n",
-    "#training_op = optimizer.minimize(loss, var_list=[weights3, biases3])  # Freeze layers 1 and 2 (optional)\n",
+    "#training_op = optimizer.minimize(loss, var_list=[weights3, biases3])  # layers 1와 2를 동결 (선택사항)\n",
     "\n",
     "with tf.Session() as sess:\n",
     "    init.run()\n",
@@ -1211,48 +1208,37 @@
     "            X_batch, y_batch = mnist.train.images[indices], mnist.train.labels[indices]\n",
     "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
     "        accuracy_val = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train accuracy:\", accuracy_val, end=\"\\t\")\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 정확도:\", accuracy_val, end=\"\\t\")\n",
     "        saver.save(sess, \"./my_model_supervised_pretrained.ckpt\")\n",
     "        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, y: mnist.test.labels})\n",
-    "        print(\"Test accuracy:\", accuracy_val)"
+    "        print(\"테스트 정확도:\", accuracy_val)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Stacked denoising Autoencoder"
+    "# 적층 잡음제거 오토인코더"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: the book uses `tf.contrib.layers.dropout()` rather than `tf.layers.dropout()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dropout()`, because anything in the contrib module may change or be deleted without notice. The `tf.layers.dropout()` function is almost identical to the `tf.contrib.layers.dropout()` function, except for a few minor differences. Most importantly:\n",
-    "* you must specify the dropout rate (`rate`) rather than the keep probability (`keep_prob`), where `rate` is simply equal to `1 - keep_prob`,\n",
-    "* the `is_training` parameter is renamed to `training`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using Gaussian noise:"
+    "가우시안 잡음을 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 300\n",
-    "n_hidden2 = 150  # codings\n",
+    "n_hidden2 = 150  # 코딩 유닛\n",
     "n_hidden3 = n_hidden1\n",
     "n_outputs = n_inputs\n",
     "\n",
@@ -1261,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1272,18 +1258,18 @@
     "\n",
     "hidden1 = tf.layers.dense(X_noisy, n_hidden1, activation=tf.nn.relu,\n",
     "                          name=\"hidden1\")\n",
-    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n",
-    "                          name=\"hidden2\")                            # not shown\n",
-    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n",
-    "                          name=\"hidden3\")                            # not shown\n",
-    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # not shown\n",
+    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # 책에는 없음\n",
+    "                          name=\"hidden2\")                            # 책에는 없음\n",
+    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # 책에는 없음\n",
+    "                          name=\"hidden3\")                            # 책에는 없음\n",
+    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # 책에는 없음\n",
     "\n",
     "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1296,23 +1282,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0440489\n",
-      "1 Train MSE: 0.0432517\n",
-      "2 Train MSE: 0.042057\n",
-      "3 Train MSE: 0.0409477\n",
-      "4 Train MSE: 0.0402107\n",
-      "5 Train MSE: 0.0388787\n",
-      "6 Train MSE: 0.0391096\n",
-      "7 Train MSE: 0.0421885\n",
-      "8 Train MSE: 0.0398648\n",
-      "9 Train MSE: 0.0408181\n"
+      "0 훈련 MSE: 0.046190277\n",
+      "1 훈련 MSE: 0.044343986\n",
+      "2 훈련 MSE: 0.04200526\n",
+      "3 훈련 MSE: 0.040973965\n",
+      "4 훈련 MSE: 0.040393338\n",
+      "5 훈련 MSE: 0.038924493\n",
+      "6 훈련 MSE: 0.0394448\n",
+      "7 훈련 MSE: 0.042411502\n",
+      "8 훈련 MSE: 0.039848443\n",
+      "9 훈련 MSE: 0.04152445\n"
      ]
     }
    ],
@@ -1330,7 +1316,7 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "        saver.save(sess, \"./my_model_stacked_denoising_gaussian.ckpt\")"
    ]
   },
@@ -1338,22 +1324,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using dropout:"
+    "드롭아웃을 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 300\n",
-    "n_hidden2 = 150  # codings\n",
+    "n_hidden2 = 150  # 코딩 유닛\n",
     "n_hidden3 = n_hidden1\n",
     "n_outputs = n_inputs\n",
     "\n",
@@ -1362,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1375,18 +1359,18 @@
     "\n",
     "hidden1 = tf.layers.dense(X_drop, n_hidden1, activation=tf.nn.relu,\n",
     "                          name=\"hidden1\")\n",
-    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n",
-    "                          name=\"hidden2\")                            # not shown\n",
-    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n",
-    "                          name=\"hidden3\")                            # not shown\n",
-    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # not shown\n",
+    "hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # 책에는 없음\n",
+    "                          name=\"hidden2\")                            # 책에는 없음\n",
+    "hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # 책에는 없음\n",
+    "                          name=\"hidden3\")                            # 책에는 없음\n",
+    "outputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\")        # 책에는 없음\n",
     "\n",
     "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,23 +1383,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.0296476\n",
-      "1 Train MSE: 0.0275545\n",
-      "2 Train MSE: 0.0250731\n",
-      "3 Train MSE: 0.0254317\n",
-      "4 Train MSE: 0.0249076\n",
-      "5 Train MSE: 0.0250501\n",
-      "6 Train MSE: 0.024483\n",
-      "7 Train MSE: 0.0251505\n",
-      "8 Train MSE: 0.0243836\n",
-      "9 Train MSE: 0.0242349\n"
+      "0 훈련 MSE: 0.027500158\n",
+      "1 훈련 MSE: 0.02639269\n",
+      "2 훈련 MSE: 0.022876455\n",
+      "3 훈련 MSE: 0.0229337\n",
+      "4 훈련 MSE: 0.022025634\n",
+      "5 훈련 MSE: 0.021996608\n",
+      "6 훈련 MSE: 0.021385597\n",
+      "7 훈련 MSE: 0.021463407\n",
+      "8 훈련 MSE: 0.021037336\n",
+      "9 훈련 MSE: 0.02082274\n"
      ]
     }
    ],
@@ -1433,13 +1417,13 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch, training: True})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
     "        saver.save(sess, \"./my_model_stacked_denoising_dropout.ckpt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1451,9 +1435,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f846a1b11d0>"
+       "<Figure size 576x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -1468,26 +1452,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sparse Autoencoder"
+    "# 희소 오토인코더"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure sparsity_loss_plot\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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Y1U5MTCQwMJBNmzZd8Vjq8yn/EBUFHTrAv/4FDz3kdDReRsu555vw8HDCw8Nz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\nAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f846b182e48>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1500,51 +1477,49 @@
     "kl_div = p * np.log(p / q) + (1 - p) * np.log((1 - p) / (1 - q))\n",
     "mse = (p - q)**2\n",
     "plt.plot([p, p], [0, 0.3], \"k:\")\n",
-    "plt.text(0.05, 0.32, \"Target\\nsparsity\", fontsize=14)\n",
-    "plt.plot(q, kl_div, \"b-\", label=\"KL divergence\")\n",
+    "plt.text(0.05, 0.32, \"목표 희소\", fontsize=14)\n",
+    "plt.plot(q, kl_div, \"b-\", label=\"쿨백 라이블러 발산\")\n",
     "plt.plot(q, mse, \"r--\", label=\"MSE\")\n",
     "plt.legend(loc=\"upper left\")\n",
-    "plt.xlabel(\"Actual sparsity\")\n",
-    "plt.ylabel(\"Cost\", rotation=0)\n",
+    "plt.xlabel(\"실제 희소\")\n",
+    "plt.ylabel(\"비용\", rotation=0)\n",
     "plt.axis([0, 1, 0, 0.95])\n",
     "save_fig(\"sparsity_loss_plot\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "reset_graph()\n",
-    "\n",
-    "n_inputs = 28 * 28\n",
-    "n_hidden1 = 1000  # sparse codings\n",
-    "n_outputs = n_inputs"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "reset_graph()\n",
+    "\n",
+    "n_inputs = 28 * 28\n",
+    "n_hidden1 = 1000  # 희소 코딩 유닛\n",
+    "n_outputs = n_inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def kl_divergence(p, q):\n",
-    "    # Kullback Leibler divergence\n",
+    "    # 쿨백 라이블러 발산\n",
     "    return p * tf.log(p / q) + (1 - p) * tf.log((1 - p) / (1 - q))\n",
     "\n",
     "learning_rate = 0.01\n",
     "sparsity_target = 0.1\n",
     "sparsity_weight = 0.2\n",
     "\n",
-    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])            # not shown in the book\n",
+    "X = tf.placeholder(tf.float32, shape=[None, n_inputs])            # 책에는 없음\n",
     "\n",
-    "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid) # not shown\n",
-    "outputs = tf.layers.dense(hidden1, n_outputs)                     # not shown\n",
+    "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid) # 책에는 없음\n",
+    "outputs = tf.layers.dense(hidden1, n_outputs)                     # 책에는 없음\n",
     "\n",
-    "hidden1_mean = tf.reduce_mean(hidden1, axis=0) # batch mean\n",
+    "hidden1_mean = tf.reduce_mean(hidden1, axis=0) # 배치 평균\n",
     "sparsity_loss = tf.reduce_sum(kl_divergence(sparsity_target, hidden1_mean))\n",
     "reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE\n",
     "loss = reconstruction_loss + sparsity_weight * sparsity_loss\n",
@@ -1555,10 +1530,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [],
    "source": [
     "init = tf.global_variables_initializer()\n",
@@ -1567,113 +1540,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train MSE: 0.134832 \tSparsity loss: 0.421739 \tTotal loss: 0.21918\n",
-      "1 Train MSE: 0.0587859 \tSparsity loss: 0.0108979 \tTotal loss: 0.0609655\n",
-      "2 Train MSE: 0.053738 \tSparsity loss: 0.0201038 \tTotal loss: 0.0577588\n",
-      "3 Train MSE: 0.0476169 \tSparsity loss: 0.0399679 \tTotal loss: 0.0556105\n",
-      "4 Train MSE: 0.0447499 \tSparsity loss: 0.0116199 \tTotal loss: 0.0470739\n",
-      "5 Train MSE: 0.0403685 \tSparsity loss: 0.0930409 \tTotal loss: 0.0589767\n",
-      "6 Train MSE: 0.0388338 \tSparsity loss: 0.0462908 \tTotal loss: 0.048092\n",
-      "7 Train MSE: 0.0378196 \tSparsity loss: 0.0758871 \tTotal loss: 0.052997\n",
-      "8 Train MSE: 0.0332092 \tSparsity loss: 0.0200693 \tTotal loss: 0.037223\n",
-      "9 Train MSE: 0.0314318 \tSparsity loss: 0.0965061 \tTotal loss: 0.050733\n",
-      "10 Train MSE: 0.0273777 \tSparsity loss: 0.0670885 \tTotal loss: 0.0407954\n",
-      "11 Train MSE: 0.0246779 \tSparsity loss: 0.0900828 \tTotal loss: 0.0426945\n",
-      "12 Train MSE: 0.0233311 \tSparsity loss: 0.0577432 \tTotal loss: 0.0348797\n",
-      "13 Train MSE: 0.0228954 \tSparsity loss: 0.0623308 \tTotal loss: 0.0353615\n",
-      "14 Train MSE: 0.0210913 \tSparsity loss: 0.0258186 \tTotal loss: 0.026255\n",
-      "15 Train MSE: 0.0220006 \tSparsity loss: 0.483207 \tTotal loss: 0.118642\n",
-      "16 Train MSE: 0.0190526 \tSparsity loss: 0.0361403 \tTotal loss: 0.0262806\n",
-      "17 Train MSE: 0.0188885 \tSparsity loss: 0.132695 \tTotal loss: 0.0454275\n",
-      "18 Train MSE: 0.0174156 \tSparsity loss: 0.0403093 \tTotal loss: 0.0254774\n",
-      "19 Train MSE: 0.0178612 \tSparsity loss: 0.110486 \tTotal loss: 0.0399584\n",
-      "20 Train MSE: 0.0168293 \tSparsity loss: 0.0291402 \tTotal loss: 0.0226573\n",
-      "21 Train MSE: 0.0183871 \tSparsity loss: 0.364209 \tTotal loss: 0.0912289\n",
-      "22 Train MSE: 0.0161226 \tSparsity loss: 0.0556278 \tTotal loss: 0.0272482\n",
-      "23 Train MSE: 0.0158919 \tSparsity loss: 0.0792573 \tTotal loss: 0.0317434\n",
-      "24 Train MSE: 0.0157006 \tSparsity loss: 0.149254 \tTotal loss: 0.0455514\n",
-      "25 Train MSE: 0.0145307 \tSparsity loss: 0.136184 \tTotal loss: 0.0417676\n",
-      "26 Train MSE: 0.0144209 \tSparsity loss: 0.110554 \tTotal loss: 0.0365316\n",
-      "27 Train MSE: 0.0138508 \tSparsity loss: 0.0744676 \tTotal loss: 0.0287443\n",
-      "28 Train MSE: 0.0139305 \tSparsity loss: 0.158476 \tTotal loss: 0.0456257\n",
-      "29 Train MSE: 0.0133762 \tSparsity loss: 0.143838 \tTotal loss: 0.0421438\n",
-      "30 Train MSE: 0.0137258 \tSparsity loss: 0.185643 \tTotal loss: 0.0508544\n",
-      "31 Train MSE: 0.0139518 \tSparsity loss: 0.0635133 \tTotal loss: 0.0266544\n",
-      "32 Train MSE: 0.013692 \tSparsity loss: 0.0577956 \tTotal loss: 0.0252512\n",
-      "33 Train MSE: 0.0134704 \tSparsity loss: 0.104171 \tTotal loss: 0.0343045\n",
-      "34 Train MSE: 0.0124406 \tSparsity loss: 0.136569 \tTotal loss: 0.0397544\n",
-      "35 Train MSE: 0.0126563 \tSparsity loss: 0.162903 \tTotal loss: 0.0452369\n",
-      "36 Train MSE: 0.0128764 \tSparsity loss: 0.0948648 \tTotal loss: 0.0318493\n",
-      "37 Train MSE: 0.0123458 \tSparsity loss: 0.108087 \tTotal loss: 0.0339632\n",
-      "38 Train MSE: 0.0121672 \tSparsity loss: 0.20089 \tTotal loss: 0.0523451\n",
-      "39 Train MSE: 0.0122532 \tSparsity loss: 0.149409 \tTotal loss: 0.0421351\n",
-      "40 Train MSE: 0.0125975 \tSparsity loss: 0.230649 \tTotal loss: 0.0587274\n",
-      "41 Train MSE: 0.0124657 \tSparsity loss: 0.100664 \tTotal loss: 0.0325984\n",
-      "42 Train MSE: 0.0116947 \tSparsity loss: 0.101108 \tTotal loss: 0.0319164\n",
-      "43 Train MSE: 0.0122123 \tSparsity loss: 0.125789 \tTotal loss: 0.0373701\n",
-      "44 Train MSE: 0.0117173 \tSparsity loss: 0.110301 \tTotal loss: 0.0337774\n",
-      "45 Train MSE: 0.0116897 \tSparsity loss: 0.165081 \tTotal loss: 0.0447058\n",
-      "46 Train MSE: 0.011611 \tSparsity loss: 0.130426 \tTotal loss: 0.0376962\n",
