14_recurrent_neural_networks.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.6\n",
      "IPython 6.5.0\n",
      "\n",
      "numpy 1.15.2\n",
      "sklearn 0.20.0\n",
      "scipy 1.1.0\n",
      "matplotlib 2.2.3\n",
      "tensorflow 1.12.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**14장 – 순환 신경망**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_이 노트북은 14장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 2와 파이썬 3 지원\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# 공통\n",
    "import numpy as np\n",
    "import os\n",
    "\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",
    "# 맷플롯립 설정\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 한글출력\n",
    "plt.rcParams['font.family'] = 'NanumBarunGothic'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 그림을 저장할 폴더\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"rnn\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "텐서플로를 임포트합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 기본 RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 수동으로 RNN 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "\n",
    "Wx = tf.Variable(tf.random_normal(shape=[n_inputs, n_neurons],dtype=tf.float32))\n",
    "Wy = tf.Variable(tf.random_normal(shape=[n_neurons,n_neurons],dtype=tf.float32))\n",
    "b = tf.Variable(tf.zeros([1, n_neurons], dtype=tf.float32))\n",
    "\n",
    "Y0 = tf.tanh(tf.matmul(X0, Wx) + b)\n",
    "Y1 = tf.tanh(tf.matmul(Y0, Wy) + tf.matmul(X1, Wx) + b)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]]) # t = 0\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]]) # t = 1\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.06640061  0.9625767   0.6810579   0.7091854  -0.89821595]\n",
      " [ 0.99777555 -0.7197888  -0.99657613  0.96739244 -0.99989706]\n",
      " [ 0.99999785 -0.9989881  -0.99999887  0.9967763  -0.9999999 ]\n",
      " [ 1.         -1.         -1.         -0.9981892   0.9995087 ]]\n"
     ]
    }
   ],
   "source": [
    "print(Y0_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.         -1.         -1.          0.40200272 -0.99999994]\n",
      " [-0.12210429  0.62805295  0.96718436 -0.9937122  -0.2583933 ]\n",
      " [ 0.99999815 -0.9999994  -0.99999744 -0.8594331  -0.99998796]\n",
      " [ 0.99928296 -0.9999981  -0.9999059   0.98579615 -0.9220575 ]]\n"
     ]
    }
   ],
   "source": [
    "print(Y1_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `static_rnn()`을 사용하여 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-9-75d973663c33>:6: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "X0 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "X1 = tf.placeholder(tf.float32, [None, n_inputs])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "output_seqs, states = tf.contrib.rnn.static_rnn(basic_cell, [X0, X1],\n",
    "                                                dtype=tf.float32)\n",
    "Y0, Y1 = output_seqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X0_batch = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 0, 1]])\n",
    "X1_batch = np.array([[9, 8, 7], [0, 0, 0], [6, 5, 4], [3, 2, 1]])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    Y0_val, Y1_val = sess.run([Y0, Y1], feed_dict={X0: X0_batch, X1: X1_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30741337, -0.32884312, -0.6542847 , -0.9385059 ,  0.52089024],\n",
       "       [ 0.9912275 , -0.95425415, -0.7518078 , -0.9995208 ,  0.98202336],\n",
       "       [ 0.99992675, -0.99783254, -0.82473516, -0.99999636,  0.99947786],\n",
       "       [ 0.9967709 , -0.6875061 ,  0.8419969 ,  0.93039113,  0.81206834]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y0_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.99998885, -0.9997606 , -0.06679297, -0.9999803 ,  0.99982214],\n",
       "       [-0.65249425, -0.5152086 , -0.37968948, -0.5922594 , -0.08968376],\n",
       "       [ 0.998624  , -0.99715203, -0.03308632, -0.9991566 ,  0.9932902 ],\n",
       "       [ 0.99681675, -0.9598194 ,  0.39660627, -0.8307605 ,  0.7967197 ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow_graph_in_jupyter import show_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "          <tf-graph-basic id=&quot;graph0.3745401188473625&quot;></tf-graph-basic>\n",
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       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시퀀스 패딩"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "X_seqs = tf.unstack(tf.transpose(X, perm=[1, 0, 2]))\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "output_seqs, states = tf.contrib.rnn.static_rnn(basic_cell, X_seqs,\n",
    "                                                dtype=tf.float32)\n",
    "outputs = tf.transpose(tf.stack(output_seqs), perm=[1, 0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        # t = 0      t = 1 \n",
    "        [[0, 1, 2], [9, 8, 7]], # 샘플 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # 샘플 2\n",
    "        [[6, 7, 8], [6, 5, 4]], # 샘플 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # 샘플 4\n",
    "    ])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.4565232  -0.6806412   0.40938237  0.631045   -0.45732823]\n",
      "  [-0.94288003 -0.9998869   0.9405581   0.99999845 -0.99999976]]\n",
      "\n",
      " [[-0.80015343 -0.99218273  0.78177965  0.9971032  -0.9964609 ]\n",
      "  [-0.637116    0.11300934  0.5798437   0.43105593 -0.6371699 ]]\n",
      "\n",
      " [[-0.93605185 -0.99983793  0.9308867   0.9999814  -0.99998313]\n",
      "  [-0.9165386  -0.99456036  0.89605415  0.9998719  -0.9999751 ]]\n",
      "\n",
      " [[ 0.99273676 -0.9981933  -0.5554365   0.99890316 -0.9953323 ]\n",
      "  [-0.02746333 -0.7319198   0.7827872   0.9525682  -0.9781772 ]]]\n"
     ]
    }
   ],
   "source": [
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.94288003 -0.9998869   0.9405581   0.99999845 -0.99999976]\n",
      " [-0.637116    0.11300934  0.5798437   0.43105593 -0.6371699 ]\n",
      " [-0.9165386  -0.99456036  0.89605415  0.9998719  -0.9999751 ]\n",
      " [-0.02746333 -0.7319198   0.7827872   0.9525682  -0.9781772 ]]\n"
     ]
    }
   ],
   "source": [
    "print(np.transpose(outputs_val, axes=[1, 0, 2])[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `dynamic_rnn()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        [[0, 1, 2], [9, 8, 7]], # instance 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # instance 2\n",
    "        [[6, 7, 8], [6, 5, 4]], # instance 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # instance 4\n",
    "    ])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val = outputs.eval(feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.85115266  0.8735834   0.5802911   0.8954789  -0.0557505 ]\n",
      "  [-0.99999595  0.9999957   0.9981816   1.          0.37679613]]\n",
      "\n",
      " [[-0.99832934  0.9992038   0.98071444  0.99998486  0.25192654]\n",
      "  [-0.7081804  -0.07723369 -0.8522789   0.5845347  -0.7878094 ]]\n",
      "\n",
      " [[-0.9999826   0.9999953   0.99928635  1.          0.51590705]\n",
      "  [-0.9993956   0.9984095   0.83422637  0.9999998  -0.47325197]]\n",
      "\n",
      " [[ 0.87888587  0.07356028  0.9721692   0.9998546  -0.7351168 ]\n",
      "  [-0.9134514   0.3600957   0.7624865   0.99817705  0.80142   ]]]\n"
     ]
    }
   ],
   "source": [
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.3745401188473625&quot;></tf-graph-basic>\n",
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       "    "
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   "source": [
    "show_graph(tf.get_default_graph())"
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   "source": [
    "## 시퀀스 길이 지정"
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  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 2\n",
    "n_inputs = 3\n",
    "n_neurons = 5\n",
    "\n",
    "reset_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_length = tf.placeholder(tf.int32, [None])\n",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32,\n",
    "                                    sequence_length=seq_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.array([\n",
    "        # 스텝 0     스텝 1\n",
    "        [[0, 1, 2], [9, 8, 7]], # 샘플 1\n",
    "        [[3, 4, 5], [0, 0, 0]], # 샘플 2 (0 벡터로 패딩)\n",
    "        [[6, 7, 8], [6, 5, 4]], # 샘플 3\n",
    "        [[9, 0, 1], [3, 2, 1]], # 샘플 4\n",
    "    ])\n",
    "seq_length_batch = np.array([2, 1, 2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run(\n",
    "        [outputs, states], feed_dict={X: X_batch, seq_length: seq_length_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-0.91231877  0.16516447  0.5548655  -0.3915935   0.20846416]\n",
      "  [-1.          0.95672596  0.9983169   0.9997017   0.96518576]]\n",
      "\n",
      " [[-0.99986124  0.67022896  0.9723652   0.66310453  0.74457586]\n",
      "  [ 0.          0.          0.          0.          0.        ]]\n",
      "\n",
      " [[-0.99999976  0.89679974  0.9986295   0.96475154  0.93662006]\n",
      "  [-0.99995255  0.96819544  0.9600286   0.9870626   0.8545923 ]]\n",
      "\n",
      " [[-0.9643544   0.9950159  -0.361507    0.99833775  0.99949706]\n",
      "  [-0.9613586   0.9568762   0.71322876  0.9772921  -0.09582992]]]\n"
     ]
    }
   ],
   "source": [
    "print(outputs_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.          0.95672596  0.9983169   0.9997017   0.96518576]\n",
      " [-0.99986124  0.67022896  0.9723652   0.66310453  0.74457586]\n",
      " [-0.99995255  0.96819544  0.9600286   0.9870626   0.8545923 ]\n",
      " [-0.9613586   0.9568762   0.71322876  0.9772921  -0.09582992]]\n"
     ]
    }
   ],
   "source": [
    "print(states_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시퀀스 분류기 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])\n",
    "\n",
    "basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32)\n",
    "\n",
    "logits = tf.layers.dense(states, n_outputs)\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\n",
    "                                                          logits=logits)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "주의: `tf.examples.tutorials.mnist`은 삭제될 예정이므로 대신 `tf.keras.datasets.mnist`를 사용하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
    "X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
    "y_train = y_train.astype(np.int32)\n",
    "y_test = y_test.astype(np.int32)\n",
    "X_valid, X_train = X_train[:5000], X_train[5000:]\n",
    "y_valid, y_train = y_train[:5000], y_train[5000:]\n",
    "X_test = X_test.reshape((-1, n_steps, n_inputs))\n",
    "X_valid = X_valid.reshape((-1, n_steps, n_inputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffle_batch(X, y, batch_size):\n",
    "    rnd_idx = np.random.permutation(len(X))\n",
    "    n_batches = len(X) // batch_size\n",
    "    for batch_idx in np.array_split(rnd_idx, n_batches):\n",
    "        X_batch, y_batch = X[batch_idx], y[batch_idx]\n",
    "        yield X_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tensorflow.examples.tutorials.mnist import input_data\n",
    "# mnist = input_data.read_data_sets(\"/tmp/data/\")\n",
    "# X_test = mnist.test.images.reshape((-1, n_steps, n_inputs))\n",
    "# y_test = mnist.test.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 배치 데이터 정확도: 0.9533333 검증 세트 정확도: 0.9322\n",
      "1 배치 데이터 정확도: 0.96 검증 세트 정확도: 0.953\n",
      "2 배치 데이터 정확도: 0.96 검증 세트 정확도: 0.9518\n",
      "3 배치 데이터 정확도: 0.96666664 검증 세트 정확도: 0.9576\n",
      "4 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9686\n",
      "5 배치 데이터 정확도: 0.93333334 검증 세트 정확도: 0.9702\n",
      "6 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.963\n",
      "7 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.971\n",
      "8 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9686\n",
      "9 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9748\n",
      "10 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9774\n",
      "11 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9752\n",
      "12 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9722\n",
      "13 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9742\n",
      "14 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9822\n",
      "15 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9762\n",
      "16 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9686\n",
      "17 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.976\n",
      "18 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9746\n",
      "19 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.976\n",
      "20 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.978\n",
      "21 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9754\n",
      "22 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9762\n",
      "23 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9786\n",
      "24 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9762\n",
      "25 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9768\n",
      "26 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.982\n",
      "27 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9808\n",
      "28 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9792\n",
      "29 배치 데이터 정확도: 0.96666664 검증 세트 정확도: 0.98\n",
      "30 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9818\n",
      "31 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.982\n",
      "32 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9798\n",
      "33 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9812\n",
      "34 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9808\n",
      "35 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9814\n",
      "36 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9816\n",
      "37 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9826\n",
      "38 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9806\n",
      "39 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9806\n",
      "40 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.979\n",
      "41 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9808\n",
      "42 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9764\n",
      "43 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9804\n",
      "44 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9742\n",
      "45 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.977\n",
      "46 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9716\n",
      "47 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9804\n",
      "48 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9806\n",
      "49 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9804\n",
      "50 배치 데이터 정확도: 0.96666664 검증 세트 정확도: 0.9808\n",
      "51 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9846\n",
      "52 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.976\n",
      "53 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9794\n",
      "54 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.981\n",
      "55 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9812\n",
      "56 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9792\n",
      "57 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9824\n",
      "58 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9812\n",
      "59 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9764\n",
      "60 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9754\n",
      "61 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9756\n",
      "62 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9816\n",
      "63 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9786\n",
      "64 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9794\n",
      "65 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.983\n",
      "66 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9798\n",
      "67 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9842\n",
      "68 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9786\n",
      "69 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.983\n",
      "70 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9818\n",
      "71 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.979\n",
      "72 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9788\n",
      "73 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.975\n",
      "74 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9806\n",
      "75 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9774\n",
      "76 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9786\n",
      "77 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9806\n",
      "78 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9742\n",
      "79 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9768\n",
      "80 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9832\n",
      "81 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9796\n",
      "82 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9814\n",
      "83 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.983\n",
      "84 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.98\n",
      "85 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9784\n",
      "86 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9784\n",
      "87 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9812\n",
      "88 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9774\n",
      "89 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.979\n",
      "90 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9772\n",
      "91 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9796\n",
      "92 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.984\n",
      "93 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9824\n",
      "94 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9588\n",
      "95 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9802\n",
      "96 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9788\n",
      "97 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9822\n",
      "98 배치 데이터 정확도: 0.99333334 검증 세트 정확도: 0.9808\n",
      "99 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9828\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 100\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
    "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n",
    "        print(epoch, \"배치 데이터 정확도:\", acc_batch, \"검증 세트 정확도:\", acc_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 다층 RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_outputs = 10\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "\n",
    "layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons,\n",
    "                                      activation=tf.nn.relu)\n",
    "          for layer in range(n_layers)]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "states_concat = tf.concat(axis=1, values=states)\n",
    "logits = tf.layers.dense(states_concat, n_outputs)\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 배치 데이터 정확도: 0.94666666 검증 세트 정확도: 0.9414\n",
      "1 배치 데이터 정확도: 0.96666664 검증 세트 정확도: 0.9638\n",
      "2 배치 데이터 정확도: 0.96 검증 세트 정확도: 0.9734\n",
      "3 배치 데이터 정확도: 0.9866667 검증 세트 정확도: 0.9738\n",
      "4 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9674\n",
      "5 배치 데이터 정확도: 0.97333336 검증 세트 정확도: 0.9744\n",
      "6 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9704\n",
      "7 배치 데이터 정확도: 0.96 검증 세트 정확도: 0.9698\n",
      "8 배치 데이터 정확도: 1.0 검증 세트 정확도: 0.9814\n",
      "9 배치 데이터 정확도: 0.98 검증 세트 정확도: 0.9764\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
    "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n",
    "        print(epoch, \"배치 데이터 정확도:\", acc_batch, \"검증 세트 정확도:\", acc_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시계열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_min, t_max = 0, 30\n",
    "resolution = 0.1\n",
    "\n",
    "def time_series(t):\n",
    "    return t * np.sin(t) / 3 + 2 * np.sin(t*5)\n",
    "\n",
    "def next_batch(batch_size, n_steps):\n",
    "    t0 = np.random.rand(batch_size, 1) * (t_max - t_min - n_steps * resolution)\n",
    "    Ts = t0 + np.arange(0., n_steps + 1) * resolution\n",
    "    ys = time_series(Ts)\n",
    "    return ys[:, :-1].reshape(-1, n_steps, 1), ys[:, 1:].reshape(-1, n_steps, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(t_min, t_max, int((t_max - t_min) / resolution))\n",
    "\n",
    "n_steps = 20\n",
    "t_instance = np.linspace(12.2, 12.2 + resolution * (n_steps + 1), n_steps + 1)\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plt.title(\"시계열 데이터 (인공 생성)\", fontsize=14)\n",
    "plt.plot(t, time_series(t), label=r\"$t . \\sin(t) / 3 + 2 . \\sin(5t)$\")\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"b-\", linewidth=3, label=\"훈련 샘플\")\n",
    "plt.legend(loc=\"lower left\", fontsize=14)\n",
    "plt.axis([0, 30, -17, 13])\n",
    "plt.xlabel(\"시간\")\n",
    "plt.ylabel(\"값\", rotation=0)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title(\"훈련 샘플\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=12, label=\"샘플\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markeredgewidth=0.5, markeredgecolor=\"b\", markersize=14, label=\"타깃\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"시간\")\n",
    "\n",
    "\n",
    "save_fig(\"time_series_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch, y_batch = next_batch(1, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.38452097,  2.05081182],\n",
       "       [ 2.05081182,  2.29742291],\n",
       "       [ 2.29742291,  2.0465599 ],\n",
       "       [ 2.0465599 ,  1.34009916],\n",
       "       [ 1.34009916,  0.32948704],\n",
       "       [ 0.32948704, -0.76115235],\n",
       "       [-0.76115235, -1.68967022],\n",
       "       [-1.68967022, -2.25492776],\n",
       "       [-2.25492776, -2.34576159],\n",
       "       [-2.34576159, -1.96789418],\n",
       "       [-1.96789418, -1.24220428],\n",
       "       [-1.24220428, -0.37478448],\n",
       "       [-0.37478448,  0.39387907],\n",
       "       [ 0.39387907,  0.84815766],\n",
       "       [ 0.84815766,  0.85045064],\n",
       "       [ 0.85045064,  0.3752526 ],\n",
       "       [ 0.3752526 , -0.48422846],\n",
       "       [-0.48422846, -1.53852738],\n",
       "       [-1.53852738, -2.54795941],\n",
       "       [-2.54795941, -3.28097239]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.c_[X_batch[0], y_batch[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `OuputProjectionWrapper` 사용하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RNN 하나를 만들어 보겠습니다. 이 신경망은 100개의 순환 뉴런을 가지고 있고 각 훈련 샘플은 20개의 입력 길이로 구성되므로 20개의 타임 스텝에 펼칠 것입니다. 각 입력은 하나의 특성을 가집니다(각 시간에서의 값 하나). 타깃도 20개의 입력 시퀀스이고 하나의 값을 가집니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_outputs = 1\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])\n",
    "\n",
    "cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
    "outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 타임 스텝에서 크기가 100인 출력 벡터가 만들어 집니다. 하지만 각 타임 스텝에서 하나의 출력 값을 원합니다. 간단한 방법은 `OutputProjectionWrapper`로 셀을 감싸는 것입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_outputs = 1\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell = tf.contrib.rnn.OutputProjectionWrapper(\n",
    "    tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu),\n",
    "    output_size=n_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "\n",
    "loss = tf.reduce_mean(tf.square(outputs - y)) # MSE\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 11.967253\n",
      "100 \tMSE: 0.52282155\n",
      "200 \tMSE: 0.14985825\n",
      "300 \tMSE: 0.08070775\n",
      "400 \tMSE: 0.06347209\n",
      "500 \tMSE: 0.059339315\n",
      "600 \tMSE: 0.055256214\n",
      "700 \tMSE: 0.047777846\n",
      "800 \tMSE: 0.050584763\n",
      "900 \tMSE: 0.047345735\n",
      "1000 \tMSE: 0.047309134\n",
      "1100 \tMSE: 0.04787014\n",
      "1200 \tMSE: 0.040914796\n",
      "1300 \tMSE: 0.04731765\n",
      "1400 \tMSE: 0.04164226\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 1500\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "    \n",
    "    saver.save(sess, \"./my_time_series_model\") # not shown in the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:                          # 책에는 없음\n",
    "    saver.restore(sess, \"./my_time_series_model\")   # 책에는 없음\n",
    "\n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-3.4066467],\n",
       "        [-2.447552 ],\n",
       "        [-1.1152307],\n",
       "        [ 0.7961146],\n",
       "        [ 2.2021127],\n",
       "        [ 3.1405406],\n",
       "        [ 3.4530869],\n",
       "        [ 3.3518317],\n",
       "        [ 2.8880975],\n",
       "        [ 2.2668018],\n",
       "        [ 1.6698229],\n",
       "        [ 1.5354451],\n",
       "        [ 1.9134911],\n",
       "        [ 2.7391908],\n",
       "        [ 3.9202955],\n",
       "        [ 5.160866 ],\n",
       "        [ 6.162185 ],\n",
       "        [ 6.6865444],\n",
       "        [ 6.6575227],\n",
       "        [ 6.084546 ]]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"모델 테스트\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=12, label=\"샘플\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markeredgewidth=0.5, markeredgecolor=\"b\", markersize=14, label=\"타깃\")\n",
    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"예측\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"시간\")\n",
    "\n",
    "save_fig(\"time_series_pred_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `OutputProjectionWrapper` 사용하지 않기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_steps = 20\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons, activation=tf.nn.relu)\n",
    "rnn_outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_outputs = 1\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])\n",
    "stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)\n",
    "outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(tf.square(outputs - y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tMSE: 13.907028\n",
      "100 \tMSE: 0.50571674\n",
      "200 \tMSE: 0.20132908\n",
      "300 \tMSE: 0.102004215\n",
      "400 \tMSE: 0.06749052\n",
      "500 \tMSE: 0.06332527\n",
      "600 \tMSE: 0.05484666\n",
      "700 \tMSE: 0.048970066\n",
      "800 \tMSE: 0.050843555\n",
      "900 \tMSE: 0.04930771\n",
      "1000 \tMSE: 0.04851123\n",
      "1100 \tMSE: 0.049693715\n",
      "1200 \tMSE: 0.04173606\n",
      "1300 \tMSE: 0.04913064\n",
      "1400 \tMSE: 0.04387053\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 1500\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        if iteration % 100 == 0:\n",
    "            mse = loss.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "            print(iteration, \"\\tMSE:\", mse)\n",
    "    \n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})\n",
    "    \n",
    "    saver.save(sess, \"./my_time_series_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-3.4355707],\n",
       "        [-2.4524438],\n",
       "        [-1.1019611],\n",
       "        [ 0.6844853],\n",
       "        [ 2.185033 ],\n",
       "        [ 3.0854373],\n",
       "        [ 3.449214 ],\n",
       "        [ 3.2966888],\n",
       "        [ 2.837772 ],\n",
       "        [ 2.2301729],\n",
       "        [ 1.6687407],\n",
       "        [ 1.5099533],\n",
       "        [ 1.8646909],\n",
       "        [ 2.6950762],\n",
       "        [ 3.8676817],\n",
       "        [ 5.0923877],\n",
       "        [ 6.0730352],\n",
       "        [ 6.614469 ],\n",
       "        [ 6.6141686],\n",
       "        [ 6.0340724]]], dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7wPDhw1m2bBmhHobY7BjU+zpWJNNl/DyjGSTz0i8dJ3GFzpO4VlYO+rKrV69m0aJF7LPPPrz33nuMHDmS0047rb2n6SGHHMLNN9/M3Llz+fjjj9m+fTs/+MEPmDRpEj/5yU+477772HPPPSksLOTII48EYJ999mHTpk1s2bKFK6+8kvfff59QKMTkyZNZvnx5p+F42459//33ueKKK/j73/9OJBJhwYIFnHnmmVxyySUUFRXx1ltvsWnTJq6//nouuOCCmG+nSDK0/fesumICwwoLP/v/C53mGX3xxdSVMVtkXlDvbRLXGII6wIYNG1ixYgXFxcVcc801tLS0sHXrVn7729+Sn5/PJZdcwrnnnsusWbPYvHkzp512Gg8//DDLli3jpZdeYujQoVx88cW7XHfhwoWUl5czd+5cALZu3cqBBx7I+vXr+eEPf9jp2KuvvprLLruMM844g4aGBo477jhOPvnk6Mds5KmnnmLjxo1MmzZNQV0yRvs8owUVrCgtI1RbE/y/LSzsNM9oD8MtyQBkXlBvm8S1y2/6eEziOnHixPZBvE444QQuvPBCrrzySvLz84FgaN0NGzbw05/+FICmpibWrl3LSSedxNChQwE4/vjj+eCDDzpdt7a2liVLlrSv77nnnj2W4X//93+ZOHEiAEVFRRx22GHtozyeffbZmBn7778/W7ZsifnziiTL7NmwdGl0ntF/r6ZgTRWRP9QR+mIJTZMqKL4yzecZzSCZl1NP4CSu69evJxwOA/Dcc8+xZMmSTkPuHnnkkXz/+99n9erVrF69murqag444AB+//vftz/kfLGbvx+POuooHn/88fb1zZs3Y2bdluGoo45q763a0NDAO++8w6GHHgrAkCGf/Q4OxswXyQwZP89oBsm8oJ7ASVzdnRkzZnDiiSfy8ccf7xJ477jjDhYvXszkyZM5+eSTeeSRRzjhhBP40pe+xHHHHccpp5zCtm3bdrnukiVL+PWvf81JJ51EeXk5tbW1PQ7He8cdd/CLX/yCKVOmUFFRwe23384ee+wR82cTSaWsmGc0Q1iya3ylpaVeW1vbadubb77JuHHjklqOrlavXs29997Lf/7nf6a0HLFKh3sp0pP6erjzTnjggc/mGb3ooqCGroDeOzN71d1L+zou83LqIpKxxoyBe+4JFkmMzEu/iIhIj1RTjyovL2+fMFpEJFOlTU1drTlip3soImkR1IcNG8bWrVsVlGLg7mzdupVhw4aluigikkJpkX4ZPXo0GzduZPPmzakuSkYbNmwYo0ePTnUxRCSF0iKo5+fnc/DBB6e6GCIiGS8t0i8iIhIfCuoiIllEQV1EJIsoqIuIZJG4BXUzO8LMPjKzW+J1TRERGZi4BHUzGwn8BFgej+uJiMjgxBzUzSwPuA/4F0ANzUWyXH09zJsHo0fDihXB67x5wXZJvXjU1G8Dnnb3l3s6wMwuN7NaM6tVByORzFVVBePHwy9/HmH+YSuZ8foirjp0Jb/8eYTx44P9kloxjaduZmcDM9x9dnT9FgB3v6Wnc7obT11E0l99fRDQW5oiVDOV8uE1DGlpJDyskNXNZUylmmEFIdat09joidDf8dRjralXAOPM7BUzewX4OvB1M3skxuuKSJrZsSOYoSjyRBWnFtUwpLkB3BnS3MCpRTVEnqiisRFaW1Nd0twWU1B396+7+zHufry7Hw8sBZa6+znxKZ6IpIuFC4Ogztq10R86aGyEujoaG4P5SCV11E5dRPqlqgoqK6Fl3AQoLOy8s7CQlrElTJ8OTz6ZmvJJIK5B3d1v6S2fLtlNrSKyW1ERrF4N5y6rIFJaFmwwg6IiIqVlnLusgjVrgs2SOqqpS1y0tYpYuhQmT4YZM6C8PFhXq4jsMHs25OdD8R4hmlZUE3lwOTtuupXIg8tpWlFN8R4h8vODiaQldRTUJWb19TBzJjQ1BQ/J5swJts+ZE6w3NQX7e6uxq5af/hYsCIL6ZZdBwYgQr+1fyfSXb+S1/SspGBFizpxg//z5qS5pblNQl5i1tYpwBw9HOOmTlbBoERO3rcTDEdzptVWEavmZYcwYePRRaGiAa6+F0lJ45hk49li47rrg3/jRR9WcMdUU1CVm7a0iIhGYOpX8r50PN98cvE6dCpFIj60i4lHLl+SpqIDDD4eWFhgxAvLyghx6c3OwvaIi1SWUmDofDYY6H2WfvDyYNAmqrljJsEvPD6pybYqKaFm2nC/fU8mLLwZxv6M334Rx4z5b374dhg797LXN+vUwdmxiP4dIOktW5yOR9lYRj96wFu/SftkbG3nkhroeW0W01/IBIhGGPh2kboY+vbL9N4DaPov0n4K6xKytVcSmfSdAQZf2ywWFfLBvSY+tItraPjf+PUjdcH6QuuH8IHXT+PeI2j6LDICCusSsrVXEMTdWQFkZkeFF7MSIDC+CsjK+eENFj60i2mr5i6dU4a/UBKkbd2howF+pYfGUKrV9FhkABXXpZDBNC9tbRTSHuOboas5qXs53uZUZLcu5tqSaxpZQj60i2mr5R+xYC01dup43NXL4jjq1fRYZAAV1aRfLsKptrSKad4R4obiS2/NuZM2ISpq2h3ptFdFWy//8rG66nhcU8vlzS9T2WWQA1PpFgNQOq1pVBeHtEb7w7ans+14NBTTSRCGbDihj/Y+rGTI0pKZykvPU+kUGJJXDqlZUwOFHhbh7ejWXDV/OLXYrlw1fzv+bXs3hR/UvoKtHqkiUuyd1OeaYY1zSz7Rp7g0N7n7rre5m7tEOog7B+qJF3tAQHJduVq1yLyhwz893nz072HbhhcF6QUGwXyTTAbXejxirmroAmTusqnqkinSmoC5A5g6rGuu4MyLZRkFdgMwdVjWWcWdymZ5BZLH+5GjiuSinnp7efTfIPz/3nHs47P7qq+6nnhq8hsPuzz4b7H/33VSXtDMz9/Jy9+ZHnnAvKur8LKCoyJsfecInTXLPy0t1SdOHnkFkJpRTl4HI1GFVYxl3Jhd1fAYRaY3wnXFBuuo741YSaY3oGUQWGJLqAkj6qKgI/jNXVwfDqjY0dB5WNd0COgRpo6VLo+POvF8IjR1GiGwbd+aP6Zc2SpW2ZxBt6Sr/QQ00NTK+oJDIlLLgHz8UYv36VJdUBks1delkzBi45x7Yti34f79tW7CejgEdYht3Jhe1P4OoqoKaGqwx6I9gjQ1QUwNVVXoGkeFUU5eM1nXcmfXPVXE0daxrKWFsSQUTexl3Jhe1NV2tPmktu3VJV9HYyI7f1zF9cTD2vWQmBXXJeJ+ljYJxZ1Y1VFJUBAdsT9+0Uaq0PYO4vWEC3y0oDGroUV5QyO1VJaypheLi1JVRYqP0i2SFTEsbpUpb09V3Dwv6I3hhEW6GFwb9Ed49rCItm65K/ymoi+SQtmcQc74Rwp6qpv625fzq4Fupv2059lQ1l349pGcQGU5BXXJeLnXE6dR09V9CfP7qSuZsuJEvLKjkuhtCadt0VfpPQV1yWtsY8kuXwuTJMGMGlJcH632NIZ+p2sa+b2kJmq7m5XVuuqphjjObgnoWyqWaZyxyuSOOnkFkL7V+yTJVVUEgiuyI8P2Tqpjx+lrqD53AjT+v4L77guZ9qokF1BFHspFq6lmkrebZ0hRhZXgq364JBre66vfnszI8lZamSNbWPAdDHXEkGymoZ5FUzl6Uido64uyoWRuN7h20dcRJwzHkRXqjoJ5F2muea7sPUtTVqebZQXtHnCcn4AWdJwZp74ijwcAkw8Qc1M3sm2b2mpnVmtk6M5sXj4LJwGXq7EWpkskdcfQwXHrUn/F5e1qAELAYKIqufw5oBj7X0zkaTz1xRowIhhE/Y1rYw+VTgvHFzdyLijxcPsXPmBZ2cC8uTnVJ00OnMeS3h/2dO5/wXx6yyN+58wkPbw+n7RjyGg89N9HP8dRjav3i7hFgYYdNW4Ed0WDfzswuBy4HOOCAA2J5S+lF2zC0xXuEaPr3agrWVBH5Qx2hL5bQNKmC4ivTc/aiVOnaEeeOOypxryRvAVz9Ppx8cvp1xOnYDDOPCN8ZVwWL1vKdcRNY3lpBa2uImTNh3br0KrckT7xz6ncBD7n7ex03uvvP3L3U3UtHjRoV57eUNm1dwC+7DApGhHht/0qmv3wjr+1fScGIEHPmoC7gXcTaESfZaZD2OVnDESJTpnLUD4IWTuNvP5/IlKl4OKKH4bmuP9X5/izAbcATwG69Haf0S2KtWuX++OPuCxYEmRcIpnJbuDDYrj/N4ycVaZBp09wbGtz9ie6n7/MnnvCGhuA4yS4kczo7M1sMHAGc7e474nFNGRx1AU+OjmmQ1laYMyfYPmdOsJ6o3qhqhil9iSmom1memd0L7A+co4CeHtQFPPHa0yAepEJO+iQYYmDitpV4OII7faZBBpO6UTNM6UusNfVpwDeBQ4Dfmtkr0eXU2Ismkr7a+wREhxjI/1qQ287/2vkwdSpEIr32CRjsQGKZ3AxTkqQ/OZp4LsqpSzYwcy8vd29+pPvcdvMjT/ikScHzjK7amlK2Hf7cc8H2Z5/9bFtPTSkztRmmxI5kNGkUyVVtaZBH/7qWCxsbsQ77vLGRR26oY83bld1OC9c+kBhAJELr40GzxIlHTsDDFRAKWgR3N5BYJjbDlOTSMAEig9CWBtm07wToktumoJAP9i3pMQ0Sa+pGD8OlV/2pzsdzUfpFskF7GuTpsO88ZYqHhxd5BPPw8CLfecoUf/apcI9pkFhSN5K7UPpFJHHa0yDNIa45upr1z1VxNHWsaylhbEkFE1tCPaZBYkndiPRF6ReRQWpLgzTvCPFCcSW3593ImhGVNG0P9ZoGiSV1I9IXC2r1yVNaWuq1tbVJfU+RdFJfHzRbXPmbCOW3T2Xn72qw5kZ8eCF5J5Tx/HXVnHFWSOO3SCdm9qq7l/Z1nNIvIkkWS+pGpC8K6iIpUFER1Nirq4PUzaqGSoqK4IDtQUpHAV0GS+kXEZEM0N/0ix6UiohkEQV1EZEsoqAuIpJFFNRFRLKIgnoa0kzxIjJYCuppZrDjbIuIgIJ6WknVFGkikj0U1NNIPKZIE5Hcph6laWThQnj4YSgcFh1nu6YGGhvJLyyEsjKorqaxJcSCBfDf/53q0opIOlJNPY20zRTfsqIKamqC6W3cg9eaGlpWVGmmeBHplYJ6GmkfZ/uGtXj7fGeB9nG2NVO8iPRCQT2NaJxtEYmVgnoaWbAgCOrH3FgBZWVEhhexEyMyvAjKyvjiDRXk58P8+akuqYikKz0oTSMaZ1tEYqWgnmY0zraIxELjqYuIZACNpy4ikoMU1EVEsoiCuohIFlFQFxHJIgrqIiJZJOagbmaTzOwPZrbOzGrN7Ph4FExERAYupnbqZjYSeAyodPffmVk58BszO9jdm+JRQBER6b9Ya+pTgbfc/XcA7r4a+BswJcbriojIIMQa1A8Bus7DUx/d3s7MLo+mZmo3b94c41uKiEhPYg3qBkS6bAt3va67/8zdS929dNSoUTG+pYiI9CTWoL4ROKDLtgOi20VEJMliDeq/Acab2VEAZnYcMBZ4OtaCiYjIwMXU+sXdt5nZOcAvzcwJUi/T3P2TuJROREQGJOahd939eeDYOJRFRERipB6lIiJZREFdRCSLKKiLiGQRBfUEqa+HefNg9GhYsSJ4nTcv2C4ikigK6glQVQXjx8PSpTB5MsyYAeXlwfr48cF+EZFEUFCPs/p6mDkTmpqgtRXmzAm2z5kTrDc1BftVYxeRRIi5SaN0tmMHNDZ+tr59e/B64onQcY7v9euTWy4RyQ2qqcfZwoWdg/rQoZ1fIdi/YEFyyyUiuUFBPc6qqqCysnNg76ixEaZPhyefTG65RCQ3KKjHWVERrF4Ns2ZBc3Pnfc3NwfY1a4LjRETiTUE9zmbPhvx8GDkSwuFgaWr67OeRI4P9F12U6pKKSDZSUI+zBQuCoH3ZZVBQAOvWwVe+ErwWFAStYPLzYf78VJdURLKReccmGUlQWlrqtbW1SX3PZKuqCmrlLzwfYf2dVZSwltdsAmPnVzCxPMSQIVBRkepSikgmMbNX3b20r+PUpDEBKiqg/u0Ih/3TVEZTw3AaafZCNj5URv43qxnz+VCqiygiWUrplwQZ83aQq3wHAAAKT0lEQVQVY7fVUEQDIZwiGhi7rYYxb6s7qYgkjoJ6oqxdu2u7xsZGqKtLTXlEJCcoqCfKhAlQWNh5W2EhlJSkpjwikhMU1BOlogLKyoIG6WbBa1mZnpCKSELpQWmihEJQXR00hamrC2roFRXBdhGRBFFQT6RQKBgzoLIy1SURkRyh9IuISBZRUBcRySIK6iIiWURBXUQkiyioi4hkEQV1EZEsoqAuIpJFFNRFRLKIgrqISBZRUO9FfT3MmwejR8OKFcHrvHnBdhGRdBRTUDezb5rZa2ZWa2brzGxevAqWalVVMH48LF0KkyfDjBlQXh6sjx8f7BcRSTeDDupmFgIOA06MTrFUASwxs8/Fq3CpUl8PM2cGE0a3tgbzikLw2toabJ85UzV2EUk/gx7Qy90jwMIOm7YCO4CMH4Zwx47O81ts3x68nngidJzSdf365JZLRKQvfdbUzWw3M3ulm2W3LofeBTzk7u91c43Loyma2s2bN8er7AmzcGHnoD50aOdXCPYvWJDccomI9MW8Y9VzsBcxuw04Gjjb3Xf0dmxpaanX1tbG/J6JlJcHkybBypW7Tl4EQUCfPh1efBEikeSXT0Ryj5m9Gk119yrm1i9mthg4gn4E9ExRVASrV8OsWdDc3Hlfc3Owfc2a4DgRkXQSy4PSPDO7F9gfOCdbAjrA7NmQnw8jR0I4HCxNTZ/9PHJksP+ii1JdUhGRzmKpqU8DvgkcAvy2Q6791PgULXUWLAiC9mWXQUEBrFsHX/lK8FpQELSCyc+H+fNTXVIRkc7iklMfiEzIqUPQDj0cDtIsd9wRtHrJy4Orr4aTT4YhQzSHtIgkT39z6pqjtAcVFUE79KeqIswcVsXYlrWsHzqBlsYKDj88xJgxqS6hiMiuFNR7MeagCHe/PRVCNUAjhArh7TI4qJosaI4vIllIY7/0pqoKamqgoSHIvzQ0BOsaI0BE0pSCem/Wru3cCwmC9bq61JRHRKQPCuq9mTBh195HhYVQUpKa8oiI9EFBvTcVFVBWFvQyMgtey8rU7EVE0pYelPYmFILq6iCHXlcX1NArKoLtIiJpSEG9L6EQVFYGi4hImsv69ItmLxKRXJLVQV2zF4lIrsnaoK7Zi0QkF2VtTl2zF4lILsramrpmLxKRXJS1Qb2qKmiw0rVDaJu22YuefDK55RIRSaSsDeqavUhEclHWBnXNXiQiuSgjgnpbW/Pi4mCiiuLivtuaa/YiEclFaT/zUVVV0PSwtTVY2uTnB8ujj/Y8FItmLxKRbNHfmY/Suqbeta15R/1pa15RAYcfDi0tMGJEENCLioKc+uGHK6CLSPZJ63bqS5bsGsy7am2FO++Ee+7pfv+YMXDPjyPc8+WqYHz0CRM0KJeIZK20Tr8UF8Onn/bvuG3betgZicDUqcGMRY2NwXjoZWXB6IsK7CKSIbIi/dLQEIfjNCWdiOSQtA7q/W1D3utxmpJORHJIWgf1trbmvemzrbmmpBORHJLWQb2trXlv+mxrrinpRCSHpHXrlzFjgnbofbVTHzOml4toSjoRySFp3fqlTX190GzxgQeC55xFRUHKZf78PgK6iEiW6G/rl4wI6iIiuS4rmjSKiMjAKKiLiGQRBXURkSyioC4ikkWS/qDUzDYDf07qm6bOXsCWVBcijen+9E33qHe5dH8OdPdRfR2U9KCeS8ystj9Pq3OV7k/fdI96p/uzK6VfRESyiIK6iEgWUVBPrJ+lugBpTvenb7pHvdP96UI5dRGRLKKauohIFlFQFxHJIgrqIiJZREF9gMws38wWmlmrmZ3XYfs3zew1M6s1s3VmNq+H8/c0s0Yze6XDsjh5nyDxerlHi6L36Pdm9gcz+6dernGFma03s9fNbKWZ7Z2c0iderPcnl79DHfYfYWYfmdktPZxv0fv5lpm9YWYPmllhd8dmm7SeJCNNfQNw4JW2DWYWAg4DTnT3BjP7HPCumf3G3d/vcv5ewMvuflrSSpx8u9yjqI+BUndvNbNRwB/N7El3/2PHg8ysHLgWOMbdN0f/4/4CqEx4yZMjpvtDbn+HMLORwE+A5b2cfzEwDShx92YzWwb8CLgyAWVNKwrqA+Tu/wpgZmd02BYBFnY4bCuwA+hueqW9gPFm9juC+/8KcJu7f5CwQidZd/couv2ODqsHAQ3AR91cYhbwoLtvjq7/GNhsZru7+7b4lzi54nB/cvY7ZGZ5wH3AvwCn93KJWcC/uXtzdP3HwLPkQFBX+iUx7gIecvf3utlXC+zn7icA5QTBf5WZWRLLlzJmdpiZvQNUA7N7CNKHAPVtK+7+MbCNINBltX7en1z+Dt0GPO3uL/dxXKfvUPTnfzCz3RNWsjShoB5nZnYb8Dl6qBG4+/ZozR53bwSuAcYChyatkCnk7u+4+2HAFOB+Mzuim8MMiHTZFiYHvq/9uT+5+h0ys7OBA9z9nv4cTufvUDj6mvXfIaVf4ij6sGoMcLa77+jvaQRftL8nrGBpyN3XRtMHk4H/67J7I3BA24qZFQB7RrfnhD7uT1e58h2qAMaZWVuefTQED03d/Zwux3b6DkV/bgA+SXgpUyzrf2slg5nlmdm9wP7AOR0DupmFzOxZM5sYXT/TzPaN/mzArcDqbMqHdsfMjjKzc9tSBNGHyWVAbdd7BDwAXNjhT+UrgJc65NizzkDuT65+h9z96+5+jLsf7+7HA0uBpe5+TrRF0Etmdlj08AeAr5vZbtH1fwYe8xzoQq+aenxMA75JkOv8bYfU5o3Ab4GDgX+IbnPgMTPLB3YC64ALk1ra1PgzMBe41sxagd2Am9z9FTMbRod75O7Pm9lPgDXRY/8K7NKsLcv0+/6Qu9+h3hQABwJtFYH7CdJRvzezMPAGOfCQFDT2i4hIVlH6RUQkiyioi4hkEQV1EZEsoqAuIpJFFNRFRLKIgrrkNDMrN7OGPo5pazt+iZm9npySiQyOgrrkBDObamZNfRwzzczCXZadwL1JKqZIzNT5SHJJcx/7q4GR0Z+dYKCsDUBdIgslEk8K6pIr9gO6GzWzXXSQrPZUjJkdC+wLrExs0UTiR+kXyRWTgNb+Hhyd+GQx8B/u/pcOu44wMzez9fEuoEg8qKYuWc/MhgBnArubWZm71/RxfBHwS4KxRM7usvtN4EvsOjSwSFpQTV1ywSXAduB64F+jQX4X0dE2KwkGZjsaONXdt3Q5bKe7f+LunyaywCKDpaAuWc3MDgCWADcBd0Y3/7SHw58B/gt4EjjW3d9NfAlF4kvpF8la0YBeDVS5+9LotnMIhkd+ALi8yynfAra4+6bkllQkflRTl2x2D/A68LW2De6+ATgZKAKGdTzY3V93901mttrMzurmes8A8xJYXpGYqaYu2WwW0NJ1tptoWmUGQA9zNY8mCPqduPtGcmhKPclMCuqStdy9r85GvRkabQXTncZcmBZNMpPSLyLdWwp82sPyuRSWS6RXms5ORCSLqKYuIpJFFNRFRLKIgrqISBZRUBcRySIK6iIiWURBXUQkiyioi4hkkf8PlcQWjsBjLMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"모델 테스트\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"시간\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 시퀀스 생성하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:                        # 책에는 없음\n",
    "    saver.restore(sess, \"./my_time_series_model\") # 책에는 없음\n",
    "\n",
    "    sequence = [0.] * n_steps\n",
    "    for iteration in range(300):\n",
    "        X_batch = np.array(sequence[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence.append(y_pred[0, -1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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el+U2J7g3aOB/2dWqAVWqsOVORPmYPx+45hqgc2fg7bfzb73eey/QpYtulLJ7t/919EpODvDmm0D37u59OQ8ffjz3fJj95z863n7eed6X5WS++/JL78ty2+bNOiRTu7b/ZYto1zxb7kT0G999B1x6qXbvTZ2q+1Hnp1w5HY8/cAB44glfq+ipOXP0y/n6690752mnAVdeqTcNBw64d16/LVqkx86dvS+rfn2dkBbGuQpbtmj9/drqNa+GDdlyJ6Jctm/XCWQVKwIzZgA1ahT+/FatdNz9xRfD2VLIzxtvaH7z/v3dPe+IEbrb2eTJ7p7XT4sWAdWrH89h7rVu3XQYIGypaLdssdMl73Ba7mGbr8DgTuQBZ4evPXs0K1txZ/o++qi2UB580NPq+WLfPuCDD7SVXVCPRWl17Kj7vY8b5+55/fT113odXq1vz6tbN12vHbZx982b7Qb3hg2Bn38O33AZgzuRy7KzgauvBjIzdSJd27bFf+3pp+vSuLffBpYv966Ofpg8GTh0yN0ueYeIzr5fvFg3ogmbvXuB1av96ZJ3OBP3wtQ1f/So9oDZmCnvCOuMeQZ38s3hw8C2bdFezw3oLmaffKJL35wEIiXx179qd+3997tfNz/985+apOfcc705/zXXaC/HxInenN9LzkYuXr03+WnQQANVmHbX275dhxFsttydzWq2brVXh9JgcCfPLVsGDBigY6+nnw4kJ+vmIf/9r+2aue/dd4G//11ndN98c+nOUbWqzp6fMQNYsMDd+vll9WptVV9/vXfdzqedprPw3303fOOhGRl6bNPG33I7ddK/S1jYXAbnYHAnyuO//9WlXe3ba2vhT3/SLT+vvFLXPrdpE95NGfKzYYPetHTpAvzjH2ULamHf5nT8eF0BcO213pZz1VX6vi9Z4m05bsvIAOrU0RtdP3XqpL1nYQlUmzfr0WZwr1lT9zbIyrJXh9JgcCfXGQNMmAA0bw689JIG9e+/B55/Xv/79de1RXrwoM4m37XLdo3LLidHW6nlymk3cUm2NM1PpUo6qW7ePODzz92po19ycvQ96NXL+7XJ/fvrez1pkrfluG3FCuDss/0vt1MnPTrL8ILOCaheZvArioiWH5YbIgeDO7lq1Sqga1dgyBCdBLNkiS7typ2RDQA6dNBMbVu36nPD2DrN7V//0kD8/PPHu/HK6sYb9T28//5wLV9avFj/rlde6X1Z1arpjmpTp4bnM3TkiA5b+N0lD+gNRcWK4Qnu27bpMFXlynbrUb8+gzvFqX37NL3qOefoF9frr+sXSLt2Bb+mc2fgmWeA6dOB117zr65u279fJ8F16uROilVHUpIujVu6VG8ewuLdd7Ub87LL/CnvssuA9es1YVAYrF6ts8BttNwTE4HU1HAF93r1bNdCW+7slqe4M3368a1Lhw3TnN/DhhUvo9Qtt2hqzL/8Rf9HDqPnn9e83c8/7/7ksWuu0ZuGe+4Jx57S2dnAe+/pHuJVqvhT5qWX6nHqVH/KKytnMp2N4A5or9mKFXqDEXRZWcEI7vXr6/dTdrbtmhQfgzuVmjHA449rspbq1XUrzldfLToTW24JCfqaY8d0fXfY7N+vQf2yy7xZ1pSQoPMWdu8GHn7Y/fO7bd483bf9iiv8K7NuXZ20OW2af2WWxYoV2jWekmKn/HbtgF9/DUdPx7ZtdsfbHfXr63fUjh22a1J8DO5Uan/9q67pvvZaID1dv2BLo3FjnTz2/vu69WmYvPKKtqgfeMC7Ms455/hKgxUrvCvHDZMmASefrC13P/Xpo/M7wtC7kZEBtG4NlC9vp3wnqdLSpXbKL65jx/RGMQgtd+cGI0xd8wzuVCqvvQY8/bTm+B4/XlsiZXH33Tq7/pZbgF9+caeOXjt2TJe8de9e+hub4nrsMe0RGTEiuF2DR4/qDdqll2qA91OPHjrp8Kuv/C23pIzR4G6rSx7QHoNTTgl+cN+xQ/+mQQjuYVzrHpjgLiJpIrJMRFaKSLqIdLRdJ8rfypXAbbdpa6ms67kdFSpo9/ymTdrVHwZTp2q34W23eV/WqacCY8bo0EdQJx/OnavDB4MH+192x446o3r2bP/LLont2/U9shncExK0N8jJkhdUTiuZwb10AhHcRaQagCkAbjHGnAXgbgBTRaSS3ZpRXkeP6tK1atW0xe5m12Jamu6K9swz4cgX/tJLulTNry7oq6/WXoL77tMgETTTp+sM/549/S87MVE3Rgl6TgBnWMVmcAd03D0jQ3ufgsqZYBuEMffq1bUBEsT/7wpiadTnBL0ArDHGLAIAY8wcEfkBQHcAH3td+KFDmjQkry++cLecWrV0fLmsXdg2vfiifilMmeJNdq1nngE+/lgTwixcqF/aQbR+vXYBjxqliWv8IKK9G61bA7ffrrPSg2T6dL1B87tL3tGjh+b037Sp+Lvw+c2ZKX/WWXbr0batfu+tWQO0bGm3LgVxgnsQWu4impDphx9s16T4ghLcGwFYn+ex9bHH/0dEhgMYDgANXMxHWNAfrHt314r4HxGgSROdTTxypAb8sPjpJ+CRRzTz2O9/700Zycna7TxoEPC3vwFPPOFNOWU1YYL+LYcM8bfcJk10EuMDD+jNpxef0dLYvFnXb994o706OD0Gs2frUswgysjQ/cGrVrVbj9at9ZiZGezgnpio6V+DoE4dBvfSEAB5pwkdQ55hA2PMWABjASA1NdW1fFR16uT/+Ny5bpWgE0O2bwfWrtV9nJ94QpdQPfqoLgHza0/nshg1Svc19mI9d26XX65fzn//u3YfDhzoXVmlkZOjSWW6d7fTZXjnnXoDdM89OkO8OPkEvDZ9uh5797ZXhxYtdFnc558HO7jb7pIHgGbN9HMT5OGvrCz9ewbh8w1oXf7zH9u1KL6gBPcsAD3yPNYAwPt+FH7SSf6nrly7Vr+k77xT1wZPnKj1CKqsLA0oQ4fqrHav/eMf+sUzZIjefPm573VRFiwANm7UGzMbKlbUG61rr9WueT/XlBdk+nSdf+DHZ6MgIto1/+mnegMWlKDg+Pln/f/ej7S8RXHW2Wdm2q5JwYKyxt1Rpw7w5Ze2a1F8Qfn4TwVwloi0BgARORdAcwABnx5Tek2b6tjymDE667pfP/2fP6ieeEK/MB96yJ/yKlYEPvxQx9v69gWWL/en3OIYP15nZvfvb68Of/iDBtInn7SfU/3IER0i6NPHfg9Uz546Gz1InxdHZqb+rWzklM9Pq1bBD+5BGG931KmjO10eOmS7JsUTiOBujNkHYBCAf4rIvwE8D6CvMSaCO34fJ6L52MePB+bM0S7N/ftt1+pEmzcD48YBN9zg70SlWrV0/LRqVeCii4KRUeuXX7S1fPnl9iaOAdoqvecenX09a5a9egDak/HzzxrcbXPmIARxvbvttLN5tWqlWy4HMVgZE8zgDmhinTAoMriLSHkR8XzlqjHmK2NMe2PMucaYzs7M+Xhw7bW62cbixdqCD1oSl8ce0xsRL7OwFaRBAw3w5ctrl+v6vNMuffbRR8CBAzo8YdvVV+uX33PP2a2HswTuwgvt1gPQL+CUFB3qCpqMDM23H5SZ/K1aaRBdvdp2TU60b59+DwYpuNetq8ewTKorTsv9ZADvFPYEETlNRP7mTpXi06BBwNtv6/KvAQM093MQrF0LvPkmcNNN9sa/UlJ0ktSvvx7vdrVl/HgdW+7a1V4dHElJOjt91iydA2DL9OnA+efb35bTkZYGzJ8fvEx+K1boEjjbQxeOVq30GMSu+SAlsHE4LfewrHUvMLiLyAARGQDgEv2n9I899rs8z2sG4EsATUSC8rENp8GDdavUmTN1TDUICSYefljHv2202nNr1Urzzm/bBlx1lZ0v7m3btBdhyJDgTNa64QYNFuPG2Sl/61ad+GhzlnxeXbvq2GiQglZOjmZ2DMp4O6DLKpOSgvU+OYKUwMbhBPcotNyfjf08BmAzgDEARgO4WESuE5HXRWQqgLkAXgEwxBjbU3vC749/1G7WKVP0v3Ny7NVl2TLdCOTPfw7GevwOHTQr3Oef2+mKfust/Xv4vba9MKefrhMO//lPOzc8zhK4IIy3O9LS9OjmUtay2rgROHgwOOPtgA51tWgR7OAepJZ7zZr6noU+uBtjzsznp5Ex5hkAewDsBZAIoCKAugB8ytMVfXfcocusJkzQFrytLvoHHtC0i3ffbaf8/Nxwg26v+tBDOmTgF2O0S/6887TFEyTXXquTfGyMM0+frnm3f/e7op/rlwYNdOgkSOPuQUk7m1dQZ8w7wd0Z5w6ChIRwZakrtHNRRPIdRTPGTDPG3GOM6QugDYBuAN4XEQZ4lzz4oO66NmmStsz8nkU/dy4wY4Zu62o7m1ZuIrrNaoUK2qPgl/R0nXgUpFa74+KLNX3y5Mn+lhukJXB5paVpcA9KX2JGhgYHZ5w7KFq10qGVoK3SycrSlnKFCrZr8lt16kRgzD1mn4jMF5HfzIMVkfoiMkREhgDoCuA9AKkAAvK/UviJAH/5i2ZCmzdPJyxt3uxP2b/+qvuHN2igKXKDpk4dvfn57DP/dgEbP16/aGzseFaUk0/WVRYffODvPI2vv9aVA0Hqknd07Qrs3BmcjGIZGZrbImiJqpzUs0HLVBe0BDaOMKWgLSq4GwD/BjBFRO7P9XgDANcD+H+x4+8B1DHGWBwhjqZrr9UgtnkzcO65wCIfFgg++aS2Ul95JXhfRo6RI3VJ0V//6n3r7PBh4J13NJ9+tWrellVaV1yhwWzOHP/KnD5dxyCDkt8+t6CNu2dkBGsynSOoM+aDtsbdEaXgngPgAQDnA7hDRIYCgDFmoTHmAgCHY8e8qWPJRT176hr4KlV0W8sJE7wra+5cHe//wx90OCCoKlbUcfelS71P4jJ5MrBnT3DzlQPaeq5cWYdx/DJ9OtClC3DKKf6VWVyNG+t4bRDG3ffu1ZvzoI23Azo34eSTGdyLq25dYNcuHZIKuuIs6EkwxqwCMALACyJSI5bYphGgXfQA6ntZSdJUo998oxO6hgzRFqvbM+k3bNC8102a6NaiQXfNNdp1N2qUt+W8+KK+/0FsoTpOOgm49FJdZXH0qPflZWUBq1YFs0se0GGtrl31ZtX2uPvKlXoMYnBPSNDJkEHqlv/1V+2FCmJwd5bD7dhhtx7FUezVusaYKQDWABgJoBmAddAEN5tx4nat5IHq1XUN/E03AU89pYlv3LqDXL9eg9eRI5rTPYitsbySknRewvz5+uOFxYt157WRI4M3aSyvK67QHoYvvvC+rBkz9BjU4A5o1/z27XrTalPQ0s7m1aKF7useFM6EtaCOuQPhmFRXVHBfA+2ad4wHMNgY860xJiHPD2fK+yAxUcfCx4zRVpobyW6+/FLH8/ft0y/tIC1rKsqwYboHvFf7vo8apTdVQZwln1evXnpT9r4PeylOn64tq6DN/s7NySJoe9w9I0M/owVtLW1b8+baE3PggO2aqCCucXeEKZFNocHdGNPaGHM410OTAAzwtkpUFGfDmeee0xnSQ4eWPoHJK6/opiy1a2sLtX17d+vqtUqV9L2YMUPH3920fDnwySd6/jD0ZFSooLPmp071dtb80aO6SiGIS+Bya9FCl1PZHndfsUJb7UF9r5xteoPSeg9ycA9TfvkSJdE0xuw2xgTkI0B33KEz2ydO1KVrJRlbzMnR5DQ336ypQxct0klIYXTzzTqL/fHH3T3v/ffreW+91d3zemnAAJ3ws2CBd2UsWqTrooPcJQ/8dtzdlmPHdDw7qF3ywPHgHpRlg0HMK+847TSdpxC54E7Bc++9mknu9dd17XdxHD6sE+eefRa45RZt6VWp4m09vVS1KnD77bpjmzN5qaw++0x7Ax5+OFhJfIrSp4+uJJgyxbsygrwELq+0NGDTJmDLFjvlr1mjE8SCHNwbNwbKlQtOcN+2TXvkgrjstFw5DfBRGHOnEHjsMWD4cB13Lirn+u7dunXqe+8Bo0cD//iHfmDD7vbbtevcjdb7wYPAbbdp0pFbbin7+fx08snaEzNlinf7EkyfDnTuHI6bHmfc3VbXvDOZLohr3B1JSRrggxTc69UL7jBG3bpsuZNPRICXXwYGDgTuvFO76vProl+/Xr+uqrxVAAAY8klEQVSU09N17fZddwX3f6CSOvVUDcjvvw98913ZznX77brRx+uv6xdf2AwYoF+Q6enun3v7dg1YQe+Sd7RurS1AW8F9xQr9DDld30HVokXwgntQhSWRTSCCu4jcJCIZIpIuIitF5GbbdQqbcuV07P2qq4D77tOjsxYzO1t3NGvfXsdjZ8/WZXRRc8cd2p332GOlP8ebb+oOa/fdF4w920ujXz/tNv/gA/fPHYYlcLmVK6eJdmyNuy9bpjcYiYl2yi+u5s11I6YgbDOdlcXg7gbrwT222UwKgPOMMakA+gB4VkQC/OcNpqQkDeKjRukX++mna3dgvXqaxrZZM02E06WL7Zp6o2ZNDfDvvlu6NKxffw2MGKFjyY884nbt/HPqqcCFF+pnwO0ELtOna7fkWWe5e14vpaVp4PrxR3/LNUaDe9u2/pZbGs2b6yqIjRvt1iMnR3uHgrjG3VGnjjacgnAjVBjrwd0Yk22MudsYczD20G4AR8AtZEslIUFneWdmahd9/fr6Rf/++5roJWjblbrt/vuBRo10DsIvvxT/df/+t7ZG69fXm4Py5b2rox8uv1yHYZYtc++cR45oqt/evcM1nGNr3H3LFk09G5bgDtjvmt+1S28ygt5yN0az6AWZb8FdRJJEZHE+P3lHNZ8HMMkYc8L8VhEZHuu6T98Z9HfWsmbNNIvdxx9rd/3AgeEPWMVRqRIwdizw/ffaii+O997TnP01amhCn5o1Pa2iLwYO1K7gt99275xz5+oSuMsuc++cfmjbVica+h3cnRurMAT3Zs30aDu4B3mNu6N2bT363RNUUr4Fd2PMEWNMx3x+/pdAVUQeB1APmuI2v3OMNcakGmNSk5OT/ao6hUz37pp7//XXdYiioFnjWVman37wYB2+WLRIW+5RUL26bvzz7rulT3CU10cfaQ77HiHbJqp8ed2Twe9x92XLdMy/dWt/yy2NU08FatWyH9ydNe5B75YHGNyLTURGA2gJYGDugE9UGo8+qmv5H3xQx1xnztRJMOvXa+78IUN0iGLyZOBvf9Mx+lq1bNfaXddco9c8c2bZz2UMMG2apritVKns5/NbWpoOVe3e7V+Zy5ZpKuegbpucV/Pm9oP71q16DPJNttNyD/qkOuvBXUQSRORV6M5ygxjYyQ2JiTocMW6cdtH37q0TwZo00aVi06YB11+vE60eeSScS96Kcuml2r35zDNlP9eSJdqqCluXvMPZ392rDYbyE5bJdI4WLYDVq+3uopeVpT0tp51mrw5FcRoBQW+5B2EUti+AmwCkA1ggx2fqPGiMmW2tVhR6IsANN2gLft483R3s5JO1hXLOOZqLPcqSkjSXwZ136u52HTuW/lzvvqs3TL//vXv181NqqmbumzfPn2v44Qf98g9TcG/eXCcA7txpL7hu3ao3pEFOrHXSSZrAicG9CMaYTwCEaO4thc3JJ4dnXbbbbrxR1/0/9ZQOR5RGTg4waZK+h0FMCVocFSpoAic/tsMFwjWZzpF7xryt4J6VFezxdkcY1rpb75YnIu9Urqwb33z0kXa5lsb8+br2+Kqr3K2b3y66SPce8ONLedky7TkKck75vIKwHG7r1mCPtztq1w5+y53BnSjibr1VuxL//vfSvf6113RjoUsucbdefuvVS4+zZnlf1rJlujdBGLYKdtSvr58TW8HdmPC03Bncici6mjU1wE+YAHz6acleu3275gH44x91eCPMzjpLJ0O5sXqgKGGbTAdoAqxmzewF9127dAe9MLTc2S1PRIHwf/+nwe3664F164r/upde0nXyI/PNPBEuCQnaNT9rlntr//Ozc6dmpwtbcAfsLocLwxp3R+3awM8/6w6SQcXgThQHKlbUNf3GaDriDRuKfs369cCYMbrJUOPG3tfRD/366Vr3r7/2royFC/XYqZN3ZXilRQtg0ybg0CH/yw7DGndHGLLUMbgTxYlmzYDPP9cWR6dOwIIFBT/32DHdRCcxUQN8VPTpozPnp0zxroyFC7WM1FTvyvBK8+Z6A7h2rf9lh6nlHoYsdQzuRHGkTRsNPpUqAeefr4luMjJ++5xDhzSD3+zZwOjRwc7zXVKnnAL07KnB3atkLQsW6PbKYcyjYHPG/NatwU9g4whDljoGd6I407w5sGqV5t2fN08DfmqqttSHD9dZ3u+8o7Prhw+3XVv3DRigY+LLl7t/7kOHgKVLNZd9GKWk6BK+0i6bLAtnH/cgJ7BxsFueiAKpcmXdHnfjRk1PW6GC7v/+0Ufaff/VV7r5ThRdcolOrvOia37JEt2ytEsX98/th5NO0vkV337rf9lbt4ajSx7QHSTLl2dwJ6KAOvVU4O67tat+507gp5+0O75bN9s1807NmpprvrQZ+wrjzGPo3Nn9c/ulVSvdZMdvWVnhmEwH6M1hrVrsliciCpT+/YHvvnN/bHnBAt0Jrnp1d8/rp1atdLOlw4f9KzNMCWwcQU9kw+BORHHH2TzGzdZ7To4usQtrl7yjVSvNA7BmjX9lhimBjYPBnYgoYOrX1xntbgb3b78F9u0L72Q6R6tWevSza95Z4x6mlnvQs9QxuBNRXBowQCfAbdnizvmc8fawt9xTUjS/gZ+T6pw17mFruf/0k7fZDsuCwZ2I4tKAAXr86CN3zjdvnrbmzjzTnfPZkpSkyyFXrfKvzDC23GvX1qGYXbts1yR/DO5EFJeaNtXJbx98UPZzZWdr9r+ePXWdeNidfTawYoV/5WVl6dKyWrX8K7Osgp6lLlDBXURaisgeEXnEdl2IKPquvFJb3Js3l+08S5dqzvrevd2pl21t22rA/eknf8rbulUT2CQEKiIVLuhZ6gLzVopINQAvAXjHdl2IKD5cc40e3367bOeZMUNb7D17lr1OQeDsaOdFFr/8bN0arvF2IPhZ6gIR3EUkAcB4APcD2Gm5OkQUJ848U3PsT5hQtlzzM2bo7PuaNd2rm03nnKPHpUv9KW/TJqBhQ3/KcguDe4yIJInI4nx+kgA8DuBzY0yhGzGKyHARSReR9J07eQ9ARGV37bWazCY9vXSv37IFWLRIt5ONimrVNA3tsmXel3X0qA4BhG0iYqVKQJUqwPbttmuSP9+CuzHmiDGmY94fAJcAaGCMebEY5xhrjEk1xqQmJyd7X2kiirxBgzS3/oQJpXv9O7GBxKuvdq9OQdC2rT/BfcsWnXUetuAOAHXrcsy9MH0AtHBa8gCGARgmIu9ZrhcRxYFq1XTr23fe0VZkSRijNwXnnQc0auRN/Wxp1043Ftq719tyNm7UY9i65QEN7nHfci+IMWaYMaZdrpb8OADjjDGDbNeNiOLDkCG6XnnatJK9Lj1dk71ErdUOHJ9U53XrfdMmPYax5V6vHrBtm+1a5M96cCcisq13b215P/lkySbWPf88cMopDO5lsXGj7uEepgQ2DqflXpbJmF4JXHA3xjxijHnEdj2IKH6ULw/cd5+2xGfNKt5rtm4FJk8Ghg3TiVVRU6MGcMYZ/gT3Bg30bxA2devqUM7u3bZrcqLABXciIhuGDNHW46hRxXu+08q/9VZv62VT27beL4fbuDGcXfKABncgmOPuDO5ERNCc6vfeC8yfr1nrCvPdd8BrrwEjRoQ3MBVH27a6t/v+/d6VsWlTeN9DJ7gHcdydwZ2IKOaGGzS/+SOPFDyOmpMDjBwJVK4M/O1vvlbPd+3a6dGrTHW//KJJYMIe3NlyJyIKsJNOAh5+GPjqK2Ds2Pyf89RT+vvRo4Gop9s491w9OtvZum39ej02aeLN+b3mbB7D4E5EFHAjRgA9egB33nliUBs/HnjgAWDwYG3lR12NGkCrVsDcud6cf906PaakeHN+r1WooCmHGdyJiAIuIQH41790I5OePYHHHgM+/RT44x+B667TwP/GG9HY2rU40tKAr78ueYKf4nCCe+PG7p/bL3XrcsydiCgU6tTRiXUXXqjd9P366c5xd92liW4qVbJdQ/+kpQE//+zNrPnvv9ehjapV3T+3X4Ia3EO4spCIyHvJydpi37BB9zVv0iQ6u76VRNeuepw7F+jY0d1zr1sX3vF2R4MGpd90yEtsuRMRFaJRIw1q8RjYAV090KKFN+Pu69aFd7zd0bChpi4+eNB2TX6LwZ2IiAqVlqaTC48dc++chw5plr+wt9ydZXybN9utR14M7kREVKi0NODAAWDFCvfOuWGDHsMe3J3d7JwNcIKCwZ2IiAqVlqZHN7vm167VYxS65QEGdyIiCpk6dTQIuxncv/1Wj82bu3dOG2rVAipWPL4vfVAwuBMRUZG6ddPg7ta4e2amjldXruzO+WwR0dY7W+5ERBQ6PXroBjJLlrhzvm+/BVq2dOdctjG4ExFRKHXvrq3Uzz8v+7mOHgXWrGFw91JggruI3CUimSKyRETSRSSOckAREQVbjRq6Bawbwf377zXAt2pV9nMFQcOGwO7duqIgKAIR3EXk/wAkA2hjjGkPoAeAw3ZrRUREufXsCSxeXPYg5kymi0rL3VnrHqTWu/XgLiLVAPQCcADAQhGZC6CTMSbHbs2IiCi3nj11Qt2cOWU7T2ambtAT9pnyjiAuh/MtuItIkogszvsDoAOAlgCyjDEdANwBYKKInLBPkIgMj3XZp+/cudOvqhMREYDzztM972fPLtt5li0DmjbVc0VBXAd3Y8wRY0zHvD8AqgPYZIwZH3vecgBfAOidzznGGmNSjTGpycnJflWdiIig+5d37Vq2cXdjdKOV9u3dq5dtycm6U2BcBvdC/ARgf57HcgBkW6gLEREVomdPYPVqICurdK/ftg348cdoBXdnrXuQEtkEIbjPB9BIRLoAgIg0A3AhgFlWa0VERCfo2VOPpW29O+vkoxTcgeAth7Me3I0xRwD0BfCCiCwHMBHAUGPMBrs1IyKivFq3BmrXBmaVsvm1ZAlQvjxw9tnu1su2oAX38rYrAPxvnL2d7XoQEVHhRIBevYCPPways4Fy5Ur2+vR0Xd8elcl0joYNgb17gX37gKpVbdcmAC13IiIKl169gD17gKVLS/a6Y8d0nXyHDt7Uy6ag7evO4E5ERCXSs6e24GfMKNnrMjI0AY6zhWyUOMvhgjKpjsGdiIhKpGZNIDUVmDmzZK9ztoyNYnBv1EiP69fbrYeDwZ2IiEqsd2/tYt+7t/ivmTsXaNIEqFvXu3rZUr265t9fu9Z2TRSDOxERlVivXkBOTvGz1eXkAPPnaxKcqGralMGdiIhCrEMHnRVe3K75FSu0ld+tm6fVsiolRXe8CwIGdyIiKrHy5YEePXRSnTFFP9+ZfHfRRd7Wy6amTTVz388/264JgzsREZXSxRdrOtnly4t+7owZuh98rVre18uWpk31uG6d3XoADO5ERFRK/frp1q0ffVT48/btA77+WifhRVlKih6D0DXP4E5ERKWSnKzbwE6dWvjzPv9cs9n16eNPvWxp0kSPQZhUx+BORESldtllwMqVwIZCdgOZPBk47TSgY0f/6mVD5cpAvXoM7kREFHKXX67Hd97J//f792se+sGDdRJe1KWkMLgTEVHInXGGrl3/17/ynzU/dSpw+DBw1VX+182Gpk055k5ERBEwZIi2Vp292nN7/XXNu96pk+/VsqJpU2DXLt1YxyYGdyIiKpPLLwcqVQJeeOG3j8+bp1np7rxTN5qJB85yONutdwZ3IiIqk6pVgZEjddw9M1MfMwZ49FGdSDdsmN36+Skoy+ECEdxF5C8islJEvokd77ddJyIiKr577gFOOQW4+Wbd1vXZZ4EvvgAeegg46STbtfNPo0a69t/2pDrrcxdF5HwAdwA4yxizW0SqA1gmIkuMMZ9brh4RERVDjRrAyy8DQ4fqcrADB4CBA4FbbrFdM38lJQFnnsngDgA7Y8dTAewGUAXao7DDWo2IiKjErr4aqF0bmDABaNFCA3u8jLXnFoTlcL4FdxFJAjAvn191BTAcwBIR2QngNAB/NMaszOccw2PPRYMGDTysLRERlUb37voTz5o2BRYs0HkHtm5ufAvuxpgjAE7ITyQiTQG8CqCnMSZdRJoA+EREthtjFuc5x1gAYwEgNTW1GPsQERER+atpU+DgQeDHH4E6dezUIQgT6i4FsMAYkw4Axph1AD4E8AertSIiIioFZ8a8za75IAT3tQC6iEgdABCRKgB6AVhjtVZERESlEIS17tYn1BljpsW64meIyCEAlQB8Cu2qJyIiCpX69YEKFey23K0HdwAwxowBMMZ2PYiIiMqqXDmgcWN2yxMREUVK06YM7kRERJHStCmwfj2QnW2nfAZ3IiIil6WkAEeOAFu22CmfwZ2IiMhlbdsCgwbZa7kHYkIdERFRlLRtC0yebK98ttyJiIgihsGdiIgoYhjciYiIIobBnYiIKGIY3ImIiCKGwZ2IiChiGNyJiIgihsGdiIgoYsQYY7sOpSIiOwFsdvm0NQHscvmcYRHP1w7E9/XH87UDvP54vv4wXvsZxpjkop4U2uDuBRFJN8ak2q6HDfF87UB8X388XzvA64/n64/ytbNbnoiIKGIY3ImIiCKGwf23xtqugEXxfO1AfF9/PF87wOuP5+uP7LVzzJ2IiChi2HInIiKKGAZ3IiKiiGFwByAiaSKyTERWiki6iHS0XScvicgqEVkiIotjP1/GHo/k+yAiiSJyt4gcFZErcz1e4PVG5b0o5Nrz/QzEfheJawcAEblJRDJi17FSRG6OPd5KRBbGHlslIv1yvabA34VNIdf/cezfi3P9VI39LhLXLyKPxa7937HP859ij8fF3x7GmLj+AVANwG4AnWL/7gZgB4BKtuvm4TVvApAQL+8DgJsB3AVgPoAri7reKL0X+V17QZ+BqH0OAJQDMBpA5di/6wE4FDuuA3BF7PEWAPYAOB3a4Mn3d7avx+XrXwSgXj6vidL13wkgMfbfyQAOAmgcD397Ywxb7gB6AVhjjFkEAMaYOQB+ANDdZqU8Vh3AXBFZLiKTRaQNIvw+GGNeNsY8CyA718OFXW9k3osCrh3I/zMAROvas40xdxtjDsYe2g3gCIAmAE4CMDn2vNUA5gHoD6B9Ib8LlUKuvxyAGgAmxf7+H4tIt9hzonT9Y4wxR2P/bAgN7qcjDv72AFDedgUCoBGA9XkeWx97PKpqGWMOiUgCgKsAzAbwD8TX+1DY371SIb+LihM+AyLSFtH+/+F5AJMA1AWwwcSaZzHONe4q5Hdh9zyAScaYLSLSxhjzCwCIyEUAPhCRXtDrjMz1i0gKgM+gNzODEUd/e7bcAcGJrZpjiPB7Y4w5FDvmGGPeBrA09qt4eh8K+7tH/jNRwGegFyJ67SLyOLQ7eiTi8G+f5/rhBPbYf88C8D60hRqp6zfGfG+MSYH2PP0LGqjj4m8fykq7LAtAgzyPNYg9Hi/KQcdg4+l9KOzvHo+fiXIA9iOC1y4iowG0BDDQGHMEcfa3z+f68xPZvz8AGGOWQ+cZlEO8/O1tD/rb/gFQFdod0zr273MB7AVQzXbdPLre9gDa5fp3XwDbAJwR9fcBwBwcn1BX4N89ip+JPNde0GcgUtcObby8Cu2KL5/r8XLQ7tY+sX83gk6cSinsd7avx8XrTwHQPde/2wH4CcCZUbl+AK2h3fBOorZ60CB9Xjz87Y0xHHM3xuwTkUEA/ikiBtoN09cY81/LVfPKQQBjRKQ2gF+hH96LjDGb4+l9KOrvHvH3oqDPQNSuvS+AmwCkA1ggIs7jD0K7oF8SkUdjjw01xnwPACJS4O9CpqDrfwnAZSLyLPTv/wuAAcaYjUBkrn8zgBEA7hWRowCSADxkjFlY2PVF5NoBMP0sERFR5HDMnYiIKGIY3ImIiCKGwZ2IiChiGNyJiIgihsGdiIgoYhjciahIItJNRA4W8RyJHa8TkUx/akZE+WFwJ6L/EZFeIvJLEc/pKyLH8vzkQBOmEFEAxH0SGyI6waEifj8Tms0OAAx0p7ENAFZ4WSkiKj4GdyLKrS6ALYU9wRiTDc1yBwAQkfYA6gD4xNuqEVFxsVueiHJLA3C0yGfFiEg5AKMBTDTGbM31q5YiYkTkP25XkIiKxpY7EQEARKQ8gEsBVBWRDsaYb4p4fmUA/4RuOjQwz69XA+iME7fQJCIfsOVORI7roBuJ3Afg5ViwP4GIJIhIP+iGJGcD6GGM2ZXnaTnGmP8aYw54WWEiyh+DOxFBRBoAeBbAQwCeiz38SgFPnw3gAwAzALQ3xqzzvoZEVBLslieKc7HAPhPAdGPMuNhjg6DbhE4AMDzPS24DsMsY86O/NSWi4mLLnYheBJAJYIjzgDFmA4CuACoDqJj7ycaYTGPMjyIyR0R+n8/5ZgO42cP6ElER2HInoisAHDbGmNwPxrrb+wNALPlcXqdDg/9vGGOyAGS5X00iKi4Gd6I4Z4wpKmlNYSrEZs3n5+e8NwxE5A92yxNRWYwDcKCAn3oW60UU14Q31kRERNHCljsREVHEMLgTERFFDIM7ERFRxDC4ExERRQyDOxERUcQwuBMREUUMgzsREVHEMLgTERFFzP8HDso2Zin2A6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(np.arange(len(sequence)), sequence, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"시간\")\n",
    "plt.ylabel(\"값\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_time_series_model\n"
     ]
    },
    {
     "data": {
      "image/png": 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/p0cPPU6b5sqQbGdVYLJ70x4RuWPWLOC++7RS3FNPnf79gQN1A/KLL7o/tnD98Yf23omy4S6xdWt/pzA5VcLVwgCCiMgF27fr8vnFFxf/vDp1gMREYOZMd8ZlN5ZwJYpca9cCV14JxMcDn35a+I12mTLAP/4BpKUB8+a5P8ZQHTsGpKfnNYILV1KSpqRu22bP+exmBRBcgQgOAwgi8pVfftG84kBqj3fpogFEpO2DOHFCqzAxgCCKPBs26ArosWOanlRc74AbbtD+Am+/7d74wrVihf5s1gbocLVurUe/rkI4HUDExQG7dgH79jlzfq8wgCAiX5kwQTuYJief+bldugB79uhsWSTZuFE/oBlAEEWW7GygZ0+tUDRxoub3F6dqVQ0ivvxS92xFAutG37rxD5cViCxaZM/57JaVBdSooYGeE0pqJSYGEETkG8ZoKcSePQPLve3SRY8zZjg7LrtZTaaaNPF2HEQUuO3b9b1pyxbt0hzIJAcA3HkncPgw8NFHjg7PNosXa+WkxER7zlezps7u+7XghVMlXC0MIIiIHLZ6tc7wBdq8KDZWl4cjLYCwPkji4rwdBxEF5uhR4IorNPVw7Njg+iO0agV07gwMHx4ZJV3//BNo0UIrKNmlQwdg7lz7zmcnBhChYQBBRL4xdaoeu3UL/DWdO2sAYYwjQ3KElXPbsKG34yCiwLzwgr7PjBwJdO0a/OvvvFMLJ0yaZP/Y7LZkiQY9durQQVde/ZjG5XQAUa8eEB3NAIKIyDFTpwL162vpw0B16aLVPVavdm5cdsvK0n0ehXXZJiJ/WbVKezpce61+hWLAAC1L7ffN1Hv26Cpwixb2nrd9ez36rRrVwYPAzp3OBhBlyuhkUUlrJscAgigC7dqlm3BLEmO0p0P37sH1RojEfRBZWVx9oNIjI0P7JFxyCXDPPXl7gCLFv/6l6TyvvRb6OWJitCv12LH+vpFcsUKPzZvbe97kZH1f/+MPe88bLqcrMFlKYilXBhBEEWTXLi0feNZZwLnn6ka+kmLFCmDr1uDSlwAgIUG7Oc+e7ciwHOH0kjmR14wBfvtNeyUkJGgKUGYm8MEHmh4TKQ0g164FvvgCGDJEe8+EY/BgPY4YEf64nLJ8uR7tDiCqVtWqTn6b6GEAEToGEEQR4sQJ4PLLtfvpU0/pG/IVV/hvSThU1v6HQDdQW0SA1FQGEERe2bgR+M9/gOuv1waQF14INGgAdOqkVdWGDtWbp/R0YOVKLR5w1VXApk1ej/zM3n1Xjw88EP65YmOBSy/VAGL//vDP54Tly3W1xIkKcd27A7//Dhw5Yv+5Q+VmALFxY+T1LCoOAwiiCPHJJ9o0bcQI4NlngSlTNKf2hhu0QkikmzZN38RD+eDq2FE/+Hbtsn1Yttu3T/OMGUBQSfDRR0CzZhokzJqlJU4rVNBypx98oDdoL7yge5sA/Xf/3Xf6e2DHTbmTjh/XLtP9+2tAZIdHH9VysMOG2XM+uy1frnvQoqPtP3e3blrO1k9pTG4VtIiL0+Bh82Znr+MmBhBEEeDIEeCf/9RKFoMG6WO1awNvvaUzem++6e347PD77zpjGcz+B4tVUtFPH0xF2bhRjwwgSpf164F//APo109vIkvCjcSoUcDNNwPnnw+sWaMpSvPnA5Mn6433LbcU3pyrWTP9M/j6a/+ltOQ3aZL+Pd10k33nTE3VleSXX9aUTb9Zvtz+9CVLly76/m6tNvvBhg2amlaunLPXsUq5+nn/S7AYQBBFgO++0xvPZ5459Qb74ouBXr30w+jwYe/GF66NGzWdIZja6vl16KCN5yIhjcmtJXPyj8mTgfPOA955R28a//MfoF07/zbWCsSaNZrT37UrMG5c8F3Vhw7VVYl//tOZ8dlh9GigWjXd/G2nl18GDh0CHnnE3vOG6/Bh3fPhVABRo4a+V//8szPnD0VWVt7NvZNKYi8IBhBEEeCddzS1p3fv07/3yCN6UzJqlPvjssucOXpMTQ3t9ZUr68bMSAggrJlnu1IiyN9Gj9ZAv3FjXS1csEAbdZUrp7/PkVR+OL8hQ7Qy0ahRoZUjrlBB37umT/fnhuoTJ4Aff9S/u5gYe8+dkAA89BDw8ce60dwvVq/WRndOBRAAcNll2lDOLytwGza4M5ljXYMBBBG5Zs0aXeYfPFhn2Qvq0QNIStISg5HUTC2/OXP0hiopKfRzdOyoKUx+7/S6ZYsew63oQv6xaVPhpUnffVc3C7dvr7/DjRvr4+edp6sSUVGaX799u6vDDdu0abo5+plnwguEb79dm2w984xtQ7PNb7/p38tf/uLM+Z94Qm8q77rLPxtrnarAlN9ll+lx7FjnrhEoY9xbgahSRVdgGEAQkWtGj9bjNdcU/n0Rnc1atgyYMMG9cdlpzhxN6Qhnpq9jR2DvXv1z8LMtW/TDpLDccIosP/2klWWsG8G1a7W6Tnq6Fje44w4NECZO1JuH/M49FxgzRm9gLr88slIQn34aqFs3ryxpqCpU0L0Q06b5bxVi7FhdYenb15nzV6oEvPKKrkb5ZfV4+XL9PElMdO4aLVvqavq33zp3jUDt2gUcOOBOAAHoRmrugSAi14werZsUi3uTu/pqnQkMp9GRV44e1VzwUNOXLNb+Cb+nMW3ZojdfFLmMAR58UGdTp03T/58wQfcBVKmi6XRffqnlln/4AahYsfDzXHCBbjb+/XfdqOv31TNAU46mT9cb/woVwj/f7bfr74PfViEmTtRytFWqOHeNK6/UiZOnn/ZHY9Dly3WVzI6/16KI6Krc5Mner7xZqwFu7Ucrab0gXAsgRGSIiPwpImkislhE7nTr2kSRat06vbm+8srin1e2rOYkT56sr4kkixfr7Gu4AcS550ZGQzkGEJHvpZdODdZFgLZtdXPsiy/mlS999tkzl8McOFBf89VXWtbUDzeSxXnmGXtWHyz5VyGmT7fnnOHaulVXBnr1cvY6UVEaPKxbp4UyvOZkBab8rr5a07a8/pmtghZurUAwgAiBiJQB0BTAhcaYFAD9ALwqItxGSFQMa5n3iivO/NybbtIbmZEjHR2S7cLdQG2JlIZyW7Zw/0MkmzULePzxvP+/5BK9KViwAHj4Yd0YfMstwQWJQ4dqCtSwYXoD98ADWjhh+nR/BRQzZmgJzkcesXeWevBgf61CTJ6sx8KKVtitf39N6XnrLeevVZwTJ4BVq9wJIJKSNE3qyy+dv1ZxvFiB2LNHv0oCVwIIY8wJY8xDxhir9+IOAEcBlHHj+kSRavRoXeKOjz/zcxs10nzdjz7yz6a8QMyZoxsp7XgT79gRWLEC2Lkz/HM5hSsQpxORriKyIHd1Ok1EwgwnnXH4sPY9sIoVdOqkQX64TahEtJfLmDGaJ/3WW8Df/66Ntxo0AD7/POyh2+KZZzT4HTLE3vNaFZmmTvVHX4hJk3Q1s21b569Vpoz+Xc+c6e3+rYwM/fd93nnOX0sE+NvfdNXJy2pMWVm6eu/WhE5cXN51SwKv9kC8AeArY8xpizkiMjj3AyQtJyfHg6ER+cPGjVpV6EzpS/ndequ+buJE58Zlt3nztDZ4KA3kCvJ7Q7nDh4HduxlA5Cci1QF8B+AuY0xrAA8BGCMiRewc8M5rr2lVNED3NXz+ub0lPi+7TDvMHzqkNxnff6/7Kq67DnjjDfuuE4qZM7Xykt2rD5YhQ7QvxN13a+NMrxij7589e+rNvRuuu07f/77+2p3rFWbpUj22bOnO9a6+Wv+srSIhXrBKuBZW3dAJJa2ZnOsBhIg8B6ABgLsL+74xZoQxJsUYk1KrVi13B0fkI1aZu2DKCF56KVCrluZgR4KDB7X2eJs29pzPaihnpUX5jdV5lgHEKfoAWGmMmQ0AxphpADYD6OnloArKygL+9a+8/3/5ZedSH6KidFXjL3/RlKkBA4D779eZca84tfpgqVABGDECWLJEU8G8smyZzoq7kb5kqVdPG/J99ZV3pbjT0/XYooU712veXIsNeBlAZGW529CzpDWTczWAEJFXALQEcIUx5qib16bSIyNDK6TcfXdeXetING6cpi41axb4a2JidDbrp58iI89y2TL9wGzVyp7zVa6sf14LFthzPrtZPSAYQJyiCYCMAo9l5D5+Ci9XqB94IK/UasuWzt1IF1SmDPDZZ5ozftttwL597lw3v19+0ZWRRx4puqKUHS6+GLjvPuB///NuxcVavXV6A3VBV12l6ZfWSoDb0tN1L4ab5aUvuUT7bXj1WbVhg3sbqAF93y9blgFEUEQkSkTeAdAIwEAGD+SUceM0h/Ott3QzcZs2kdkb4fBh/cDu3z/41J6rr9bSqGPGODM2Oy1ZosfWre07Z7t2DCAijAAouGvnOAr5fPJqhXrs2FNnSocPP3N1JTtVqKDvZxs2AC+84N51AX0vefBBTaW604Xaia+8krfiMny489craNIkDdbcvLEE8hqsjR/v7nUt6enupS9Z+vXT/XrWpnU3nTihDSDdXIGwVhYZQASnP4Ah0BmlWSIyJ/frIpeuT6XA/PlaErFFC12FWLdOZ6Ovu073BUSSadM0D/rii4N/rdUzwst82kAtXqw3R01Om2sOXbt2QHZ2XrqQnzCAKNRGAAVv12JzH/fcpk1a4cxy/fVA587uj+OCC/S97LXXgMxM967773/rrPhrr2m3eKeVKQN88YWmY955p6Y1ueXIEX3vdTN9ydKgga7EehFAHD2qFZjc2ECdX8eOQLVq3vzMmzdrEOF2oFiSmsm5VYVprDFGjDHtjTGp+b48iDupJDpyBLjxRuCss/TNqEEDzdcdPVrz7J980usRBmfcOL2x7tYt+NdajXomTtROm362ZInOetm5WbFdOz0uXGjfOe1iBRC1a3s7Dp8ZA6C1iLQCABHpAKAZAFcz/rdvB37+WTcuz5mjNxjTp2tu+o4d+pzatb3dzPzCCzqL+eij7lzv22+B557TrtrWDLkbYmKAb77RFdghQ7SynBt+/10nbtxOX7L07at7XtxOU1u1Cjh+3P0AIjoa6NHDmy7k1iqA2wFESeoFwU7UVCI8+6zm048YcerNWdOmWl/9008jZz+EMXoj06NH6NVOrrpK68f/8IO9Y7PbkiX2pi8BeRuy/ZjGtGULULOm5sGSMsbsATAQwEgRmQut0tffGLPbjevv3KndkOvU0RW/AQN0ZrR+fQ3gM3J3Z5Qpo1WXzj7bjVEVrlEj4KGHdLOt0/1Oxo0DrrlGe6u8/baz1ypMuXIawFx0kQYRixc7f82JE/WmNpSJGzv07avv227fUFsbqN0OIADt9p2R4f6KsVVK1c0UJkADiE2bNGCLdAwgKOKlpWln2Jtu0hmrgh59VD+Mhg1zfWghWbkSWLs2tPQlS0qKbsD2cxrT1q3Atm32baC2VKumXan9GkAwfel0xpipuSvUHYwxF1gVmZy2di3Qvj3w/vvAyZNFP69sWf1d6umDulBDh2rVnvvvL37M4ZgyRZtXtm6tkxmVKztznTMpX16Dtho1gEGDnO9vM26c9vaoUsXZ6xTlggv0s2rqVHevm56uAXJiorvXBfRnBnT1x01erkCcPKlptpGOAQRFtCNHtLFTnTqao1uYmjW1ac1nnwF797o7vlCMG6fHwoKhQInofpDJk/1bjcnaQG13AAH4dyM1Awj/yMoCunTRIMLStq3m3rdqpTetjRrpnoelS3Vlwg8qVwaef157nTjRyXfWLE1XatpUqy9Vq2b/NYJRq5ZO/ixaBHz8sXPXWb9e35MuvdS5a5xJ+fJ6Q+12ALF0qf59u7HHpaB27TRlzYsAompV/XJTSSrlygCCItpzz+nsyYgR+oFflDvuAA4cAEaNcm9sofr5Z90XYHWtDNUll+gyqZe144vjRAUmS7t2uoneb3tAGED4w9atQPfumkoA6A3MF19o0Pnjj5ous3Onfsh/+qneXPnJjTfqv/FHH9U9XnZZskQnLho21MkHL9O18rvqKk2leuop55rMWX13LrnEmfMHqnt3DZasfTduSE/3Jn0J0KAlJcX9ACIry/3VByDvc70kbKRmAEERa8EC3VR4441nTvdp314/cIcP965RTyD27gVmzAgvfcnSsSNQvXreiobfLF6sK0dOVONs21aCNbQJAAAgAElEQVSPftpIbQwDCD9YsyYv7xrQnPcff9RVykgRFQW8/rreBN15pz2pTLt2aeO6ypU1halOnfDPaRcRbWSXna0ryU4YOxZISNAvL/Xoocfp09253sGD+rvgdgnX/JKT9fPAqZS8wrjdA8Ji7bngCgSRR06cAAYP1pvPQKqiiOgqxJIlzm8+DMeUKbpqEE76kiU6GujTR1c03HxjDpQTG6gtVgDhpzSmvXu1vwcDCO+kpWm3cit4iIrSNKA+fbwdVyi6dNGb6o8/1t4v27aFd7577tGbmm+/1RUIv+nVSyeBXn7Z/r0Q+/cDv/7q/eoDoJNdFSu6l8a0cKFObljV67zQurX+Haxb59413e5CbalUSVf2GEAQeWT4cO378Prrxacu5XfttZrv+M47zo4tHBMn6gygtbEsXH376o2FV91Ni2KMdl1t0cKZ89eqpTdBixY5c/5QsAeE9xo1ytsQHBOjG6OvuMLbMYXjqaf0hvqHH3Q29S9/0VSso0G2ah09WtM7n3xSVy79SERTtlatsr+63OTJ+mfmhwAiJkY3cv/6qzvXS0vTY0qKO9crTFKSHv/8053rHTyoZZu9WIEASk4pVwYQpVB2ti57N2miy5Z+T+spaPNm4IkndEbq6qsDf12lShpEjB7t343FEydqDqxdZT6t5fApU+w5n122btU9Keee69w1WrbU0r5+wQDCe3XqaGf6hARNFYzk4AHQm+qHH9Yc9sGDdTb52mt1Rve33wI7x5YtujqbnAw8/riz4w3XgAH6nvHii/Z+Zv34o24W79TJvnOGo0cPfe9yo7RpWppW9apf3/lrFaVlS10NdKNUL5DXWNaLFQig5DSTYwBRysyerUuVH3ygx2rVNJh48MHICSIee0w30r31ln6ABuO227RR0BdfODO2cKxdq192NjGKjdUPXLdmswK1Zo0enQ4gli93vvRjoBhA+EOLFnpzdv75Xo/EPomJwH//qykgY8ZoqlznzhoQFPe+boz2wNi/H/jkE//3JylTRj+r0tK02Z8djhzR5oGXXeafn797dz260Q8iLc3b1QdAU7aaNnVvBcKrEq6W2FgNICLlnqsoDCBKkU8+0QY5lStrasfo0TpLdc89mgrk554BlhUrtCrKXXeFVhmlXTttNPb++/aPLVxWtSS7u6D26KEb8vzUuMbKQT/nHOeu0bKl3ki5mVdbHGs2kV2ovWdn53M/iYrSG+H0dODWW7XIxJAhRQfRI0fq5uEXX3QundBu11+vE19vvmnP+caPB3bvBq67zp7z2aFdO023dXriZ+9e7TvkdQAB6KqZVZnPaX4IIPbv928mRKAYQJQCx4/rMvegQbpEO3cu0Ly5fk9E+ye0bw/cfbf/yl4W9O9/a3fmRx4J7fUiugoxf76/KvQAGkA0amR/M5+uXfWDwk/7IDIy9GYn3FK1xbFuiPySxrR9u/77O+ssr0dCJV3lylra+okngPfe05vjY8dOfU5GBnDffTrbfe+93owzFJUra++fb77JW9ULx6ef6p4pPzQJtERH6yZ5pzdSz5mjs+Cpqc5eJxDNmulkT7D7d0KRlaXvxQ0aOH+twpSUXhAMIEq4bduA3r2BV17RVKUJE06/gYmO1g+ZHTv0eX61eTPw1Vc6sxbOLO6112rDnnfftW9s4TpxQvcp9OoVfFrWmVgfDnYt+dthzRoNHmJinLuGFUD4JXDavl1/90rq7Df5i4j2yXnpJX3f7N9f30MBIDNTK09FRwMffaTBfCS5804NiN57L7zzZGVpytegQfpn4SfduwOrV+fl6zvht9/0794PAURCglYLzN/Y0SkbNmgqqZOfP8Up9QGESN5tjohkisiV9gyJ7LJ4sa4szJ6tHxJvvVV0jmdSkm5IfuON8MsBOuWdd3Q15Z57wjtPjRq6DP7xx3pT5wdpabqMbnf6EgDEx+sMm58CiIwMZ9OXAE0BaNTIPysQOTnO9LwgKs7QobrnbdYsLZzRubOuQG/bpuk7XqVxhKNpU60w9847p6+sBMMqIHLXXfaNzS5WAQwnVyF++027rrvdjbkwVv+NlSudv5ZXJVwtJaWZXEABhIiki8jxfF8nAJwUER/8s6PCjB+vzZKOH9cPjkGDzvya//s/3WD8v/85P75gnTwJfPihzprZsfH2/vs1P3748PDPZQcr19WJZXQRLc3op/4Xa9Y4u4HakpioZR/9YPt2oGZNr0dBpdEtt+gG1Vtu0feDG27QfHM/zDyH6q67tKJgqCVdd+zQSbW//hVo3NjWodmidWtdsXQqgDh+XCeVLrzQmfMHywog3Hi/9qqJnKV2bV39KC0rEB0AVM/9qgpgGIDfjTF78z2noohUFxGf1DEovWbN0nrgTZvqfofk5MBe16wZcPnl+qa6f7+zYwzW9Ok6axBIIBSIFi2Afv10I97hw/acMxwzZ+qYnJqhTk3VmZ2dO505fzB27dJxOL0CAeiH0qpV/qh2wRUI8lJCgr63z5ih+yOc3H/khn79dHU11M3Uzz+vn3PPPGPvuOwSFaX715zaSL1okZbS9kvp2urV9cba6QDCGO8DiKgoXQEpFQGEMeagMWa/MWY/gHMA3Arg6QJP+xjALgARXlk7sm3frrXNGzfWnPpgNwk98oje4PmtStFnnwFVqmiAY5cHH9Rl/FGj7DtnKE6c0KXkzp2du4Y10zh3rnPXCJQbFZgsCQmaGuaHVDWuQBDZp0wZXYWYMQP444/gXpuWBgwbppuxW7Z0Znx26NFD01ycqCRnBSZWyVg/sCZ8nLRzp2ZaeJnCBJSMZnJB7YEQkQEAfgXwijFmUoFvDzTGiDHmS9tGR0G77z79BRk9OvAOzfmlpuqN7GuvhZdbaqeTJ4GffgIuvVQrMNmlRw8t6frKK3oNryxerFWSunRx7hopKTrr4Yc0JiuAcCOFyc1l8eIYwwCCyG5Dhujv1JNPBv6a7du1kEbduv4uGgLk3dxPnmz/uX/9VVe9/dSXJiHB+T0QXpdwtZSEZnJnDCBEpKaI3CkicwF8BOARY8y/HB8ZBW3uXJ1Nf+QR3RgVqkce0XShL30SCs6fr+kf/fvbe14R/VlXrNBKHF6ZOVOPTq5AVKkCnHeePzZSWwFEkybOX8svAcTu3brSxBQmIvtUrqzN8iZPDmwvxNatmvqUlaWVqapXd36M4WjRQrMJvvvO3vMePaqfO34qXQvo+/XWrc72R8jK0qMfViCys/0zURuKQFYgagK4B8AEAOcYY0YW8pzhAFYUdxIR6SoiC0RksYikiUgEb9/yp6ee0tmYUHskWPr315vNl1/2R+74+PF6s9+7t/3nvvJKvZF98UXvftaZM3U2wuk3tI4ddanfy9UWQDdQ16sHVKrk/LXi4rTymNcBhJVCxRUIInvdfbeuJN9xB7BpU+HPOX5cA4bkZC3r/M03/tk8XBwRYOBADZDs3L82ezZw8GBepSe/sHogrV7t3DX8sgIRG6v3HEX9m40EZwwgjDErjDHNAXwNYA8AiEgnEYnN/e/LAeQYY9KLOoeIVAfwHYC7jDGtATwEYIyIVLThZyDoLP3EiVqyr0qV8M4loo3n0tOBH3+0Z3zhmDBBU3CcmL2Njtafde5cYNo0+89/JsZoDq+T6UuW88/XmR2vb6bdKOFqiY7WANHJD6RA5OTokSsQRPYqWxb45BO9Ie7eXff+HTqkQfuECcBjj+lEwt/+Bpx9thYZueQSr0cduIEDNQCyc5X8l1/0vdFP+x8Ad1aMs7KAcuW8fy8uCb0ggtkDsQSAFbM9B8CKXTsBOFMs3wfASmPMbAAwxkwDsBmAIwtoIs59vfeeP6r2FDRsmC7nDh5sz/muuUZ/mZ94QlMvvLJzp86a9+vn3DVuugmoU0cbLrlt9WrdyO1k+pKlXTs9Llrk/LWKs2aNewEEoAGEG82JisMVCCLntGqlwcK+fcBFFwEVK+oNYr9+upLepg3w/fc60Wa9D0aKlBRNYxo92r5zTpigK9LVqtl3Tjucc47eZzm5D2LDBqBhQ++bJ5a2AAIA/hSR/QDy3+6cB+BMfYGbAMgo8FhG7uOnEJHBuSlOaTnWtJ2PDB6seYl+qGZj2bZN9yvcfLN9bwhly2oX06VLteSfVyZO1JQbJwOI8uWBv/9dZ2UyCv4rdZgb+x8szZvr36uXAcTBg5r36cYGakt8vDNVTIJhvZUxgCByxgUX6ETBl18C//438PrruhqxZw8wbpyWNvdbt+lAiGiq7aRJupcqXFu3AgsXaiM+vylXToMlJ1cgvC7harHGEMkbqYMNIFoaYyoDmAkAIhIP4AIA54tI82JeJwAKzmMfL+z6xpgRxpgUY0xKLa/XmArxyy96Q9u1q97c+sGnn+pGnL//3d7zXnmlzuYMHerdP/Lx43XZuX17Z69z221aFvDdd529TkFz5mizICv300kxMVqycOFC569VFGslwM0ViPh4/eC148M3VNYKhA/f0ohKjAoVgKuv1o3V992nOf6VK3s9qvANHKif8aE2zctvUm79zD59wj+XE5wu5ep1F2pLhQr6eVCaViDKiEg0NCCoAWAUgLcB/AvAaBEpqiDYRuSlP1licx+3nTHOffXurTWkExN1RmPxYid+guB+1pEjdTmyeXEhXAislC1jgNtvd3+T8cmTutTau7fe3DupQQPgssuAjz/WfFO3zJ0LdOigf9ZuaNNGAwivNoy7WcLVEh+vx8xM965Z0PbtutJVkbu+iChI7dtrKuann4Z/rgkT9Ma1bdvwz+WExERNYXLiM+r4cd207IcVCCDye0EEG0CsAXAMQBcADwPYAeD/ALwFYBGAoubAxwBoLSKtAEBEOgBoBqBgL4mIULOm/hJWr65N27yc2Zw7F1i2DLjlFmfO37ix5pBOmuR+KtOiRZqe5WT6Un7XX6/XmzrVnesdOKAb1Tt0cOd6gAYQOTnAli3uXTM/N5vIWawAwss0JqsLtVuBIhGVHCLAoEHauyGcbICTJzVzondv7/cAFCUhQT8bs7PtP/fmzfpnwADCHgH/E8ptEvf/v6ArCAOMMUeNMScB3GiM+b8iXrsHwEAAI3P7SbwBoL8xxsNb7/DUraul4DIzdROuVzO6I0fqrOZVVzl3jTvu0FSm++/XngluGT9ej06Uby1M//5A1arAF1+4c72FC/XNzOn0rPysWSev9kGsWaOB91lnuXdNPwQQbCJHROEYNEgDiY8/Dv0cCxfqZIZf05eAvHReJ9KYrJt1P6QwARpArF/vj3L5oQg5BjXGHDfGHMv3/8XW6jHGTDXGtDfGdDDGXGBVZIpkF16os/NjxgBvv+3+9Q8c0JvdgQP1xtcpUVH6plWxonbwPHLEuWvlN3681u2uU8ed65Uvr2lp33/vThqTtRHfzQAiKUmPXgUQGRnupi8BGrBUreqPFQgiolDExemejo8+Cr2Xz5gx+nnuxw3UFiuAcKISk196QFji4vQ+btcur0cSGp8uYkWO++7TX8aHHtJUIjd9842WrXMqfSm/+vV1tWPhQuBfLvQh37VLm924lb5kuewyTUn77TfnrzV3rr6BuBUgAVqlKz7e2xUIN9OXAJ2187oSE1cgiChcN9+s72MzZoT2+h9+ADp18vdkRoMGusHYiQDCL12oLZFeypUBRJhEgA8/1EoPbs7OA7rBOTHRnRKggN5cX3898J//6I2gkyZNcr58a2F699ZSp2PHOn+tefPcXX2wWBup3XbsmC7Xur0CATCAIKLI99e/6mrqhx8G/9qMDGDJEl1l97OoKN0H4dQKRPXq4TfbtQsDCELdujo7/+ef2njNDUuXAr//rtWR3NyY+fLLWg708cedvc6ECUCNGto92U1VqgDdujkfQGzfriVN3dxAbWnTRgPAffvcve769dqU0O0VCEADiMxMb3JNjxwB9u7196wfEflfxYpapnb06ODfv60SsH4PIACdGHViD0RWln/SlwAGEJTr0ku1D8OrrwI//uj89d57T2fKb7zR+WvlV68ecO+9+ga2dKkz1zDGvfKthendWzeLO1EFwjJvnh69CiCM0dkoN3lRgckSH69N7LZtc//aO3bokSsQRBSum2/W97Kvvw7udT/8oHvgrKISfpaYqCvGdmd0bNjgn/QlAKhdW5vnRWozOQYQNnr1VU1JufZaZ/tDHD6s9aAHDPBmVvP++3Um5OWXnTn/n39quTW305cs3bvrcdo0564xb56uHLVr59w1imJtpP7zT3ev60UPCIuXlZisLtRcgSCicKWm6g32Rx8F/ppt23Rf31//6tiwbJWYqCnM1meGXfzShdoiEtmlXBlA2KhCBY3yq1fXFQmnau2PGgXs3AkMHuzM+c+kZk0tKffVV3kddu1klW/1qtRcmzb6d+hkP4i5c4EWLbzJxYyN1Z/P7QBizRr9HalXz93rAt4GENbvCFcgiChcIroKMWsWsHp1YK/58UdddY6E9CVA90AA9qYxHTig901+WoEAGEBQPvXr6y/r9u3aV2DvXnvPf+IE8NJLOnNtzZR74c47dXlx5Ej7zz1+vPYrqFtUX3OHlSkDdOniXABhTF4Hai+IAK1be7MCcc453jRTa9xYjwwgiCjS3XCDbjYOdDP1Dz/oe2Dr1o4OyzZOlHK1KjD5aQUCYABBBbRrB3z7reaYDxhgbx7fF1/orMOjj3rb1bZlSy0H9+GH9m5M3blTN4f372/fOUPRvbve8FpvOnbasEHTWryowGRJStJ/n6HWEw+FFyVcLZUqab4pU5iIKNLVrw9cconuhTx0qPjn7tsHTJ6s6Ute3jMEo2pVnUAsLQHE5s3A0aNejyR4DCAc0rcv8MEHwJQp2qnajhu1Awc0cEhOBq64IvzzhevGG3Wz8fz59p3zp590lcXrpVZrdceJVQirgZxXKxCABhAHDmglKDecPKnX8iqAALwr5WqtQLjZfZuISrYHHtD3lk8+Kf5548frJKbXn6nBSky0N4DwWxdqS1ycTsJu3Oj1SILHAMJBN94IvPgi8OWXwIMPhj9T/8QTwKZNwBtv6PKl1wYO1AoCn35q3zm//15/wZOT7TtnKFq1As4+27kAIiZGr+EVtzdSZ2fr5n8vNlBb4uK8qXaRk6PBQ3S0+9cmopKpSxf9nHztteInKD/9VFcsLrzQvbHZISHB3j0QWVm6AtOggX3ntEMkl3L1wW1oyTZ0qJY9feMNYPjw0M/zww/AsGHAPfdo6pAfWJvFv/hCm4SFa+9e4JdfdKbE66XWqCiga1dnAoh583SPR0yM/ecOVMuW+jO6FUB4WcLVEhenHyJupm0BbCJnEZElIjJPRObkfv2a73tdRWSBiCwWkTQRSfVyrER+J6ITk6tWAePGFf6crVt1BeL6670piR6OxER979y5057zbdigBTzKlrXnfHZhAEFFEgFef11Lkj7wQGjlXWfOBK65RnPmX3rJ/jGG44YbdIZ14sTwz/XttzpLfe214Z/LDt266Yy1nbPWJ04AaWnepi8BWg0pIcH9AMLrFYgjR9zvBbF9O/c/5KoC4HxjTGruVw8AEJHqAL4DcJcxpjWAhwCMEZGKHo6VyPeuvFJX7F94ofAMh48+0s+cQYNcH1rY7N5IvX69fgb4jZVSxQCCChUVpb/INWoAf/ubNoEJ1JIlOssfFwf8/LPe+PlJ376a6mNHGtMnnwBNm7rffboonTvrceZM+865fLnuPfByA7UlKcnZfiX5rVmjKTxebmCzru12GlNODlcgcp0FYLqILBSRr0WkTe7jfQCsNMbMBgBjzDQAmwH09GaYRJGhbFngySeB2bPzOk1bDh3SyctevbRkeKSxO4BYt86fTfTKlwfq1InMZnIMIFxSu7beIC9fDjz0UGCvyc7WakSVKukMvx9vQmJiNCgaMwbYsyf086xerY3bbrzR+/QlS6tWQLVqwIwZ9p3Tyw7UBSUlAZmZ4f29BSojQ4NgL/cBWLNPbr9Rl6YUJhGJyZeilP8rBkAdY0xnAMkAxgCYLCKxAJoAKNgyKiP38YLnH5yb4pSWY5W3IirFbrlFA4R//OPU3lPDhmkK0xNPeDe2cDRurJ8XduyDOH5c01etct5+E6mlXBlAuKhXL01jGj5cc/2Lc+CArjzs2qX5jX4rPZbfDTdo6tG334Z+jjff1NmU226zb1zhKlNG95vYuQIxd64GJU2b2nfOUFkbqd1YhVizxtv0JSAvgHDzjdqY0pXCZIw5mi9FKf/XUWPModznnDTGjAIwH7r6IABOFDjVcRTy+WSMGWGMSTHGpNQqLX+oRMWIjgY++wzYsUNTpTdu1PuLp57S0q1dung9wtCULat75uxYgdi0SYMIBhD2YgDhsn//G2jeXGcNdu0q/DknTgDXXQcsWqTdntu0Kfx5ftGhg94Qh5rGtGuX9pO4+mrvmscVpXNnLVVrV978nDmavuSHKlpuVWIyJq+JnJeqVdMvN1cg9u7VAgOlZQUiSGUA7AWwEUDBKZLY3MeJ6AzattUJvNWrNae+b1+gSRMtJe+XFf1Q2FXKNTNTj35MYQLyAgg7e2q5wQe3MaVL+fJ6o71tG3DrrYVXhBk6VFOChg0DLr7Y/TEGS0RXIaZNCy2KfvZZXXEZOtT2oYXNmr2ZNSv8c+3fr7P9HTuGfy471K+v+1ecDiB27NA0Ka8DCEDfqN0MIKweEKV9slxE2otIcr7/7w+gOYBfoOlMrUWkVe73OgBoBmCSF2MlikR9+2qBjmef1dKuCxfqvstIlpioQdGJguuTQbL6//h5BeLgQf2sjCQMIDyQnKzVlL7/Hnj88VOjzjfe0F/+e+8F7r7buzEG6/rr9ThqVHCvW7VK05duvdXbvghFSU7Wjet27INIS9OA0S8BhIg7G6lXr9ajH9K23O4FYaXpcwUC+wE8l7uBeg6AuwH0NsbsNsbsATAQwEgRmQvgDQD9jTG7PRwvUcRp1kxTl+6/H6hYAmqYNWumHZrDbXiamamfd35NBfcivdYOrmxpFJEhAO4EcAxADIB3jDFvu3Ftv7r/fr15fukl3dxz/fVaRWHECGDAAA0iIkl8vO4X+OADXUkItOb00KF6g/6vfzk7vlDFxACpqfbsg5g9W49+qTIFAK1bA+++qzM8TtUJtwKIhARnzh+MuDh7VpMCZa1AlPYAwhizHEC/Yr4/FYAPapMRkV9Yk4rp6eFNQK1bpw3kvOy9VJz8vSDatfN2LMFwfAVCRMoAaArgQmNMCvRD5FUR8Vk/QHeJAG+/rbMF336r1ZZGjgTuu0/3PURa0xdAx56REfhm6h9+0FStxx7TMmZ+1aWL7kfZHeZ86OzZuiR71ln2jMsOSUla7m/NGueusXq17vnwQ/5pXJz+Pe7d6871rBWI0p7CREQUrJYt9V5pyZLwzuPXEq4Wr0qMh8vxAMIYc8IY85AxZn/uQzsAHIVuoCvVoqI0X3H9ep0VXb9e6zZ7WeoyHH/9qy45PvfcmXMWMzJ0I3lysnbT9LMePTT1aPr00M9hjG6g9kv6ksWNjdSrVumbtx9mf9x+o+YKBBFRaCpW1L1z4QYQmZn+3f8A6OdDuXJaLSqS2BZAnKH+d35vAPjKGFNotldprPNdpw5w4YW6qTWSRUUBTz+tv+yffVb087KzgT59dGbhyy/9cWNZnNRUfSObPDn0c6xdq7PRfgsgWrTQgNXJAGL1an/sfwDc7wWRk6MfDJUru3M9IqKS5Lzzwgsgjh7V0rZ+XoEQ0fu/7GyvRxIc2wKI4up/W88RkecANIBuoCvqPKzzHcGuukpz/B96CFi27PTvz52rN+RbtwJjx3rfGyAQMTFA167hBRDW/ofUVHvGZJdy5XTVyKmN1MboCoQf9j8A7m9Wy8nR9KVILqVIROSVVq10EurQodBen5Wln0N+XoEASnkAcSYi8gqAlgCuyB9UUMkioh23o6N178CoUbp8OHWqVlq64AJ93owZ/puNL85FF2k/CKscXLDmzAGqVNGcTr9JSnJuBWLLFi3R65cViDp1NCB0awVi2zbufyAiClWbNppCHOoqhPWZ7ecVCEADiFKbwlQUEYkSkXcANAIwkMFDyZeQoAFCo0ZaXSo+XvcRfP45cNdd+kbQtq3XowzOgAF6/PLL0F4/a5Y23PPj5vjWrXWWZudO+8+9apUe/bICERWl/y7dTGGqXdudaxERlTQpKXqcNy+011tN5LgCYT83ViD6AxgCoAmAWfn2RlzkwrXJI02barrSlClamnbcOE1bGjZMuwFHmsaNdZ/K558H/9rt23WGv0cP24dlC2sjtRNpTH7qAWFxsxeElcJERETBa9RIJ2FCDSDWrdOJu4YN7R2X3Ro00Gaz+/Z5PZLAOV7vxxgzFgAzgEuhsmX1ptmvN87BuvbavBWUYJreTZumR7/+ObRpo8cFC4Bu3ew99+rVmjLkpwY+cXHAL7+4c61t27gCQUQUKhGgfXttxBqKzEwNQvxe3dIqopOdreXeIwE7URMFaOBAnckIdhViyhTd/2AtxfpNnTq6wjJnjv3nXrVKy/D5KXUrNhbYvFmrczjp4EH94goEEVHoUlK0KMv+/Wd+bkFr1/o/fQnICyAiaR8EAwiiANWqBfTuDXzxhW7qCoQxOtvdtau/Z0BSU50JIFav9s/+B0tcnP69ZGU5ex02kSMiCl9qqr5nz50b/Gv9+BlUmAa5rZUjaR8EAwiiIFx3nebPjx8f2PMXLdIczL/8xdlxhSs1VW+o7Zz9OHlSO1z7af8D4F4viG3b9MgUJiKi0HXsqKlMs2YF97odO/QrEgKIevX0yACCqIQaOFBTch577MzdtgFg9GhN37n8cufHFg6rP4WdqxBZWcCRI/5783arFwRXIIiIwletmlYLnDkzuNdZRTz89hlUmCpV9IspTEQlVEwM8PzzupH6tdeKf+6JE1r2tWtXbVXvZ23balM5OwOI5cv16LcNYVY1DqdXIERdEJkAABjNSURBVBhAEBHZo3Nnbch6/Hjgr/FbGfEzibRSrgwgiII0cCBwxRXA448Dv/9e9PO+/143cN1xh3tjC1VMDNCunb0BhNX457zz7DunHcqV0+VipjAREUWGzp21Ken8+YG/ZtUqzQDwexM5S4MGDCCISjQR4L33tLLDX/6i+xwKOnYMeO454Nxz85rQ+V1qqpbKO3bMnvMtWaIzKmedZc/57ORGL4icHA1WKld29jpERCVdz57aCPTnnwN/zerVGjzExDg3LjtxBYKoFKhRAxg7VisrdegAvPRS3tKqMcDDD2vzuBdf9FcJ0+KkpgKHD+u47ZCeHly/DDfFxbmzB6J2bQ04iYgodGefrZ9R48YF/poVKyInfQnICyCM8XokgWEAQRSixETt3nzZZcCjj+pqw80361LrsGHadO6KK7weZeCsjdSzZ4d/ruPHtW63XwOI2FgNIAItxxuKbdu4/4GIyC4XX6wpTIHM0h87pvvw/PoZVJgGDbQ/0Y4dXo8kMAwgiMJQsybwzTfAmDFAs2ba82H3buB//9OvSNKokc6A2LEPYs0arcDkt/0Plrg4faPeutW5a+TkMIAgIrKLNSE3YsSZn7typQYRrVs7OyY75e9GHQkYQBCFSURXISZM0F/89HTg7rsjL3VFBLjgguI3hgcqPV2Pfp39caMXxLZt3EBNRGSXxEQtif7f/565K/XixXpkAOEcBhBE9P917AhkZgKbN4d3niVLdMNb8+a2DMt2bvSC4AoEEZG9Hn0U2LVLC5kUZ/FioGxZ/5URL47VjTpSekEwgCCi/++CC/QY7j6IJUt0T0iFCuGPyQmxsXp0agXiwAHg4EEGEEREdkpNBbp1A159VdNQi7JkiU5glS3r2tDCVreuHrkCQUQRx2ooF2zHz4L8XIEJ0M6m1ao5F0BYTeSYwkREZK/HHtNZ+nfeKfz7xmhJ8jZt3B1XuMqV02pTDCCIKOKUK6dVpCZPDv0cBw/qJmo/BxCAs70g2IWaiMgZvXrp1z//WXghjFWrdA9a587ujy1c9eo5W9zDTgwgiOgUvXrpCkKo+yCWLdMZIL9WYLJYpVydwBUIIiJniGiVw4MHdU9EQdOn67FLF3fHZYe6dYEtW7weRWAYQBDRKXr10mOoqxALFujR78vHjRvrhnEnmvZs26ZHrkAQEdkvMRF48EHgo49Orxw4YwZQpw7QtKknQwsLAwgiilhJSXrjO3FiaK+fOxc46yygSRN7x2W3c88F9u7NWy2wE1OYiIic9eSTQMOG2rT1xAl97MgRYNIkoGvXyCulDuQFEJHQjZoBBBGdIioK6NlTVyBCeRObOxfo0MH/b94JCXpcvdr+c+fkAOXLA5Ur239uIiICKlUCXnsNWLQI+M9/9LHPP9cV4Ftv9XZsoapbFzh0CNi3z+uRnJnrAYSItBSRnSLytNvXJqLA9OqlsyBWQ7hA7d8PLF2qAYTfWcvbq1bZf+6tW3X/g9+DKCKiSHbllcBVVwGPPw688ALw7LO6im6l4kYaq5RrJKQxuRpAiEh1AG8B+MLN6xJRcKw330mTgnvdggXAyZOREUA0bgxERzsTQGzerNU0iIjIOSLAyJG6av744zqJ9eabkTt5wwCiECISBeBjAI8DKDLrWEQGi0iaiKTlOJGcTERn1KiRblILNoCYNk3fuFNTHRmWraKjgXPOcSaAyM4G6te3/7xERHSqSpV0z96MGboC3qmT1yMKXakNIEQkRkTmFPIVA+A5AJOMMb8Xdw5jzAhjTIoxJqUWdyASeaZXLy2Hd+RI4K+ZNAlo106b4USChARn9kAwgCAico+I9n2I9NLZpTaAMMYcNcakFvwCcCmAWGPMm3Zej4ic06uXbuYqWCKvKPv2AXPmRFbuadOmGkCcPGnfOQ8dAnbtYgBBRETBqVEDKFu2FAYQxegHoLm1IgHgNgC3icg3Ll2fiILUrRtQpkzg5VynTQOOH4+sACIhATh8GNi40b5zWg34GEAQEVEwoqK0hwUDiFzGmNuMMcn5ViTeB/C+MWagG9cnouBVraqdPL/9NrByrt99p6+58ELnx2YXJ0q5WgEEN1ETEVGwIqWZHPtAEFGRrrtOb67nzi3+eUePAj/8AFx+OVCunDtjs4MTpVyzs/XIFQgiIgoWA4hiGGOeNsY87cW1iShwV16pDdE+/bT4502ZAuzeDQyMsDXF+vWBihUZQNhFRMqKyEMickxE/lbge11FZIGILM6ttJcayPeIiEoTBhBEFPGqVQMuuwz48ktdZSjKe+9p5aXevd0bmx2iovI2UtslOxuIiQHOOsu+c0aQ2wEYAHPyP5jbA+g7AHcZY1oDeAjAGBGpWNz33B06EZH36tbVbtonTng9kuIxgCCiYt1wA7BjBzBhQuHf37ABGDMGuP32yEpfsjRtav8KRP36kdvIKBzGmLeNMa8CKPjR1wfASmPM7NznTQOwGUDPM3yPiKhUqVNHg4cdO7weSfEYQBBRsfr0AWrV0m6fhXn1VT3ecYd7Y7JTQgKwdm3xKyzBKOk9IM7Q76coTQBkFHgsI/fx4r5X8NpsNEpEJVqk9IJgAEFExSpbFhgyRFcZFi489XsZGcDw4cBttwFxcd6ML1znnaezPStW2HO+7OySXYGpqH4/xpjiQjDB6asSx6GfQcV9r+C12WiUiEo0BhBEVGI8+KA2uLn/fu31AOjxlls03//ppz0dXliSkvT455/2nG/z5pK9AhGijQBiCzwWm/t4cd8jIipVGEAQUYlRvbqmKk2frqsRK1YA118PzJgBvPtuZM+4JyTo3o1Fi8I/14EDwJ49DCAKMQZAaxFpBQAi0gFAMwCTzvA9IqJSJVICiGivB0BEkeHmm7Va0Qsv6H6IqCjgxRe1V0Qki44GWrWyZwWCXagLZ4zZIyIDAYwUEQNNUepvjNkNAMV9j4ioNKlcGahUiQEEEZUgzz+vvSEWLQI6dND9AyVBUpLu8TAmvOpJpbkHRH7GmG6FPDYVQPsinl/k94iISptI6AXBFCYiCkq7drr3oaQED4AGENu3560ghMoKICI5pYuIiLzFAIKIKAJYG6nD3QfBFQgiIgoXAwgioghgVyWm7GygfHnddE5ERBQKBhBERBGgWjWgcePwAwirhGtp7EJNRET2qFsX2LULOHLE65EUjQEEERF0FSLcFKYNG4AGDewZDxERlU5WKdetW70dR3EYQBARAWjTRsvUHjwY+jkyM4H4eNuGREREpVAk9IJgAEFEBKBtW+DkydDTmI4cATZtYgBBREThYQBBRBQhkpP1OH9+aK/PytI+EgwgiIgoHAwgiIgiRIMGQJ06QFpaaK9ft06PjRvbNiQiIiqFatfWIwMIIiKfEwFSUkIPIDIz9cgVCCIiCkdMDHD22Qwg/j8ReVBE0kVknoikiUhFN69PRFSc5GRg+XLgwIHgX7tuHRAdzSpMREQUvrp1tTS4X7kWQIjIMwBqAWhjjGkP4CIAh926PhHRmaSk6EbqUMq5ZmYCsbFAmTK2D4uIiEqZBg20MIdfuRJAiEh1AH0A7APwm4hMB9DRGHPSjesTEQXC2kgdShpTRgbTl4iIyB4NGwIbN3o9iqLZGkCISIyIzCn4BeB8AC0BbDTGnA/gPgCfi8g5hZxjcG56U1pOTo6dwyMiKlb9+kC9esFXYjIGWLECaNbMmXEREVHp0rCh7oE4dszrkRTO1gDCGHPUGJNa8AvAWQAyjTEf5z5vIYApAPoWco4RxpgUY0xKrVq17BweEdEZhbKReutWYO9eIDHRmTEREVHp0rChTk75dR+EW3sgtgHYW+CxkwBOuHR9IqKApKToasK+fYG/ZsUKPXIFgoiI7NCwoR79msbkVgAxE0ATEekEACKSCKAHgIkuXZ+IKCApKTrrs2BB4K+xAgiuQBARkR1CCSAOHgRWrgSOHnVmTPm5EkAYY44C6A9gmIgsBPA5gEHGmLVuXJ+IKFApKXqcNy/w16xcCVSsmPeGT0REFI5QAoi0NF0Jnz7dmTHlF+38JVTuvodkt65HRBSK2rW1m3QwAcSKFbr6EMXWnEREZIPq1XViKpgAwmpo2rixEyM6FT/uiIgKaN8emDs38OenpwMtWjg3HiIiKl1Egi/lagUQsbGODOkUDCCIiAro0EHfiAOpJL1zp77BJyU5PiwiIipFGjcG1gaR7J+ZqeXIy5VzakR5GEAQERXQvr0eA0lj+vNPPTKAICIiOzVrpimyJwNsu5yZ6U76EsAAgojoNMnJup8hkDQmK4Bo3drZMRERUenSvDlw4EDgaUwMIIiIPFS5su5pCHQFonZtoG5d58dFRESlR/Pmely+/MzPPX4cyMpiAEFE5ClrI7UxxT9v0SKmLxERkf2sAMLqNVSc7GwNIhhAEBF5qEMHYPt2YP36op9z6JBWYLL2TBAREdmlVi2gRo3AViDWrdMjAwgiIg916KDHOXOKfs6iRTrjYz2XiIjILiKaTrts2Zmfu2qVHs8919kxWRhAEBEVonVroFIl4Lffin7OH3/okQEEERE5oU0bYOHCM1diWrpUG8/FxbkzLgYQRESFiI4GUlOBWbOKfs7cudrop14998ZFRESlR0oKsH8/sHJl8c9btkxXK6JcurNnAEFEVIQLLwQWLwb27Sv8+3PmcP8DERE5x/qMSUsr/nlWAOEWBhBEREXo1EmXjWfPPv17mZm6aa1rV9eHRUREpUSzZpqaVFwAsWcPsGkTAwgiIl+44AKgbFlg8uTTv/frr3q86CJ3x0RERKVHmTJAu3bFF/SwqjQxgCAi8oFKlXQVYuLE0783ZYo2j3PzDZuIiEqfPn10z11RZcWtpqetW7s3JgYQRETF6N1bu01v2ZL32PHjuirRo4eW2SMiInLKtdfq8fPPC//+5MnAOee4V4EJYABBRFSs3r31+PPPeY9NmQJs2wZccYU3YyIiotKjSRMt6vH++9rANL9jx4CpU91Pp2UAQURUjLZtgaZNgQ8/zHvs00+1O+jFF3s3Lj8SkbIi8pCIHBORvxX43hIRmScic3K/fs33va4iskBEFotImoikuj96IiL/evppYO1a4OGHT3187lytFNirl7vjiXb3ckREkUUEuPVW4NFHtQ53hQrAt98CgwYB5cp5PTrfuR2AAVDYdr8qAJKMMae0QxKR6gC+A3CJMWa2iHQDMEZE4o0xB50eMBFRJLjoIuD++4HXXwcSE4F77tHHX3oJqFxZU2rd5EoAISIPA7gBwCEAFQB8aYx53o1rExGFa9Ag4JlngJtvBqpX16Disce8HpX/GGPeBgARubSQb58FYLqIVAawGsDzxphFAPoAWGmMmZ17jmkishlATwA/uTNyIiL/e/llLR/+j39odaZdu4CfftLHa9RwdyyOBxAi0hnAfQBaG2N2iMhZABaIyDxjzCSnr09EFK66dTVtaeBAwBjglVfc3azmJyISA2BGId/qYow5WsxL6xhjDolIFIBrAEwWkXYAmgDIKPDcjNzHC157MIDBABAbGxvK8ImIIlZ0tG6k7tMHuOsufeyyyzSgcH0sLlwjJ/dYA8AOAFWhey+2unBtIiJbXHEFsGiRzvI0auT1aLyTGyQEvUfBGHMo93gSwCgRuRG6+iAAThR4+nEUskfPGDMCwAgASElJMcGOgYgo0lWooKXFf/tNP4/atfNmHLYFEMXNSkFnjOaJSA6A2gBuMcYsLuI8nGEiIl9ys8Z2KVAGwF4ARwAUrB8SC2C06yMiIooA5csDPXt6OwbbAoiiZqVEJAHAOwB6GWPSRORcAGNFJNsYc9pGO84wERGVLCLSHsBJY8z83P/vD6A5gF+gm65fE5FWxpglItIBQDMATHElIvIpN1KYLgMwyxiTBgDGmDUi8j2Aa1F4pQ4iIipZ9kODhLrQFYedAHobY3YDgIgMBDBSRAw0fam/9T0iIvIfNwKIVQD+ISL1jDGbRaQqNO/1AxeuTURELjPGdCvw/8sB9Cvm+VMBtHd4WEREZBPHAwhjzI+5aUsTROQQgIoAxkHTmoiIiIiIKIK40gfCGPMagNfcuBYRERERETnntDJ5RERERERERWEAQUREREREAWMAQUREREREARNj/NtqIbfx3PoQX14TwHYbh+M1/jz+xp/H3/jznCrOGFPLrsF4IczPB4D/JvyOP4+/8efxt3B+noA+H3wdQIRDRNKMMSlej8Mu/Hn8jT+Pv/HnoYJK2p8hfx5/48/jb/x5gscUJiIiIiIi+n/t3V+sXFUVx/Hvr8V/CQbin6gUqxKbSJA/EStGEyHB0NA0KJKivmhfFCXGJxJjlBf1kSIPgsQQHjSalIihwajFatDQtGoVhSZogBJrjTygolxbaWmXD/dcczu9f07be+ecM/f7SSbt7Nm9WbtrulfX7DMzrdlASJIkSWptkhuIb3UdwBJzPf3mevrN9WjUpP0dup5+cz395npO0cS+B0KSJEnS0pvkEwhJkiRJS8wGQpIkSVJrNhCSJEmSWpu4BiLJlUl+l+SxJHuTvLfrmM5EkseT/CbJnub2865jOlVJXpbkliRHk3xs1vggc7XAegaZqyQ3JflDk4PHktzcjL8zya5m7PEkm7qOtY0F1vNgc3/PrNs5Xce7mCRfbdbz6+bfy2eb8UHmp2tD3XfmM9R9Z4b1od+sD/3WZX04a6l/YJeSnAv8ANhUVbuTXAVsT/K2qjrUbXSn7dXApVV1vOtAzsCngAL2zAwMPFcnracxuFwlWQ2sA95fVVNJ1gBPJdkOPAB8qaq2JbkQ2JXkkqo62GXMC1lkPa8Drq2qv3Ya5Kn7J/Duqjqa5PXAM0keYoD56drA9535DG7fGWF96CnrwyB0Vh8m7QRiA/CnqtoNUFUPA38Dru4yqDP0GuAXSR5Ncl+Sy7oO6FRV1V1VtRU4Nmt4sLmaZz0wwFxV1bGquqWqppqhvwNHgLcDrwLua+Y9AfwSuL6TQFtaYD2rgdcC25r8PNj8p6T3qur2qjra3H0rMAWczwDz0wOD3XcWMLh9ZzbrQ39ZH/qvy/owUScQwAXA0yNjTzfjQ/WGqjqcZBXwcWBnkndV1YGuAztD5qqf7gC2AecB++vEz3keYn7uALZV1YEkl828epnkGuD+JBuqam+3IS4uyTrgR0wXuRuZnPyMm/vOMJinfrI+9FBX9WHSTiDCyV3/Swx4nVV1uPn1eFV9F/gt06/ODJ256pkkXwPWAJ9jAvIzsh5mX/pQVQ8B36fnr5jNqKonq2od06/AfpvpQjDo/HRk8M/rUUPfd+ZhnnrG+tBfXdWHwSS7pYPA2pGxtc34pFgN/LvrIJaAueqRJLcBFwE3VNURBp6fOdYzl8HkZ0ZVPQrsZjr2weanQ4N+Xrc0uOf1HMxTj1gfhmHc9WHSGojtwCVJLgZI8h7gHcBPO43qNCVZn+TyWfc3AhcCO7qLasmYqx5IsirJ3cCbgc2zNtNdQCW5tpl3AdOvlj3QTaTtzLeeJOuSXD1r3uXAdTTXiPZVkouT3Jgkzf01wBXAzxhgfnrAfWcYzFMPWB+sDwuZqPdAVNW/kmwG7k1STB/ZbKyq5zsO7XRNAbcneSPwIvAP4JoBr+f/zFVvbARuAvYCjzT7EMCXmT6+vTPJV5qxT1bVk+MP8ZTMt547gQ8l2cp0fg4BH6mqZzqJsr0/A58BvpDkKPBy4Naq2pVkiPnplPvOMJin3rA+9Fun9SEnvsdCkiRJkuY3aZcwSZIkSVpGNhCSJEmSWrOBkCRJktSaDYQkSZKk1mwgJEmSJLVmAyGdpiRXJZlaZM7M5zNvSbJvPJFJkrpkfdCks4GQFpFkQ5JDi8zZmOSlkdtx4O4xhSlJGjPrg1aqifoiOWkZHV7k8R3Auc3vCzgC7Ad+v5xBSZI6Z33QimMDIS3uPODAQhOq6hjT3zYKQJL1wJuAHy5vaJKkDlkftCJ5CZO0uCuBo20nJ1kN3AZ8r6r+Muuhi5JUkj8udYCSpE5YH7QieQIhLSDJWcB1wDlJrqiqXy0y/2zgXuAtwA0jDz8BvA84thyxSpLGx/qglcwTCGlhW4AXgS8CdzUF4yRJViXZBOwFLgU+WFXPjUw7XlXPV9ULyxmwJGkstmB90AplAyHNI8laYCtwK/D1Zvib80zfCdwP/ARYX1VPLX+EkqQuWB+00nkJkzSHpjjsAH5cVfc0Y5uBR5J8B/j0yB/5PPBcVT073kglSeNkfZA8gZDm8w1gH/CJmYGq2g98ADgbeOXsyVW1r6qeTfJwkg/P8fN2AjcvY7ySpPGwPmjF8wRCmttHgf9WVc0ebI6erwdovkR01PlMF5ATVNVB4ODShylJGjPrg1Y8GwhpDlW12BcDLeQVzadtzOU/o0VHkjQc1gfJS5ik5XAP8MI8tzUdxiVJ6pb1QRMhNruSJEmS2vIEQpIkSVJrNhCSJEmSWrOBkCRJktSaDYQkSZKk1mwgJEmSJLVmAyFJkiSpNRsISZIkSa39D3POgrI6s8odAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_time_series_model\")\n",