-      "47 Train MSE: 0.0117358 \tSparsity loss: 0.163508 \tTotal loss: 0.0444374\n",
-      "48 Train MSE: 0.0116507 \tSparsity loss: 0.459586 \tTotal loss: 0.103568\n",
-      "49 Train MSE: 0.0116655 \tSparsity loss: 0.188114 \tTotal loss: 0.0492883\n",
-      "50 Train MSE: 0.0114275 \tSparsity loss: 0.19221 \tTotal loss: 0.0498695\n",
-      "51 Train MSE: 0.0113954 \tSparsity loss: 0.266457 \tTotal loss: 0.0646867\n",
-      "52 Train MSE: 0.0119332 \tSparsity loss: 0.379985 \tTotal loss: 0.0879303\n",
-      "53 Train MSE: 0.0113018 \tSparsity loss: 0.129771 \tTotal loss: 0.037256\n",
-      "54 Train MSE: 0.0153057 \tSparsity loss: 0.434827 \tTotal loss: 0.102271\n",
-      "55 Train MSE: 0.0134004 \tSparsity loss: 0.0833025 \tTotal loss: 0.0300609\n",
-      "56 Train MSE: 0.0123188 \tSparsity loss: 0.297605 \tTotal loss: 0.0718399\n",
-      "57 Train MSE: 0.0122943 \tSparsity loss: 0.247148 \tTotal loss: 0.061724\n",
-      "58 Train MSE: 0.0239939 \tSparsity loss: 0.215717 \tTotal loss: 0.0671373\n",
-      "59 Train MSE: 0.013596 \tSparsity loss: 0.203553 \tTotal loss: 0.0543065\n",
-      "60 Train MSE: 0.0179108 \tSparsity loss: 0.180449 \tTotal loss: 0.0540006\n",
-      "61 Train MSE: 0.0132258 \tSparsity loss: 0.231824 \tTotal loss: 0.0595906\n",
-      "62 Train MSE: 0.0136533 \tSparsity loss: 0.498429 \tTotal loss: 0.113339\n",
-      "63 Train MSE: 0.0143277 \tSparsity loss: 0.333901 \tTotal loss: 0.0811079\n",
-      "64 Train MSE: 0.0119968 \tSparsity loss: 0.176848 \tTotal loss: 0.0473664\n",
-      "65 Train MSE: 0.0156422 \tSparsity loss: 0.173917 \tTotal loss: 0.0504256\n",
-      "66 Train MSE: 0.0150095 \tSparsity loss: 1.02187 \tTotal loss: 0.219383\n",
-      "67 Train MSE: 0.036823 \tSparsity loss: 0.323619 \tTotal loss: 0.101547\n",
-      "68 Train MSE: 0.0148193 \tSparsity loss: 0.230714 \tTotal loss: 0.060962\n",
-      "69 Train MSE: 0.0126409 \tSparsity loss: 0.454552 \tTotal loss: 0.103551\n",
-      "70 Train MSE: 0.045501 \tSparsity loss: 0.745102 \tTotal loss: 0.194521\n",
-      "71 Train MSE: 0.0143786 \tSparsity loss: 0.229362 \tTotal loss: 0.060251\n",
-      "72 Train MSE: 0.0151026 \tSparsity loss: 0.826014 \tTotal loss: 0.180306\n",
-      "73 Train MSE: 0.0136122 \tSparsity loss: 0.316737 \tTotal loss: 0.0769596\n",
-      "74 Train MSE: 0.0309757 \tSparsity loss: 0.289552 \tTotal loss: 0.0888861\n",
-      "75 Train MSE: 0.0304744 \tSparsity loss: 0.489417 \tTotal loss: 0.128358\n",
-      "76 Train MSE: 0.0204102 \tSparsity loss: 0.201982 \tTotal loss: 0.0608067\n",
-      "77 Train MSE: 0.0211023 \tSparsity loss: 0.32347 \tTotal loss: 0.0857964\n",
-      "78 Train MSE: 0.0178777 \tSparsity loss: 0.533425 \tTotal loss: 0.124563\n",
-      "79 Train MSE: 0.018841 \tSparsity loss: 0.424661 \tTotal loss: 0.103773\n",
-      "80 Train MSE: 0.0159234 \tSparsity loss: 0.115559 \tTotal loss: 0.0390352\n",
-      "81 Train MSE: 0.0129649 \tSparsity loss: 0.912508 \tTotal loss: 0.195467\n",
-      "82 Train MSE: 0.0162278 \tSparsity loss: 2.17347 \tTotal loss: 0.450922\n",
-      "83 Train MSE: 0.0146708 \tSparsity loss: 0.681089 \tTotal loss: 0.150889\n",
-      "84 Train MSE: 0.0150686 \tSparsity loss: 0.292309 \tTotal loss: 0.0735305\n",
-      "85 Train MSE: 0.0250247 \tSparsity loss: 0.949989 \tTotal loss: 0.215023\n",
-      "86 Train MSE: 0.0146914 \tSparsity loss: 0.685326 \tTotal loss: 0.151757\n",
-      "87 Train MSE: 0.0122667 \tSparsity loss: 1.44823 \tTotal loss: 0.301912\n",
-      "88 Train MSE: 0.0197259 \tSparsity loss: 0.861047 \tTotal loss: 0.191935\n",
-      "89 Train MSE: 0.0331342 \tSparsity loss: 0.291833 \tTotal loss: 0.0915009\n",
-      "90 Train MSE: 0.0295548 \tSparsity loss: 0.445159 \tTotal loss: 0.118587\n",
-      "91 Train MSE: 0.0145762 \tSparsity loss: 0.0887034 \tTotal loss: 0.0323169\n",
-      "92 Train MSE: 0.0147775 \tSparsity loss: 0.390856 \tTotal loss: 0.0929486\n",
-      "93 Train MSE: 0.0166543 \tSparsity loss: 0.155326 \tTotal loss: 0.0477195\n",
-      "94 Train MSE: 0.012198 \tSparsity loss: 0.12071 \tTotal loss: 0.03634\n",
-      "95 Train MSE: 0.0141104 \tSparsity loss: 0.107212 \tTotal loss: 0.0355529\n",
-      "96 Train MSE: 0.018834 \tSparsity loss: 0.230255 \tTotal loss: 0.0648851\n",
-      "97 Train MSE: 0.0134663 \tSparsity loss: 0.102045 \tTotal loss: 0.0338754\n",
-      "98 Train MSE: 0.013678 \tSparsity loss: 0.0839055 \tTotal loss: 0.0304591\n",
-      "99 Train MSE: 0.0245401 \tSparsity loss: 0.335841 \tTotal loss: 0.0917084\n"
+      "0 훈련 MSE: 0.13819474 \t희소 손실: 0.41844434 \t전체 손실: 0.22188361\n",
+      "1 훈련 MSE: 0.059185907 \t희소 손실: 0.010747161 \t전체 손실: 0.06133534\n",
+      "2 훈련 MSE: 0.053931113 \t희소 손실: 0.019957827 \t전체 손실: 0.05792268\n",
+      "3 훈련 MSE: 0.04771372 \t희소 손실: 0.039470375 \t전체 손실: 0.055607796\n",
+      "4 훈련 MSE: 0.044841796 \t희소 손실: 0.011593379 \t전체 손실: 0.047160473\n",
+      "5 훈련 MSE: 0.04044193 \t희소 손실: 0.09246979 \t전체 손실: 0.058935888\n",
+      "6 훈련 MSE: 0.03886659 \t희소 손실: 0.045811094 \t전체 손실: 0.04802881\n",
+      "7 훈련 MSE: 0.037868652 \t희소 손실: 0.07498116 \t전체 손실: 0.052864887\n",
+      "8 훈련 MSE: 0.033122517 \t희소 손실: 0.020321798 \t전체 손실: 0.037186876\n",
+      "9 훈련 MSE: 0.031449173 \t희소 손실: 0.095670365 \t전체 손실: 0.050583243\n",
+      "10 훈련 MSE: 0.027398113 \t희소 손실: 0.065706335 \t전체 손실: 0.04053938\n",
+      "11 훈련 MSE: 0.024734963 \t희소 손실: 0.08889374 \t전체 손실: 0.042513713\n",
+      "12 훈련 MSE: 0.023367295 \t희소 손실: 0.0576614 \t전체 손실: 0.034899574\n",
+      "13 훈련 MSE: 0.023007901 \t희소 손실: 0.061935436 \t전체 손실: 0.03539499\n",
+      "14 훈련 MSE: 0.02123353 \t희소 손실: 0.025881436 \t전체 손실: 0.026409818\n",
+      "15 훈련 MSE: 0.02206469 \t희소 손실: 0.48820436 \t전체 손실: 0.11970556\n",
+      "16 훈련 MSE: 0.01910253 \t희소 손실: 0.032202266 \t전체 손실: 0.025542984\n",
+      "17 훈련 MSE: 0.01898219 \t희소 손실: 0.12995036 \t전체 손실: 0.044972263\n",
+      "18 훈련 MSE: 0.017487913 \t희소 손실: 0.035002217 \t전체 손실: 0.024488356\n",
+      "19 훈련 MSE: 0.017776337 \t희소 손실: 0.11024549 \t전체 손실: 0.039825432\n",
+      "20 훈련 MSE: 0.016910141 \t희소 손실: 0.030320741 \t전체 손실: 0.02297429\n",
+      "21 훈련 MSE: 0.018679725 \t희소 손실: 0.36376768 \t전체 손실: 0.091433264\n",
+      "22 훈련 MSE: 0.016285753 \t희소 손실: 0.055482324 \t전체 손실: 0.027382217\n",
+      "23 훈련 MSE: 0.015950203 \t희소 손실: 0.079365596 \t전체 손실: 0.031823322\n",
+      "24 훈련 MSE: 0.015672017 \t희소 손실: 0.14289412 \t전체 손실: 0.04425084\n",
+      "25 훈련 MSE: 0.014612305 \t희소 손실: 0.13330609 \t전체 손실: 0.041273523\n",
+      "26 훈련 MSE: 0.014524785 \t희소 손실: 0.10948229 \t전체 손실: 0.036421243\n",
+      "27 훈련 MSE: 0.013598451 \t희소 손실: 0.07208246 \t전체 손실: 0.028014943\n",
+      "28 훈련 MSE: 0.014069845 \t희소 손실: 0.15789555 \t전체 손실: 0.045648955\n",
+      "29 훈련 MSE: 0.013441612 \t희소 손실: 0.14449221 \t전체 손실: 0.042340055\n",
+      "30 훈련 MSE: 0.013608929 \t희소 손실: 0.18507166 \t전체 손실: 0.05062326\n",
+      "31 훈련 MSE: 0.014197283 \t희소 손실: 0.06788807 \t전체 손실: 0.027774898\n",
+      "32 훈련 MSE: 0.013759435 \t희소 손실: 0.057576656 \t전체 손실: 0.025274767\n",
+      "33 훈련 MSE: 0.013667367 \t희소 손실: 0.11363616 \t전체 손실: 0.0363946\n",
+      "34 훈련 MSE: 0.0126469685 \t희소 손실: 0.13469556 \t전체 손실: 0.039586082\n",
+      "35 훈련 MSE: 0.012872889 \t희소 손실: 0.16850437 \t전체 손실: 0.046573766\n",
+      "36 훈련 MSE: 0.012669464 \t희소 손실: 0.09313129 \t전체 손실: 0.03129572\n",
+      "37 훈련 MSE: 0.012427666 \t희소 손실: 0.105387196 \t전체 손실: 0.033505104\n",
+      "38 훈련 MSE: 0.01233604 \t희소 손실: 0.19235855 \t전체 손실: 0.05080775\n",
+      "39 훈련 MSE: 0.012337869 \t희소 손실: 0.14231703 \t전체 손실: 0.040801276\n",
+      "40 훈련 MSE: 0.012626693 \t희소 손실: 0.23267308 \t전체 손실: 0.05916131\n",
+      "41 훈련 MSE: 0.012383292 \t희소 손실: 0.104892224 \t전체 손실: 0.033361737\n",
+      "42 훈련 MSE: 0.011781142 \t희소 손실: 0.10161172 \t전체 손실: 0.032103486\n",
+      "43 훈련 MSE: 0.012044032 \t희소 손실: 0.1359405 \t전체 손실: 0.039232135\n",
+      "44 훈련 MSE: 0.011620717 \t희소 손실: 0.11472766 \t전체 손실: 0.03456625\n",
+      "45 훈련 MSE: 0.011669101 \t희소 손실: 0.15798101 \t전체 손실: 0.043265305\n",
+      "46 훈련 MSE: 0.01167006 \t희소 손실: 0.13549636 \t전체 손실: 0.038769335\n",
+      "47 훈련 MSE: 0.011549011 \t희소 손실: 0.15662336 \t전체 손실: 0.042873684\n",
+      "48 훈련 MSE: 0.011729614 \t희소 손실: 0.4430182 \t전체 손실: 0.10033325\n",
+      "49 훈련 MSE: 0.011562036 \t희소 손실: 0.17581894 \t전체 손실: 0.046725824\n",
+      "50 훈련 MSE: 0.01135992 \t희소 손실: 0.18881696 \t전체 손실: 0.049123313\n",
+      "51 훈련 MSE: 0.0115385335 \t희소 손실: 0.26335293 \t전체 손실: 0.06420912\n",
+      "52 훈련 MSE: 0.0120425625 \t희소 손실: 0.3675837 \t전체 손실: 0.0855593\n",
+      "53 훈련 MSE: 0.011238733 \t희소 손실: 0.12488687 \t전체 손실: 0.03621611\n",
+      "54 훈련 MSE: 0.013967819 \t희소 손실: 0.43314826 \t전체 손실: 0.10059747\n",
+      "55 훈련 MSE: 0.012766779 \t희소 손실: 0.07719517 \t전체 손실: 0.028205812\n",
+      "56 훈련 MSE: 0.012239235 \t희소 손실: 0.30152494 \t전체 손실: 0.072544225\n",
+      "57 훈련 MSE: 0.012724373 \t희소 손실: 0.24817757 \t전체 손실: 0.062359888\n",
+      "58 훈련 MSE: 0.023815982 \t희소 손실: 0.2323372 \t전체 손실: 0.07028343\n",
+      "59 훈련 MSE: 0.013152219 \t희소 손실: 0.19813177 \t전체 손실: 0.052778576\n",
+      "60 훈련 MSE: 0.019204749 \t희소 손실: 0.19425029 \t전체 손실: 0.058054805\n",
+      "61 훈련 MSE: 0.01287946 \t희소 손실: 0.22912267 \t전체 손실: 0.058703996\n",
+      "62 훈련 MSE: 0.014201862 \t희소 손실: 0.47936517 \t전체 손실: 0.1100749\n",
+      "63 훈련 MSE: 0.014247858 \t희소 손실: 0.3340563 \t전체 손실: 0.08105911\n",
+      "64 훈련 MSE: 0.01188848 \t희소 손실: 0.17818896 \t전체 손실: 0.04752627\n",
+      "65 훈련 MSE: 0.016912188 \t희소 손실: 0.18625271 \t전체 손실: 0.054162733\n",
+      "66 훈련 MSE: 0.015350101 \t희소 손실: 0.984547 \t전체 손실: 0.21225952\n",
+      "67 훈련 MSE: 0.037533756 \t희소 손실: 0.31189525 \t전체 손실: 0.09991281\n",
+      "68 훈련 MSE: 0.015359703 \t희소 손실: 0.21824294 \t전체 손실: 0.059008293\n",
+      "69 훈련 MSE: 0.0130188465 \t희소 손실: 0.43986538 \t전체 손실: 0.10099193\n",
+      "70 훈련 MSE: 0.04249404 \t희소 손실: 0.7454435 \t전체 손실: 0.19158275\n",
+      "71 훈련 MSE: 0.014368004 \t희소 손실: 0.24009731 \t전체 손실: 0.06238747\n",
+      "72 훈련 MSE: 0.015802076 \t희소 손실: 0.7972901 \t전체 손실: 0.1752601\n",
+      "73 훈련 MSE: 0.014027514 \t희소 손실: 0.31637588 \t전체 손실: 0.077302694\n",
+      "74 훈련 MSE: 0.033579413 \t희소 손실: 0.32424852 \t전체 손실: 0.09842911\n",
+      "75 훈련 MSE: 0.037583392 \t희소 손실: 0.51026374 \t전체 손실: 0.13963614\n",
+      "76 훈련 MSE: 0.018015897 \t희소 손실: 0.19957174 \t전체 손실: 0.057930246\n",
+      "77 훈련 MSE: 0.019651866 \t희소 손실: 0.30766442 \t전체 손실: 0.08118475\n",
+      "78 훈련 MSE: 0.018113064 \t희소 손실: 0.5368116 \t전체 손실: 0.12547539\n",
+      "79 훈련 MSE: 0.019586327 \t희소 손실: 0.4225994 \t전체 손실: 0.10410621\n",
+      "80 훈련 MSE: 0.016286958 \t희소 손실: 0.116765514 \t전체 손실: 0.03964006\n",
+      "81 훈련 MSE: 0.012678387 \t희소 손실: 0.8773603 \t전체 손실: 0.18815045\n",
+      "82 훈련 MSE: 0.015514646 \t희소 손실: 2.136949 \t전체 손실: 0.44290447\n",
+      "83 훈련 MSE: 0.014462668 \t희소 손실: 0.6624632 \t전체 손실: 0.14695531\n",
+      "84 훈련 MSE: 0.0139609 \t희소 손실: 0.2704364 \t전체 손실: 0.06804818\n",
+      "85 훈련 MSE: 0.02716384 \t희소 손실: 0.93438864 \t전체 손실: 0.21404156\n",
+      "86 훈련 MSE: 0.014590265 \t희소 손실: 0.6826812 \t전체 손실: 0.1511265\n",
+      "87 훈련 MSE: 0.012223512 \t희소 손실: 1.41284 \t전체 손실: 0.29479152\n",
+      "88 훈련 MSE: 0.01925102 \t희소 손실: 0.8619595 \t전체 손실: 0.19164293\n",
+      "89 훈련 MSE: 0.029023234 \t희소 손실: 0.2942158 \t전체 손실: 0.087866396\n",
+      "90 훈련 MSE: 0.03109355 \t희소 손실: 0.45987695 \t전체 손실: 0.12306894\n",
+      "91 훈련 MSE: 0.014538759 \t희소 손실: 0.09905543 \t전체 손실: 0.034349844\n",
+      "92 훈련 MSE: 0.015151837 \t희소 손실: 0.38795644 \t전체 손실: 0.09274313\n",
+      "93 훈련 MSE: 0.01682431 \t희소 손실: 0.1613949 \t전체 손실: 0.04910329\n",
+      "94 훈련 MSE: 0.011993256 \t희소 손실: 0.11752173 \t전체 손실: 0.035497602\n",
+      "95 훈련 MSE: 0.0139113525 \t희소 손실: 0.10376862 \t전체 손실: 0.034665078\n",
+      "96 훈련 MSE: 0.018343525 \t희소 손실: 0.23694368 \t전체 손실: 0.06573226\n",
+      "97 훈련 MSE: 0.013478864 \t희소 손실: 0.10932048 \t전체 손실: 0.03534296\n",
+      "98 훈련 MSE: 0.013734443 \t희소 손실: 0.089936554 \t전체 손실: 0.031721756\n",
+      "99 훈련 MSE: 0.0233883 \t희소 손실: 0.33332965 \t전체 손실: 0.09005423\n"
      ]
     }
    ],
@@ -1691,13 +1664,13 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        reconstruction_loss_val, sparsity_loss_val, loss_val = sess.run([reconstruction_loss, sparsity_loss, loss], feed_dict={X: X_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train MSE:\", reconstruction_loss_val, \"\\tSparsity loss:\", sparsity_loss_val, \"\\tTotal loss:\", loss_val)\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", reconstruction_loss_val, \"\\t희소 손실:\", sparsity_loss_val, \"\\t전체 손실:\", loss_val)\n",
     "        saver.save(sess, \"./my_model_sparse.ckpt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1709,9 +1682,9 @@
     },
     {
      "data": {
-      "image/png": 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optJpKiXouESgSuO5TRHVMdw1qNRaSZLNjUtS16tqbkxRyXgn9Zm761JKvh9H\nvTd33JLRTC5NqnCnBQAIg6YFAAiDpgUACIOmBQAIY0rJAzAAAM4m7rQAAGHQtAAAYdC0AABh0LQA\nAGHQtAAAYdC0AABh0LQAAGHQtAAAYdC0AABh0LQAAGHQtAAAYdC0AABh0LQAAGHQtAAAYdC0AABh\n0LQAAGHQtAAAYdC0AABh0LQAAGHQtAAAYdC0AABh0LQAAGHQtAAAYfwP7yJU8LUryWEAAAAASUVO\nRK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8467498c18>"
+       "<Figure size 576x432 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -1726,15 +1699,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the coding layer must output values from 0 to 1, which is why we use the sigmoid activation function:"
+    "코딩층은 0에서 1사이의 값을 출력해야 하므로 시그모이드 활성화 함수를 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [],
    "source": [
     "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.sigmoid)"
@@ -1744,15 +1715,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To speed up training, you can normalize the inputs between 0 and 1, and use the cross entropy instead of the MSE for the cost function:"