    "\n",
    "    sequence1 = [0. for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence1[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence1.append(y_pred[0, -1, 0])\n",
    "\n",
    "    sequence2 = [time_series(i * resolution + t_min + (t_max-t_min/3)) for i in range(n_steps)]\n",
    "    for iteration in range(len(t) - n_steps):\n",
    "        X_batch = np.array(sequence2[-n_steps:]).reshape(1, n_steps, 1)\n",
    "        y_pred = sess.run(outputs, feed_dict={X: X_batch})\n",
    "        sequence2.append(y_pred[0, -1, 0])\n",
    "\n",
    "plt.figure(figsize=(11,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(t, sequence1, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence1[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"시간\")\n",
    "plt.ylabel(\"값\", rotation=0)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(t, sequence2, \"b-\")\n",
    "plt.plot(t[:n_steps], sequence2[:n_steps], \"b-\", linewidth=3)\n",
    "plt.xlabel(\"시간\")\n",
    "save_fig(\"creative_sequence_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 심층 RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MultiRNNCell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 2\n",
    "n_steps = 5\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "\n",
    "layers = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "          for layer in range(n_layers)]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch = np.random.rand(2, n_steps, n_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    outputs_val, states_val = sess.run([outputs, states], feed_dict={X: X_batch})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5, 100)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 여러 GPU에 심층 RNN 분산하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 사용해서는 **안됩니다**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.device(\"/gpu:0\"):  # 이 할당은 무시됩니다\n",
    "    layer1 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "\n",
    "with tf.device(\"/gpu:1\"):  # 이 할당은 무시됩니다\n",
    "    layer2 = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "대신 `DeviceCellWrapper`를 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class DeviceCellWrapper(tf.contrib.rnn.RNNCell):\n",
    "  def __init__(self, device, cell):\n",
    "    self._cell = cell\n",
    "    self._device = device\n",
    "\n",
    "  @property\n",
    "  def state_size(self):\n",
    "    return self._cell.state_size\n",
    "\n",
    "  @property\n",
    "  def output_size(self):\n",
    "    return self._cell.output_size\n",
    "\n",
    "  def __call__(self, inputs, state, scope=None):\n",
    "    with tf.device(self._device):\n",
    "        return self._cell(inputs, state, scope)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 5\n",
    "n_steps = 20\n",
    "n_neurons = 100\n",
    "\n",
    "X = tf.placeholder(tf.float32, shape=[None, n_steps, n_inputs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "devices = [\"/cpu:0\", \"/cpu:0\", \"/cpu:0\"] # 만약 GPU가 세 개 있다면 [\"/gpu:0\", \"/gpu:1\", \"/gpu:2\"]로 바꿉니다\n",
    "cells = [DeviceCellWrapper(dev,tf.contrib.rnn.BasicRNNCell(num_units=n_neurons))\n",
    "         for dev in devices]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또 다른 방법으로 텐서플로 1.1부터 `tf.contrib.rnn.DeviceWrapper` 클래스를 사용할 수 있습니다(텐서플로 1.2부터는 `tf.nn.rnn_cell.DeviceWrapper`가 되었습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-3.29020061e-02  1.17019236e-01 -4.58992608e-02 ...  3.84477153e-02\n",
      "   -5.24900258e-02  5.80436960e-02]\n",
      "  [ 3.54288332e-02 -7.42610693e-02 -6.42375052e-02 ... -2.45757148e-01\n",
      "   -1.77626804e-01 -3.26535292e-03]\n",
      "  [ 2.59726584e-01  1.44605249e-01  3.29312116e-01 ...  3.38821977e-01\n",
      "    5.98853417e-02 -8.31450596e-02]\n",
      "  ...\n",
      "  [ 1.18043743e-01 -2.41663873e-01 -8.27798247e-01 ... -1.85782462e-01\n",
      "    1.89682379e-01 -1.35261893e-01]\n",
      "  [ 2.87830859e-01  3.92953992e-01 -3.15236300e-01 ... -7.85605535e-02\n",
      "    4.32724983e-01 -6.61824420e-02]\n",
      "  [ 7.49779761e-01  6.11052871e-01 -3.01685542e-01 ...  1.95649698e-01\n",
      "   -1.62457466e-01 -5.23968339e-02]]\n",
      "\n",
      " [[-6.68027624e-02  7.35576972e-02 -2.90275402e-02 ... -7.79968277e-02\n",
      "   -3.65773253e-02 -4.66752201e-02]\n",
      "  [ 1.71546549e-01  4.28418722e-03  2.18812120e-03 ...  3.68538052e-02\n",
      "   -8.13241452e-02  9.01001915e-02]\n",
      "  [ 3.45635489e-02 -9.57525298e-02  1.59450233e-01 ...  1.63022086e-01\n",
      "   -6.83046281e-02  1.07990295e-01]\n",
      "  ...\n",
      "  [ 1.96414590e-01 -2.15230390e-01 -5.82537949e-01 ...  3.02532643e-01\n",
      "    5.47862291e-01 -1.06282756e-02]\n",
      "  [ 2.01206997e-01  2.52655987e-02 -5.02945222e-02 ...  9.56110135e-02\n",
      "   -2.03766495e-01 -1.52696133e-01]\n",
      "  [ 4.77758050e-01  1.66605622e-01 -1.56402126e-01 ...  1.67080894e-01\n",
      "   -2.03233853e-01  7.59940522e-05]]]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    print(sess.run(outputs, feed_dict={X: np.random.rand(2, n_steps, n_inputs)}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 드롭아웃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "n_inputs = 1\n",
    "n_neurons = 100\n",
    "n_layers = 3\n",
    "n_steps = 20\n",
    "n_outputs = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.float32, [None, n_steps, n_outputs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노트: `input_keep_prob` 매개변수는 플레이스홀더로 훈련하는 동안에는 어느 값이나 가능하고 테스트할 때는 1.0으로 지정합니다(드롭아웃을 끕니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_prob = tf.placeholder_with_default(1.0, shape=())\n",
    "cells = [tf.contrib.rnn.BasicRNNCell(num_units=n_neurons)\n",
    "         for layer in range(n_layers)]\n",
    "cells_drop = [tf.contrib.rnn.DropoutWrapper(cell, input_keep_prob=keep_prob)\n",
    "              for cell in cells]\n",
    "multi_layer_cell = tf.contrib.rnn.MultiRNNCell(cells_drop)\n",
    "rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.01\n",
    "\n",
    "stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])\n",
    "stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)\n",
    "outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])\n",
    "\n",
    "loss = tf.reduce_mean(tf.square(outputs - y))\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 훈련 MSE: 16.386942\n",
      "100 훈련 MSE: 4.2519865\n",
      "200 훈련 MSE: 3.7323227\n",
      "300 훈련 MSE: 3.674717\n",
      "400 훈련 MSE: 2.4364638\n",
      "500 훈련 MSE: 3.7355957\n",
      "600 훈련 MSE: 3.9596407\n",
      "700 훈련 MSE: 4.507475\n",
      "800 훈련 MSE: 4.067928\n",
      "900 훈련 MSE: 3.6636028\n",
      "1000 훈련 MSE: 3.3427944\n",
      "1100 훈련 MSE: 3.8968458\n",
      "1200 훈련 MSE: 2.9589307\n",
      "1300 훈련 MSE: 3.3112516\n",
      "1400 훈련 MSE: 3.723128\n"
     ]
    }
   ],
   "source": [
    "n_iterations = 1500\n",
    "batch_size = 50\n",
    "train_keep_prob = 0.5\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for iteration in range(n_iterations):\n",
    "        X_batch, y_batch = next_batch(batch_size, n_steps)\n",
    "        _, mse = sess.run([training_op, loss],\n",
    "                          feed_dict={X: X_batch, y: y_batch,\n",
    "                                     keep_prob: train_keep_prob})\n",
    "        if iteration % 100 == 0:                   # not shown in the book\n",
    "            print(iteration, \"훈련 MSE:\", mse) # not shown\n",
    "    \n",
    "    saver.save(sess, \"./my_dropout_time_series_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_dropout_time_series_model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_dropout_time_series_model\")\n",
    "\n",
    "    X_new = time_series(np.array(t_instance[:-1].reshape(-1, n_steps, n_inputs)))\n",
    "    y_pred = sess.run(outputs, feed_dict={X: X_new})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5PPHEE6xYsYJXX32VAw88kCuvvHK/6y5atIjy8nLmz58PwPbt2zn66KPZtGkTP/jBDzode+ONN3LttddywQUX0NDQwKmnnsrUqVNjL7ORn//852zdupXzzz9fQV3yRsd5Rp98Eg46qH1fx3lGexhuSfoh/5pfTjpp/6/6oUNhwoSULz1lypR9g3idfvrp3HnnnUybNo3i4mIgDK374IMPUl5ezpw5c2hqamL9+vWceeaZHBj7HXnaaaftd93a2lrmzJmz7/HIkSN7LMN//dd/MWXKFABKS0s57rjj9o3yOGvWLMyMI488km3btqX8ekUyRfOMZk7+BfXKSpg0KXz1m4X1pElhe4o2bdpEa2srAGvWrGHJkiWdhtw94YQT+O53v0tNTQ01NTWsXr2ao446il//+tf7bnK+0s3vxxNPPJHnnntu3+P6+nrMrNsynHjiift6qzY0NPDuu+/yuc99DoDBg9t/WIUx80Xyg+YZzZz8C+qDBoU5rR57DO66K6xXrw7bU+TuXHzxxUyePJkdO3bsF3iXLl3K4sWLqaioYOrUqTz55JOcfvrpnHHGGZx66qmcddZZ7Ny5c7/rLlmyhGeffZYzzzyT8vJyamtrexyOd+nSpfzoRz9i+vTpVFZW8v3vf59DDjkk5dcmkk2aZzRzLNM1vokTJ3ptbW2nbW+//TZjx47NaDm6qqmp4aGHHuLf/u3fslqOVOXCeynSk7o6uO8+ePTR9nlGr7gi1NAV0HtnZq+7+8S+jsu/G6UikrfGjIFly8IiAyP/ml9ERKRHqqnHlJeX75swWkQkX+VMTV3ZHKnTeygiORHUhwwZwvbt2xWUUuDubN++nSFDhmS7KCKSRTnR/DJ69Gi2bt1KfX19touS14YMGcLo0aOzXQwRyaKcCOrFxcUce+yx2S6GiEjey4nmFxERSQ8FdRGRCFFQFxGJEAV1EZEIUVAXEYmQtAV1MxtvZh+Z2Z3puqaIiPRPWoK6mY0AHgQeS8f1RCR31dXBggUwejQ880xYL1gQtkv2pRzUzawIeBj4W6Db3kNmNs/Mas2sVh2MRPJXdTWUlcHy5VBRARdfDOXl4XFZWdgv2ZWOmvo9wC/c/Vc9HeDuP3T3ie4+cdSoUWl4ShHJtLo6mD07TEPX0hJmK4KwbmkJ22fPVo0921LqUWpms4Cj3P1v01QeEclRe/Z0nvN99+6wnjwZOg7btGlTZsslnaVaU68ExprZa2b2GvBV4Ktm9mTqRRORXLJoUeegHptrfd8awv6qqsyWSzpLqabu7l/t+Die+eLud6ZyXRHJPdXVMHMmrFwJQ4fuv7+xEWbMgG7mXpcMUp66iCSktBRqamDuXGhu7ryvuTlsX7s2HCfZk9ag7u53qpZeuJTqFm2XXw7FxTBiBLS2hqWpqf3vESPC/iuuyHZJC5tq6pIWSnWLvqqqELSvvRZKSmDjRrjoorAuKQlZMMXFsHBhtkta2BTUJWXpSHVTLT/3jRkDTz0FDQ1w880wcSK88AKccgrccktoU3/qqXCcZI+CuqQsnurmHpYzzgjb46lu7mF/S0v356uWnz8qK2HcONi1C4YNg6Ki0Ibe3By2V1Zmu4RimZ4XdOLEiV5bW5vR55SBNWMGPPFE9xkRcY2N8OUvw89+1nl7XV0I3E1N4fGaNSGwr1kD06eHbfGf+qoBCm1t4Vt+/Xo46aTwLTJoULZLlRFm9rq7T+zruJyYzk7yWyqpburQIglra4PzzoN168KHZuhQmDQJVq8umMCeCDW/SMpSSXVThxZJWHV1COgNDeEbv6EhPFb7XCcK6pKyVFLd4rX8joG9o3gt//nnB/Y1SB5Yv37/D0pjI2zYkJ3y5CgFdekkmSyUVFLd1KFFEnbSSfu37w0dChMmZKc8OUpBXfZJNgsllVQ3dWiRhFVWhjb00lIwC+tJk5Ry04WCugCp55onm+qmDi2SsEGDwk3Rxx6Du+4Ka90k3Y9SGgWAt9+GsWPbH+/eHW5WxtdxmzbB8cen97mrq0OtfO1aWLo03AMrKoIbb4SpU2Hw4L4rY3V1sGQJPPccPPAAfPObcOGF4UtDqZASBYmmNKqmLkB2s1BS7dCizkuSkLa2kHd7991h3daW7RINCNXUBQiBdNq0xHLNc+n/gjovSUIikOOumrr0S75moaQ6RIEUiALKcVdQFyB/s1DUeSk5BTeAWgHluCuoC5C/WSjqvNR/BXkPooBy3BXUBcjfYVXztdkoW9IxTHJeKqAcdw3oJftUVob/zKtXhyyUhobOWSi5FtAhNBstX9652WjPHjjggNxuNsqWgh1ALZ7jXl0dmlwmTIjsCI/KfpG8Fs9+Wbky5LS/8Ub4pXHvvfCFL4Ra+gUXKPslLpVhkiW7lP0iBSFfm42yRfcgok9BXfKeZuNJnO5BRJ+CukTCmDGwbBns3Bn6mezcGR6rht5ZvqauSuIU1EUKSE6krhZId/1sUVCXgldIHXGyfg8i3l3/K1+BO+4I6/POU2BPIwV1KWiF2BEnq/cg8rm7fp78wlCeuhSsjh1xoHNHnH/5l9AZZ/bsaKZDxu9BLFuW4Sfurbv+zJkZLkw/5NGAYKqpR1AhNSekQoOBZUG+dtfPo18YCuoRU4jNCcnSYGBZkK/d9fNoQDAF9Qgp2HE9kqSOOFmQr1PS5dEvDLWpR0jBjuuRpI4dcZ58Eg46qH1fx444w4dnrYjRNGhQ+DbN5Tb0ruK/MLq2qefgL4yUa+pm9nUze8PMas1so5ktSEfBpP9yojkhTzIEIL874ui+SYbl0y8Md096AQYBi4HS2OPPAM3AZ3o65+STT3YZGGbu5eXuDQ3d729ocJ82zb2oaIAK0NrqPn26e2lpKExpaXjc2jpAT5iazZvdS0rc16wJRXz9dfezzw7r1lb3F18M+zdvznZJO1u1KpSruNj98svDtssuC49LSsJ+yTGtre4//an7XXeFdRL/J4BaTyQuJ3JQogswBNgJHNXTMQrqA2fYsPAvOmOGe1NT531NTWE7uA8fPkAF+OlPQyBvTx4Jj3/608TOT8MHv79WrXJ/7jn3qqrwPQThS2/RorA91wJk/Iso/vauWRO2v/hi+7Zc/CIqaGmq7CQa1NN9o/R+4HF3f6/jRjObF2ueqa2vr0/zU0pc1psTUskQSEdPwySaflLtiJPpZhClYeahTKdDJhL5E1mAe4CfAgf0dpxq6gMn680JqdTU01HLz3DTTzaaQc4/v+fmtbiGhnCc5Ii77mr/GRhfzNzvvrtflyGTNXUzWwyMB2a5+550XFP6L23jeiR7szOVHORU84AzXBvKVvqo0jDzUIbTIVMK6mZWZGYPAUcCcxTQsy/lcT1SaQZJJUMg1Q9+hjuHpKMZJJmmG42Hnocy3eEqkep8TwswE3DgN8BrHZazezpHzS85LtVmkGSl2nyS4XKn2gySbNPNN74RjrnsMvdPPnFvaXFvbAzrTz5pv8Z11yX/2mQAxJMA7r47v7JfElkU1HNcmtr/kpLKBz/DbeqppI+mksGS9fsmkjWJBnX1KJXO4s0gDQ3t2zLVHTqVnoYZni0+ld6oqfT87XrfZOnScM4pp8CNN4bJtzUna2HT2C/SWb4OuATtXwq33RbWA9jbL5X00VR7/mpOVumNeceqQQZMnDjRa2trM/qc0k9tbRmr8earurow6uXKlaF2/MYboeZ8773whS+EWvoFF3Q/FntREUybFs7tem8Y2jNYXnklp0dZkAwzs9fdfWKfxymoiySnujrUyteubW8GKSpqbwYZPLj7WvPw4fDppyFwd9d0M2cO/Oxn4bidOzP3eiS3JRrU1fwikqRkm0Gy3vM3HfJo4LZCo5q6SIal0nSTE/JoarcoUU1dJEelredvtuTR1G6FSEFdJAvyOoMlj6Z2K0TKUxfJkjFjYNmysOSVbPZlkD6ppi4i/ZPPfRkKgGrqItI/Ge69K/2joC4i/ZePk0cXCDW/iIhEiIJ6DtJM8SKSLAX1HFNdHTqmLF8OFRVw8cVQXh4el5UpFVhEeqegnkOyNUWaiESHbpTmkFTG2ZYUxUemXL8+5GErm0PylIJ6Dlm0CJ54on041t7G2f7ZzzJfvsjSWCYSIWp+ySGaKT5LNJaJRIiCeg7RTPFZorFMJEIU1HNIJMbZzkfxsUw60lgmkqcU1HNIVVUI2tdeCyUlYTztiy4K65KSkAVTXAwLF2a7pBGjsUwkQjRJRo5Jdoo0SZHmZZUcl+gkGcp+yTGVlSEPffXqMM52Q0PncbZzduKEfKexTCQiVFOPKuVdS1/0GckrqqkXMuVdS1/0GYks3SiNIuVdS1/0GYksBfUoUt51ZrW1wcqVcPfdYd3Wlu0S9U2fkchS80sUaQ7JzMnXZgx9RiJLNfUoUt515uRrM4Y+I5GVck3dzKYB98WutQe43t1fS/W6kgLNIZk5vTVj5HJ6pD4jkZVSUDezEcDTwEx3/08zKwf+w8yOdfemdBRQkqS868zI52YMfUYiKdXml/OAd9z9PwHcvQb4EzA9xeuK5IdUmzHy8Sar5LRUm18+C3Sdh6cutn0fM5sHzAM46qijUnxKkRySSjNGqjdZ1XlIupFqUDega9WilS6/ANz9h8APIfQoTfE5RXJLss0YHW+yQuebrH1dK1+zbmTApdr8shXoWvU+KrZdRHqTSq54vmbdyIBLNaj/B1BmZicCmNmpwPHAL1ItmEjkpTKOuzoPSQ9San5x951mNgf4ZzNzQtPL+e7+cVpKJxJl8ZusXZtQErnJms9ZNzKgUs5Td/eXgFPSUBaRwpLKTdZUvhAk0jT0bq5SZoP0RRN7FBQNvZvPlNkgiVDnIemGxn7JRcpsEJEkKagPkLo6WLAARo+GZ54J6wULwvY+KbNBRJKkoD4AqquhrAyWL4eKCrj4YigvD4/LyhKocKeS6iYiBU1BPc3q6mD2bGhqgraWNr49Nozr8e2xK2lraaOpKezvtcauYVFFJEm6UZpme/bEWk5iNzv9e+ugqZGykqG0TW+/2blpUy8X0bCoIpIk1dT70s9R9BYtigX12M1Oaww3O62x/WZnYyNUVfXxvPHMhttuC2sFdBFJgGrqvUkitTA+FtPqM9dzQDc3O/f8egMzFs/klVcyUH4RKTiqqfcmidTC0lKoqYHvP38SXtL5ZqeXDOX71RNYuzYcJyKSbgrqvUkitfDyy6G4GDYfV0nbxEn40FLcDB9aStvESWw+rpLiYrjiigEuu4gUJAX13iSRWlhVFYL6NV8bhP18NXX3PMaPj72Lunsew36+mqu/OojiYli4cIDLLiIFSWO/9CbJ7vrV1dDaCmvXwtKloeWmqAhuvBGmToXBg5WdKCL9o7Ff0iHJ1MLKypCHvno1DBsWmuJLS6G5GcaNgzFjMlR+ESk4qqmLiOSBRGvqalMXEYkQBXURkQhRUBcRiRAFdRGRCFFQFxGJEAV1EZEIUVAXEYkQBXURkQhRUBcRiRAFdRGRCCmMsV/a2sL4LevXh5EXNTWciERU9IN6kiMtiojko+g3vyQxe5GISL6KflBPYvYiEZF8Ff2gnsTsRSIi+Sr6Qb2yMrShl5aCWVhPmpTQ1EN1dbBgAYweDc88E9YLFoTtIiK5KKWgbmZfN7M3zKzWzDaa2YJ0FSxt4rMXPfYY3HVXWCdwk7S6GsrKYPlyqKiAiy+G8vLwuKxMTfIikpuSnvnIzAYB9wJ3unuDmX0G2Ax8zt3f7+m8fJj5qK4uBO6mpvB4zZoQ2NesgenTw7aSEti4UVPTiUhmDPgcpe7eBizqsGk7sAfI+zzBPXs631vdvTusJ08OCTRxmzZltlwiIn3ps/nFzA4ws9e6WQ7ocuj9wOPu/l4315gXa6Kpra+vT1fZB8yiRZ2D+oEHdl5D2F9VldlyiYj0JS0TT5vZPcAXgFnuvqe3Y/Oh+aWoCKZNg5Ur90+cgRDQZ8yAV14JfZtERAZaxiaeNrPFwHgSCOj5orQUampg7lxobu68r7k5bF+7NhwnIpJLkg7qZlZkZg8BRwJzohLQAS6/HIqLYcQIaG0NS1NT+98jRoT9V1yR7ZKKiHSWSk39fODrwGeBX3Zoaz87PUXLnqqqELSvvbY9y+Wii8K6pASuuSbsX7jgXs0MAAAHYElEQVQw2yUVEeksLW3q/ZEPbeoQ8tBbW0Mzy9KlIeulqAhuvBGmToXBgxPqvyQikhYZa1OPqspKGDcOdu2CYcNCQC8tDW3q48YpoItIblJNXUQkD6imLiJSgBTURUQiREFdRCRCFNRFRCJEQV1EJEIU1EVEIiTyQV2zF4lIIYl0UNfsRSJSaCIb1OvqYPbsMBBXS0sYrwXCuqUlbJ89WzV2EYmWpGc+ynWavUhEClFka+qavUhEClFkg3p1Ncyc2TmwdxSfvej55zNbLhGRgRTZoK7Zi0SkEEU2qGv2IhEpRHkR1OO55sOHh3HNhw/vO9dcsxeJSCHK+fHUq6tD6mFLS1jiiovD8tRTPU9YodmLRCQqIjGeetdc844SyTXX7EUiUmhyOqgvWbJ/MO+qpQXuu6/n/WPGwLJlsHMntLWF9bJlYbuISNTkdFD/yU8SC+qPPpqZ8oiI5LqcDuoNDek9TkQk6nI6qCeaQ65ccxGRIKeDejzXvDfKNRcRaZfTQT2ea94b5ZqLiLTL6aA+ZkzIQy8p2T+4FxeH7U89pUwWEZG4nA7qEHLJN26EefM69yidNy9sV665iEi7nO9RKiIiEelRKiIi/aOgLiISIQrqIiIRkvE2dTOrB36f0SfNnkOBbdkuRA7T+9M3vUe9K6T352h3H9XXQRkP6oXEzGoTubFRqPT+9E3vUe/0/uxPzS8iIhGioC4iEiEK6gPrh9kuQI7T+9M3vUe90/vThdrURUQiRDV1EZEIUVAXEYkQBXURkQhRUO8nMys2s0Vm1mJml3TY/nUze8PMas1so5kt6OH8kWbWaGavdVgWZ+4VDLxe3qO7Y+/Rr83s/5nZN3q5xnVmtsnM3jSzlWZ2WGZKP/BSfX8K+TPUYf94M/vIzO7s4XyLvZ/vmNlbZvYTMxs64AXPAYOzXYA89DXAgdfiG8xsEHAcMNndG8zsM8BmM/sPd3+/y/mHAr9y93MyVuLM2+89itkBTHT3FjMbBfyPmT3v7v/T8SAzKwduBk529/rYf9wfATMHvOSZkdL7Q2F/hjCzEcCDwGO9nH8lcD4wwd2bzWwFcC9w/QCUNacoqPeTu/8DgJld0GFbG7Cow2HbgT3AoG4ucShQZmb/SXj/XwPucff/HbBCZ1h371Fs+9IOD48BGoCPurnEXOAn7l4fe/z3QL2ZHezuO9Nf4sxKw/tTsJ8hMysCHgb+Fji3l0vMBf7J3Ztjj/8eeJECCOpqfhkY9wOPu/t73eyrBY5w99OBckLwX2VmlsHyZY2ZHWdm7wKrgct7CNKfBeriD9x9B7CTEOgiLcH3p5A/Q/cAv3D3X/VxXKfPUOzvPzOzgwesZDlCQT3NzOwe4DP0UCNw992xmj3u3gjcBBwPfC5jhcwid3/X3Y8DpgOPmNn4bg4zoK3LtlYK4POayPtTqJ8hM5sFHOXuyxI5nM6fodbYOvKfITW/pFHsZtUYYJa770n0NMIH7ZMBK1gOcvf1seaDCuC/u+zeChwVf2BmJcDI2PaC0Mf701WhfIYqgbFmFm9nHw3hpqm7z+lybKfPUOzvBuDjAS9llkX+WysTzKzIzB4CjgTmdAzoZjbIzF40symxxxea2V/E/jbgLqAmSu2h3TGzE83sy/EmgtjN5ElAbdf3CHgUuKzDT+XrgFc7tLFHTn/en0L9DLn7V939ZHc/zd1PA5YDy919Tiwj6FUzOy52+KPAV83sgNjjbwJPewF0oVdNPT3OB75OaOv8ZYemzduAXwLHAn8W2+bA02ZWDOwFNgKXZbS02fF7YD5ws5m1AAcAt7v7a2Y2hA7vkbu/ZGYPAmtjx/4R2C+tLWISfn8o3M9Qb0qAo4F4ReARQnPUr82sFXiLArhJChr7RUQkUtT8IiISIQrqIiIRoqAuIhIhCuoiIhGioC4iEiEK6lLQzKzczBr6OCaeO36Vmb2ZmZKJJEdBXQqCmZ1nZk19HHO+mbV2WfYCD2WomCIpU+cjKSTNfexfDYyI/e2EgbK2ABsGslAi6aSgLoXiCKC7UTP3iQ2Sta8pxsxOAf4CWDmwRRNJHzW/SKGYBrQkenBs4pPFwL+6+x867BpvZm5mm9JdQJF0UE1dIs/MBgMXAgeb2SR3X9fH8aXAPxPGEpnVZffbwBnsPzSwSE5QTV0KwVXAbuBW4B9iQX4/sdE2ZxIGZvsCcLa7b+ty2F53/9jdPx3IAoskS0FdIs3MjgKWALcD98U2/2MPh78A/DvwPHCKu28e+BKKpJeaXySyYgF9NVDt7stj2+YQhkd+FJjX5ZRvAdvc/YPMllQkfVRTlyhbBrwJ/J/4BnffAkwFSoEhHQ929zfd/QMzqzGzL3ZzvReABQNYXpGUqaYuUTYX2NV1tptYs8rFAD3M1TyaEPQ7cfetFNCUepKfFNQlsty9r85GvTkwlgXTncZCmBZN8pOaX0S6txz4tIflM1ksl0ivNJ2diEiEqKYuIhIhCuoiIhGioC4iEiEK6iIiEaKgLiISIQrqIiIRoqAuIhIh/x/JyfAl3RQTpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"모델 테스트\", fontsize=14)\n",
    "plt.plot(t_instance[:-1], time_series(t_instance[:-1]), \"bo\", markersize=10, label=\"instance\")\n",
    "plt.plot(t_instance[1:], time_series(t_instance[1:]), \"w*\", markersize=10, label=\"target\")\n",
    "plt.plot(t_instance[1:], y_pred[0,:,0], \"r.\", markersize=10, label=\"prediction\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"시간\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이런 드롭아웃이 이 경우엔 크게 도움이 안되네요. :/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-88-858b095e28bf>:3: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is deprecated, please use tf.nn.rnn_cell.LSTMCell, which supports all the feature this cell currently has. Please replace the existing code with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "lstm_cell = tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_steps = 28\n",
    "n_inputs = 28\n",
    "n_neurons = 150\n",
    "n_outputs = 10\n",
    "n_layers = 3\n",
    "\n",
    "learning_rate = 0.001\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])\n",
    "y = tf.placeholder(tf.int32, [None])\n",
    "\n",
    "lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)\n",
    "              for layer in range(n_layers)]\n",
    "multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)\n",
    "outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32)\n",
    "top_layer_h_state = states[-1][1]\n",
    "logits = tf.layers.dense(top_layer_h_state, n_outputs, name=\"softmax\")\n",
    "xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "loss = tf.reduce_mean(xentropy, name=\"loss\")\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "correct = tf.nn.in_top_k(logits, y, 1)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
    "    \n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_3:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_4:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_5:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_6:0' shape=(?, 150) dtype=float32>),\n",
       " LSTMStateTuple(c=<tf.Tensor 'rnn/while/Exit_7:0' shape=(?, 150) dtype=float32>, h=<tf.Tensor 'rnn/while/Exit_8:0' shape=(?, 150) dtype=float32>))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'rnn/while/Exit_8:0' shape=(?, 150) dtype=float32>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_layer_h_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "에포크 0 배치 데이터 정확도 = 0.97333336 검증 세트 정확도 = 0.9602\n",
      "에포크 1 배치 데이터 정확도 = 0.96666664 검증 세트 정확도 = 0.9782\n",
      "에포크 2 배치 데이터 정확도 = 0.9533333 검증 세트 정확도 = 0.9714\n",
      "에포크 3 배치 데이터 정확도 = 0.99333334 검증 세트 정확도 = 0.9828\n",
      "에포크 4 배치 데이터 정확도 = 0.98 검증 세트 정확도 = 0.9856\n",
      "에포크 5 배치 데이터 정확도 = 0.9866667 검증 세트 정확도 = 0.9862\n",
      "에포크 6 배치 데이터 정확도 = 0.99333334 검증 세트 정확도 = 0.988\n",
      "에포크 7 배치 데이터 정확도 = 0.9866667 검증 세트 정확도 = 0.987\n",
      "에포크 8 배치 데이터 정확도 = 1.0 검증 세트 정확도 = 0.9872\n",
      "에포크 9 배치 데이터 정확도 = 1.0 검증 세트 정확도 = 0.99\n",
      "테스트 세트 정확도 = 0.988\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 10\n",
    "batch_size = 150\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n",
    "            X_batch = X_batch.reshape((-1, n_steps, n_inputs))\n",
    "            sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
    "        acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n",
    "        print(\"에포크\", epoch, \"배치 데이터 정확도 =\", acc_batch, \"검증 세트 정확도 =\", acc_valid)\n",
    "    \n",
    "    acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test})\n",
    "    print(\"테스트 세트 정확도 =\", acc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_cell = tf.contrib.rnn.LSTMCell(num_units=n_neurons, use_peepholes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 임베딩"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 섹션은 텐서플로의 [Word2Vec 튜토리얼](https://www.tensorflow.org/versions/r0.11/tutorials/word2vec/index.html)을 기반으로 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from six.moves import urllib\n",
    "\n",
    "import errno\n",
    "import os\n",
    "import zipfile\n",
    "\n",
    "WORDS_PATH = \"datasets/words\"\n",
    "WORDS_URL = 'http://mattmahoney.net/dc/text8.zip'\n",
    "\n",
    "def mkdir_p(path):\n",
    "    \"\"\"디렉토리 생성, 이미 있다면 그냥 통과\n",
    "    \n",
    "    이 함수는 파이썬 2 버전을 지원하기 위해서입니다.\n",
    "    파이썬 3.2 이상이면 다음과 같이 쓸 수 있습니다:\n",
    "    >>> os.makedirs(path, exist_ok=True)\n",
    "    \"\"\"\n",
    "    try:\n",
    "        os.makedirs(path)\n",
    "    except OSError as exc:\n",
    "        if exc.errno == errno.EEXIST and os.path.isdir(path):\n",
    "            pass\n",
    "        else:\n",
    "            raise\n",
    "\n",
    "def fetch_words_data(words_url=WORDS_URL, words_path=WORDS_PATH):\n",
    "    os.makedirs(words_path, exist_ok=True)\n",
    "    zip_path = os.path.join(words_path, \"words.zip\")\n",
    "    if not os.path.exists(zip_path):\n",
    "        urllib.request.urlretrieve(words_url, zip_path)\n",
    "    with zipfile.ZipFile(zip_path) as f:\n",
    "        data = f.read(f.namelist()[0])\n",
    "    return data.decode(\"ascii\").split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = fetch_words_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anarchism', 'originated', 'as', 'a', 'term']"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사전 구축"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "vocabulary_size = 50000\n",
    "\n",
    "vocabulary = [(\"UNK\", None)] + Counter(words).most_common(vocabulary_size - 1)\n",
    "vocabulary = np.array([word for word, _ in vocabulary])\n",
    "dictionary = {word: code for code, word in enumerate(vocabulary)}\n",
    "data = np.array([dictionary.get(word, 0) for word in words])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('anarchism originated as a term of abuse first used',\n",
       " array([5234, 3081,   12,    6,  195,    2, 3134,   46,   59]))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join(words[:9]), data[:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cycles originated as a term of abuse first used'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\" \".join([vocabulary[word_index] for word_index in [5241, 3081, 12, 6, 195, 2, 3134, 46, 59]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('culottes', 0)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[24], data[24]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 배치 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from collections import deque\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "    global data_index\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "    buffer = deque(maxlen=span)\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window  # buffer 중간에 타깃 레이블을 둡니다\n",
    "        targets_to_avoid = [ skip_window ]\n",
    "        for j in range(num_skips):\n",
    "            while target in targets_to_avoid:\n",
    "                target = random.randint(0, span - 1)\n",
    "            targets_to_avoid.append(target)\n",
    "            batch[i * num_skips + j] = buffer[skip_window]\n",
    "            labels[i * num_skips + j, 0] = buffer[target]\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "    return batch, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_index=0\n",
    "batch, labels = generate_batch(8, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3081, 3081,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
       " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch, [vocabulary[word] for word in batch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[5234],\n",
       "        [  12],\n",
       "        [   6],\n",
       "        [3081],\n",
       "        [  12],\n",
       "        [ 195],\n",
       "        [   2],\n",
       "        [   6]], dtype=int32),\n",
       " ['anarchism', 'as', 'a', 'originated', 'as', 'term', 'of', 'a'])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels, [vocabulary[word] for word in labels[:, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # 임베딩 벡터 차원\n",
    "skip_window = 1       # 고려할 왼쪽과 오른쪽 단어의 개수\n",
    "num_skips = 2         # 레이블을 생성하기 위한 입력의 재사용 횟수\n",
    "\n",
    "# 가까운 이웃을 샘플링하기 위해 랜덤한 검증 세트를 만듭니다.\n",
    "# 검증 샘플은 가장 흔한 단어인 낮은 ID 번호를 가진 것으로 제한합니다.\n",
    "valid_size = 16     # 유사도를 평가하기 위해 랜덤하게 구성할 단어 세트 크기\n",
    "valid_window = 100  # 검증 샘플을 전체 샘플의 앞 부분에서만 선택합니다\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "num_sampled = 64    # 부정 샘플링(negative sampling)의 수\n",
    "\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "# 입력 데이터\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary_size = 50000\n",
    "embedding_size = 150\n",
    "\n",
    "# 입력을 위해 임베딩을 조회합니다\n",
    "init_embeds = tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
    "embeddings = tf.Variable(init_embeds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs = tf.placeholder(tf.int32, shape=[None])\n",
    "embed = tf.nn.embedding_lookup(embeddings, train_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NCE 손실을 위한 변수를 만듭니다\n",
    "nce_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                        stddev=1.0 / np.sqrt(embedding_size)))\n",
    "nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "# 배치에서 NCE 손실의 평균을 계산합니다.Compute the average NCE loss for the batch.\n",
    "# tf.nce_loss는 자동으로 손실을 평가할 때마다 음성 레이블에서 새로운 샘플을 뽑습니다.\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n",
    "                   num_sampled, vocabulary_size))\n",
    "\n",
    "# Adam 옵티마이저\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "# 미니배치 샘플과 모든 임베딩 사이의 코사인 유사도를 계산합니다\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), axis=1, keepdims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "# 초기화를 위한 연산\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델 훈련"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "반복: 0\t스텝  0 에서의 평균 손실:  289.90948486328125\n",
      "would에 가장 가까운 단어: viewpoint, lured, rossini, vara, teflon, glaciation, losing, eurasia,\n",
      "on에 가장 가까운 단어: thatched, falsely, wi, vinegar, euskal, consequentialist, baroness, tallinn,\n",
      "four에 가장 가까운 단어: hom, rez, siege, translators, khalil, pegged, hellene, ynys,\n",
      "his에 가장 가까운 단어: pointers, ponty, charged, achieved, rk, blooming, value, juggernaut,\n",
      "often에 가장 가까운 단어: presses, convenience, arabidopsis, jewry, mapuche, guts, macrinus, implemented,\n",
      "in에 가장 가까운 단어: furnishing, qi, renderer, tuning, mckenna, illustrate, advances, parser,\n",
      "an에 가장 가까운 단어: gutierrez, dyess, privations, archaeological, bijection, kon, joh, insemination,\n",
      "eight에 가장 가까운 단어: malacca, liquidation, davidic, comical, ochre, wild, redman, unconditional,\n",
      "these에 가장 가까운 단어: pedro, condoned, neck, ssn, supervising, doug, thereto, melton,\n",
      "nine에 가장 가까운 단어: cocker, struck, dame, tensile, unifies, operetta, develop, opinions,\n",
      "called에 가장 가까운 단어: paradoxes, wings, krantz, consults, weblogs, supergirl, constitutionally, zoologists,\n",
      "about에 가장 가까운 단어: heavyweight, cisco, fatou, triphosphate, predecessors, abkhazia, swordsmanship, wexford,\n",
      "up에 가장 가까운 단어: clair, drives, steadfast, missed, nashville, kilowatts, anal, vinland,\n",
      "one에 가장 가까운 단어: imagines, tijuana, hindrance, motorcyclist, steadfastly, lords, letting, hutchinson,\n",
      "and에 가장 가까운 단어: pierre, savannah, fridays, laminated, reestablish, counters, asymmetric, unchecked,\n",
      "been에 가장 가까운 단어: powerpc, maccabean, precarious, hounds, gol, linear, hazael, schuster,\n",
      "반복: 2000\t스텝  2000 에서의 평균 손실:  132.04802772903443\n",
      "반복: 4000\t스텝  4000 에서의 평균 손실:  62.340319894790646\n",
      "반복: 6000\t스텝  6000 에서의 평균 손실:  41.113937734365464\n",
      "반복: 8000\t스텝  8000 에서의 평균 손실:  31.317131091713904\n",
      "반복: 10000\t스텝  10000 에서의 평균 손실:  25.686956456661225\n",
      "would에 가장 가까운 단어: can, tarski, unprotected, morpork, afc, hashes, cannot, mileva,\n",
      "on에 가장 가까운 단어: shrewsbury, in, hearing, actinium, ufos, doubleday, of, mosque,\n",
      "four에 가장 가까운 단어: nine, five, one, three, zero, two, eight, six,\n",
      "his에 가장 가까운 단어: the, nen, abscess, modeling, adobe, and, adherence, nhk,\n",
      "often에 가장 가까운 단어: ampere, baudot, scythian, are, and, canaris, it, breastfeeding,\n",
      "in에 가장 가까운 단어: of, and, altaic, romanus, buchan, nine, UNK, the,\n",
      "an에 가장 가까운 단어: a, is, this, beers, which, threw, achill, parsley,\n",
      "eight에 가장 가까운 단어: nine, zero, four, five, one, six, seven, two,\n",
      "these에 가장 가까운 단어: displaced, snowball, tuned, antigua, seceded, fallacy, or, kournikova,\n",
      "nine에 가장 가까운 단어: four, one, eight, six, zero, seven, two, three,\n",
      "called에 가장 가까운 단어: and, are, ampere, or, specifying, rhazes, gide, dissociation,\n",
      "about에 가장 가까운 단어: blankets, limiting, chung, argonauts, and, webster, in, ball,\n",
      "up에 가장 가까운 단어: integrity, a, flow, laughton, peake, rn, bodyguard, natively,\n",
      "one에 가장 가까운 단어: nine, four, five, three, two, seven, six, UNK,\n",
      "and에 가장 가까운 단어: of, in, the, which, as, or, ufos, UNK,\n",
      "been에 가장 가까운 단어: have, stopped, has, jealousy, essendon, nationally, had, a,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 10001\n",
    "\n",
    "with tf.Session() as session:\n",
    "    init.run()\n",
    "\n",
    "    average_loss = 0\n",
    "    for step in range(num_steps):\n",
    "        print(\"\\r반복: {}\".format(step), end=\"\\t\")\n",
    "        batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)\n",
    "        feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}\n",
    "\n",
    "        # 훈련 연산을 평가하여 스텝을 한 단계를 업데이트합니다(session.run()에서 반환된 값을 사용합니다)\n",
    "        _, loss_val = session.run([training_op, loss], feed_dict=feed_dict)\n",
    "        average_loss += loss_val\n",
    "\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            # 평균 손실은 2000개 배치에 대한 손실의 추정입니다.\n",