+    "훈련 속도를 높이기 위해 입력을 0과 1사이로 정규화하고 비용 함수로 MSE 대신 크로스엔트로피를 사용합니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [],
    "source": [
     "logits = tf.layers.dense(hidden1, n_outputs)\n",
@@ -1766,12 +1735,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Variational Autoencoder"
+    "# 변이형 오토인코더"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1782,7 +1751,7 @@
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 500\n",
     "n_hidden2 = 500\n",
-    "n_hidden3 = 20  # codings\n",
+    "n_hidden3 = 20  # 코딩 유닛\n",
     "n_hidden4 = n_hidden2\n",
     "n_hidden5 = n_hidden1\n",
     "n_outputs = n_inputs\n",
@@ -1813,11 +1782,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
-    "eps = 1e-10 # smoothing term to avoid computing log(0) which is NaN\n",
+    "eps = 1e-10 # NaN을 반환하는 log(0)을 피하기 위한 안전항\n",
     "latent_loss = 0.5 * tf.reduce_sum(\n",
     "    tf.square(hidden3_sigma) + tf.square(hidden3_mean)\n",
     "    - 1 - tf.log(eps + tf.square(hidden3_sigma)))"
@@ -1825,10 +1794,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [],
    "source": [
     "loss = reconstruction_loss + latent_loss\n",
@@ -1842,63 +1809,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train total loss: 32440.1 \tReconstruction loss: 25031.5 \tLatent loss: 7408.61\n",
-      "1 Train total loss: 30017.4 \tReconstruction loss: 23093.3 \tLatent loss: 6924.14\n",
-      "2 Train total loss: 23337.9 \tReconstruction loss: 20221.0 \tLatent loss: 3116.88\n",
-      "3 Train total loss: 21724.7 \tReconstruction loss: 18698.8 \tLatent loss: 3025.89\n",
-      "4 Train total loss: 28219.0 \tReconstruction loss: 21493.3 \tLatent loss: 6725.66\n",
-      "5 Train total loss: 25906.5 \tReconstruction loss: 19582.4 \tLatent loss: 6324.09\n",
-      "6 Train total loss: 19198.3 \tReconstruction loss: 15831.6 \tLatent loss: 3366.69\n",
-      "7 Train total loss: 17638.8 \tReconstruction loss: 14539.6 \tLatent loss: 3099.17\n",
-      "8 Train total loss: 16688.3 \tReconstruction loss: 13615.9 \tLatent loss: 3072.4\n",
-      "9 Train total loss: 17007.3 \tReconstruction loss: 13783.2 \tLatent loss: 3224.1\n",
-      "10 Train total loss: 16550.5 \tReconstruction loss: 13333.8 \tLatent loss: 3216.75\n",
-      "11 Train total loss: 16248.7 \tReconstruction loss: 13009.1 \tLatent loss: 3239.6\n",
-      "12 Train total loss: 16346.3 \tReconstruction loss: 13150.0 \tLatent loss: 3196.26\n",
-      "13 Train total loss: 16067.2 \tReconstruction loss: 12777.2 \tLatent loss: 3290.02\n",
-      "14 Train total loss: 16512.1 \tReconstruction loss: 13058.1 \tLatent loss: 3454.07\n",
-      "15 Train total loss: 16099.5 \tReconstruction loss: 12739.1 \tLatent loss: 3360.35\n",
-      "16 Train total loss: 20827.6 \tReconstruction loss: 16602.6 \tLatent loss: 4224.96\n",
-      "17 Train total loss: 38965.4 \tReconstruction loss: 24849.1 \tLatent loss: 14116.2\n",
-      "18 Train total loss: 29396.9 \tReconstruction loss: 24286.1 \tLatent loss: 5110.81\n",
-      "19 Train total loss: 27910.6 \tReconstruction loss: 21005.3 \tLatent loss: 6905.23\n",
-      "20 Train total loss: 26797.9 \tReconstruction loss: 20202.2 \tLatent loss: 6595.64\n",
-      "21 Train total loss: 18686.1 \tReconstruction loss: 15251.4 \tLatent loss: 3434.69\n",
-      "22 Train total loss: 17034.8 \tReconstruction loss: 13890.0 \tLatent loss: 3144.77\n",
-      "23 Train total loss: 16404.0 \tReconstruction loss: 13102.6 \tLatent loss: 3301.37\n",
-      "24 Train total loss: 16214.5 \tReconstruction loss: 12803.4 \tLatent loss: 3411.13\n",
-      "25 Train total loss: 16253.4 \tReconstruction loss: 12823.9 \tLatent loss: 3429.48\n",
-      "26 Train total loss: 16326.2 \tReconstruction loss: 12934.0 \tLatent loss: 3392.18\n",
-      "27 Train total loss: 16161.3 \tReconstruction loss: 12767.4 \tLatent loss: 3393.91\n",
-      "28 Train total loss: 16990.3 \tReconstruction loss: 13471.8 \tLatent loss: 3518.54\n",
-      "29 Train total loss: 15728.4 \tReconstruction loss: 12465.1 \tLatent loss: 3263.28\n",
-      "30 Train total loss: 16505.3 \tReconstruction loss: 13219.9 \tLatent loss: 3285.37\n",
-      "31 Train total loss: 16961.6 \tReconstruction loss: 13379.0 \tLatent loss: 3582.55\n",
-      "32 Train total loss: 17671.7 \tReconstruction loss: 14372.1 \tLatent loss: 3299.55\n",
-      "33 Train total loss: 16640.7 \tReconstruction loss: 13332.3 \tLatent loss: 3308.39\n",
-      "34 Train total loss: 21943.6 \tReconstruction loss: 15878.3 \tLatent loss: 6065.31\n",
-      "35 Train total loss: 15656.0 \tReconstruction loss: 12254.1 \tLatent loss: 3401.86\n",
-      "36 Train total loss: 15697.0 \tReconstruction loss: 12231.0 \tLatent loss: 3465.93\n",
-      "37 Train total loss: 15769.4 \tReconstruction loss: 12409.7 \tLatent loss: 3359.68\n",
-      "38 Train total loss: 17182.6 \tReconstruction loss: 13943.9 \tLatent loss: 3238.67\n",
-      "39 Train total loss: 18285.6 \tReconstruction loss: 14796.2 \tLatent loss: 3489.34\n",
-      "40 Train total loss: 20053.2 \tReconstruction loss: 14899.0 \tLatent loss: 5154.25\n",
-      "41 Train total loss: 16290.2 \tReconstruction loss: 13008.1 \tLatent loss: 3282.09\n",
-      "42 Train total loss: 27364.1 \tReconstruction loss: 22713.0 \tLatent loss: 4651.08\n",
-      "43 Train total loss: 15450.8 \tReconstruction loss: 12009.0 \tLatent loss: 3441.87\n",
-      "44 Train total loss: 15567.6 \tReconstruction loss: 12068.6 \tLatent loss: 3499.0\n",
-      "45 Train total loss: 15348.8 \tReconstruction loss: 11840.9 \tLatent loss: 3507.95\n",
-      "46 Train total loss: 15435.6 \tReconstruction loss: 11949.6 \tLatent loss: 3486.03\n",
-      "47 Train total loss: 15210.5 \tReconstruction loss: 11804.4 \tLatent loss: 3406.18\n",
-      "48 Train total loss: 20627.8 \tReconstruction loss: 16485.7 \tLatent loss: 4142.07\n",
-      "49 Train total loss: 15147.4 \tReconstruction loss: 11587.1 \tLatent loss: 3560.29\n"
+      "0 훈련 전체 손실: 33711.305 \t재구성 손실: 24141.234 \t잠재 손실: 9570.07\n",
+      "1 훈련 전체 손실: 27055.078 \t재구성 손실: 22278.68 \t잠재 손실: 4776.3984\n",
+      "2 훈련 전체 손실: 28769.135 \t재구성 손실: 23155.71 \t잠재 손실: 5613.4233\n",
+      "3 훈련 전체 손실: 26304.252 \t재구성 손실: 21337.469 \t잠재 손실: 4966.783\n",
+      "4 훈련 전체 손실: 31762.32 \t재구성 손실: 24771.611 \t잠재 손실: 6990.71\n",
+      "5 훈련 전체 손실: 25975.902 \t재구성 손실: 21666.244 \t잠재 손실: 4309.659\n",
+      "6 훈련 전체 손실: 21084.105 \t재구성 손실: 17912.25 \t잠재 손실: 3171.8557\n",
+      "7 훈련 전체 손실: 20691.05 \t재구성 손실: 17767.701 \t잠재 손실: 2923.3506\n",
+      "8 훈련 전체 손실: 18301.613 \t재구성 손실: 15335.816 \t잠재 손실: 2965.7979\n",
+      "9 훈련 전체 손실: 18003.305 \t재구성 손실: 14857.625 \t잠재 손실: 3145.6807\n",
+      "10 훈련 전체 손실: 16868.87 \t재구성 손실: 13742.127 \t잠재 손실: 3126.7417\n",
+      "11 훈련 전체 손실: 16322.584 \t재구성 손실: 13176.514 \t잠재 손실: 3146.0708\n",
+      "12 훈련 전체 손실: 16848.775 \t재구성 손실: 13641.574 \t잠재 손실: 3207.201\n",
+      "13 훈련 전체 손실: 16411.53 \t재구성 손실: 13193.485 \t잠재 손실: 3218.0444\n",
+      "14 훈련 전체 손실: 16841.875 \t재구성 손실: 13629.753 \t잠재 손실: 3212.122\n",
+      "15 훈련 전체 손실: 18734.246 \t재구성 손실: 15469.811 \t잠재 손실: 3264.4355\n",
+      "16 훈련 전체 손실: 15549.877 \t재구성 손실: 12475.354 \t잠재 손실: 3074.523\n",
+      "17 훈련 전체 손실: 16549.727 \t재구성 손실: 13114.041 \t잠재 손실: 3435.6865\n",
+      "18 훈련 전체 손실: 16204.832 \t재구성 손실: 12911.634 \t잠재 손실: 3293.198\n",
+      "19 훈련 전체 손실: 19072.146 \t재구성 손실: 15676.701 \t잠재 손실: 3395.4456\n",
+      "20 훈련 전체 손실: 18689.686 \t재구성 손실: 14211.309 \t잠재 손실: 4478.3774\n",
+      "21 훈련 전체 손실: 16398.697 \t재구성 손실: 13045.562 \t잠재 손실: 3353.135\n",
+      "22 훈련 전체 손실: 16106.504 \t재구성 손실: 12735.91 \t잠재 손실: 3370.5942\n",
+      "23 훈련 전체 손실: 20430.426 \t재구성 손실: 15874.012 \t잠재 손실: 4556.413\n",
+      "24 훈련 전체 손실: 15652.191 \t재구성 손실: 12415.114 \t잠재 손실: 3237.077\n",
+      "25 훈련 전체 손실: 15460.601 \t재구성 손실: 12243.658 \t잠재 손실: 3216.9421\n",
+      "26 훈련 전체 손실: 15832.671 \t재구성 손실: 12465.408 \t잠재 손실: 3367.2627\n",
+      "27 훈련 전체 손실: 37082.61 \t재구성 손실: 25035.375 \t잠재 손실: 12047.233\n",
+      "28 훈련 전체 손실: 26629.25 \t재구성 손실: 21904.469 \t잠재 손실: 4724.7803\n",
+      "29 훈련 전체 손실: 23798.465 \t재구성 손실: 19210.568 \t잠재 손실: 4587.8955\n",
+      "30 훈련 전체 손실: 20755.977 \t재구성 손실: 17576.453 \t잠재 손실: 3179.5234\n",
+      "31 훈련 전체 손실: 17813.498 \t재구성 손실: 14805.74 \t잠재 손실: 3007.7578\n",
+      "32 훈련 전체 손실: 17064.127 \t재구성 손실: 13854.3125 \t잠재 손실: 3209.8142\n",
+      "33 훈련 전체 손실: 16516.027 \t재구성 손실: 13270.247 \t잠재 손실: 3245.781\n",
+      "34 훈련 전체 손실: 16060.129 \t재구성 손실: 12752.525 \t잠재 손실: 3307.6035\n",
+      "35 훈련 전체 손실: 16070.643 \t재구성 손실: 12676.658 \t잠재 손실: 3393.9841\n",
+      "36 훈련 전체 손실: 16754.852 \t재구성 손실: 13328.36 \t잠재 손실: 3426.492\n",
+      "37 훈련 전체 손실: 16162.049 \t재구성 손실: 12813.662 \t잠재 손실: 3348.3872\n",
+      "38 훈련 전체 손실: 16215.911 \t재구성 손실: 12828.523 \t잠재 손실: 3387.388\n",
+      "39 훈련 전체 손실: 15912.985 \t재구성 손실: 12628.198 \t잠재 손실: 3284.787\n",
+      "40 훈련 전체 손실: 15275.752 \t재구성 손실: 11982.837 \t잠재 손실: 3292.9146\n",
+      "41 훈련 전체 손실: 16066.625 \t재구성 손실: 12757.855 \t잠재 손실: 3308.7693\n",
+      "42 훈련 전체 손실: 17823.701 \t재구성 손실: 14552.526 \t잠재 손실: 3271.1743\n",
+      "43 훈련 전체 손실: 24408.918 \t재구성 손실: 19846.832 \t잠재 손실: 4562.0864\n",
+      "44 훈련 전체 손실: 24694.812 \t재구성 손실: 19852.523 \t잠재 손실: 4842.288\n",
+      "45 훈련 전체 손실: 30566.844 \t재구성 손실: 22793.793 \t잠재 손실: 7773.05\n",
+      "46 훈련 전체 손실: 24413.105 \t재구성 손실: 19724.02 \t잠재 손실: 4689.086\n",
+      "47 훈련 전체 손실: 23234.568 \t재구성 손실: 18196.725 \t잠재 손실: 5037.8438\n",
+      "48 훈련 전체 손실: 17091.879 \t재구성 손실: 13951.564 \t잠재 손실: 3140.3154\n",
+      "49 훈련 전체 손실: 15979.056 \t재구성 손실: 12713.727 \t잠재 손실: 3265.3289\n"
      ]
     }
    ],
@@ -1916,13 +1883,13 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch})\n",
-    "        print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 전체 손실:\", loss_val, \"\\t재구성 손실:\", reconstruction_loss_val, \"\\t잠재 손실:\", latent_loss_val)\n",
     "        saver.save(sess, \"./my_model_variational.ckpt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1933,7 +1900,7 @@
     "n_inputs = 28 * 28\n",
     "n_hidden1 = 500\n",
     "n_hidden2 = 500\n",
-    "n_hidden3 = 20  # codings\n",
+    "n_hidden3 = 20  # 코딩 유닛\n",
     "n_hidden4 = n_hidden2\n",
     "n_hidden5 = n_hidden1\n",
     "n_outputs = n_inputs\n",
@@ -1974,75 +1941,75 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Generate digits"
+    "## 숫자 이미지 생성"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's train the model and generate a few random digits:"
+    "모델을 훈련시켜 랜덤한 이미지를 생성해 보겠습니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 Train total loss: 17792.6 \tReconstruction loss: 14122.9 \tLatent loss: 3669.64\n",
-      "1 Train total loss: 17332.2 \tReconstruction loss: 13560.0 \tLatent loss: 3772.24\n",
-      "2 Train total loss: 16350.7 \tReconstruction loss: 12579.3 \tLatent loss: 3771.48\n",
-      "3 Train total loss: 16581.4 \tReconstruction loss: 12810.6 \tLatent loss: 3770.78\n",
-      "4 Train total loss: 16223.9 \tReconstruction loss: 12450.0 \tLatent loss: 3773.86\n",
-      "5 Train total loss: 15628.1 \tReconstruction loss: 11819.6 \tLatent loss: 3808.51\n",
-      "6 Train total loss: 16080.9 \tReconstruction loss: 12179.7 \tLatent loss: 3901.24\n",
-      "7 Train total loss: 15772.8 \tReconstruction loss: 12021.3 \tLatent loss: 3751.55\n",
-      "8 Train total loss: 16276.5 \tReconstruction loss: 12404.6 \tLatent loss: 3871.83\n",
-      "9 Train total loss: 15589.6 \tReconstruction loss: 11740.6 \tLatent loss: 3849.02\n",
-      "10 Train total loss: 15931.3 \tReconstruction loss: 12031.4 \tLatent loss: 3899.94\n",
-      "11 Train total loss: 16112.7 \tReconstruction loss: 12238.3 \tLatent loss: 3874.35\n",
-      "12 Train total loss: 16002.0 \tReconstruction loss: 12185.1 \tLatent loss: 3816.83\n",
-      "13 Train total loss: 15357.7 \tReconstruction loss: 11667.4 \tLatent loss: 3690.35\n",
-      "14 Train total loss: 16208.4 \tReconstruction loss: 12264.4 \tLatent loss: 3943.96\n",
-      "15 Train total loss: 15970.0 \tReconstruction loss: 12158.5 \tLatent loss: 3811.52\n",
-      "16 Train total loss: 15551.6 \tReconstruction loss: 11783.1 \tLatent loss: 3768.49\n",
-      "17 Train total loss: 15330.0 \tReconstruction loss: 11555.7 \tLatent loss: 3774.3\n",
-      "18 Train total loss: 15251.3 \tReconstruction loss: 11584.5 \tLatent loss: 3666.81\n",
-      "19 Train total loss: 15196.0 \tReconstruction loss: 11516.6 \tLatent loss: 3679.44\n",
-      "20 Train total loss: 15323.9 \tReconstruction loss: 11525.9 \tLatent loss: 3797.99\n",
-      "21 Train total loss: 15358.7 \tReconstruction loss: 11515.6 \tLatent loss: 3843.17\n",
-      "22 Train total loss: 15297.9 \tReconstruction loss: 11582.5 \tLatent loss: 3715.37\n",
-      "23 Train total loss: 14673.0 \tReconstruction loss: 10940.7 \tLatent loss: 3732.34\n",
-      "24 Train total loss: 15293.5 \tReconstruction loss: 11561.7 \tLatent loss: 3731.75\n",
-      "25 Train total loss: 15256.3 \tReconstruction loss: 11540.8 \tLatent loss: 3715.53\n",
-      "26 Train total loss: 15305.4 \tReconstruction loss: 11475.4 \tLatent loss: 3830.01\n",
-      "27 Train total loss: 15276.9 \tReconstruction loss: 11449.7 \tLatent loss: 3827.24\n",
-      "28 Train total loss: 14980.6 \tReconstruction loss: 11318.0 \tLatent loss: 3662.56\n",
-      "29 Train total loss: 15232.8 \tReconstruction loss: 11520.1 \tLatent loss: 3712.69\n",
-      "30 Train total loss: 14872.4 \tReconstruction loss: 11172.9 \tLatent loss: 3699.47\n",
-      "31 Train total loss: 14890.3 \tReconstruction loss: 11144.1 \tLatent loss: 3746.17\n",
-      "32 Train total loss: 15246.7 \tReconstruction loss: 11439.3 \tLatent loss: 3807.4\n",
-      "33 Train total loss: 15063.5 \tReconstruction loss: 11282.1 \tLatent loss: 3781.41\n",
-      "34 Train total loss: 15046.7 \tReconstruction loss: 11310.2 \tLatent loss: 3736.47\n",
-      "35 Train total loss: 15293.9 \tReconstruction loss: 11599.5 \tLatent loss: 3694.4\n",
-      "36 Train total loss: 15134.5 \tReconstruction loss: 11362.8 \tLatent loss: 3771.74\n",
-      "37 Train total loss: 14705.7 \tReconstruction loss: 11054.7 \tLatent loss: 3650.98\n",
-      "38 Train total loss: 14913.9 \tReconstruction loss: 11077.0 \tLatent loss: 3836.93\n",
-      "39 Train total loss: 14848.1 \tReconstruction loss: 11198.5 \tLatent loss: 3649.57\n",
-      "40 Train total loss: 14694.2 \tReconstruction loss: 10991.5 \tLatent loss: 3702.73\n",
-      "41 Train total loss: 15223.9 \tReconstruction loss: 11465.1 \tLatent loss: 3758.8\n",
-      "42 Train total loss: 14585.3 \tReconstruction loss: 11019.3 \tLatent loss: 3566.01\n",
-      "43 Train total loss: 14579.1 \tReconstruction loss: 10931.2 \tLatent loss: 3647.84\n",
-      "44 Train total loss: 15049.1 \tReconstruction loss: 11381.9 \tLatent loss: 3667.18\n",
-      "45 Train total loss: 14855.6 \tReconstruction loss: 11125.6 \tLatent loss: 3730.04\n",
-      "46 Train total loss: 14777.7 \tReconstruction loss: 11093.4 \tLatent loss: 3684.3\n",
-      "47 Train total loss: 14408.9 \tReconstruction loss: 10788.5 \tLatent loss: 3620.39\n",
-      "48 Train total loss: 14479.2 \tReconstruction loss: 10864.3 \tLatent loss: 3614.88\n",
-      "49 Train total loss: 14637.6 \tReconstruction loss: 10926.0 \tLatent loss: 3711.55\n"
+      "0 훈련 전체 손실: 17845.31 \t재구성 손실: 14199.457 \t잠재 손실: 3645.853\n",
+      "1 훈련 전체 손실: 17223.674 \t재구성 손실: 13457.104 \t잠재 손실: 3766.5708\n",
+      "2 훈련 전체 손실: 16330.138 \t재구성 손실: 12578.04 \t잠재 손실: 3752.098\n",
+      "3 훈련 전체 손실: 16323.255 \t재구성 손실: 12578.617 \t잠재 손실: 3744.638\n",
+      "4 훈련 전체 손실: 16297.912 \t재구성 손실: 12533.176 \t잠재 손실: 3764.7368\n",
+      "5 훈련 전체 손실: 15681.15 \t재구성 손실: 11960.288 \t잠재 손실: 3720.862\n",
+      "6 훈련 전체 손실: 16120.67 \t재구성 손실: 12325.412 \t잠재 손실: 3795.258\n",
+      "7 훈련 전체 손실: 15783.96 \t재구성 손실: 12021.791 \t잠재 손실: 3762.169\n",
+      "8 훈련 전체 손실: 16208.828 \t재구성 손실: 12365.744 \t잠재 손실: 3843.0837\n",
+      "9 훈련 전체 손실: 15524.169 \t재구성 손실: 11801.077 \t잠재 손실: 3723.092\n",
+      "10 훈련 전체 손실: 16092.582 \t재구성 손실: 12279.52 \t잠재 손실: 3813.0625\n",
+      "11 훈련 전체 손실: 16111.75 \t재구성 손실: 12282.848 \t잠재 손실: 3828.9019\n",
+      "12 훈련 전체 손실: 15926.159 \t재구성 손실: 12180.764 \t잠재 손실: 3745.3953\n",
+      "13 훈련 전체 손실: 15271.382 \t재구성 손실: 11631.805 \t잠재 손실: 3639.5774\n",
+      "14 훈련 전체 손실: 16211.1875 \t재구성 손실: 12341.906 \t잠재 손실: 3869.2817\n",
+      "15 훈련 전체 손실: 15781.817 \t재구성 손실: 11920.02 \t잠재 손실: 3861.798\n",
+      "16 훈련 전체 손실: 15513.055 \t재구성 손실: 11780.989 \t잠재 손실: 3732.0657\n",
+      "17 훈련 전체 손실: 15282.611 \t재구성 손실: 11568.492 \t잠재 손실: 3714.1196\n",
+      "18 훈련 전체 손실: 15077.107 \t재구성 손실: 11422.83 \t잠재 손실: 3654.277\n",
+      "19 훈련 전체 손실: 15184.711 \t재구성 손실: 11399.879 \t잠재 손실: 3784.8323\n",
+      "20 훈련 전체 손실: 15317.0625 \t재구성 손실: 11541.074 \t잠재 손실: 3775.988\n",
+      "21 훈련 전체 손실: 15375.975 \t재구성 손실: 11662.023 \t잠재 손실: 3713.9507\n",
+      "22 훈련 전체 손실: 15253.931 \t재구성 손실: 11569.6 \t잠재 손실: 3684.3308\n",
+      "23 훈련 전체 손실: 14920.597 \t재구성 손실: 11226.293 \t잠재 손실: 3694.304\n",
+      "24 훈련 전체 손실: 15152.121 \t재구성 손실: 11461.097 \t잠재 손실: 3691.0242\n",
+      "25 훈련 전체 손실: 15161.9795 \t재구성 손실: 11527.926 \t잠재 손실: 3634.054\n",
+      "26 훈련 전체 손실: 15351.327 \t재구성 손실: 11591.437 \t잠재 손실: 3759.8906\n",
+      "27 훈련 전체 손실: 15188.266 \t재구성 손실: 11508.098 \t잠재 손실: 3680.168\n",
+      "28 훈련 전체 손실: 14944.834 \t재구성 손실: 11275.673 \t잠재 손실: 3669.1616\n",
+      "29 훈련 전체 손실: 14949.296 \t재구성 손실: 11173.085 \t잠재 손실: 3776.211\n",
+      "30 훈련 전체 손실: 14824.383 \t재구성 손실: 11233.498 \t잠재 손실: 3590.8848\n",
+      "31 훈련 전체 손실: 15053.771 \t재구성 손실: 11336.643 \t잠재 손실: 3717.1284\n",
+      "32 훈련 전체 손실: 15209.582 \t재구성 손실: 11480.851 \t잠재 손실: 3728.731\n",
+      "33 훈련 전체 손실: 15029.901 \t재구성 손실: 11239.151 \t잠재 손실: 3790.75\n",
+      "34 훈련 전체 손실: 15018.421 \t재구성 손실: 11247.266 \t잠재 손실: 3771.1555\n",
+      "35 훈련 전체 손실: 15295.654 \t재구성 손실: 11592.32 \t잠재 손실: 3703.334\n",
+      "36 훈련 전체 손실: 15067.271 \t재구성 손실: 11287.572 \t잠재 손실: 3779.6997\n",
+      "37 훈련 전체 손실: 14845.694 \t재구성 손실: 11252.967 \t잠재 손실: 3592.7273\n",
+      "38 훈련 전체 손실: 14872.638 \t재구성 손실: 11183.228 \t잠재 손실: 3689.4102\n",
+      "39 훈련 전체 손실: 14961.719 \t재구성 손실: 11297.793 \t잠재 손실: 3663.9263\n",
+      "40 훈련 전체 손실: 14667.465 \t재구성 손실: 10977.892 \t잠재 손실: 3689.5732\n",
+      "41 훈련 전체 손실: 15164.336 \t재구성 손실: 11456.284 \t잠재 손실: 3708.0515\n",
+      "42 훈련 전체 손실: 14535.691 \t재구성 손실: 10862.055 \t잠재 손실: 3673.6365\n",
+      "43 훈련 전체 손실: 14729.365 \t재구성 손실: 11080.487 \t잠재 손실: 3648.8777\n",
+      "44 훈련 전체 손실: 15044.16 \t재구성 손실: 11426.916 \t잠재 손실: 3617.2446\n",
+      "45 훈련 전체 손실: 14839.276 \t재구성 손실: 11058.229 \t잠재 손실: 3781.0476\n",
+      "46 훈련 전체 손실: 14890.201 \t재구성 손실: 11144.563 \t잠재 손실: 3745.6377\n",
+      "47 훈련 전체 손실: 14605.932 \t재구성 손실: 10951.763 \t잠재 손실: 3654.1694\n",
+      "48 훈련 전체 손실: 14460.681 \t재구성 손실: 10878.129 \t잠재 손실: 3582.552\n",
+      "49 훈련 전체 손실: 14584.321 \t재구성 손실: 10932.154 \t잠재 손실: 3652.1667\n"
      ]
     }
    ],
@@ -2063,7 +2030,7 @@
     "            X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch}) # not shown\n",
-    "        print(\"\\r{}\".format(epoch), \"Train total loss:\", loss_val, \"\\tReconstruction loss:\", reconstruction_loss_val, \"\\tLatent loss:\", latent_loss_val)  # not shown\n",
+    "        print(\"\\r{}\".format(epoch), \"훈련 전체 손실:\", loss_val, \"\\t재구성 손실:\", reconstruction_loss_val, \"\\t잠재 손실:\", latent_loss_val)  # not shown\n",
     "        saver.save(sess, \"./my_model_variational.ckpt\")  # not shown\n",
     "    \n",
     "    codings_rnd = np.random.normal(size=[n_digits, n_hidden3])\n",
@@ -2072,14 +2039,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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YMDGYuisZULusNRqNRqOJA+LeQgbEvTt//nyWL1/O5MmTAbjiiiukFABiq5l7vV7JIgwE\nAjidTundWlxcHDa2/Px86WUNlga6ePFi4EDfWrAyDqOZ0RzaAL61tZXExESZ38suu0y0QNUgINaW\nkMqyLikpwe/3i7Z+9913U1RUJOPNz8/nl7/8Jddddx0Af/zjH5k6dWrMXavV1dWA1TgmLS2Nbdu2\nAVanK5XRnpycjN1uj3qC4tGwfft2aVrjcDjE9RsPhN7NnJ2dLWfIpk2bqKmpkZCAy+USa6ytrY2W\nlhaxgDIyMiR8lJCQEJUEKkVoMmtNTQ333HOPNF5xOp1itUUr4ROsTOiqqirp7te/f385H7reSpeY\nmMjo0aPFW/jee+/JGr7qqqtISEgQN/Wjjz7K2rVrAcviv/rqqyN67hmGwcaNGwHLki8tLRULubGx\nkTfffDPMy6nO5Pb2djwej1j948aN4ze/+Q2A7NcTJa4FsmmaeDwefvGLXwDWS+3fvz8//OEPAcuF\nolxPsWo1qDbvu+++KweszWajtLSU733ve8DB8ezQTkGqjZ+Kb7a3t4ugUVnl0cJms8kB5PF4SEpK\nknkNXXDxIpDVWPPz85kwYQKXXHIJYAmx0LHZ7XYuuugiKa3YvHkz9fX1R1WXHKnnNE2TFStWAFYc\nsL29XVp/pqWlHZSJH2+Ypskf//hHEWxZWVlxJZAVNpsNp9MpoZgzzzyTfv360dbWBljK8nnnnQdY\n66SxsVGUjF27dskzuVwu8vPzD3m7XGNjY1i54oliGAaNjY0S1iouLiY3N1dcvA0NDSKQv4rQa0iP\nt/Qz9Pxavny5XHoxdOjQIyqKdrtdqjYWLVpE7969AUuQ9+rVS66X3Lhxo4zxkksu4aKLLoromvd6\nvbLX+vbty0UXXSSu/zVr1rBy5UrpYZCbm8vo0aMBSwDn5+dL97ElS5ZIV8XevXt3S7g07gXyli1b\nREO54447+NnPfiZCoqmpSXqexur+WJVo8f7774f1aB0/fvxRa3mdnZ1SBwcHhF+ka5APhZrHb3zj\nG2zatIl+/foBVsJLqEXZNfEoFkJDHUp+v5/p06eLInOojZGQkCC1hUuXLqW1tfWoBHKknisQCEh9\nfVlZGbNnzxYh4fV6JTmmax1tJMd0LAQCAT777DNZE2PHjpWciXgkdM727dsnYz3//PPlzu/U1FRS\nU1NlT7e2tspBHXr9IVhrTL2T7hDGoVdCrl27lq1bt4rHJzc3F9M0ReGfM2eOCDeVX3M0nOi6MU2T\nhQsXcsYZZwDhc9D1pi2lUKo69YSEBFFuVqxYQV1dHZs3bwastaQs7YsuuuiQjZO6AzW2xsZGyTcZ\nNmwYffv2FSWsqqqKHj16SLLwlVdeyVlnnQUcuHZWKXBTp06VpNdvfetb3SKQdQxZo9FoNJo4IO4t\n5PLycrF8fvazn4WVD5WXl0s3plmzZsVkjKG3xiit0OFwSCedr8Lv9/PUU0+JtuhyuTj//POB6Lvh\nbTabWJ2LFy+mtLRUmr737t1bnk+VdIVqsdGy4NTn+Hw+ab5yyimnHNV92OqOVa/XG9bsPhYEg0G5\nDWzJkiXs3r1bvBNqvcOBkEa8xZAbGxvDmmpMmjQpbhrGdEXdPATwk5/8hLq6OvGOTJ8+XdzZLpcr\nbH579OgRtt7sdrtYVn6/v1tK59Seam5uZsGCBYDVWCgtLU2a8nz00UeUl5dLdUlnZyeTJk0C4Dvf\n+c4Rf393dij0eDyUl5dLDPW///u/xVJXn6Gex+/3U15eLt4Dm80mjU3q6+tpaGiQn8nIyODHP/4x\nAGeccUZE2pcahiEVAZs2bZKLcvx+P36/X2LY1dXVXHHFFZx99tmA1ZQl9D560zQl/ycrK0vOkf8v\nyp5M02TTpk1MnDgROOAyUK6dxx57TFzWXTdTtFBJUA0NDbJ5nU4niYmJ8rK6uq5D3VMvvfQSTz75\npAj2vn37yuKMdl9d0zTZsGEDYC3MCy+8MCyG1vUWnUN1rIl0i1I1hh07dsgBdskllxykIHSlubmZ\nL7/8Uv4da4Hs8XikDGvjxo0EAgFxT19wwQXy97a2NhITE+OmXama/zfeeCMsjhmtevljRXXmeuGF\nFwCrZt1ms0lXtDPOOOOwim/oelLnS2iv967u2mPFNE0Rbu+8845cVQhWXoHqPLdy5UoaGxvD9p9q\no1ldXU3v3r3lrOiOcR2OmpqasLajixcvZsaMGcCB2nN1jrW2tjJnzhy++OILwEpWVaHHQCCAy+WS\nNXPHHXdIwmWkWoEahiH7f968eWFtdCdNmiRfGzFiBOecc46ce6Edu1Q8XtVWB4NBKQHrLuJaIBuG\nQc+ePSXGo/rQqozk7du3SwF5LA6s0JfT2toqG0FZDSqhQW0SpZVt2bKFm266CYAvv/wSv98vsdp5\n8+Z1+0s+FtTG9vl85OXliRUQmunbtXG/3++Xr0Wyb3ioIvPGG2/IJkpLSzvixReGYfDWW2+J8jRu\n3DhpchIr3G63CIKOjo4w67K2tlaSRex2O4MHD6ZXr14xGWdXlIW4fPlyDMOQeU9JSYmrZiaha7O8\nvJy5c+fK1woLC/nZz34GfPWdtur3dL10pTvWt2EY0uTo73//u9Se9+vXjz59+shnbN68mdbW1rB2\nmcrae/XVV7n++usl0eqrFNMToaioiB49esgefPfddykrKwOsOHdNTY0I69mzZ7No0aKweu/Qu6nz\n8/OZPXs2YDUdimSjHvXZKkdj9+7dcp/0vn37cLlcoqD17t2blJQUUSy6JuB++eWXUj1TX1/fbdnV\nivj0MWk0Go1G8/8ZcW8hJyQkSLZhMBiko6NDuroMHjyYm2++GYhdbE3F+/x+v2iA7e3tfPzxxxJf\nMQyD1atX8/777wOWZqW0TIfDQe/evSUGPnjw4Jg9i91ulw5A/fv3F+1Wocal2t4pjbOxsVG0yJ49\ne0a0vlfV6r7zzjsyVqfTeUjrLNRK+uKLL8QCufvuu2PeOjMhIUGyVT/55BM8Ho/M77Jly8Ku3hsx\nYkS3t+g7HkIvp1fXRaos5OHDh8dVDFm9+6amJv7whz+I5ZaYmMi1114rrSePZT670/NjmiZNTU3M\nmTMHsNzBKpa9Zs0aTNOUOGZJSUlYtUloJ7cvvvgiLP8gMTHxIGu+u3C73aSnp8u+/+STT8TS9Hg8\ntLW1iRequbmZQCAQ5j5Xe+7mm2/mvvvuE09gNK519fv94kVISUkRD4NpmjQ3N8t5XFFRQXV1tYx7\n8ODB8l6ampq48847KS8vB6zqiO7uThfXAtlmszFw4EBqa2sBK8lixYoVEud86623Yhpbs9lskrSg\nbjsBK2noo48+EneU3W6ns7MzbHEq1/CQIUN49tlnGTdunHwtVthsNik/mDBhAnPmzJEDd+zYsTJm\nr9dLRUWF9AMeOnQoU6dOBSLrMgu9Fcbj8UgcbePGjfTs2TNsLYQKj9WrV7N3714pZZg4cWLMhYfT\n6ZS66SVLlrBy5UpRZNra2uQqySlTpoT1Oo8lpmlKzkZnZyc2m01cdikpKXGTcBZKR0cH27dvlwN3\n1KhR3H777cesNHad/2AweMKCxOFwkJqaKr9PlTUVFxdjt9tlPZ999tnMmDFDxtDW1iYKxpo1a+jR\no4cIa5fLJWdSd+fVuFwuJkyYwLvvvgtYirhqXnK4cIUac2lpKY8//jhg3Xkc7Zyf0Ksir7nmGjGW\nioqKGD58uHyto6ODbdu2Sbgx9Maqzs5OUlNTOfPMMwGrNFSFvrprf8Z+l2s0Go1Go8EWxUSMY/4g\n5RZVGXFffPEFzz33nKSd/+UvfxHX3glyJFXtiONW2tM999zDn/70JwCxhkMTneBAsld+fj7f/va3\nAbj11lvp3bv38WqLh/uh436paj3s3buXu+66i6qqKsByYavnWLBgAYmJiZKJ+NRTT0lSRFpa2ldp\ni8c916FZqTfccINclD5y5EjOOussSf7Lz8/H4/GIx6K8vJy9e/dy1VVXHfQsR8lxj/lIqLluaWlh\nxYoV4oEYNGiQNK5wOp0kJCTExfowDEM6yt1www20t7dLid6bb77ZXWV63TLXKvGwrq6O++67j//8\n5z8AXHzxxdx///0nXMFwiA5uxzzXfr9fMu3Xrl0r93cPHTo0rJ1jdnY2brc7zKpXruGqqqqwG59C\nwwipqanHuxcPO+YtW7ZwwQUXANalKF2rFdQ43G432dnZ0q3wzjvvlDV9gpbxca2PUDlnGEbYv7uW\nbHZ2dsrZoaocALnARq3z5ORk+ftRhDOO6qHjWiCD5crZsWMHYB38dXV1/PrXvwas2Eo3uQpOSEiA\nFTN55JFHACsDODRztqioiCuuuIJp06YBlkBQL/IEx9/tAll+wf9lhav2jrNmzZIShoyMDG6++eaw\nWj3l8jmK5zmhAzc0hvk///M/gOVmSklJkbK4wYMHS7tBsFyDffr0kQPhOOY8IgI57JdEpkVnt64P\n1TkPrDhgdna25HOcgFLZlW4VyDU1NcyaNUvyIe69996wDOZu5LjmWmXzdo23HuqGr0PV/SvXufp3\nV+HyFW71Yx6zYRjyzp9++mlR2E3TJDk5WdrTXnnlldx4442yJ7sxThzxvXjQLz0KGXkU6+nrIZAD\ngYDECl977TV69+4tvaxDGyicIFF/yd1ExATyIX9pSFu80HKAY+SE5lqNobOzU66G3LNnDytWrJBD\nt6SkhIEDB8p1homJiWJpHid6fUSPbp1rlXegBN/RNJA5Tr5Oc31URojf75fmJb169Qqz4iMYH/66\n7cUwdAxZo9FoNJo4IO4t5NCWZwsXLqS2tlZKnbqxk9XXTes6GccM8Tvuk3HMoNdHNPk6zfXJOGY4\necd94JviXSDDwT78KMZ/4OR8ySfjmCF+x30yjhn0+ogmX6e5PhnHDCfvuAXtstZoNBqNJg6I68Yg\ninhsOKDRaDQaTXcSTZe1RqPRaDSaw6Bd1hqNRqPRxAFaIGs0Go1GEwdogazRaDQaTRygBbJGo9Fo\nNHGAFsgajUaj0cQBWiBrNBqNRhMHaIGs0Wg0Gk0cEM3GIPFc8Px1a8d2Mo4Z4nfcJ+OYQa+PaPJ1\nmuuIjlndFqeuyLTb7cfS/Onrtj7C0BayRqPRaDRxQNy0zlTaEhz9BfJdL+PWaDQaTfwQekZ3dHQA\nUFlZyT//+U/5/9tvvx2HwxH1sR0K0zRjKkviRiCrF3c0L0YJ7/379+Pz+QDIzMzE7XYftTDXaDQa\nTXTw+XzU19cD8OSTT9LR0UFRUREAtbW19OrVC4idYaXkj/ozVuPQ0kuj0Wg0mjggLixk0zSP2mVh\nGAYNDQ0ALF26lAEDBgCQkpKC0+kUzUa7sDUajaZ7OdxlRIc7b5U3c9++ffz6178GrHP7jDPO4JZb\nbgEgPz8/pue1aZoyTsMwcDgcMRtPXAjkr3r4UHdCZ2cnW7ZsAWDnzp18+eWXAEybNk1cIPGOaZoE\ng0EAAoEAAE6n9SpiuRhOVmId99GcPJimGSZUuoa4Qr9us9lisq66jvFYxmIYBrW1tQB8/vnnJCYm\ncv755wPgcrlOeGyhY/iqfWeaJh6PB4C//e1vLF26VH7HjTfeSH5+PnD0OUORwDRNvF4vu3btAiz3\neWlpKT179gSOLoR6uN+r3uGxPF9cCORQQh/EMAyCwaAkA9TU1LBt2zZWrFgBQEtLC8XFxQC0t7fH\nbAMdCtM0RdgahkFrayvz5s0D4OWXX6aqqgqAYDBIRkYGkydPBuD+++8nIyMDiK2Vb5omfr9fFurb\nb7+N2+0GoLi4mMmTJ5OcnAyEKxHRGnMwGKSzsxOAqqoqfD6feEtOxlwCwzDkeSorK2UeU1NTsdls\ncjCkp6eTmJgY03GG/hn63o/23Ud7XZumSXNzMwDr16+nubmZkSNHApCXlyfKcDAYxOPx0NTUBEDP\nnj1JSkqK6jjBirdWVlbS3t4OQElJCUlJSbIGDjV/6mc3bdrEnXfeCUBFRQVXXHEFF1xwQUTG+1Xv\nMRgM8q9//QuAefPmkZWVBcAPfvADRo0a1S0KwvGi5qu+vp67775bzmbDMBg+fDivvPIKAAMGDDhu\noXw86/zkOrU0Go1Go/maEjcWcqhVrGLEX375JcuXL2f79u2A5XJpa2sTi9npdJKamgpAVlZWzC1K\nZTW0tbV5yOFIAAAgAElEQVRRXl7OqlWrANi+fTurVq2SLMP9+/fT2toKgNfrpba2VtxM/fr147/+\n67+A2FjIfr8fgD/+8Y+8+OKL7Nu3D7CsTmUtpKam8te//pXrr78egEmTJsl7iDRqnbS0tHDXXXcB\nMHfuXEzTZMSIEQBcf/31TJ48mYKCAuBAOCCeCLWI6+rq+Oyzz2S9tLa2ypirq6vZvXu3PMvMmTMZ\nNGgQEN31oTw+e/bsAaw4YN++fRk+fDhg5XCEjsfn88k+dblcsj6U50J9bzTCDcFgUMpsnnrqKSZO\nnMiECRNkPGrfVlZW8sgjj8gzqX0YDUKt+CeffJIlS5YwbNgwAH74wx9SXFx82Axg0zSl2uTxxx9n\n+fLlACQkJHDhhRceNu4bSUzTpLGxkffeew+Abdu2MW3aNMAKLypvWywwTVPW5syZM5k7d6641h0O\nBx6Ph02bNgFQVFQkHqmjWaehcux4LOu4OKlM0xQBtWLFCl577TXAcieYpsnZZ58NWJve7Xazbds2\nwNro6mu5ubkxdVP6fD55iffffz9bt26lX79+AJSVlTF9+nQRuh6PRwTdkiVLaGxsFPfU22+/za23\n3goQ9UXr9/vFvbV06VJKS0u57777ADj99NNFWNfV1bFw4UKWLFkCWC61wYMHA5EXEmrBb9y4kTlz\n5gBWuKJHjx54vV4AXnvtNR588EEJZ7zyyiuUlJTEPJyhxu7xePjf//1fFi9eLP+fnp5OWloaAKNG\njRL33uLFi1m7dq0oqVVVVSKQoznmjo4OXnvtNWbNmgVYB9e1115LYWEhYAk2tT727dvH7Nmzqa6u\nBmDgwIHccccdwIE1Hc3ykvr6elnHKSkp3H777WRmZsrnq3EvXLiQRYsWcdtttwGWQIsWhmHwySef\nAPDqq68SCAQYN24cYCU9HSm3xDRNPv30UwBmz54dVgpaVFQUM8V+w4YN8kx9+vTht7/9LUBMQy5g\nzfWyZcsAeOutt0QYgzW2hIQEFixYAEBGRoYob0lJSQfF0EP/brPZwvKdjmeNa5e1RqPRaDRxQMwt\nZMMw8Hq9/OpXvwLgww8/FEtnzJgxjBkzhtGjRwOWttLc3Mz+/fsBGDx4MLm5uYClzcZCE1SJWzNn\nzuQPf/gDAJ2dnUyYMIGf//znAJx66qk4HA7RxG02m1gPzzzzDG+99ZYkklRXV4srM5oWcjAY5Oqr\nr+bjjz8GYMKECcyfP18stVDXXmtrK0uXLhUrP5rJGcrV9Mwzz0imel5eHr/4xS/45je/CcAXX3zB\nT37yE1auXCnP8vjjjzN9+nTAcrmHupOisW6CwSB79+4FrKS+RYsWSRLaddddR3FxMenp6YBlxam5\nTkpK4vXXXxdrTVn90UJ5ru6//35ee+01GdfZZ5/NsGHDSElJke/dsWMHAL/97W9ZuXKluN3dbrdY\nbeo5orVXDcPgL3/5C3l5eYA19wUFBWHeNDW21atXk5KSwimnnBK1MSorqq6ujp/+9KcANDQ0MGzY\nMG688UYAkpOTD+v9U67um266CbCeRe3Hm266ieLi4qh2wQp9npdfflnc8DfffLOs71g3/6ivr+ep\np54CoKmpCZvNJlb7qFGjGDhwoJy98+bNk6qeIUOG0NjYKOdKRkaGeBRLS0vDLP/jrZaJmUAOdd9t\n2rSJd955B4Dm5mYuuugiAG688UZKSkpkctra2li+fDl9+/YFYPz48fTo0QOITeq8YRjycp5++mla\nWloAGDduHC+88AK9e/cGDqTOq8PINE369OkDwBVXXMGSJUtoa2sDLJdJNONrivLycmbPni0H1/vv\nv09mZuYhP7+trY2lS5dy5plnAtC7d++oHV7r168HLFeuWgcLFiyguLhYxlBSUsKpp54qSt6bb77J\nPffcI27L4uJinnrqKYYMGQJYQk8JDxUrVe/sRA6z0PK29evX88YbbwBW9uukSZP4wQ9+AFj5D10z\n1ZXgq6mpob6+Xkr6CgsLo7YmfD4fTz/9NAB/+tOf8Pl8Mmff+c53GD58uAjkQCAgytzSpUtpbm4W\nF3xxcbEorl3XdKTWuDpfdu3axZNPPslvfvMb4OCsWVVKCbBmzRrKysqi6lJVxsdPfvITdu7cCVgK\nzK233kpJSQlw5DUYDAa5/PLLpWrDbrfTv39/AH70ox9FPZNZzfvq1atZsGCBjP3GG2+Mes5D189T\ne3Hbtm2Ul5cDlms9KSlJSsNuvfVWcnNzWbt2LWCFFB9//HEAGhsbRcEAK0yqjMNf/vKXhyyhg2NT\nQGJuIXu9XpqamiRe0tnZyTnnnANYG7lHjx5yOHm9XhoaGmTycnJyYtoD1ePxyKHa2NgoG/mRRx6h\nT58+ByWwhKIEQEFBAfn5+dTU1ABWqYWKaahEmEguZLVoHnvsMRISEpg9ezbAIYWxinNfd9111NbW\n8v3vfx84OLYSKQKBAM8//zxgHTw33HADYK2T0M1gs9koLCzk2WefBaw5fv7558ULsXHjRr7//e9z\n2WWXAZZSFBoL3bx5swiQUaNGnfCYwcqNUIfmyJEj+fGPf3zE96sOjwceeIBgMMh3v/tdIDpeE7Um\namtr+ctf/gJYe6+goICHH34YgHPPPRe32y1jb29vZ+7cuYC1FwBRIr7xjW+I4O76rJFaN+rMeOGF\nFzjttNPkXR/qvFC5EGvXruWJJ56ImnJvmqaUcM6fP1/WSv/+/bn88suPeLYpb9stt9zC4sWL5XnT\n09NFiUpLS4u6EFTKzSuvvIJhGOIlzM7Ojto44NCJb2pP7d69W/4/ISGBvn37ct111wGWN81ms4mh\nt379eurq6gDLEDFNU87uQYMGMWPGDODQXozjSabTMWSNRqPRaOKAmFnISoNxOBxkZmZy+eWXA5aL\nThXtK/eh0jSCwSDnnnuuuM2O5I4J1Yjsdnu3a72GYbBp0yaJm5mmycCBAwFLy/qqOz7VMzU2NoY1\nfsjIyAhzn0Zaw1Wa9s6dOykqKhJPRdfP9fl8EqNds2YNjz76qMQzo6WFezwe/v3vfwPWux86dOgR\nP1/N44QJE3j11VfFiujRowdut1vc806nU+YhKSmJQYMGdVuGrRrDwIEDJVN62rRpB5UJhWKapngC\ntm7dSmpqKvfeey8QvXg3WN2VVJ6A6vikwhQJCQlheQUVFRVSZWCaJikpKRKnGzJkyGH3aqTWuHIF\nt7W1MWXKlLBYdyiGYfDII48A1po6/fTTu30sh8M0TfFAeDweORPcbvcRM3g9Ho+0nXz77bcxTVPW\n689+9jN5R9GO1Xq9XqmQWbNmDaeffrqMM9YVDqFnbHFxsZS2rV+/nuLiYlnz6vuUVbxr166w3B+X\nyyXzO2vWLAkPdId1DHHgsk5NTWXQoEESD9y5c6e45dxud1i9Znp6Orm5uUfc3Grytm3bJqUAkyZN\norS0NCw2e6IC2jRNtm3bFtb6UrnSv+owD60b3LNnT5iLNCkpKayWLdLuMzVfTU1NjB07NqxmV7WV\nAytGsnr1agBuu+027rjjjqjH7VtbW6WL2fbt20UYHOpQN01TXOyLFi0iOztb1lhhYSHjxo3jtNNO\nA6x1pbqO2e32bqtbDj0ExowZI27x1NTUI77bhoYG7r//fnmOu+6667ACJRKoNbFixQpJoktOTsbh\ncFBZWQlYLsi2tjZx//385z+XUItqjaiUO5fLFfUDWe0vn8/HiBEjDvv5+/btE0XiRz/6UdRLndTZ\nFtrHYMuWLdx+++1SB52SkkJzczOff/45AM8//7y8B8MwcDqdfOtb3wKsRC51fkZzzv1+Px988AFP\nPPEEYIUwRowYIWG8eGhvq/bi8OHDJQSUmpoaVkrrcDjYsGGDtPn897//LWvJZrNRVFQk/Q/69et3\n2D18vM+qXdYajUaj0cQBMbeQHQ4HKSkpor1kZmYe5K5RFrGyKpR7QX0dLDdbS0sLixYtAuChhx4S\nF2FOTg7z5s2jtLQU6J4yHbvdTmpqqlhswWBQuhgFAoFDatqhlq/K1lu2bFlYokD//v3FQoHIa7lq\nLnv27Mlpp50mFrHNZqOzs1MyDP/85z9Lgfzvfve7mCTT5eTkyDvcs2ePdF+aPHlyWGKZYRhUVFRw\n6aWXApanZcqUKeKmcjqdDB8+XN5djx49jpiAdyKo35eSkiIuclW6p8pAlAWprNHf/e534jEpKiri\npz/9aVStC/VZzc3NYZeeVFdX89ZbbwGWJ6u8vFzWscfjkZ/r1asX9913X0wsNYX6zISEBNatWyfv\nXu19ZfU8+OCD8jM//vGPozpWu90uiZH/+Mc/xE3q8Xh4/fXXefPNNwHLa6a6FMKBREGwnjMvL4+r\nrroKQDw90cbv9/OnP/1JLvtxOp1UV1eL1yQhIUG8AXa7nYSEhLASuGjMu/qMxMRELrzwQsDKlJ43\nbx7/+Mc/AHjppZfYs2dPmOdCrZkePXowZcoUzjrrLHmOo+FYvAMxF8jKrac2fltbm2zyzs5OMjMz\npRZyx44d5OTkyGXWdrtdspO9Xi/JyckSxz3nnHPCumHdcMMNfPDBBwDSpUd9/vGOe/To0WHKgnIp\n7dy5k5KSkrBm8KEx7Y6ODmkHWllZGVZm097eflDj/kiiNsWMGTMoKCgQ99327dv54IMPWLhwIWCV\ni6i4ZqyawjudTk499VQAli9fLpvd4/GQkJAgB9Xq1auZNm2aKGRnnHEGZ511lszxvn37SE1NFYH4\nVfH+7kApnmBt0KqqKrm4o7CwEK/XK9m+W7duFff2okWLot7ZSM3FoEGDWLNmDQB9+/Zl0qRJonSu\nX7+ehoaGMAVOrSVVOnKkGPmhPq87UcpAUVER69atCyvRCwaDPPDAAwC8/vrr0tJRrYdoYbfbxa3/\nzjvviBt19+7dYZ2eOjo6wmLMcGDOUlNTGThwoGS2x6JNJljPkpiYKGeXMpxU1cbGjRvlbAG48sor\nOe+88wBL0Y5mSMZut8vnjRs3DqfTKblAu3btEmUNrH2rejEMHTpUWmvCV2exh3btOmkEMoRv5qys\nLLl5Y/fu3fTu3VsshwULFlBWVib1vePHj2fs2LGAdaglJCTIJPz+97+XGuH9+/fj9Xqlzi8jI6Nb\n4p+9evUSzbu6ulrqkFetWkVBQUHYQRoIBCTus3LlSlkA9fX1dHR0yAHS0tISVrMZadS8X3rppWzb\ntk2EwhtvvEFTU5MIwEsvvVSUnViiDrAPPvhADqGtW7cyaNAgeb/PPPMMgLTzfPzxx+nTp48knKi+\nwGoNHGotRKK1o1I609PTxeIEK1YbGrfasGGDrKucnJyw8XT3mA6FUlzOOussaWZy5plnMn78eGmS\nUFNTw4YNG6QOs7GxUZ5v5MiRYXPadR0f6frD7kKt6xtuuIEFCxbIOPfv38+aNWt4//33AcLq1WOB\nmrMzzjhDlPTdu3fz/PPPS5nc4sWLxbgASyFWvc2nTJlCVlaWtFP1er1Rb74ClsdB1faC9VzJycly\njeG6devkfBw0aBCdnZ289NJLgOUVvPbaa4Ho9ZNQn5OQkEBeXp6UOblcrjAPbGJiogjvqqoqAoGA\ntEDOycmR93ekcR/LM+kYskaj0Wg0cUBcWMhwQJtLT08X18bHH38s2ZxwwC2iLpQ466yzxP2sOh0p\n7dvlcon7RLXXPNJ9oseD3W6XwvcVK1aIBnj//ffz8ccfS3mWw+GgoqIirGRHuccqKiro6OgQLWrv\n3r1i+RUUFEQ8O1H97uTkZLKysuRGp1tuuYURI0aI1jtkyJCY3zFst9vlfScnJ4vL94knnqBXr14S\ne3c4HFx88cVScjFkyBCqqqrCGgJMmjTpiPfKRhKbzUZSUpJkfefk5FBQUCCXTXR0dEgjBZvNRiAQ\nkLlXWbXqa5FA7ZNTTz1Vmqn079+fzMxM8ZIkJiYyePBgadL/6aefikdo0KBBB3XjOtJNRZF4DjVf\nOTk5XHrppRITrKmpoby8nLKyMsBq86mszUiO52hQ77W4uJiZM2eKB0WVYqm9efPNN3P33XcD1j5o\na2uT2HHoGRhtEhIS5MwNBoMUFRVJCevgwYPlfOzfvz/r16+XtdPQ0MAll1wCRCdsEAgExAreuXMn\n8+fPlwoSdUuT2gNpaWnioe3o6CA1NVVCqOpZIdw9rTie8zLuBHKPHj3EXffhhx+yY8cO8vPzATjv\nvPO46qqrxG0ZGndQm175/7dt2yYJKC6Xi7Kysm6/Jcdms8mCu/vuu3nssccAa9PPmTNHXEder1de\nKliCVgld1f1FbaiysjJx80RDAKoF5HA4yMjIkI5GGRkZ+Hw+6Xa1atWqqNZoHgqbzSYtR/v168eG\nDRsAyw3Zp08fOWT79+/P4MGDxWXtcDjYt29fWMz2cB2v1IEW6e5oNptNDuDU1FRSUlIkPut2uyWG\nrMajNr9hGBG/KUn93vz8fBlHVVUVFRUVEiKaMGECxcXFokQEg0FZ78r9pzAMQ5Ql1XM+mq5JNb9g\nCbDzzjtPXNh9+vQJK5E63h7E3Y3NZhOBVVNTg81mk0SkmTNnhq3fjIwMETCh6yOaJCYmhgnTjo4O\n5s+fL2fG0KFDJYnS5XLR0NAgCWDt7e1UVFQAMHr06G5ZG4faw2oNbt++Xdzlc+bMob29XZSdgQMH\nsm/fPokTqzUB1lpyOBySQxMMBmWuD3Wt6PEQNwJZ4XK5pM6roqKChQsXilWUlZWFz+dj69atAGFx\n2v3797Nq1So++ugjwLI0lWV9yy23cNlll8mkd/d4wbo7VVnic+fOpbm5WZKK2tvbaW9vl0YW+fn5\nEu/s7OwkOTlZvnbxxReHNayINKH9k0PvPLbb7TQ3N8vdqmlpaWEWaCyw2WxysI4ZM0Y2tNvtJj8/\nX+atsLCQc845R5QcwzDo6OgQzdY0Tdxud7dnVh+LIA89dAKBAH/7298kQXHIkCF873vfA6yDrmvv\n5WgJjMTERBHAixYtIj8/XxIqk5KS2LVrl2QGBwKBwyafhSof6t/RJDSL1+12U1ZWJmvD5XKJQK6t\nraVv374x9wSBlah4++23A9bc9u7dm5dffhk4uN991yTLWIzf5XLxk5/8RBo8+Xw+ueQFrEoI1aeh\nR48e/POf/xSjxOv1snnzZoCwd3M8HE4QGoYhHrLf//73kkPQ0tJCRkaGeKuysrJEboDlYVGKZlpa\nGhMnThSD6UgXGuk6ZI1Go9FoTmLizkK22WxyS8xPf/pTCgoKxNVYX1/Piy++KKUWEydOFIuptbWV\nuXPnSqxo6tSp4uIZM2ZMxNPqExMTufLKKwG44IILaGpqEmtTxeGUG+Szzz4Li93m5eWJV2DUqFFR\nsYwVahxOpzPs1iPDMDAMQ6z++vp6mfdYXjCuPnvGjBlSNtHZ2YnL5aK+vh6wLOTc3NyDLpxQLrPh\nw4eTlJTU7ZbE0WrFXd1aDQ0NLF68WCydW2+9VS5m6OqNiHY9snJDXnrppWHlecFgkMzMzLDSMUVb\nWxs9e/YMa4/b9V3EkoKCAlnnTqdTLKDOzs6YxV9DMQyDyy+/XMo/U1JSeOWVVw55GUnXkIbdbo/J\nM9hsNqZMmcKsWbMAq767ra1NwkrV1dVieY4bNw6HwyGhyJKSEonjn+jZdySLVXnQiouL5fMcDgdj\nxoyR3Ijly5dTWFgoN22dcsop8nOVlZUMGjRI5FPXdd2Vk/K2p0OhHnLIkCE8+OCDUiJUV1fHhg0b\nwtLOVVw4ISGBb37zmyJAcnNzRSBGy8WqDtTs7GzZPGDVPXd2doqrPTc3V2rbMjIyuPjii+UO1mgK\n41BUPXjoInK73UycOBGwanvj4bAKrb8Mrev1+/1hm6yrAPD7/RLbHDly5BE3SbQEhor7rVy5kp07\nd0orz4svvjgu3KZwYC5cLhculyssZpaZmSm3YZWXl8v8tre3R6XW+FgITbpxOp2yV0PH5XA4CAaD\nMauzV3z88cf861//EiFbVlbGmDFjjjiHkezbf7TY7Xa5Nemaa66htbVVrjFcu3athDtOPfVUXC6X\nlFQWFxdLT+hInX8qkRLg3nvv5bbbbgMsQ6Ouro5169YB1rnSq1cvOffy8vLkPaxfv56srCxZ51+1\npo9HIMfHrtdoNBqN5v9z4tpCdrlcOJ1Ohg0bBlgah7qvEsKTG7q2X4tluz7VeUwldQUCAXbt2hWW\n5q+s9rPPPpuxY8fGtM2g+tzQtqSBQIDm5uYwzVu50JTrN5aEvu+ul4UcKlM2MzNT3FChndpihWma\n0nRj1qxZpKSkyI1OqampMbcoD0foGs/MzGTy5MmA1QJWWUBerzcuvCmhdL2wpWtZlvpaIBAI65QX\nzfeg9torr7wiiYcAd95551feOR7NSzGOROj6yMjIYNKkSYB1zoV6KUpKSsKSRKPRnVCdEUlJSRL6\nysrKoqioSMoMU1NTyc7Olq+HhgDGjBkTVhJ1tJ93LMSlQFaoDRH6YLF2Jx0tdrtdsgVra2vDMlLT\n09OlJ/PYsWOPeBVfNAkdQ3V1Nb/61a9YsGABYLUgVK6aeLi5pSuh66JrjNY0TZKSkjj33HMByy0W\n6/H7fD7pELV69WqmTp3K6NGjgdhkyR4rKqShsrCvvPJKESjxEosNJTTGGqqwhWYpt7S00NraKoJQ\nfW+0UDkaTqeTgoICKe88/fTTj7gmYr2Wj4ZDKTdqbqPZha7r5zgcDhITExkwYID8f9eQlxpfSkrK\nQbe0dXcJYlwL5JMV9VJVzCI5OZl+/fqJhRmaVJCZmRmzMqIjsWvXLioqKmTxnX/++UcsR4h0bexX\nEaokhFo8YHkk3n77bWlDOWrUqMPWIUcD0zT54osvpKQsIyODO++8M6bJcseDzWYTb8O1114rddRe\nr5dAIBDTUqeuhDZWgXAhoPaf3++nrq5O3kM0PUGhF8zccccd9O3bV5pl5OTkhDX8ONRcRupylGgQ\n6zHbbLZDKvRd/608KxG14iP2mzUajUaj0Rw12kKOAMrNruI62dnZEqsAwrI8Q+Pg8UDoJd4Oh0OK\n4L/5zW/GRWbykT6/qwWhGj5s2LABj8cjjQhiHW/z+/288MILMtdTp06lpKQk5nN4rIRaFmlpaWJ9\ntrS0hHWMirULPtR70tXKCW3r2dnZyfLlyyUDN5rYbAcu2Bk9ejSnnnrqMVm98ehlOxlQ5Z1HO382\nmy0s/NHde9YWxVhPfAWVwjnSrHb7uLsxBnu4X9ItYw699QS6bdNHbK67CmT176amJn7+859LW9Cz\nzz77WMsrunXMgUCAiooKKY3r3bt3pA7UiK6Pg37p/8231+vF5XId7zNFdS92I1Gd627i6zRmiNK4\njzM8d1TfrF3WGo1Go9HEAdpCtoi51nWcaA33KDlBr4ReH9FDz3X0+DqNGY5j3MFgMFru/qM6fKIp\nkDUajUaj0RwG7bLWaDQajSYO0AJZo9FoNJo4QAtkjUaj0WjiAC2QNRqNRqOJA7RA1mg0Go0mDtAC\nWaPRaDSaOEALZI1Go9Fo4gAtkDUajUajiQOieblEPHcg0d2BosfJONcn45hBr49o8nWa65NxzHDy\njlvQFrJGo9FoNHGAvn4xiqg2pYFAQPqnxvpqOo3mcPj9flmfDoeD0Da7XW/UOtmujtRo4hEtkCOA\nOqTUn4ZhYLPZaGhoAODzzz+X7x03bhwZGRkkJiYC+mDTxB61bmtra+WKyNTU1DAB7ff7aWhooL6+\nHrDumB4yZAjAsV5tqTkB1NlyMpwb8aq8hV48041X4x4X2jzTaDQajSYO+Fpdv3gC2k23JAr4/X7A\nuqB937598m+fz0dpaSmGYcg4y8vLAXjqqacwTZOHHnoIgNLS0mNxY+ukjOgQkTGrvef3+/F4POJB\n2bFjB3V1dQCMHTuW7Oxs0tLSgGO2Pk9ofQSDwbB/G4YRZj1v2LCBzz77DIChQ4dy2WWXAYi35ziJ\n6FyrPaj2WDdaQ1Hbi8FgkPb2dgBqamooLCw8Xg9bRMdsmiadnZ0AtLS0kJycDEBycvKJXHl4XOuj\nq2xQ68Dr9VJeXs67774LwLnnnsvw4cNlv9nt9u5aI0f1S05q35JpmuzcuROA22+/ndTUVJ588kkA\nCgsLoz6WxsZGAB599FH279/PAw88AMCAAQMOErLDhw8HrA30ySefMHfuXADuvffeKI76+DFNMyym\nGA+x8NDxGIYhm87n8+FyuUSYxctYvV4vABs3buSFF14QJQ2gra1N/u71ern66qsB+P73v09OTg4Q\nedef3W6XObTZbNjtdpnjnJwcsrOz5XsrKyvp6OgATlggdyvBYJCdO3fy9NNPA7B//34ATj/9dACu\nu+66sHVhs9lEEXE4HPK1eHCzqnHV1tbyP//zPwAMGzaMvn37xsX4QlHCeMWKFQC8//775OXlATBj\nxgwKCwujug9D58c0TXw+H2Apv8888wwLFy4E4MUXX2TChAncddddAIwZMwa3233Q74gUJ6VAVodC\nZWUlkyZNAmDPnj0UFhaSnp4eszH9/e9/B2D+/Pmcc845ohQcauGpl5yWlkZzczNffPEFEJmX3tUq\nOBZCE9GU1bZt2zbWr1/Pl19+CVgHxe233w5A//79I3rhd9f4PFgWZkdHhxy2q1at4t1332XVqlUA\nVFdXk5WVxS9+8QsArrzySpn/WGGaJvv27QNg3rx5rFy5Uixkt9stB8b+/fvxer389re/BaCjo4Nf\n//rXQHQOCLV2wFo/ag25XC4GDhwo+2/Lli0y5nhAjXvDhg2ce+65tLS0yNcSExPxeDyApRhnZGQA\n0NnZye7du9m9ezcAubm5TJs2TX4mShfZHxLTNGltbQXg5ptvZvny5QDcdNNNXHzxxTEbV1eUkvne\ne+/x0EMPsWPHDsBSilU+wr/+9S+eeeYZiouLgejnHJimKcrjqlWrWLFihawHwzBYunSp7MVvf/vb\nTJ8+HbCUUKfTGdF9F3tTQaPRaDQaTXxYyIeLYyu3aGgGXDAYJBAIADB79mzJ8jQMg5ycHNHCok0w\nGCtVLbQAACAASURBVKSqqgqAXr16cddddx1R81PPVFFRQUdHR1SsC8MwvtJKVpaFcvmq+Z01axZL\nly4FLKsjEAjIM9hsNrGen3nmGTIzMyOiRYa6mhoaGli5ciVgWWe7du0Sl++XX35JR0cHzc3NgGX5\nNDU1MXPmTACmTp1KQkJCTN18ofG1jRs30tTUREJCAgAFBQUkJSUBlkutqqpKxrpr164T8ngcC4Zh\nyF4DyyoOfeeJiYkUFRUB8Pe//11CNnl5eTGf282bNwNw4YUX0tTUJGdMQkICo0aN4tZbbwXglFNO\nkbFWVlby1ltvsX79esDy9px66qkAlJSUdGc88ZgJBoP8/ve/B2DBggUy71dffbWsm1jT2trKNddc\nA1hj9Pl8cgampqbicrkAWLduHU899RQ/+tGPACgqKoq6law8ZBkZGQwdOlQs5Pb2dlwul3j/nnji\nCd577z0AJk+ezPXXX0+PHj2AyHioYi6QldBV8RG/3y8HlXIrKDe0cheozdXc3Cw/Z7fb+cEPfhCz\n+KDNZuOcc84BrJfar1+/I36/Oug2bdqEYRiyWCPxkrvGTw73GaFxYcMwaG9v5+233wYst29mZiYA\n+fn51NfXS5zT5/NJKdfWrVsZPXp0t20wNZ5gMEhnZ6e49v/3f/+XBQsWAFa81efzyZw6nU68Xm+Y\n8ABEYaqqqgqLf8YC0zTFxb5nzx5sNhsFBQWApdANHToUgIEDBzJ//nyZ65ycnIgLBbWnOjo6xK2e\nnJxMenq6HP6maWK320VB2rhxo7yPAQMGxMS9q9aKx+ORXIz6+npM05TxXHjhhbzwwgtkZWUBlpKh\nfi4zM5OdO3eyZ88e+dmNGzcClnCOJfX19bIXi4qKRDiXlpbGXPkB68ybMmWKJPmZpkleXh4XXHAB\nYJV3qvW+ZcsWTNNk7dq1AGRnZ8sZH41nsdlscj6NGDGCXr16hYVi3G63PFdjY6M80+rVq1m7di1P\nPPEEYIUbu1veaJe1RqPRaDRxQMwtZJXVqLTy3bt3s2bNGsByS5aVlYnbSLmMlMuupqZGfi4xMZGL\nLrooBk9wAJXWP2rUqK+0EFTZQmdnJ3a7nTPOOCNi4zqWovdQjc/tdjNjxgzA8lwoC/O1117jo48+\noqmpSX5GaZzdnSwVqoFv3ryZF198EYAPPvhA3ExpaWlkZWXJGLxeL263W7J9Gxsbqa+vF8/LY489\nxssvvxxTV59hGPzzn/8ELO9DR0cHe/fuBaCsrIz8/HzAeraEhARZTx6PJ6JWRKi3qrq6WsIA+fn5\nDBw4MCzr2GazyTvIzs6W57nhhhskUSqaKA/JAw88wCeffAJY1r7D4eD8888H4PXXXyc5OTlsnas1\nlpSUxL59+2SduFwuWSORTuY5EsFgkNdff11cpY8++ijjx48HrEzwWFrIynPz0EMP8Z///EfGMnXq\nVO69915KSkoA64xQyXJjx44N61bY2Ngoe9Xtdkf8eUK9rK2trTQ0NIhMCbWO1bjVWd3c3Mxf//pX\nCYvOnDlTQkvdRcwFMliLSk1IQ0ODxHBcLhfZ2dniFktMTMQwDHF9rF27ViavT58+IhBjgc1mk7rj\nsWPHfqXLVmXx2e123G43ZWVl8nviAXXgKld6U1MTs2bNAmDZsmXs3btXDu6cnByuvfZaoHvdlV03\nRkJCgmwOwzDE7VxYWEheXp7E1err68nIyJAQwj/+8Q/+/Oc/i/BYuHAhzc3N5Obmdss4jxXDMFi/\nfj0vvfQSYB1IhmHIfNbV1ZGSkgJYilBovD405tndqJCFWsfbt28XIVtaWkp+fr4IMrXXlCt3+vTp\nvPnmm4CVRTtlypSoZrKbpilZ9bNmzZJ3bbfbGTt2LK+88oqMu6ubUc1nS0uLuLgBevTowWmnnRb2\nPbGgurqap556inPPPReA8ePHh9Udx6q7lN/vl7ySBQsWkJiYKIrPs88+S1pamozL4/HQq1cvwMqR\n8Pl8otAHg0H5e3p6Oi6XS86QSOWiKHmzbt06VqxYITkwTqeTtLQ0caF3ze3xer0sWbJEnr+7OyzG\nhUAO9en369dPNnlVVRXNzc2iWSUnJ7Nr1y6WLVsGWMk7aiIKCgoOmxwWDYLBoBxSKvHscIvKNE1Z\nyIZh4HQ6Revq7s2lWut9FV3rig3DoLKyUiyNxx9/XEoYlOBQQuPuu+/mBz/4AWApTd0x/q7vsqsF\nXlxcLPW4o0aNIjMzkzFjxsj3DB06VH6mrKyMDz74QGrWvV4vn3/+uXhUon2Ytbe38+CDD8ohoNb/\ngAEDAKsWXf196dKlYTXJkYwZKu+Tyt2orKyktrYWsCyJzMxMscwKCwvDFLbKykpWr14NwF133cXc\nuXO57bbbACtOp/ZGpHI8AoEAzz33HIAIY7CSdv785z/LWjnU56vD+d1338Xn88n3lJWVxayMMnRc\n999/P3V1ddLXICkpKSz5MtqE5nWonI7CwkKmTZvGTTfdBEBWVhbV1dVs374dsPIiVJmT2+0OU6i9\nXi/vv/8+YCn0w4YNk/MwUmtdKZ3Lli2jrq5OyrUCgQBOp1OU+/Hjx0t+zPbt2zFNU4Rw18Y53YGO\nIWs0Go1GEwfEhYUMBzTXlJQU0ab37duHaZpiEbe1teHxeKS8IlRDT0tLo62tTWI+0S5RCG30sGPH\nDqZOnSpxQGUpK42qvb1dWrV5vV5KS0ulVduhbtQ5UY70e0IbbYS6Taurq3n99df54x//CFhuVPW9\ndrudoqIiyWS98cYbu72bTdff43Q6CQaDMoaCggKZ39LSUjIzMznllFMAaw2Fus2Liop4+OGHpdTF\n7/czZ84cvvWtb8nvjgZqbjds2MCGDRvkWbKzs3nwwQe56qqrAKs0R1k+7e3tdHR0yDpX2deRIjS+\n5vP5JJ4aCARoaGiQNe52u2lra5PSs+eee07CMKZp8u677/Kvf/0LsKzUK664ArCs/6SkpG63lP1+\nP1u2bJHPV7//4YcflpKlw6GsoyeffBLTNOX8ueWWW+TnlAcpmlUcqmxv7ty5XHbZZVLl4PP5xJXq\ndrvDXLzRJBgMMnLkSABGjhwZ5lHw+/00NzfTp08fwLKglUdNeQVDf4/yaqxYsYKsrCwGDhwIRO6y\nB5VvsGvXLrxeb1hHusTERK6//noATjvtNObPnw9YJZ0+n0+8V5HIK4gbgayw2+3iJtuxYwdbtmwR\nF/bQoUM588wzxdW3du1aOTBGjBhBbW2tbK6cnBwRztG4DaW9vV3ibXv27BGhDFZsasWKFZK0s2bN\nGnGf2u12cnNz5aALbYPXHYvxqw6QriVR6hB49dVXeeedd2Tjhya4DB06lL/85S9S2hXJhJdQ4TBv\n3jxqamoAK6at3EptbW2cddZZcpB2PZzsdjvnnXeeCDWfz8fmzZvl2UI7UEUStVaXLFmCz+eThJA7\n77yTa6+9NuyQUuPZvn07Xq9XFDbVfjCSqDnfv3+/dLgaMWIEPXv2lDXw6aefMnv2bFGWGxsb5eec\nTidut1t+trm5mZdffhmwSou+973vdasCp+Leas4SExNlbX73u989YuKTYRgyNlVzr+a4f//+skZM\n08TlckW0PLErShlub2+nsLBQ9mZLS4v83el0Mnr06JgIZKfTybhx4wBrz4XuoUAggN1ulz0ZqmSq\nuQt1t6swyZ49e8LCZ5GaZ+V2Puecc1i2bJkIaJXTo2ROUlKSnNWGYZCYmMjgwYPlmbob7bLWaDQa\njSYOiDsL2TAMsYDtdjs9e/aU7MKysjJ69eolWuu6devEshw0aBAvvviiaDMTJkzg29/+NgC9e/eO\nWGcmpeWtXr2aOXPmAAdcZqq3dVNTE+3t7fK9DodDkhZSUlLIzMwUl00wGAxraBHNDErDMKioqAAs\nrTwtLU365yYkJEj52e23306/fv2i2nh/5cqVLFu2TN4vHLDkBg0aRM+ePY9oJXg8HtHglWtehT56\n9ux5UBORSKASSWpqasjNzeXyyy8HLAv5cG7zN954A9M0xd0XjcxlNY81NTViBXi9XhwOB7t27QKs\nrNpPPvlEst7hQAOfK664gqlTp0oji5UrV0pi2n/+8x8uv/xyCTd013w3NzeLm7OlpYUJEyYAlqUW\n2m1MrQEVPli1ahWPPfYYYHkwHA6HZPz+5je/YeLEiYBVOTF06FAZb6TLoAzD4M9//jNgzavdbqey\nshKAvf+PvfOOj7JKF/93StqkJySE3jsC0kFRQAQLgqgf9dpQL/a6ruWn67qufS2rq95Vr+zqqqwu\nKIiiKEqzAFKlhRIIJZCQRvok097398d7z+NMBETJTCZ6vv+YmJA573mfc87TT2EhW7duBayLJtq1\na0fHjh2ByCYoOp3OELkNztB3u93s2bNHKmJOO+006e3vdDpDLipxu91s3rwZsDxCU6dOpVevXmF7\nHpvNJuto+vTpJCYmyt5dXFxMXFyclN6uXbtWkrp8Ph85OTnSvz0cXomoOZCDY6fBXVQGDRokiysj\nIwOHwyHdczZu3CiLp7q6mrVr18oBkpeXx+mnnw4g6fZH+9wTeelKAD/99FNx0TmdThITE2VsdXV1\nIW4Yh8MhLsj+/fvTo0cPyUA80lgidbG36q4D1sZfWVkpylFWVpY0WZ8wYULE6h/VZvraa6+xfv16\nkY34+HhZ4AMHDgzpuHSkv5GbmxtSwlBaWip11ZmZmSHutnApbko2O3XqxHPPPScL+0iHsXKfqkoC\n1ZIwEu0yg+PEKi7cs2dP2rRpE1KqEhMTI+PJzs7m4YcfBn5wE6uSoeeee47PP/8csGS/pqZGDuSm\nHLeqfQ5u3blixQpM0wzJvK6srOSbb74BrJafwT+z2+3yntavXy/uSXXblTrIles6XJimKTkRDoeD\nAQMGSIx8//79Mp+BQICSkhJZC81VnqUy9JXiNWfOHDZs2CC10wMGDKBdu3byu2o+wbqsRil6SUlJ\ndOrUKSI3mYF1plx33XVMnz4dgE2bNvHSSy9JCV9ZWZmsxbi4OE477TQpUQ3HWoy6A9nn83HqqacC\nltbbsWNH0bwdDgeFhYU8+uijgKV5q9T5qqoq6uvr5UC5/PLLRaCPVWx+Ii/eMAx27doFQG5uriRd\nZGVl0blzZ1nYSUlJFBYWyvc2m01iXKWlpZx00klSExusOTbFGFUa/0+hEuRUzGfPnj3k5eXJxjN1\n6lQR2qYqbToe1IGwa9cu3G63aLYTJkyQEhzl/Wh8E1SwBv7uu+9KnEq1BVX1hO3btxe5aeqNNjgG\nrnIIhg0bxpAhQ476WYZhSEmWKqdTfX8j0TRBbY6FhYVSLtShQwd69uwpm1CnTp2w2+1S9nLRRRdJ\n4pYqa1FWfceOHUNagTb11Xs2m42UlBRR3oItrsWLF4fcTd64XCh4rTkcDhwOh6zFMWPGSDvTzMzM\nkBac4cZms4nlrnIc1q5dC8C3334ra7pr165kZWVFPIH1SP/P5/MxZ84cwEqAyszMFC9Qt27dxNui\n5lApyF999ZUYM4MGDYpoL/TgvAOwlPsJEybItZHBzaeUDIXzOkYdQ9ZoNBqNJgqIOgvZ6/WKRbxt\n2zbsdru4ojweD6+++qo0JXc4HPTv3x+wNPjgdPWePXuKJhMO14JpmpSXl4tGeOjQIWl/OXDgQIYO\nHSqalYoRK41w27Zt0pLwwIEDJCYmys8at2ILtvzU9z+Hn1vSo7qkzZ07F8MwxFvx9NNPN3lXmp9D\ncnIyiYmJIgsXX3yxuI6Uxa6sHtXcQX2/ZcsWli5dGnJLksvlkkzV4Azdpka9u+LiYukkNWLEiCO+\nF/W7O3fuFGsILFlWIY5wE2wFFhUViQvSbrcTHx8vVk7nzp157rnnxIWt7ooFRNaDWw6qnImzzz67\nydsNgmW1jB49GoBVq1ZJfkBRUVFIuCg40x4sWVYWcZ8+fejRo4e0i23fvr3IfHp6Ok6nU+Ym3Lkd\nweOqq6ujqKgopKxLeRwmTpwYttvVjkTwDWDqRjiw5vPAgQOSN1BTU8OAAQOYMGECYOXKqDGqi2JU\nWdyCBQtkXs8///ywt7Q9UrMk9X1MTAwjR46U+d27d2+INyU/Pz+s+SZRcyCrh0tISJDFU1ZWRmpq\nKkVFRYA1IQkJCQwcOBCw4hLq64yMDHr06CG9UyPREzU/P1+C/5WVlRKLGj58OL1795bPj4uLw+fz\nifvV5XJJgobauIJdYY0PhxM9LI43Bu31ennkkUcAS/lJTEzkqaeeAiLrpg5GhST69etHUVGRhCFO\nOumkH5U5NXalqYX00UcfUV1dLeOPj4+nV69ekpuQmZkZtkWmxrR69WpJ8ouNjaVfv34hndwMw5CY\n9sSJE0WZczqd/PWvf41Y/avdbmfIkCGAFXpRyUMLFy5k2rRpUleanJxMcnKyHBoej0fGvHHjRlau\nXCnrtrCwkD59+gBWAl445MjhcEi898ILLxTF0jRN0tLSRI527NjB8uXLQ9yPqpb2+uuvZ8iQIfJM\nDodDDu7Y2NiQRK5I3UoElrL/xRdfiBu1Q4cOEh4bOXKkhAYigdfrlduwqqur5XCura1l3bp1cpgO\nHz6cm2++mdatWwPWXq4SvPLy8vjiiy/47rvvAEvm1ME9fPjwsMl6sPKg1p7KhQkOcwVf8RuM6t2u\nfhYOpSxqDmT1ElRQH6y4ZUpKiiwQm81Gz549pRY1ISFBFlZ+fj7JyckRqxNUL1BZbOPGjZPWjT16\n9CAlJUU2KI/Hg9/vF8vA4XDI15WVlRQUFIil0bjXblO2ofypBiGLFy+WjcxmszF27FhOOumkJhvH\nL0FZXXfddRf9+vUT2cjJyflRa1L1vZo/tXDy8/Px+XyyWXTq1IkrrrhCNgFlBYUDVRf/6aefyh29\nS5YsYfz48SGJTe+++y5//OMfAWsDVs80ePDgsF480hi73S4eqttvv13GXFlZyezZs6VneXZ2Ng6H\nQ2Rc1XaD1cd4y5YtIne9e/fmpptuAsKXJR4TEyNZ1t27dw+Jz9vtdklOvP/++/F4PPLzrKws8bIl\nJiaSkpIS0hqysUxFah0EW/UNDQ3k5+dLvopqUwnWOohUYxvDMKiurua9994DLK+PUgacTicxMTFy\n3WLXrl0xTZMPP/wQsNogq317zZo1FBcXS7LtTTfdxDXXXAOEz5AyDEP6AFRVVcn+GxMTg9PplL3C\n7/eTn58vzxUfHy8y7nA4wu6N0DFkjUaj0WiigKixkBUOh0PcHFlZWT9yQ8bHx0sqfVVVlbjFsrOz\nycjIiJhrz2az0adPH+644w7AyhRUMayYmBj8fr9oVoZh4HA4JGNVtUMEK4v4m2++YfHixYBVMtLU\n9b3HmpPg8MC9994bEve+//77I6Z9Hw01Bx06dODiiy+W8QTHiBtbL8oFrDq+FRYWSotBsDI5J0+e\nHJGaXjWfu3fvlvKJpUuX8tJLL0km9VdffcXrr78uFoRpmtKe78MPPwx7iU1jlFU4cuRIafE6a9Ys\nDhw4ICGNTp06MXLkSPE6LFu2jLlz5wKWR8IwDKmNfeCBB455uUNTYLPZJE4dXJ7k9/tDMtxzc3OJ\nj48XF/pZZ50leRIxMTEUFhaKezspKelH4ZBIliAGXxF44MAB8bacd955Ye0WdaSxgCXLS5Ys4csv\nvwSsrloqtyE1NZVhw4aRkZEBwNatW9m/f79k4btcLrE6hw4dSmxsLNdeey1gVR1EwrOp9osDBw6I\n2728vDzkIpry8nLy8vLkd10ul1jPNpsNv98vuREJCQm/Xpd1MMEuyOAD2TRNWWBgxTCUq7dNmzYR\nbR9ns9lITEyUhdGpUyd5qapNnNrwlbCp8WVkZMj/CwQC1NbWymZtGEZY+lkfDbVxzZo1i0OHDsmY\nL7vssrDF+34OwbKQmpoqIYq6ujrZoGJjY0PqkBsn3O3duxfTNKXU7JJLLglJMgnnM6o492mnnSZJ\nXYcPH+bNN9+UZgQ1NTVS9gFWo5JXXnkFsJKlmusdxMXFyZzdd999FBUVsWzZMsByq3/55ZcSB1y3\nbp3kUDgcDvr06cOLL74IHN/94CdK4/a4ShaKioqYP3++KAslJSVkZ2eLa/WSSy6RcsSKioqQcMG2\nbdvEDa6SuiKFzWaTpMMVK1awadMmSWIcPHhwWEtvjobf7yc2Nlaa89TW1soa7Nq1a0jJHFghAKXs\ntGrVStpsdu/enbS0NHmGSOzbNptN1mL//v3lkD1w4ABz586VJMDy8nJKS0vle5/PF7JPuFwuMQLT\n0tJClOXGsehfonxql7VGo9FoNFFAVFrIRyMQCJCfny+Zn7GxsSFJF5G2JFQpCPy4XKkxwRl56enp\nUp71wQcfkJ6eLlmTwZdeRwJlcW7cuBG73S7jGD58uLgAowHV2UdppAkJCdJRKrjdKlha78aNG8Xd\nWldXR3p6OrfffjtgNXwIVyvVxigtecaMGZJlnZeXR01NjVhmyj2mmpO8+uqrYllES8ggNjaWjh07\ncsUVVwBWEtemTZvEW1VcXCxfjx07lieffFJcv5G8IanxuB0OR0hCFFgZ+xMnTpTvlRwVFhbi9/vl\nXmen0ynvJCUlRUJPEP6yJ1ViA/DKK6/g8XikFXBycnJE97pgGRg1apTc8qYSacFaU+p9g1W54Ha7\nZV/MzMyUr5tjrw7+vLi4OEnA7dSpE2vWrBFvVWFhIXv37hVZDk7sS0lJoXXr1rI/B3szGz+PYRi/\nSO6j/kAOri9VX6u4SUZGhhwaSUlJzfKSj/czg3/P4XCIK3Dx4sW43e6QPq+Reo5AICDul5qaGlJS\nUsJau32iBM+3ak8KSAcvlSH+/fffs3LlSikzS0lJYerUqdJK1eVyRWyOg2Pg6vae+++/n9zc3JDO\nbT169OBvf/sbYNUpB99UFi3YbDbZnBISEhgxYgTDhg0DrDr14Jh+JBWJo7VMBeRqPSXP6nY15Wrd\ntWuXfF1eXi5uTbDWpso4d7lctGvXTp7rWJ/ZVKi1OXbsWM466yypj26utanuFlAZ8xA6D8c6aKNB\njoNdz+rrnJwcJk6cKNUkixYtYs6cORIuKCkpkX2mb9++XHXVVXT+vzbHMTExP3ouNR/H2yHxR2OM\nhGD9H7/4g4KTClSLTCBk8TQ0NEgpBvxsATjWL0dsgn4BRxv3McccXE6h7vp85ZVX2L59uxzIkydP\n5r777hMLIdhqP8HF1SRzHXx3cyAQCLGSd+7cycyZM8Xyyc7O5uqrr2bcuHHADy0Sf8bG1iRjVvNu\nGAZVVVUy/tjYWBISEsKR2PKL5KOZOeG5Vnd7g3Wt39atW5k9ezZgyfzw4cNFrrOzsyXBJycnB5vN\nJo2Hzj33XClrVDJzjE22Sefa5/OxatUq+frkk08W5aAJD+TfpHwc8R8GxX49Ho8o8/X19SFKWVxc\nXMgZc7S1egQPynEt6ugzgzQajUaj+Q3SIizkxgRfbB1ctH0CmuNvwkJWpRRq/gKBgMTj//3vf5OX\nlyfN9C+55BIGDBggWl5wCcgJaugnNNfB8hosB6ZpSqa6zWbjf//3f6UzkIp9qpKMX9Dk4TchH1FC\nk1jI6t2qygyFKpdTPw92sx4pLny00rqfMe5fNNfB67TxOJuQ36R8NBPH9fJa5IEcBn5tL/lnu1F9\nPh9ut1ti8mGMAYZ9rlX7O9WZp3H3s1/Ab1Y+mgE915Hj1zRmaLnjFrTLWqPRaDSaKEBbyBa/Nq2r\nJY4ZonfcLXHMoOUjkvya5roljhla7rh/+KUIHsgajUaj0WiOgnZZazQajUYTBegDWaPRaDSaKEAf\nyBqNRqPRRAH6QNZoNBqNJgrQB7JGo9FoNFGAPpA1Go1Go4kC9IGs0Wg0Gk0UoA9kjUaj0WiigEje\nhxzNHUh+bd1fWuKYIXrH3RLHDFo+Ismvaa5b4pih5Y5b0BayRqPRaDRRQCQt5F81R7q6zefzAeD1\nepk4caLcoDR79my5ID0MV6ppNBqNpgXSog5kdUeout/UMAxiYmIA6z7k5jzcGn+2aZrU1dUB8O67\n7xIIBKisrARg165dtG7dOuJj/DkEAgH8fj/V1dWA9Xxq3jMzM3E6nVqZ0ByVIymo0YTq4W8YBh6P\nB7BkPj4+vqnu/Y4oje9O1rRM9JvTaDQajSYKaBEWstL+ysvLWblyJWvXrgVg9+7dtG3bFoArrriC\n7t2743K5gCNbrMFfq5+HU4uPj48HYNmyZZSWlpKeng5A+/btw/aZPwev14vX6wXA7Xbz/vvvs3jx\nYgC+++473G43NTU1gGU9qDm02+3ExsYyduxYAN544w2x+KPZKlKo54jEWNVnmab5IxkMtmRsNluL\nmLtglMcKrPCMCtHU1dXh9XpJTU0FwOVySbgmWp5RvYvDhw/z3HPPAZCfn8/UqVOZNm0aAAkJCVEz\n3qMRCAQ4cOAAn3zyCQAdO3ZkwoQJwA/7jyYU0zTF21dZWcm2bdtk31u2bBlpaWmceuqpAJx//vly\nxrhcrrDLQ9QfyIZhUFFRAcD777/P559/zsGDBwHo3LmzuIULCgpo166dCKHdbg+ZPJvNFuLWCffE\nmqbJ9u3bAcjLy6O8vJz6+noAYmNjw/rZx4vD4ZAD+Z133uHLL7+ksLAQgKysLEzTpKqqCgCPx0Mg\nEACsw9vv94ti9Mgjj/DCCy8A0fNsjfH5fCxZsgSADz74gDvuuIO+ffsC4ZMF0zRlzg4cOMCjjz4K\nwNatW6moqJC5jY2NpWPHjlx11VUATJs2TZS35g7FNEatodraWrZv3877778PWDLhdrsB6yDr3r07\ngwcPBqB///6kpKQAREWoI/i9uN1u9u3bB8CaNWvo1q0bU6ZMAaJHeVAoJcLv97N//34Ann/+eT79\n9FMaGhoAOOecczjzzDObbYzBBCuhjffi5sQ0TdavXw/AE088wZdffimyC9b4lILzxBNPcMYZZwDw\n0ksvkZOTE9bxR/WBbBgGO3fu5NJLLwVg37592O12+vTpA0CfPn3o168fAMnJydTV1ZGUlARA0nOx\nTgAAIABJREFUTExMiAVimqZo8DabTWLP4ZpcwzDYtm0bYC0gu91OWloagFgLzYXaVN1uN++++y4A\n8+fPx+v1ctFFFwFw4YUX0qZNGxISEoBQhcbj8fD666/z6quvArBnz56oUzaCN4P6+npeeOEFXnrp\nJcA6MG699dawjyHY6k1JSRGtvKSkhIMHD4o82u12iouLyc3NBWD58uWcfvrpAEyZMoXs7OyoiAs2\nNDTw+uuvA5YCd+DAAZljl8tFly5dABg/fjxDhw6lU6dOgLUWVVy2uTdjsGRDKaL5+fkUFBQA1rqo\nqakJ8WpEw3jBsoSLiooAePnll1mzZg1gKUY1NTXiGRwwYIDsbZEmOG9m1apVzJkzh2+++Qaw5nbA\ngAEAdOrUiTvvvJNWrVoB1n4YSfm22Wyyr23fvh2v1yvy6XK5SElJkbVZXl7O3LlzAVi7di1bt24l\nMTExbGNr/lWu0Wg0Go0mOi3k4PjOzTffzK5du+RnY8aM4Y477gCgR48eJCcnAxAXF4fNZpN/G6z1\nBAIBNm7cKO7YMWPGkJmZCYRPY6+rq+Prr78GoKqqCpfLJZZ9TU0NWVlZYfnc40HNUWVlpXx9zjnn\ncOGFF9K5c2fg2K7SmJgYhg8fzjPPPAPAhg0bxGWm4oaRJjieqawfpeXOnz+fl19+mfLycgC6du1K\nly5dImL9qM9wOBwMGTIEsDwKpmlKdm99fT2BQIDa2loA5s2bx4oVKwBYtGgRDzzwgFgXkfauKPmo\nrq7muuuuY8GCBfL/k5KSJN46Y8YMOnToID8rLCyU54sGN7VCuauVt2LdunVi1aWkpJCVldVsFubR\nqK+vZ/78+cyaNQuw9rP+/fsDUFZWhmEY4o2YNGlSROdayYfH4+HZZ5/l5ZdfBizLUs0xWOtA7eNO\np5MPP/yQ//7v/wZgyJAh9O7dG4CkpCS8Xq+EHuPi4oiLi2vSMdtsNvm8P/3pT+zYsUPCVyNHjiQp\nKUm8JjfeeKOE5kpLS1mzZo3kzoSDqDyQ1Yt8/fXXxY0H1gH87LPPivA1dkkDIYfw4cOHAVi6dCmP\nPvqoLLz//u//5vzzzweQja4pMU1TEtDAOpD79esnh11wck+kCXbDpaWlccEFFwCWyz8xMfGYrqNg\nF/CDDz5ISUmJ/Fs175Ei2CXt9/spLy9nx44dAOzfv5+ysjKJDS5cuJDKykp57ssuuyysbqdg1GfG\nxcWJG7qmpoaNGzdKLkRFRQUej0eUmqSkJIkhFxQU8Ne//pXf/e53AAwcODCih7JaMzNmzGDu3Lny\nPKeffjqzZs0St2Ow3Hi9XhoaGkQmIi0bP4XNZpMY8r59+6QcMTY2lp49e0ZNApqKa86aNYuPPvpI\nFMzf/e539OrVC7Di3p999hljxowBLHdwpMZtGAZbt24FYOrUqezduzckZux0OmWdxcXFhRhPvXv3\nluf55JNP+OijjwArLNKhQwdxzz/22GNNfiDDD6G1Sy+9lEAgIHOmco9UePHjjz/mnnvuAaxy1XAb\nUtplrdFoNBpNFBB1FrJpmpSVlQGwZMkSampqRPuePHky7du3D9GYlFURnIgBUFRUxCuvvALAhx9+\nyMGDByXhq6SkhB49eoT1ORYtWiQWZExMDL169RIN0efzRbT0Jpjgz4uPjxdNsXESXGNM0xSNfe7c\nuWzZskVceyNGjAhLiUWwNd/4/fr9fslSnjVrFp988olYnH6/H5fLxaBBg4AfMsQzMjIAuO222yKW\nRKLGHxsbK0lPgwcP5ttvv2XPnj0AUiJ07rnnAjBo0CBJCMzNzWX9+vU8//zzAPzxj38U2Q33M9TX\n1/Pss88C8Pnnn+N0OiXBcubMmT9yRQd7fjwej6y3aMoUV2NUsrJ8+XKRo65du3LKKac0e9IlWDKh\nXNTz588H4OabbwZg9OjREp5R3hXlwo5EUqWybB9//HGprlBzqOZu8ODBjB49WjKUhw8fHhJSSktL\nk7/z8ccfs2jRIsBKnEpKShIZD3fplt1ux263y9iCLWWwvIjKtX7gwIGwN3Rqfsk7Aspl7Xa78fl8\nsvFXVlZSWVkpk1ddXc3nn38OwKZNm/B4PPKzvXv3sm7dOsDaWOx2u8SNTznlFD799FPAyiZualRG\npHKLDRo0iMsuu0xi2DU1NfKMzRmvstvtx+xK1Lg8ZNOmTYDlAu7YsSPdu3cH4Lzzzgu7W1LlBygF\nbM+ePcyZMwewMn7LyspkM2jfvj3Tpk2TTeqzzz4DkIx85Y6KJHa7XTJhBw4ciM/nC6nxjomJIScn\nB7CeVS388vJyCgsL2bhxIwBz5szh7rvvBsK3Wal3fscdd/Dee+8BVvbp448/zo033ggcW249Hg8b\nNmyQcFCbNm3CMs5fSm1tLY899hhguSHVJjx69GiysrKaXXkwTZMtW7aIElZfX89NN93E6NGjAcvl\nqzrorVu3Dp/PJ2G8cI89EAjw4IMPAvDiiy9KnoDNZiM1NVVyCm644Qa6d+8eUu7W+BnVXj1lyhR2\n794NWOGmuro6UV4j1StAyXzjz3S73WJI9e/fX7Kzw0VUHshKs+7QoQObNm0SrTY3N5d58+aJNvbN\nN99IwN3j8eD1esV6jouLEw3M6XSSnp4uwfjTTz9dNr9wYBgGpaWlIlRTp06lT58+tGvXDrA2BHWw\nNGfCiyrHUgQnxRmGEdIYpLa2Vt7Lvffey+LFi+X5vF5viEA3FUeaF/U5BQUF7Ny5E7CshLi4OAYO\nHAjALbfcwvjx4yWJpKKigkAgIMpXc5UQKaUlKSmJCy64QOa2rKyMhoYGiZs5nU5pHlNZWUlubi6H\nDh0CLM+LKk3r1atXk8uOYRi8/fbbgNXyVW24d911FzfeeONxKZBVVVWsXr1a1mK3bt2OqbBFskGL\nz+fjjTfekHKchoYGidfPmDEjLPHKn4vf7+ehhx4SBf6ss87iyiuvFEXSZrOJhX/48GH69Okjsdpw\nz2Vubi7/+Mc/AEQ2wOoJ8dhjjzF06FAA0tPTSUhIOK61ZhiG7I3nn38+JSUlXHHFFQBhPwAVKo9g\nzZo1bN68WbybwSWy3bt3JzU1VcYUjn1Ex5A1Go1Go4kCotJCVu6NM888k+3bt1NcXAxYbuhnnnlG\nXB01NTUhzRXi4+Ol7KZ3795iwdXX19O3b19xsaalpYXVSqqsrKS4uFg02lNOOQWbzUZpaSlgxa2U\n+9zlcv2oq1ikMAxDSoScTidOp1Ms97y8PPbv3y9uxy5dusicNTQ0kJCQIN1sDh06xPjx4wFITEwM\n67Oo952fny8xWKfTyaRJk/jDH/4QMlZV0K9K4MIRnvg5qHlJSEhg8uTJonnn5uaybt06TjrpJAAm\nTJggFk9MTAylpaUiO5s3b+bNN98ErC5CTT3XZWVlPP3004DlFVG3kv2///f/ftI6Vu/mzTffZMeO\nHXTs2FH+jpIdu91OIBCQNR7pDOzS0lLmzZsnFpDD4eCaa64BLI9DczZgUVb8jh07WLZsmcz3zTff\nHBK7rK2t5ZFHHgHg+++/58ILL5R5DHczk6+++kqaAAHikn722WcZPXq0jNntduNwOGQ+g2UnEAgQ\nCATE07lr1y6GDRsmf6++vj6kIiXce6NhGLJ3zJ8/n4qKipBGIWotJicnU1ZWxg033ABYJZ5NPbao\nPpBHjRpFWVkZH374IWAdyGVlZeK2tNlsEpdr06YNw4cPZ9y4cYBV2xa8IaiEHkD+TVOjFtSmTZvY\nvn279EBNSkqivLyct956C4Di4mIR1JtuuomUlJSQjSlSh7PH4xFXaFpaGl6vV1w3LpeLs846S5JE\ngscUFxeHw+GQcEFzxGSLioqkrlj171VxNKfTSSAQYOHChYA19szMzGat/Q7G6XSSmpoqiSsZGRnk\n5OSIm7p9+/ayBqZMmUJmZibLly8HLGVIxcQffvjhJo0jm6bJvn37pFUtIIfVT7lyDcMQefjyyy8p\nKCiQsNBXX30lCmggEKBfv37iJo6UMqr2jOeee441a9ZIDkd2djZ33XUX0Pxd5tSYHnzwwZC48Mkn\nnwwgdeo333yzJEHFxcVRV1cnh6TT6QyrkjNw4EAxeuLj47nyyisBGDt2bMi+6nK5QvIkgm+L8/l8\nrFixgtWrVwNw0kknSf/tzMxMHA5HxDu7KXlUiavq87Ozs2UtlpWV8dRTT8m433rrraPenfBL0S5r\njUaj0WiigKi0kJUbtaysjIKCArHiamtrQ1wYDodDXCZnnHEGN9xwg1ilqampIe6SmJiYsGvAykJe\nuXIlRUVFoj1t3bqVvLw8sTxUdx31b9xut1ggMTExEXHRgKXVKc26tLQUp9Mpbv5WrVr9SNMOvu3J\n5XJJIw7VmQkQF2c4ME1TEp/27Nkj7z4nJ4eEhATRxlNSUkLKnJxOp7iZogFliX7xxReA5Srt06eP\ndA9yOBwiD/Hx8YwbN066fM2dO1eaMaxZs0YaQjQFKlNWJa24XK7jbpzj9/vFct+9ezdJSUkcOHAA\ngH//+9/ye06nk7vuuivEQg43pmlKAuDbb79NQ0ODrLHp06eL56S5s6tVWGL9+vXExsZK2V5JSQn1\n9fU8+eSTAMyePVvWcEZGBl6vV2Q/KSkprCWVQ4cOlf7333zzDVdffbV8buMEVYfDEdKARSVZLlu2\njJKSEvGsDRo0SN6BcnNH8l3Y7XYeeughAC644AIOHz4s+2BOTo6U4T788MPMnTuXpUuXAvCf//yH\n6dOny7ibgqg5kINbsKmuLR999BFr166VjELDMHC5XHKwxsfHS7u+zp07h2xkwansqhXbkWomm/LF\nB2coB1/xtW3bNtavXy8xz+TkZIlnOxyOkIsvfD6fHOTh2KwMw5A4cWVlZYjrLiUlRTKpVVmCWviG\nYchcGYbBhg0b5HB0OByyIYQr5tN4PKZpyuHfqlUr3nnnHf7+978DVlZvQ0ODlGnZbDa2bdsmnbti\nYmLwer0iO8F1spHYCPx+PytXrhTFs7S0lH79+omLt3F2anx8PE888QRgxbjUO1u0aFGTHsgAbdu2\nZcSIEYClWCq5PNJ7DS5d2bdvn8S2KysrcTgcsgG3bt1a5D01NTVEWY4EgUCAm266CUCUYnUYXHvt\ntVHRScw0Tens5/P5SEpKkrFedtll7Ny5U2KupmlKqEJ1AFSlOeEmLi5OlMPOnTvLfnG0daPec01N\nDa+99hoAW7ZsISUlRUrounTp8qPLfiJ5INtsNokTDx8+/Ec/Vz+75ZZb2Lhxo5ScrV+/nssvvxz4\nFR7IamFv3ryZp556CrASiwKBgLzU1NRULr74YokNb9q0SV5cZWUlW7dulYWWnJwsQnuk1o7heOHq\nb/r9fhoaGti7dy9g1e1WV1dLzHD69OlyNZ1KplItCjdv3kzPnj2B0PhFU2EYhljFypIBq4yosrJS\nShkqKyupr6+XhDqn0ymNK9auXcucOXNkzOnp6XK4hAulYKmFO27cOPLy8gDYuHEjmzdvllapX3/9\n9Y/KsD788EO589ThcJCamsrjjz8OwLnnntvksaBj4ff7qaysFPlMSkqie/fuR93cbDYb3bp1A6zm\nFeq5VYOZphxzYmKiHP5///vf5UB2u90h9wMbhkFdXZ3Ejf/0pz+JghYTE0MgEJBSlpycHFGe+vfv\nH+JFiUTSzp49e9iwYYN8ntPp5JZbbgGseH1zW8YKtZ4SEhIwDEMUyIKCAtxut+yD8fHxUsJ55513\n0r9/fzFEwq3oBOftOJ3Ooxo2SoFX+/pHH33EqlWrAMvoysjIYPLkyfI80fIOjjQOdXZ07NiR008/\nXZ550qRJTd4GWceQNRqNRqOJAqLGQlaayfLly0UzVNq5clGPHTuW++67T77/4IMP+PLLLwHLJbJ7\n926JRQwdOlQ0m0g131CfcdVVV/Hcc8+JG3fDhg106NCBa6+9FrDcIsEt2g4fPixZk//85z9F2339\n9debtFm8YRhUV1dLCGDBggVSTrF48WK2bt0qWdbKra007qSkJGnn6PV6OXjwoMzv0KFDJUM42KPR\nlKi7hZV1dc4554hLNCMjgyFDhogGvmbNGurq6kK098TERGnI3759+xDXbKQ0dDWeQCBATU2NuNTH\njh1LUlLSMceg5nrMmDES+lCeoqbEZrPJ373wwgulo92DDz7IyJEjxY26c+dODhw4IJe/7Nu3T9Zl\nZmYmvXv35swzzwQsj8CoUaOAH7odRfIChFdeeSXkLvTs7GyuuuoqoHk75TVGdZJLTEykoqJCMn6z\ns7PJzMyU71u1asWdd94JwLBhw4iLiwuphgj33Abn5qh5VR4pZRGrMJDyxs2fP188AA6HQ+4dVn8v\neK1Gi7WsUM8UCATIycmR74cOHdrkHsyoOZAVXbp0+dHLUYfGjTfeSGZmpiQQrVu3TlxR+fn5dO/e\nXVw5cXFxEb+1RX1O165dGThwIN999x1gufsqKyullWdCQoIkCtTX1/PBBx/w1VdfAVbimkp4+f77\n7+nYsWOTjd/v97NlyxaZv4aGBlF++vXrh8vlknEYhkFCQoIsuJSUFFn0aWlpGIYhf6dr166yuMI5\n106nU9y6KSkpcjirOm+1UPLz87nrrrvkwPP5fFx99dXSC1g9i9qMI9VrWW1ac+bM4fvvv5fYVHBJ\n3k/92x07dsiGGK4xq7/ft29faU+6ePFiFi1aJBtseXk5DodD5MPj8YgLvlWrVpx66qlyILdq1UrW\nYkpKSoiCHO55d7vdfPbZZzJ/Ks9EudfDodT8UlRp2ODBg2loaJDa3C5durB+/XpJpho8eLDcsuVy\nuSKeA6EIPkjdbjdFRUVS392+fXvq6+ulvnfXrl3yu/Hx8QwcOFDi3pE+gINzH+DY8V+/3y+K/7x5\n89i5c6eUeiUnJze58RE1B7J6Kb169ZKN3+PxSPNvsNrEfffdd5L0tWTJEllY9fX1pKenyyYdGxvb\nbJqWw+Hg888/F4vs0KFDFBcXS2zO4XDI5uXz+XC73SKssbGxshAHDRp0ws8QHONYv349H3/8sVjg\nQ4cOFa1c9X3esmULYNV8FxUV8e233wKWhaxigvn5+fh8Pvk75eXlkugQrpakSjk7Vqa8kpNOnTox\nffp0afFnt9s588wzZWzN0a7UNE3xTHzyySdUVFRIAolqgnC0f2cYhlj/O3bsEEVC3eHa1Ki5cblc\nYol17dqV8vJyWW/Lly+npKRErDZV6w2WZX3ttddKtnZw0kzweo4Eu3bt4sCBA5IIpyozguP1wReX\nNJeFZrPZxLt36623EhsbKz3A6+rqePPNN2WNxcbGiiIR3HyjOcasZLG4uJjnnntOPJaJiYnU1dWJ\n0m6aZoiid/PNN4cYTJFQKIKVh/z8fMBSFrOzs0MO5eCe+evWrZMLVjZt2sSQIUNCLvJo6vHqGLJG\no9FoNFFA1FnInTt3Fs1/9erV+P1+0cpvvfXWkIxkpZ2Dpc2feeaZUsrS3HGIlJQUqX0cOXIkO3bs\nCGk5p7KZHQ4H2dnZ4r65/PLLpbF6U7irgzvkeDwevv/+e7EUJ0yYIHOtLE9VC5uVlcWePXtEq3Q4\nHFJ2kZaWxqFDh2T+6+rqxPrr3r17s7YfBGusWVlZ4tbzer0kJiZGvPtPMF6vl9///veAZV22bt1a\nOjApKyM4VBN8zeTChQu5/fbbAeuGM9Vis0+fPmEPEajcgBkzZlBXV8f+/fsB6NGjB2+++abc0pOS\nkiLW9NVXX018fPwR22NGeu6/+eabEA+U3W5nxIgRUvHQ2DIKnvtIj1VlL/fv3x+v1yvzt2HDBrZu\n3Srj6tq1a4jHoTlRY8zOzqZnz57iUauoqKCmpiak54Hq8HX99dfTrVu3I1rI4UTtgx999JF0f7zg\ngguYPHmy7H+BQICKigpmzpwJWDfFqfrwNm3acPbZZ0slTzh6RkTNgaxITEyUa8cuvfRS9u3bJ+4D\ndYgF98VV8dYrr7yS22+/XdyozX0gA3LIbt68mf3798tLnjdvntyKMn36dAYMGCAutOB7iZv6GVJT\nUyktLZVks5kzZ0qctV+/fqxbt07aURYVFWG32yU23KZNGxlX69atiYmJkfDAiBEjpM60uTcIsDbW\ntLQ0ic1u3LgRv98f4r6PRLmN+hywNihVIlRVVUVDQ4M0zMjMzKRv375yOKjrO8GSlRdffFFyDlq1\naiX11krhCCdqjtSGpdyoMTExcu80WDcSqVuoVFwz3LHuY6Hm3ev1htx3m56ezo033njEG3vUwdDU\npSzHi5onlSyl8jmefPJJSkpKJCRw5plnRsWdzcEkJCRw9dVXh5Qebtq0SWQ6NjaW0047DbCSGIMT\n+yIlH6o0c+3atZLPU1VVRUFBgcj3hg0b2LBhgzSjgh9CSmPHjmXatGlhPWOaf/fUaDQajUYTfRay\nzWYT6/Hrr7/mxhtvlLtLVUan0hSHDx8uGW8TJkwI26URJ4rdbqfz/90XCvDYY4+FaOHh1hCDP6tP\nnz5yUcH+/ftZsGABYCXF+f1+0bwzMjJITk6WBiZnnXUWXbt2BawsSYfDIa7WxMTEqPJMqIxaZd3H\nxsaye/dueZa4uLiIjVONJTipJRAI4Ha7Ze5zc3NDLJ5AICDZqlVVVTidTrnr+YUXXpBOSZH0Rths\nNpxOp5Q9LVq0iP3794tn5+STTxZvVXO0Pzwao0ePplevXhIuuvHGG+nTp88xwxfNPW673U5VVZW0\nyly1ahWBQIA+ffoAVlgpGjxRwdhsNlq1asX1118PWE2HNm/eLHKdlJQkoZbGmfaRQr3zxMRE8TgV\nFBSwfPnyH61TJctDhw6VUNPQoUN/sjzxRIm6Axl+2GjatWvH/PnzxWXd0NBAfX29bLTBcarmXkQ/\nl0iOV81Rr169eOSRRyQGv2/fPpYsWQLAwYMHaWhokEXfv39/evfuLd//lCBGw/w37rWtvs/MzGTJ\nkiWcffbZwA834kRizEqWExMTpc3ezJkzqa6ulk1Add5SG4bP55Ms/N69e3PVVVcxbdo0wAoXNFer\nR5vNJq7efv36UVtbK/Ixfvx4UdCi4TBWnz948GAWLFggse/BgwdHVCH7JdhsNpKTk8XN7nQ6adeu\nnYQqwn3F6S9FdcAD6wanbdu2Sfy1R48eIsMZGRnNolAot/TEiRP5+OOPAatqwe/3y8+ysrKYMWOG\nXNXapk0bidcfz55xovkHtgjGS5onMHN8HGv2WuK4W+KYoYnG7fP5qKiokOsX8/PzKSkpEYsjPj7+\n527KJzxm0zRFEdqzZw+5ubkhtehFRUUUFBQA1mammvanp6fjcrl+abytyeVDKRF1dXVUVVWJhRzc\nnvYEDwu9Fvnh0hmwmh5lZGSE43KcsI15+fLlzJs3T8oo77nnHlGIT7ART5PIR+N7B4IbNYVJ2Tmu\nPxpdfg+NRqPRaH6jaAvZQmvlkSMic22apsQNDx8+TH19vWRL/oJmClo+Gv+BRvtGE1oVeq4jR9jG\n7PP5KCwslE6AAwcOlDDMCTbU+LXJR+gv6QMZ+PW95JY4ZojecbfEMYOWj0jya5rrJh9zE5YZ/trk\nIwTtstZoNBqNJgrQB7JGo9Fowko0ZoVHI5F0WWs0Go1GozkK2kLWaDQajSYK0AeyRqPRaDRRgD6Q\nNRqNRqOJAvSBrNFoNBpNFKAPZI1Go9FoogB9IGs0Go1GEwXoA1mj0Wg0miggktcvRnPB86+tHVtL\nHDNE77hb4phBy0ckCetc+/1+6c2+fv160tLSAMjOzqZ169bye4ZhhNyt/RNo+YgcunWmRqPRaDQt\nhUhayE2GupM1Gi5C12g0mnCibi7r2rUrADExMfh8PgCefPJJrrnmGrm97GdYx5ooJCrfnmrn2fiw\nNU2T6upq/vWvfwEwYcIEevfuDfBzr9PT/AZQcuT1evH7/XL9m2EYIcqclh1NNGOaJnV1daSnpwOQ\nmJjIk08+CcC4ceOw2WzaMGlilNHndrvl66SkpLArPHon0mg0Go0mCohKC/lo2l4gEOAf//gHS5Ys\nAaB9+/ZiIWuaBmVV+v1+ysrKqKmpAaBVq1YkJiYCJ3zBeNgxDAOv18uePXsAmD17Nt999x3Z2dkA\n9OjRg6ysLCZOnAhATk4ODocDsNyBmh9Q8mAYBvX19ZSXlwOwYcMG0tPTadeuHQAul0sSjeLj47XX\noQlQc79lyxbuvPNOOnXqBMDLL79Mjx49AKLeOg6WH4X6Wq25aJIV0zTxeDy88MILALz++uvU1dUB\ncMEFF/DMM8/IPhgOovJAPhper5evv/6aDRs2AFBbWxvVwtjSME1TYlMFBQU8++yzrFmzBoBRo0Zx\nxRVXADBo0CDi4uKabZxHI9hFvX37dr766isAcnNzKSwslN9LSUlhx44ddOnSBYCsrCxiY2MjP+Ag\nTNOUjUopFAClpaWsXbsWt9sNWO9BjdvhcIRd/tWcVlVVsWDBAl566SUA9u7di8PhICkpCYChQ4dy\n2mmnAXD++efTunVr2XCbg+Bb7EzTlHlqSfuF3+8H4MCBA4wePZp77rkHgNTU1OYc1nETCASoqKgA\nYO3atSxbtgywxj9w4EAGDRoEQHJyshxyzX04G4ZBQUEBH3/8MWCtP/Ueli5dSn19PS6XCwiPLLWo\nA7miooLVq1eL1aY2g5aC2nAbGhpYsWIFYGlga9askZc7atQo/va3vwGQkZER8Q1EjbGoqAiPx0NR\nUREAM2fOZNasWQDceuut/OlPf2rWDbcxpmnK4l+xYgWrVq0S5SIzM5MrrrhClIiqqip27drFp59+\nCkDfvn1JSEiQv/NL5/yn/m3jQ0KNr66ujpKSEubPny/jX758ufwsEAjI2Hv06MFnn30GWJZ9OOXD\nNE1qa2sB+Mc//sHLL78s8hAIBLDZbJSVlQGwf/9+PvnkE8Cy4K677jquu+46wIp5RlKOvV4vixcv\nBuDdd99lxYoVYsmPHTuWadOmiWctNja22Q+Bo1FcXAzAwoULOf/880lOTm7mER0/pmmicN03AAAg\nAElEQVRSVVXFvffeC8Cnn35KdXW1/Lxt27Zi8Xft2pUHH3wQsLyezbGvqLXp8/lYvXq1lJjZ7fYQ\nS76uro7MzMywjSM6JVGj0Wg0mt8YLcJCVtrLypUrxQoCGDx4cLO7oI6WEd74d2pra8Xl+9lnn4kl\n6nQ68Xq98nf27dsnGvsbb7wRcW1RjSMrK4sbbriB6dOnA1Yc9tVXXwXg0Ucf5eqrr5YyjGjA7/ez\nbt06wGqc0KVLFwYPHgxAmzZtSEtLE9fT999/z9q1a9myZQtguaVUfPlEsiiPJANHiqH5/X4qKiok\nF2LlypUUFRWxdOlSAGpqasR6VqhMz2HDhpGRkXHUz2tKTNNk9+7dALz66qscOnRInsNut2O320Oe\nT7nVd+/ezTPPPMO+ffsAeOKJJ8QDEc4xKzf/+++/z+233w5YXjWbzcbBgwcB2LRpE3PmzOGqq64C\n4L/+67+ksYZ6nuASoubaXwKBgKy3ZcuWceedd0atJX8kAoEA69evZ9euXYAl80pu/X4/lZWV8r52\n7dpF3759Abjlllua1UIuLi5mxYoVEjcOBAIynvT09LDLQ4s6kJcuXYrP5xNXtXrBzcnxvKBdu3Yx\nYsSIEGVCbVCTJk0iJiaGL774ArBclOrgaI7NQMVSu3TpElIaNHjwYNlwZ82aRUlJSVQdyDU1Naxc\nuRKw3Lp9+vQJcUvabDbZ0DweDxUVFSJXcXFxctD4/f6wbMR2u10OVY/Hw86dO9m0aRMA5eXlbN26\nVdxkNptNNgGbzUZ8fLwcIM8884y4ryNxICs3Y0NDA06nU9Zev3796Nixo7isd+7cKb/r8Xiora2V\nXI+8vDz69+8PELbN1jAM/v73vwNWba5aa6ZpEh8fL+NOTU3FZrMxe/ZsANatW8fAgQPlGb1eL2ef\nfTZgxcXVv4v0Wjx06JCMMSYmhrZt2x71d4NDIdFioHg8HvLz8+U9nHTSSVx00UXye7t27WL//v2A\npRAHu4FPJGz0S1FrMz8/P0RZMAxDnslut4c9d6blqFwajUaj0fyKaREWsrIcPvzwQ/x+f4iWcjwu\n4+Zi8+bNAAwZMgSfzycW2vjx4/nnP/8JQOvWrWloaOAvf/kLAB999BEPPPAAEPmMw2ArUlnHan4T\nEhLo2bMnYGns0eQ+CwQCfP7551RWVgJWklbfvn1/5H5WbuAVK1awZ88esYyys7N/9NzhQFnhHo8n\npLwqEAjQ0NBA+/btZTwejwewEl4mTJjAZZddBiAZnpHAbrfLmE4++WQCgQDDhg0DYMqUKZimKWVQ\nmzZtYu7cuYCVFezxeCQJqba29kelLk1NbW0tzzzzDGBZXOod5uTkMGnSJMaNGwdYyUSbN28WV/y2\nbdt48803ActdaZomixYtAuC1115j6NChYR33kTAMg1mzZpGXlwfAlVde+ZOWWTRYyaZpinevuLiY\ndevWyVgGDhwo8tC2bVu6dOki7uySkhJxWZum2SwWspJP5eVRFnLwWFJSUkhJSQnr2KL+QDYMg7fe\neguw3Dg2m42TTjoJsGI8ytUQ7OKLBhYuXMh5550HWBtuUlIS77zzDgDnnXdeyIHm9/vlmYYNG0av\nXr0iP+BGNJ7H+vp6Fi5cCFguXtX1Khpwu90sWLBANs/u3buHbKA2m41AICBZzG+88QbJycmceuqp\ngJWtHy75UX8veMP0+XwsXLiQrVu3AnDw4EG6du0qLr0xY8bIeFq1akVCQkJED2KFzWaT7OT77ruP\nvXv3imvRNE2KiorYvn07YNXKqjG3b9+erKwsKYPq0aNHyDw09RybpsnevXtlMzVNU0qDZs6cyfjx\n40OUs9GjR0tM+c9//jOrV68GLJd1IBBg7969wA9uzEjjdruZP3++vPO77777iF0LFaoTHVhrM1yy\nfKR3F5xDUFdXJy7q7777juLiYlHounXrJiFGJdPqHXXr1k1CA5Eo5TsSaj/Ozs6mqqpKwnN+v1+U\n5+zs7LArZlF/INfW1or1aBgGLpeLp556CrAsNbVoDMPA5/PJhDkcjhCrJ5hwv/B9+/YxZcoUGVty\ncjJbtmyhY8eO8jtKkOvr63n44Ydlg3j00UebrZwouBbWNE0CgYBYaqtXr5avx4wZIyUL0YDP56NX\nr16iAMXHx4c0TAgEAmzYsEFKK+rr65k6dSpXXnklEJlGJ6ZpyiJftGgRubm55OfnA5ZC0LNnT2n2\nkJycLJtAXFxcszZiUePo1asXhw8fFqWmoKCA3NxcKUFMTExk+PDhAJx99tl07txZPBCpqamyBsPx\nHIZh8PTTT4t8OhwOzjzzTMBqLRkXFxfyuQ6Hg86dOwPQoUMHeca6ujpM05TDO9Jlh2q/mD9/PgUF\nBWLVt2nTJiQpEH7YPwKBAAcPHpRnyMjICJvyFqwEgPUulSXp9Xqpr6+nqqoKsLwkFRUVspdlZmbS\nr18/wPK2+f1+aSSTnJzc7E1C1Dvv1KkTXq83JKlSze0555yjW2dqNBqNRvNbIKotZNM0KSwslGYE\nDoeDq6++mgEDBgChHY18Ph9ut1tiLbW1tbRp0wawNJxItJhT1sLQoUNDLjPYvHkzHTp0kN8zDEOs\no6uvvpotW7Zw+umnA5Y7p7m0RJ/PJ3N96NAhYmNjRVOsrKxkypQpgNVcIZqaFCQmJjJu3DiysrKA\nH+LAyqpYu3Ytl1xyiWj41157LbfddpuUOjWlXBwtp8Hn80mXq1deeYWKigpxM9rtdvLy8iQGm5qa\nSvfu3QGYMWMG48aNa7bbfNRzOJ1OvvzyS/7zn/8AllvV5/PJz2tqasQFP2jQICZNmiTdl8LthgwE\nApSUlMj7jomJYciQIcCRvR8qhAFWxzGVo6JKnlQ8s02bNhFdizt27ACsxioDBgzgzjvvBKy5r62t\nlW5zrVq1EtkpKiqipqZGrGKHwyF7YFPJSrA13rgCQVm28fHxxMXFiev53HPPxW63S4jjrLPOkv1Q\nNZVR44umdrUul4tDhw6JtyX4GU499dSwy0NUH8iGYfDKK6/IoZCVlcV9990ni+ngwYOsX78esGo5\nY2JipJzo8OHDXHzxxYCVUBDubkF+v5+pU6cCVu1jYmKilOG0b98ewzAkvrJ69WpJJNm0aROBQIBz\nzz0XIKx9Uo+FclcrReHzzz/HNE1xo/bs2VNiQP369YuaWD1Ym26fPn1oaGgArEVlGAbff/89YB1q\n1dXV/OEPfwDgtttuEyWtqWn8N5Ws7t+/X8pygg8PhVIs1c/Ve1i1ahUTJkzg7rvvBqzkqkhuYOp5\nYmNj8Xq98jzqv8G9z1WSzt/+9je2bNki892zZ8+wtia12Wx4PJ4QpUXFgb1er4QwFMEdyPLy8kLc\nkzabTcIxkYzbNzQ0SIc+u93OlClTxOXv8XhYtmyZ1M07nU5KSkoA60D2+Xyy702ePJlLLrkEaDpF\nKLjtaOO/1/jQV7LZs2dP2rRpIwdbIBAIqWF3OBzyu5EOKR6Lffv2cfDgQRmrzWaTkj3lYg8n2mWt\n0Wg0Gk0UENUWstvt5u233xYtvEePHlRXV7N27VrA6h6lugElJSWRnp5OaWkpYKXdHz58GIDnn38+\nbH2v1dgWL14s2ZoOh4Nrr71WNG2fz8f27dulF3SHDh2k0N/v95OQkCDu4OZK6LLZbMTFxTFq1CgA\nOnbsyN69e8UN+cUXX8gcduvWLeoa3Pt8PrEqU1JSWLBggbhXa2pqeOKJJ5gxYwYQWbev0vbdbrd4\nGKqqqvB6vT+68ED9N9jt6PV6WbRokcj5k08+ySmnnAJE9tYth8PBKaecIs1Mtm/fLlnJ8EO5Cljh\njXnz5omHaPbs2WEtH3I4HMyYMUNu9yorK5OKgP79+3P55ZeLBWm32/F4PMyZMwdAZEb9zG63Szim\ncRJTOFCW2HfffSdjGTVqFBdeeKF4ywoLC9m1a5dY9aqLlBrjoUOHQvpEn3POOQBN3sTieGQtOMTh\ncrnEK+j1esX1qxIVgxP91Fw3R8kT/PAeZs6cSU1NjXwfFxcn4Y9I7BtRfSAfPnyYuro6mYgOHTrw\n/vvv8/bbbwOWa1jFDUeOHEl8fLx0Djp8+LC4ycLpBlYb0pw5c0Kyqm02G9999518/vr168Ut3b17\ndyZPngxYrqpevXpJPLM5UV2hADp37hxyLeGsWbNEEWrdujVXXHFFxOOZRyMQCLBx40b+/Oc/A1Ys\nzufzycH1ySef0LZt22Ytp+jXrx/ffvstgBzGSj5jYmJwu90hG60qJ/rLX/7C1q1bOXToEGCFElRL\n0HC53Y/2HOPGjZPPy8vLIzk5mW7dugFW5qy6EONf//oX5eXlEvO89tpr5bKH1q1bh6UL2tSpU6V1\n6qxZs0Qxf/DBB3n//fdFgayqqiI/P19+7vV6Q5SfQCDAqlWrAOvgU27VcMyzaZpSO//Xv/5VLpMY\nP3687CFguc579eolnecGDhxIeno6YNVc//vf/5bS0DVr1ohcNdWY1eGkeikc7991OBwSijlw4IAo\nOi6X66ju9OZyV6txLlq0SOLzYI117NixQGSMpejYURuhtCW3201aWhqtWrUCYPjw4axatUriJzEx\nMbI5nXXWWWzbtk0Oibi4OLnvNhJX61111VUS4+natSvjx4+XNP+kpCQGDx4cknCmLE/TNBk5cmRU\nNdoAa5OLj4/n5JNPBqwYqIrRbty4kYsvvrjZD2QlJ9u3b2fGjBlSOuZwOBgwYIDUfSv5aU6cTucx\nY1Aul0vGaZomOTk5gCUrTz/9tGzWxcXF8h4i4aUItlxSUlI4//zzAWvtBVvoNpuNSZMmAZaFNm3a\nNCmB2bNnD19//TVASPvEpiQ5OZk//vGPgOV1+uijjwBrvr799tuQqy2DLV+HwyEbbSAQkJpmsJQO\nZR2FYzM2TVMs+Q0bNojXrHHb2pSUFCZOnBiSBKX2i4SEBHJycsQY6Ny5s3gDmgKv1yuepksvvfS4\n/51qEqK8FomJiXIgNy5Da25UTT1Y3pVg+UhNTY1oi+DoOgU0Go1Go/mNEpUWsiIrKyukwUZ+fr7E\npcAqgledYDZs2MCsWbMkznL66aeHuBrCpZEprfWUU04Rl53T6QyxeBt/9jvvvCNuEYfDIZp9NKE0\nXKUtjhgxQlyqbrebQ4cO0aVLF6D53EwFBQWAdUFHUVGRuBdPP/10Xn75ZXHrHYloaDV4NIJLQvr2\n7Ut2draU1AXHt8JNcKOY+vp6nE6neHmO5C5XMn/yySeTlpYm7lifzyfr9sILLwxbdruK0T/11FNS\n8fDQQw+xY8eOkHCAYRhi8bZr106yrNXeEfyM4aS+vl5u/Dp8+LB0RUtKSgrJaI6JiQmx0E3TFC9J\nQUEBL774oniHLrrooia15isrKyWcVlxcTKtWrY55uYmSF6/XS3l5uViXbdu2Fctdja/x/eBH+5vh\nxuv18sYbbwA/lK4qWc7JyYmIh1UR1Qey8t+r3rIbNmygpqZGBCIrK0v6Refn53P48GEp0/nd734X\nljrToxHs+joaSlhVOQhYMTXlnowGgi/qLigokGSRjIwMKcNQbqjmZPv27RKSKCsrIy0tTVymkyZN\nwufzyQabmJgY8m4CgUBIqUt8fHzIjS7RgFJCS0tLqa2tJSUlBbBqY9XX4cYwDAnDLFq0iAkTJtCn\nTx/gyGEgNeZ169ZRXl4ekuCj4vnhXIvqbyclJXHGGWcAlqJcWFgosuD1ekMSuXr27Mlzzz0HWNc2\n+nw+md9wX7cXFxcXotSquPbChQuZNm2a5HM0NDRQV1cnMfm8vDyJl2/dupWDBw9K57GbbrqpSWU4\nJSVF9tTly5czfPhw6akQ7Do3DINAICD7RXl5OXa7XdzwLpfrR+NSa87r9TbblZfqmkhlTDVWDNq0\naRPRhLOoPJDVQ6usR7V5HjhwAL/fLwu/vLxchDg9PZ2zzz6bhx9+GLBqxqLN8lFabFVVlYztxhtv\njJpDwDAMaZJQWFjI0qVLZSM777zzRIN3Op1yh2wkMU1TlIEzzjhDcglat27Ngw8+KHLy1Vdf8fbb\nb8smdc4550j2OFgHcCAQkGQ/v9//Iwvkl8pOU1jehmGIXD///PPk5eWJcjlgwIAmT9ppjHqG0tJS\n8d7k5uaydu1aqYceNGgQDodD1mJRURH33XcfAAsWLKC+vl7GecYZZ0hCYyST0MA6CFSTFbCebejQ\noSFtVadNmwbA3LlzcTgcUn8c7j3E4XDIfHbs2JEFCxYAsGTJEr7++mt5DzU1NWzZskUu8khJSRHl\nvmfPnkyfPp3rr78esGLITTnm4NyHcePGsXPnTrk2MTs7Ww5nv9/PoUOHpD1sq1atSEtLC7GGgxuM\n+Hw+SaSqqamR/IlI56X4/X6WL18u3jZ1wY4at9PplEThtLS0EM9QOGQjOk4CjUaj0Wh+40SlhRyM\nz+eTko+ampqQlPSGhga5Ueb++++nV69ezZ75ezT8fr+4VA3DkIzDe+65pzmHJfj9fmpra8Uy+/bb\nb9m2bZvUHge3kMvOzm6WTMny8nIuuOACwIpnqZjUKaecQufOneXig0WLFuFyuaQkp6qqiuLiYokx\npqWlhYy/sUV8ItaxslwCgcBxhTHUv4MfQhp79+7l8ssvB6wSLr/fz+jRowHr2sNIdeqqrq6WTngl\nJSUUFRWJC3vo0KFkZGSIq2/Hjh3SlUm1oBwzZgxglQRGy+1gjbtNORwOqYZwOBz4/X6Jz4a75Wdw\nmeGVV14pGegff/wxy5Ytk7W4f/9+SktL5frTiy66SEIAffv2Dbl9ran3P4fDIRay6oam3OV+v18+\nr7S0lOrqannnqqRLebE6deokcuvz+Vi7dq383Z49ezbbbX1+v5+CgoKQvAy73S5h0ZSUFPESBgKB\nsK+96Dy9/o/Y2FjOPvtsPv74Y8DasAzDEFfYmDFjeOGFFwDrNpFoc1ErAoEAr732mrQWdDgcPPTQ\nQ0DTF+//XJTLsba2lh07dvDFF18AVi/rkpISuTUnMzNTFr0KJUQSv9/PvffeS25uroxbzV1cXBzv\nvfee1O4OGjSIa665RuKI8fHxP3I1hevWITWfwX2eG3+maoQQXN/pdrsltnnVVVfJfb2GYdCqVStu\nvfVWwFKGwi3n6u/n5OSIq7ykpASPx8POnTsBK47Z+HYwhdPp5IwzzpDmG9FyGP8Uyq2qXKnl5eWi\nkEZizpWr/JJLLuHiiy8OUe5sNtsRDy0lS+EaX7DcOp1OBg0aJA2XNm3axNKlSwFrv2jbtq2syU8/\n/ZRdu3aJTMfHx4sc9OzZk7Zt28re0rp167DWex8Lm81GWlqauNoVajwlJSVyIKu5COcYtctao9Fo\nNJooIKotZLvdTkZGBo8//jhgFcGvWrVKsmsfeeQRKW2JJutYadrK9TV79mzuv/9++XnHjh25/fbb\ngeYfd/DlB++9955YNXa7nUmTJskdwykpKc2afOb1eqmrqxMrITY2Vm7l6d69O/379+eOO+4AoHfv\n3sTFxUV8vMHac0lJCfHx8eLSi4+Pl6Szuro6Dh48KG0oi4uLWbp0KRs3bgR+KL0AyzMxe/ZsSUqL\n5DMlJSXJhQe///3vyc3NDbkZKRi73S6NSh544AFuv/32iJaLnAgquS8mJgaPxyPtHvPy8iRpKdIt\nbYMt4p/67EjtISqrWlm2Q4YMkbDQihUrJBscLJnev3+/uKyzs7MZNmwYYF2AMXHiRAkhRbLjXGNi\nY2MZMWIE7777LvBDwq1aZ23atJFk1kisPVsk+rX+H7/4g4LdYoZhyCbXhC/xWH/ouMcdnEVYXV3N\nbbfdBsC8efPweDyyQX377bfSYewEOdq4j2vMpmlK3G/p0qXMnDlTXJLjx4/nD3/4g2Q/NqEw/qK5\nNk2T6upqKXPbv3+/KGbB2ZwQlg3quMYc3AXK7XZTWFgoIQCn0ym5EKWlpWzcuFEyxuvq6sQtCVaG\nqso3ePDBB0+k7ecJyYd6JrDct//5z3/44IMPACtm7HQ65Sac2267TeKHJ3izWpOsxeNFXfEKVgZ7\nRUVFSFz3f/7nf4Djis2e8Fw3A79ozME5D8roqKuro7a2NsS97fV6Rbns1q2buIGdTueJxOebVD6U\n4fTiiy8C8O677+J0Ovn9738PWNdIqlyVEyzJOq5/2CIO5Ahwwi9ZNdIAKyb46KOPirVZVFREfHy8\ntHI866yzmurQOOEDWVnIbreblStXysE2atQoXC5Xsx1uUcbPHrPP56OqqkpiwW+99ZZ87fF4KCgo\nkLiV1+ulQ4cOUud99913SwLPCVoPTXpIBJeuyAc0fQlIxA9k1eJz9OjR7N69W5TPmTNnSrvI47CQ\nfzMH8tE41lnSUuWjCTmuCdAxZI1Go9FoooCojiG3JIJjPoFAgNraWnHROBwORo0aJVm/zR03VgS3\naExKSmLixIlRM7aWTkNDA6mpqRKaGDhwoLjv4uLiQrLE7XZ7RLLAT5RoHdeJYLPZJJP6vvvu4y9/\n+Yt0zps8efKv7nnDiZ6rE0e7rC2a3A3i9Xrl1pi4uDg6dOgQjqSAX5ObDKJ33C1xzKDl42ehwk4q\njPMza+1/TXPdEscMLXfcgnZZazQajUYTBWgL2eLXpnW1xDFD9I67JY4ZtHxEkl/TXLfEMUPLHfcP\nvxTBA1mj0Wg0Gs1R0C5rjUaj0WiiAH0gazQajUYTBegDWaPRaDSaKEAfyBqNRqPRRAH6QNZoNBqN\nJgrQB7JGo9FoNFGAPpA1Go1Go4kC9IGs0Wg0Gk0UEMnLJaK5A8mvrftLSxwzRO+4W+KYQctHJPk1\nzXVLHDO03HEL2kLWaDQajSYK0Ncvan4RpmlSUVEBwOrVq0lNTQVg+PDhx3OZu0ajOU4atzfW1xz+\netEHcjMRvMhaygJTY66vr+fJJ5/kpZdeku8zMjIAWLp0Kb169Woxz6TRRAtqfdlsNkzTlO8Nwwi5\ni1qvreZFXc8ZjneiXdYajUaj0UQBLcpCNgwD0zRDtJKWpC0ahgHAoUOH+PrrrwE46aST6N27N3Z7\ndOtGgUCAw4cPA3DrrbeycOFC3G43AAkJCQwaNAiA9PT0ZhujRvNrwDRNDMPA5/MBUFlZidPpxOVy\nAeBwOIiNjQW0tRwJ/H4/YIXmrrnmmv/f3plHR1WdAfw3SyYJCZAESAgQCEhYhEYFRRERCKsFwWrw\n4MZSj1JLiwuWttYe9VStHrRFXEDQ01ZtZVNPZdMCLihV2SooqwFZAknYwpBk9nmvf7xzP2ZYLAoz\n87T39w+BJMx99917v/277N+/H4CSkhJGjRoFwKRJk2jWrNk5n+O2FsimaRKJRNi3bx8A8+bNIxwO\n0717dwAGDRpEdnY2gO0FGliuXYC1a9fyz3/+E4CtW7fywAMPyAazG2oxfvTRR9x7770A7Nq1i6ys\nLBHCY8eOZcyYMQBkZWXZ8pAwDINQKARY6yojI8OW47Q7yo3q8/lYt24d8+fPByzB0Lx5cwDat29P\naWkpRUVFADRq1IjMzEz5uVQTuxYcDoecHW632xbjM02TQCDAe++9B8DixYsJBoMMGzYMgGHDhpGW\nlgbYYz6/LYZhEIlEaGhoACAUComykZGRgdt9Qiyl6vnUOm9oaODOO+8E4I033sAwDJE5ubm59O7d\nG7DOPSWvwFKavsvYbSmQY2Mn+/fvZ8aMGQAsX74cv99Pz549Acu6VC8vLS0t7kWCvRaraZpUV1cD\n8Pzzz7Np0yYAgsHgKUkbdiESifDWW28BMHnyZI4dOwZAt27duP3227nxxhsBaNq0qSRy2WnO1eao\nqalh27ZtfPjhhwCUlZXRt29f2yWfqXVgmqZYR+FwmLS0NBnrd93o52t8KpFv2rRpLFu2TKyFvLw8\nEbqhUAiXy0Xnzp0BmDhxIgMGDABSM37TNOXwnzNnDk8++SQHDx6U76m5vfnmm5k9ezYZGRlJHZ8a\nh4pNhkIhXnzxRZ5++mkAvF4voVCIhQsXAjBmzBj5XnZ2Nk6n01b7Lha1piORCHV1dQBs376dVatW\nydmyc+dOrrnmGgAeeeQR2rVrl3IDq76+HoDbb7+dBQsWAJYAHjBgAH369AGgZcuWdOnSBUBi/uc6\nbvublRqNRqPR/B9gOwtZxU/A0lI2bNjAxx9/DMC+fftIS0tj8+bNgGVpDho0CICcnBwKCgrIysoC\nIBAISDwzLS0Nj8cjFnSsJpMszTIcDos1sW3bNtEWDx8+nJTP/7aEw2EWLVrEk08+CVjeiiuuuAKA\nP//5z3Tu3FksCbto57GelWPHjvG3v/0NsOb7q6++EjffHXfckTQNXI1JWT9gzW00GhUr2O12E41G\n+eKLLwB46aWXWL58OQDHjx/H6XRy6aWXAjB//nxxDSebcDjMvHnzAFiyZAlOp1Msm8LCQqqqqgDY\nvXs34XCYFi1aAMRZPKmwjmtra/nxj38MwMaNGwmHw3GetWAwCMCCBQsoKSnhd7/7HZDcMFhsjsa6\ndeuYM2eOnA2xljPA0qVLxftwyy23kJ2dLSEvt9ttG29VNBpl586dAEyZMkWeJxAIcOzYMSorKwHL\nelbeq+3bt1NUVJRSC9nn83HfffcBsHDhQnJycgB47LHHKC8vl7mur68X93VaWlqcp+K7zr1tBHKs\nu04tvPXr17Ns2TJ5kcqto9xNb7/9tgi53NxcXC6XTNDx48dp2bIlAAMHDqRFixYcP34csA4IVaaT\njNitYRgYhnFaV1g0Gk25eyYW5eb97LPPePPNNzly5AgA/fr149FHHwWsGKFd4m1wwsVbU1MDwFtv\nvcXChQvlHRcUFNCnTx9++tOfAtC6deukjV19jtPpFAFcVVXFv/71L7Zs2SLjj0ajrFy5EoD9+/cT\nCATkewCffPIJYB0KTz31FEBSXe6GYbBv3z6JGaenpzN69GiJoZmmKfsrOzubnJwcSkpKACsumKo1\nXldXR79+/di+fTsAjRs3ZuzYsfz85z8HrIRE5f79y1/+wty5c5kyZQqAxDWTgYnqpqQAABflSURB\nVGEY4iZdtWoVhw8fFkEcm8iqnmnZsmWAJTwyMzO56qqrAOu9NGvWDLDWvcfjSeo6UevV6/Xy29/+\nljlz5gDWOde4cWMAunbtSqNGjeLWhFLerrjiipSGksLhMEuWLBFlPiMjQ3JnJkyYgMfjEYMxNvxy\nvsIG9pEEGo1Go9H8H2MLCznWTR0Oh9mxYwcAb775JuvWrRNXjsqOTE9PByxtUCUa5ebm0rJlS3FF\neTwe8vPzActKqauri0vyKSgoSMpzgaUdOhwOeQ6fzyfPW1RUZJvkIsMw2L17N2Alv2zcuJHLL78c\ngIcffpj27dsD9spGBaitrWX58uXi9nI4HJSXl0vyhcfjIS8vTzwmqbDWYkv0/H4/FRUVfPnll4Bl\nybjdbrESCgoKZH00NDSwd+9esa4rKirk62Sum1AoxFtvvSWJiVdddRXNmzeP24tNmjQBoE2bNjRt\n2lT2YirWivKy3XPPPWzbtk1CWatXr6akpETmzjRNHnroIcCqJKirqxNLNRkWslrD4XBY3Ltffvkl\nfr8/zmvocDhkzFlZWRLy+uyzz/B6vbz99tuAtV5UAlskEqFHjx5MmzYNQPZvoohEInJ+lJeXSwgG\noFmzZhQWFsrXe/bskfM4JydHxtikSZOUrBe1p3bv3s1zzz0n3sybbrqJcePGAda5FyurIL6Zizrn\n4bvvTVsIZDixgWpqanj55ZcBK92/qqpKXpyK/So3c5s2bRg8eDAAffv2JRQKSdw4Nzc3Lm4Vm025\nYcMGSktLE/5M6sWpl6Vi3z6fT8Zy/fXX20K4wYlDF6yF2bFjR2699VYA2rZtm9ID9mRM05SD8w9/\n+AP19fUSz+zXrx8ej0fGWVtbKxnAqSL2QHW5XOzfv19c6l26dKFjx46iQGZmZorytmPHDhYsWMCe\nPXsAK6NdubOTkQ2s3Kaffvops2fPlsPe6/Vy4MABDhw4AFg1mRdddBFgHaqpVNpM02TFihWAFXN3\nOBw89thjAHTq1ClOIXM4HJJbUFxczCeffCJhmhYtWiT8GWIrLJSQPXn+1J/qPAkGgxK2q6+v59Ch\nQ1JSqfIT1O9VV1fLefmPf/wjYc8RDodZsGABDz74IGDl+2RlZUl2/cCBA2VeV65cyZEjR2T9Pv30\n0/Tt2xdIjbIcawS+8soreDweLrzwQgB69uwpMiUSiRCNRiXnID09Pa787Hz0xbCFQI5Go7LYKioq\nJF5WU1NDJBKJ00gAObhGjRrF0KFDAUvLMgxDeiq7XK64ulO/3y8xRq/Xm/CNpuLGYL2scDgspU7h\ncFjiPH369LGNgNu3b58oDa1bt6asrIyrr74awHZ1u+FwmNWrVwNWvsCoUaNEOfN4PESjUXbt2gVY\nSWjXXXedxNmysrJSZiWDZSF0795d1mdZWRlFRUWy8aPRqFii77//PgcPHhRhXlBQIIdAojFNU3I0\n7r//fo4ePSqH0fr161m3bp0cqmVlZVKXnmoMw5BYdzgcpm3btowdOxY49cA3TVOUis2bNxOJROQZ\nk7neMzIyJHFPjXnRokWAlXNQX18vgrahoUHG6HK5xGBRz3Ny4ySl3CUCNaYtW7bwpz/9SRSFiy++\nmGHDhjF+/HjAMkKUQrB3716ysrIkp+OWW245pWQ1WUQiERk7WHNWWloq1vygQYNkv0UiEQKBgKyh\n9PT0U5IVz3XN6BiyRqPRaDQ2wBYWstPpjPPFq2zN01kxTqeTH/3oRwBcfvnl4vbLyMiQ1HP1c8pV\nozKZlVWak5MT93mJQDWEV19XVlZKKYtpmuISUXHDVBONRvn000/Fi9CvXz9GjhwpcTTl9lekylpW\nXod9+/bxn//8B7DiPL1795b3HQ6HeeKJJ3j++eeBE+VzSlsfMmQIrVu3lvjnyS7MRKH+b4/HQyAQ\niMsxaNy4sXw/EAjwxhtvAFZ3oIaGBjp06ADAbbfdljT3eyQSEVfvjh07iEajMscul4t27drRsWNH\neSblkcjJyaF58+Ypy42IRqPiWs/KyuL2228/o3vf5/MxadIkwGpQkZeXl/BYayyxe0pZZeXl5ZSX\nlzN58mQAfvOb3zBv3ry49aLWrsfjobCwkE6dOgFWFz1ltR48eJD09HTJdk/E2JV34cUXX+To0aNM\nnDgRsEoL27VrJ5Z8dXW1uIUdDgeTJk2S8jL1LMlEzWV9fT0zZsxgw4YNAFx22WWMGzeONm3aAFYe\ngTpzQqEQmZmZsgcS0ZDFFgLZ4XDIS7n44ou57bbbAKt+VMUbwAqqFxUVcdlllwGWq1KVRDVp0gSP\nxyMCJFY4gzWxqp5s8ODBCRcoLpcrbgMtXbo0ruZYudpT5apRxJYpvPvuu6IMtW/fPq5G0+Vyxbln\nnE5nSmpL1Vp47bXX5PN79OiBy+Vi7969ANx111289957kqihBNhXX30FWO61CRMmcMEFFwBWKUyy\nS4hik1q++uqruLrdzZs3M3fuXMCKf1944YXcddddgFUykqz5jkajsmcuueQSunXrJm7Vnj170rJl\nS1kfu3fvZu3atYC1L4cPHy4liMnGNE1xn3u9Xvr37y9rRXVUUjWwEydOlL7y0WiU4uLipHXqinUv\nO53OU9agSkJ88MEHWb16tbQQjo1jZmdnM2LECK6//nrA6rfs9XoBq6Z3x44dIlzON4ZhSGjlwIED\nlJeX88ADDwDWnootYV2zZo383qBBg7jnnntSmtehzr1jx47FhQMuvvhiUYzAUtjU9+rr62nRokXc\nuXe+96J2WWs0Go1GYwNsYyErS7Fp06ZSmO/3+1m1apX0cE1PT+fCCy8U98zatWv5/PPPAUt7ady4\nsST2FBcXy//p9/tPG4BP9DPFfk6sxebxePjJT36StLF8E8odM2/ePFasWEG3bt0Aq3nK3r17xaoP\nBoO0atUKsJKSTNOUEEBmZmZSkqRM02TdunWA1a1IZVVv27aNjz/+WEonamtryc7OFk/KfffdR6dO\nnSSLNRgMUlJSclqXdSJR7zoYDFJVVSUuXofDQSAQkPldu3atWB5ut5thw4YxevRogLjs8UTj8Xgk\na1Y1tlFWnJozZT24XC65DOHVV1+lsLBQkuiSnUDndrvFYvR6vezfv1+8J36/n7lz58qZUltbKyVR\nTqeTm2++OWleq4aGhm9cg+rf2rVrxy9+8QtmzZoFWNnY6veuvPJKrrvuOtmLZWVl/Pvf/wasxLv8\n/HxGjBiRkPE7HA7atWsHwKOPPkp+fn7cZT+RSETc1B9++KF4gEaOHEleXl5Kz77YS1KaNGki2eAl\nJSUcPnyYrVu3AnD06FEZd48ePU5ZG+c79GkLgRxLrGvU4/EwZMgQKVEKBoNxtwl17dpVXCF//etf\ncTqdcuiq2BZYGcOXXXZZyhbA8ePHRZCA5YpSCznVqHaHs2bNwuVySeelnTt3smbNGukIdMEFF0ib\n0uLiYjp27CjZm8XFxdKFJ5GuX8MwpNZ4x44doiy8/vrrHD58WBSeQYMGMW3aNImdxbZMhRMu91Rd\n+N6oUSN69OghbsedO3eSm5sra2T37t1ytWXr1q0ZMWJESm5LcjqdcsCe6XPVXm3ZsqV8vXr1ap54\n4gkpoUt2jNDpdEq1RbNmzVi1ahUzZ84ErHBFQ0OD5J5ce+210pq3pqaGXr16JU25nDp1qrjWx48f\nf8augQ6Hg7KyMnGlZ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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8465cb9780>"
+       "<Figure size 576x3600 with 60 Axes>"
       ]
      },
      "metadata": {},
@@ -2087,7 +2054,7 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(8,50)) # not shown in the book\n",
+    "plt.figure(figsize=(8,50)) # 책에는 없음\n",
     "for iteration in range(n_digits):\n",
     "    plt.subplot(n_digits, 10, iteration + 1)\n",
     "    plot_image(outputs_val[iteration])"
@@ -2095,21 +2062,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure generated_digits_plot\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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ii9jtdvLy8mSbmczMzJBaNsKKO9qW96HguBJQhyOcmpqaWLlypewPEx0dLQs1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\nPhz4ddG5XC6Z3PHuu+9yyim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tjcDs6F0Eq1xWsAnHmER8s7NnCuU/VG7Z30IXUCFATKrw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\noVCEJUpAKRQKhSIs+T8ReevT56akNAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f84623f0be0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2128,15 +2088,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the latent loss is computed differently in this second variant:"
+    "여기에서 잠재 변수 손실은 조금 다르게 계산된 점을 주목하세요:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 59,
+   "metadata": {},
    "outputs": [],
    "source": [
     "latent_loss = 0.5 * tf.reduce_sum(\n",
@@ -2147,19 +2105,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Encode & Decode"
+    "## 인코드 & 디코드"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Encode:"
+    "인코드:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -2184,12 +2142,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Decode:"
+    "디코드:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2210,19 +2168,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot the reconstructions:"
+    "재구성 이미지를 그려봅니다:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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LFy5sxOlEGaK3N9fzduRPza0bdt91111JrtugeO211yb53HPP7fWYJjJEB6BxFAgAIQoE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\nHXc5YbwAAAAASUVORK5CYII=\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f846b0829e8>"
+       "<Figure size 576x540 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2242,15 +2200,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Interpolate digits"
+    "## 숫자 이미지 보간"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 63,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -2261,9 +2217,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8462250c18>"
+       "<Figure size 792x324 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2271,9 +2227,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8465411198>"
+       "<Figure size 792x324 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2281,9 +2237,9 @@
     },
     {
      "data": {
-      "image/png": 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IuN89r10pNVNxLEFgepr7iPP32dQ4T2vle1b/Fz8vg369mki8vuUN5UvXR44XG61zXGorztvsw14q22j7vTPI308se+EezldddVXEvNaxa8Zovp9LJTf8Djr//PMbj2d6nKukX3zxxYhZGsLrIVdS5yVy++yzT6vX34YzjJIkSaryhlGSJElVnU5Jc6r3+OOPj5hpFTriiCMa/z7ttNMi5spQ4pQ9Gxe//e1vjzhf7XTddddFzGljphWYOmJKrbYibRCm/Nu+Bh43NhLlyk02o+WKr7x0gKkWHiuuzjz55JN7/m2WDuTHkykAjhNTeYOWWuknvjeupMtTmf/VdoVkqQSBx/t73/texCwtYGo8pWZqhqUGTzzxRMTsZMCVpcO8DzgxlXjcccdFzGOUUnOjApZ5sGyHY8frHa9dF198ceN5mfrn+E2V82gs8uPC84VdPVjCwe8eruBt25SZJQSMSyUE/Ezke1+XvrsGDVf8n3vuuRFzZTLTutwMIC+TKR1njh3TyA8++GDEN954Y8QrVqxoPJ5jTLyO8Vxl6pnn6oEHHth4fOneZjScYZQkSVKVN4ySJEmq6lxKmtO+nN698sorI+aU8ZFHHhnxN77xjcZztUlDcwqeqVXuU8sUQUrN6Wimi0qNQUsr1bhKMaX/Xf00GdqmobnX75e//OWIOU6c6l+8eHHEH//4xxvPy2bMpb2oZ82aFfG3vvWtiM8444yI2Qw1peYeusO213Ab/AyyYfMdd9wR8SOPPBIxU4/5iko+V2k189lnnx0xV/sddthhETO1klKzQfc111wTMdPQCxcujHgqjiPPSaaaWXKTUrMrANPTN910U8/Hcx92jnfeFeJnP/tZxEuWLImYK4CHuXH6SHG88k4RHKNbb7014qeffjri+fPnRzzSRun575UeU1rxm5c5sCxoLA2txxu/u5mGZqqafv3rX0c8b968xv/xvfG6yfNl+fLlPWPes/B15HivwHKhPfbYI2KWce21114R87s3pf6W5ngWS5IkqcobRkmSJFV5wyhJkqSqTtcwsjUH6wa5O8Qpp5wScV4fVcJ2PWzlwR0pWAOU7yDCWi/+TdbilepYWDPJ95RSsxYh3yFgMuRtVvg+WItz/fXXR8wamKeeeqrnz6dNm9Z43ve+970Rsz61VL+zwQYbRMxdf1ibl1JzRx5u2M7PzyDX5YwV68p4zB577LGIzzvvvIjPPPPMiPP6X34WWEPDz/+xxx4b8Zw5cyJmbS5rJlNK6bLLLuv5XHvvvXfErOWZ6rVy/IzmY8TjzBYerNHirhB8/NKlSyPOz6PVq1dHzPrVYTtfxkO+Sw7bF7Hejd8dO+64Y8SjOcalljltduRi7X7+mEG2ySabvOrvlHZCYo1v/ntsmfPAAw9EXKo/ff7554t/n99tM2bMiPjEE0+MmLX6/P4i7liTUn+viVP76ipJkqRX5Q2jJEmSqjqXkiamfjk1ztQLp/zzFgbE5fGXX355xFdffXXETFVyOniLLbZoPNdHP/rRiDnNzBRPqbUB47xFyKBN/+fpEKZvmUKZPXt2xJy2L6WwTz/99Mbz3n777RFzun3u3LkR8zhzh4Rly5ZFnLfV4S4iN9xwQ8Qf+chHImbLlmFrE8LP05Zbbtnz5zz29957b8T5hvY8/kwR83PANAvHnqUJF154YeN5mf5csGBBz9fbld0mJlqe7uQuESyp4W4hHDuez2w/wtKclJrnNFuF5OlL/a98jH75y19GzOvT4YcfHjFLM0bzeS99D7IcijF35Mqve10537gD1KJFiyL+yU9+EjGveywHuO222xrPxe9+xvzsM+YY8zrJMqCUmunms846K+Ldd9894lIZVqm0oN+6/60nSZKkceUNoyRJkqo6l5LmNCxTVFdddVXEnAK+4oorev5+Ss2VUOeff37Ed999d8Rczclu6gcddFDEeeqltJq5tFMLp5D52pk2Sul/U9+DhmOz0UYbRcwddrhajbu+cKX5qlWrGs/77W9/O2KmH5kq4THn8eSK93zVOY/1XXfdFfGdd94ZMcd2l112ibiWnu5KmobHjLsVMd3M1AzTJJ/73OcazzVz5syImabhseFYMN127rnnRsxdQ1Jq7njAVBI/X1053hMt/7yXSjvWW2+9iHmNYuqS6ex81SjPI57Hw1C2MR54fWLqN6WUpk+fHjE7BvC7h2nNtjhGL7zwQs/XwvEuncNdPdd4zL75zW9GzN3GLrnkkoh57vC6lVKzfI2lGjx+PI9YOvfud787Ynb/SKl53eWOS3ztI92Zp/aY0fCMliRJUpU3jJIkSapaZxJW3vbtD7KBJtNat9xyS8Scjs1TndwsnNPJnEI+6aSTImZKjKsJ81Qzp/DbrF7ilDdX8uYNct/3vvdFzFVf2ZRzv+af+zZOfN+czmezbKYDvvvd7zYez8bAPLaMOX78XPDn+dQ8XxfTcttvv33Ep512WsTc+J3lAflq9tKq90w/xqlvY8TjxPPkE5/4RMT33XdfxFylnFIzJb3nnntGzJQyx3vlypURc6Vifrw+85nPRMwm/ByvcTRQY9Tqj+Ez/dxzzzX+jylpnjtcJc1U2IoVKyI+4YQTImZ6OqVmGpVdJXbYYYeIxzGV2ekxevjhhxv/d+mll0bM76H58+dHvNNOO0VcSk/nK6GZYmWpE89PXscY9yElPVBjVPo+YgkAVznn3TXYiJubHPC8YKr76KOPjpidPfLNN1giNJZyjlGmpFv9kjOMkiRJqvKGUZIkSVWdTkmXVpsxhcl9jPOmwGwWzZQam6QeeuihEdcamLZ5jYw5TcwVbEwd5H+DaYLKXtIDl5Iu/oHCCr18dSdTzFxdu3bt2ojZVJop1dpe0EzH8Hn5Wdhvv/0iZqPVPqySHqg0DXEsmHK54IILIuZeqSk1VwsyFcqUGRt0l/YEZ9lFSs29rLlyc4JWaw7sGBX/GM6p/DxiVwLuDV3qzMDSHl5T8zToySefHPGpp54acakrRJ91boxYgsQUdErN7yh+3rnf91FHHRUxN4OoXes4FqWynlLcB50Yo9K9UP5zni/cG5qpapbscBxrY9QvpqQlSZI0abxhlCRJUlWnU9IlTKnVGjcztcJ0L3/OKeTxwtfIOF8J2jJN0JmU9HgZzWe69JjSfp0tV0LXdC5Nw7IPlnOk1Exdc7UhV9ry51xJvWTJkoi32267xvPmK9AnWCfGqCRvOMyuC2xevHz58ojXrFnT8/HTpk2LON/r/UMf+lDEo2kqPUadGyN+D51zzjmN/2PHAJZ28Hz57Gc/GzFXTPP7Ii9ZKl2jLO3onzbfIQPMlLQkSZLGzhtGSZIkVQ1lSrr4h8dxj8WxKO19XFkJXTPlU9IdMSXSNB03VGP08ssvR8zUM1dDs7k3uwUccMABEbPRd0qjvk71S+fGiM2iv/Od7zT+7wc/+EHE3FiCe9p/8YtfjHjhwoURcyX0oHy3/b/OjdEUZEpakiRJY+cNoyRJkqq8YZQkSVLVlKphnCKsYewG63oGn2M0+Do3Rmz7xl2s8v/j7lNsmcPdXfq8I8t46dwYTUHWMEqSJGnsvGGUJElSlSnp4WNKuhtM0ww+x2jwdW6MSjtGDbHOjdEUZEpakiRJY+cNoyRJkqomfKd4SZKmqimShtYQcoZRkiRJVd4wSpIkqWoyVklLkiSpQ5xhlCRJUpU3jJIkSaryhlGSJElV3jBKkiSpyhtGSZIkVXnDKEmSpCpvGCVJklTlDaMkSZKqvGGUJElSlTeMkiRJqvKGUZIkSVXeMEqSJKnKG0ZJkiRVecMoSZKkKm8YJUmSVOUNoyRJkqq8YZQkSVKVN4ySJEmq8oZRkiRJVd4wSpIkqcobRkmSJFV5wyhJkqQqbxglSZJU5Q2jJEmSqrxhlCRJUtX/ARR1a6POKltgAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f84623ac278>"
+       "<Figure size 792x324 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2291,9 +2247,9 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f846557df60>"
+       "<Figure size 792x324 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2324,7 +2280,7 @@
     "collapsed": true
    },
    "source": [
-    "# Exercise solutions"
+    "# 연습문제 해답"
    ]
   },
   {
@@ -2333,15 +2289,6 @@
    "source": [
     "Coming soon..."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
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