    "            print(\"스텝 \", step, \"에서의 평균 손실: \", average_loss)\n",
    "            average_loss = 0\n",
    "\n",
    "        # 이 코드는 비용이 많이 듭니다 (500 스텝마다 ~20%씩 느려집니다)\n",
    "        if step % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            for i in range(valid_size):\n",
    "                valid_word = vocabulary[valid_examples[i]]\n",
    "                top_k = 8 # 가장 가까운 단어의 개수\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log_str = \"%s에 가장 가까운 단어:\" % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = vocabulary[nearest[k]]\n",
    "                    log_str = \"%s %s,\" % (log_str, close_word)\n",
    "                print(log_str)\n",
    "\n",
    "    final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막 임베딩을 저장합니다(물론 텐서플로의 `Saver`를 사용해도 됩니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"./my_final_embeddings.npy\", final_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 임베딩 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_with_labels(low_dim_embs, labels):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"임베딩보다 레이블이 많습니다.\"\n",
    "    plt.figure(figsize=(18, 18))  # 인치 크기\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i,:]\n",
    "        plt.scatter(x, y)\n",
    "        plt.annotate(label,\n",
    "                     xy=(x, y),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "plot_only = 500\n",
    "low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])\n",
    "labels = [vocabulary[i] for i in range(plot_only)]\n",
    "plot_with_labels(low_dim_embs, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 기계 번역"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`basic_rnn_seq2seq()` 함수는 간단한 인코더/디코더 모델을 만듭니다. 먼저 `encoder_inputs`를 상태 벡터로 인코딩하는 RNN을 실행하고 그다음 `decoder_inputs`을 마지막 인코더 상태로 초기화시킨 디코더를 실행합니다. 인코더와 디코더는 같은 RNN 셀 타입을 사용하지만 파라미터를 공유하지는 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "reset_graph()\n",
    "\n",
    "n_steps = 50\n",
    "n_neurons = 200\n",
    "n_layers = 3\n",
    "num_encoder_symbols = 20000\n",
    "num_decoder_symbols = 20000\n",
    "embedding_size = 150\n",
    "learning_rate = 0.01\n",
    "\n",
    "X = tf.placeholder(tf.int32, [None, n_steps]) # 영어 문장\n",
    "Y = tf.placeholder(tf.int32, [None, n_steps]) # 프랑스어 번역\n",
    "W = tf.placeholder(tf.float32, [None, n_steps - 1, 1])\n",
    "Y_input = Y[:, :-1]\n",
    "Y_target = Y[:, 1:]\n",
    "\n",
    "encoder_inputs = tf.unstack(tf.transpose(X)) # 1D 텐서의 리스트\n",
    "decoder_inputs = tf.unstack(tf.transpose(Y_input)) # 1D 텐서의 리스트\n",
    "\n",
    "lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons)\n",
    "              for layer in range(n_layers)]\n",
    "cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)\n",
    "\n",
    "output_seqs, states = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(\n",
    "    encoder_inputs,\n",
    "    decoder_inputs,\n",
    "    cell,\n",
    "    num_encoder_symbols,\n",
    "    num_decoder_symbols,\n",
    "    embedding_size)\n",
    "\n",
    "logits = tf.transpose(tf.unstack(output_seqs), perm=[1, 0, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_flat = tf.reshape(logits, [-1, num_decoder_symbols])\n",
    "Y_target_flat = tf.reshape(Y_target, [-1])\n",
    "W_flat = tf.reshape(W, [-1])\n",
    "xentropy = W_flat * tf.nn.sparse_softmax_cross_entropy_with_logits(labels=Y_target_flat, logits=logits_flat)\n",
    "loss = tf.reduce_mean(xentropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부록 A 참조."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 임베딩된 레버(Reber) 문법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 문법에 맞는 문자열을 생성하는 함수가 필요합니다. 이 문법은 각 상태에서 가능한 전이 상태의 리스트입니다. 하나의 전이는 출력할 문자열(또는 생성할 문법)과 다음 상태를 지정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, seed\n",
    "\n",
    "# 일관된 출력을 위한 유사난수 초기화\n",
    "seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "default_reber_grammar = [\n",
    "    [(\"B\", 1)],           # (상태 0) =B=>(상태 1)\n",
    "    [(\"T\", 2), (\"P\", 3)], # (상태 1) =T=>(상태 2) or =P=>(상태 3)\n",
    "    [(\"S\", 2), (\"X\", 4)], # (상태 2) =S=>(상태 2) or =X=>(상태 4)\n",
    "    [(\"T\", 3), (\"V\", 5)], # 등등..\n",
    "    [(\"X\", 3), (\"S\", 6)],\n",
    "    [(\"P\", 4), (\"V\", 6)],\n",
    "    [(\"E\", None)]]        # (상태 6) =E=>(종료 상태)\n",
    "\n",
    "embedded_reber_grammar = [\n",
    "    [(\"B\", 1)],\n",
    "    [(\"T\", 2), (\"P\", 3)],\n",
    "    [(default_reber_grammar, 4)],\n",
    "    [(default_reber_grammar, 5)],\n",
    "    [(\"T\", 6)],\n",
    "    [(\"P\", 6)],\n",
    "    [(\"E\", None)]]\n",
    "\n",
    "def generate_string(grammar):\n",
    "    state = 0\n",
    "    output = []\n",
    "    while state is not None:\n",
    "        production, state = choice(grammar[state])\n",
    "        if isinstance(production, list):\n",
    "            production = generate_string(grammar=production)\n",
    "        output.append(production)\n",
    "    return \"\".join(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본 레버 문법에 맞는 문자열을 몇 개 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTXXTTTTVPXTTTTTVPSE BTXSE BTXXTVPSE BTXXVPSE BTSSXXTTVVE BTXSE BTSSSXSE BPTTTVVE BTXXVVE BPTTVVE BTSXXTTTTVPSE BPTTVVE BPTVPSE BPTTVPXVVE BPVPXTTTVPXTVPSE BTXSE BPTTTTVPXTTTTTTTVPXVVE BPTVVE BTXSE BPTTTVVE BTSXXVPSE BTXXTTTTTVVE BPTTVPSE BPVVE BPTTTVPXVPXTTTTTVPXTTVVE "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_string(default_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 이제 임베딩된 레버 문법에 맞는 문자열을 몇 개 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPBPTVVEPE BTBPTVPXVVETE BPBPTTTVVEPE BPBTXSEPE BPBPTTTTTVPSEPE BTBTSXSETE BPBPVPSEPE BPBPVVEPE BPBTXSEPE BPBTSXSEPE BTBPTTVVETE BPBPVVEPE BTBTXSETE BPBPTTVVEPE BTBTSXXVVETE BTBTXXTVPXTVPSETE BTBPTVVETE BPBPVPXTTVPXTVVEPE BTBTXSETE BPBTXSEPE BPBTSXXTVPSEPE BPBPVVEPE BPBPTTTTTTTTTTVPXVVEPE BPBPVVEPE BPBPVVEPE "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_string(embedded_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋네요, 이제 이 문법을 따르지 않는 문자열을 생성할 함수를 만듭니다. 무작위하게 문자열을 만들 수 있지만 그렇게 하면 너무 문제가 쉬워지므로 대신 문법을 따르는 문자열을 만든 후 하나의 문자만 바꾸어 놓도록 하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_corrupted_string(grammar, chars=\"BEPSTVX\"):\n",
    "    good_string = generate_string(grammar)\n",
    "    index = np.random.randint(len(good_string))\n",
    "    good_char = good_string[index]\n",
    "    bad_char = choice(list(set(chars) - set(good_char)))\n",
    "    return good_string[:index] + bad_char + good_string[index + 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "잘못된 문자열 몇 개를 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BPBPVPPEPE BPBVXSEPE BPBPTVVSPE BTBPBPSETE BTBPVVPTE BPBTSSXXTVTVVEPE BPXTSXXTVPSEPE BPBTXSXPE BTBPTTTVPSSTE BPBTSXXTTTETTVVEPE BPBSXXVPXTVPXTTVVEPE BPBPTTVEEPE BPBPVVEBE BPSPTTVVEPE BPBPVBSEPE BPBTTXXVVEPE BPBPTTTVPXVVETE BPBTSSXVEPE BPBPVEEPE BXBTXSEPE BTBPTVPXVPXVVETP EPBTSXXTVPXVPSEPE BPBTSXXTTTVTSEPE BPBPVPXVVXPE BTBTSTSETE "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_corrupted_string(embedded_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문자열을 바로 RNN에 주입할 수는 없습니다. 먼저 벡터의 연속으로 바꾸어야 합니다. 각 벡터는 원-핫 인코딩을 사용하여 하나의 문자를 나타냅니다. 예를 들어, 벡터 `[1, 0, 0, 0, 0, 0, 0]`는 문자 \"B\"를 나타내고 벡터 `[0, 1, 0, 0, 0, 0, 0]`는 문자 \"E\"를 나타내는 식입니다. 이런 원-핫 벡터의 연속으로 문자열을 바꾸는 함수를 작성해 보겠습니다. 문자열이 `n_steps`보다 짧으면 0 벡터로 패딩됩니다(나중에, 텐서플로에게 각 문자열의 실제 길이를 `sequence_length` 매개변수로 전달할 것입니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_to_one_hot_vectors(string, n_steps, chars=\"BEPSTVX\"):\n",
    "    char_to_index = {char: index for index, char in enumerate(chars)}\n",
    "    output = np.zeros((n_steps, len(chars)), dtype=np.int32)\n",
    "    for index, char in enumerate(string):\n",
    "        output[index, char_to_index[char]] = 1.\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0]], dtype=int32)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "string_to_one_hot_vectors(\"BTBTXSETE\", 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 50%는 올바른 문자열 50%는 잘못된 문자열로 이루어진 데이터셋을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_dataset(size):\n",
    "    good_strings = [generate_string(embedded_reber_grammar)\n",
    "                    for _ in range(size // 2)]\n",
    "    bad_strings = [generate_corrupted_string(embedded_reber_grammar)\n",
    "                   for _ in range(size - size // 2)]\n",
    "    all_strings = good_strings + bad_strings\n",
    "    n_steps = max([len(string) for string in all_strings])\n",
    "    X = np.array([string_to_one_hot_vectors(string, n_steps)\n",
    "                  for string in all_strings])\n",
    "    seq_length = np.array([len(string) for string in all_strings])\n",
    "    y = np.array([[1] for _ in range(len(good_strings))] +\n",
    "                 [[0] for _ in range(len(bad_strings))])\n",
    "    rnd_idx = np.random.permutation(size)\n",
    "    return X[rnd_idx], seq_length[rnd_idx], y[rnd_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, l_train, y_train = generate_dataset(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "첫 번째 훈련 샘플을 확인해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0]], dtype=int32)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터셋에서 가장 긴 문자열 때문에 패딩된 0 벡터가 많습니다. 문자열 길이가 얼마나 될까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "타깃 클래스는?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 좋습니다! 올바른 문자열을 구분할 RNN을 만들 준비가 되었습니다. 앞서 MNIST 이미지를 분류하기 위해 만든 것과 매우 비슷한 시퀀스 분류기를 만듭니다. 차이점은 다음 두 가지입니다:\n",
    "* 첫째, 입력 문자열이 가변 길이이므로 `dynamic_rnn()` 함수를 호출할 때 `sequence_length`를 지정해야 합니다.\n",
    "* 둘째, 이진 분류기이므로 출력 뉴런은 하나만 필요합니다. 이 뉴런은 각 문자열에 대해 올바른 문자열일 추정 로그 확률을 출력할 것입니다. 다중 클래스 분류에서는 `sparse_softmax_cross_entropy_with_logits()`를 사용했지만 이진 분류에서는 `sigmoid_cross_entropy_with_logits()`를 사용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "possible_chars = \"BEPSTVX\"\n",
    "n_inputs = len(possible_chars)\n",
    "n_neurons = 30\n",
    "n_outputs = 1\n",
    "\n",
    "learning_rate = 0.02\n",
    "momentum = 0.95\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, None, n_inputs], name=\"X\")\n",
    "seq_length = tf.placeholder(tf.int32, [None], name=\"seq_length\")\n",
    "y = tf.placeholder(tf.float32, [None, 1], name=\"y\")\n",
    "\n",
    "gru_cell = tf.contrib.rnn.GRUCell(num_units=n_neurons)\n",
    "outputs, states = tf.nn.dynamic_rnn(gru_cell, X, dtype=tf.float32,\n",
    "                                    sequence_length=seq_length)\n",
    "\n",
    "logits = tf.layers.dense(states, n_outputs, name=\"logits\")\n",
    "y_pred = tf.cast(tf.greater(logits, 0.), tf.float32, name=\"y_pred\")\n",
    "y_proba = tf.nn.sigmoid(logits, name=\"y_proba\")\n",
    "\n",
    "xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits)\n",
    "loss = tf.reduce_mean(xentropy, name=\"loss\")\n",
    "optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,\n",
    "                                       momentum=momentum,\n",
    "                                       use_nesterov=True)\n",
    "training_op = optimizer.minimize(loss)\n",
    "\n",
    "correct = tf.equal(y_pred, y, name=\"correct\")\n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32), name=\"accuracy\")\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련하는 동안 진척 상황을 확인할 수 있도록 검증 세트를 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val, l_val, y_val = generate_dataset(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  훈련 손실: 0.6943, 정확도: 52.00%  검증 세트 정확도: 58.50%\n",
      "   1  훈련 손실: 0.6739, 정확도: 64.00%  검증 세트 정확도: 64.14%\n",
      "   2  훈련 손실: 0.6221, 정확도: 72.00%  검증 세트 정확도: 67.14%\n",
      "   3  훈련 손실: 0.6702, 정확도: 64.00%  검증 세트 정확도: 69.74%\n",
      "   4  훈련 손실: 0.4848, 정확도: 78.00%  검증 세트 정확도: 81.56%\n",
      "   5  훈련 손실: 0.5440, 정확도: 82.00%  검증 세트 정확도: 67.82%\n",
      "   6  훈련 손실: 0.3464, 정확도: 86.00%  검증 세트 정확도: 85.04%\n",
      "   7  훈련 손실: 0.0582, 정확도: 100.00%  검증 세트 정확도: 96.20%\n",
      "   8  훈련 손실: 0.0315, 정확도: 100.00%  검증 세트 정확도: 97.98%\n",
      "   9  훈련 손실: 0.0112, 정확도: 100.00%  검증 세트 정확도: 98.24%\n",
      "  10  훈련 손실: 0.0088, 정확도: 100.00%  검증 세트 정확도: 98.62%\n",
      "  11  훈련 손실: 0.0029, 정확도: 100.00%  검증 세트 정확도: 99.98%\n",
      "  12  훈련 손실: 0.0017, 정확도: 100.00%  검증 세트 정확도: 99.98%\n",
      "  13  훈련 손실: 0.0011, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  14  훈련 손실: 0.0008, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  15  훈련 손실: 0.0007, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  16  훈련 손실: 0.0006, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  17  훈련 손실: 0.0005, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  18  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  19  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  20  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  21  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  22  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  23  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  24  훈련 손실: 0.0003, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  25  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  26  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  27  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  28  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  29  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  30  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  31  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  32  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  33  훈련 손실: 0.0002, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  34  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  35  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  36  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  37  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  38  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  39  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  40  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  41  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  42  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  43  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  44  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  45  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  46  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  47  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  48  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
      "  49  훈련 손실: 0.0001, 정확도: 100.00%  검증 세트 정확도: 100.00%\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 50\n",
    "batch_size = 50\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    init.run()\n",
    "    for epoch in range(n_epochs):\n",
    "        X_batches = np.array_split(X_train, len(X_train) // batch_size)\n",
    "        l_batches = np.array_split(l_train, len(l_train) // batch_size)\n",
    "        y_batches = np.array_split(y_train, len(y_train) // batch_size)\n",
    "        for X_batch, l_batch, y_batch in zip(X_batches, l_batches, y_batches):\n",
    "            loss_val, _ = sess.run(\n",
    "                [loss, training_op],\n",
    "                feed_dict={X: X_batch, seq_length: l_batch, y: y_batch})\n",
    "        acc_train = accuracy.eval(feed_dict={X: X_batch, seq_length: l_batch, y: y_batch})\n",
    "        acc_val = accuracy.eval(feed_dict={X: X_val, seq_length: l_val, y: y_val})\n",
    "        print(\"{:4d}  훈련 손실: {:.4f}, 정확도: {:.2f}%  검증 세트 정확도: {:.2f}%\".format(\n",
    "            epoch, loss_val, 100 * acc_train, 100 * acc_val))\n",
    "        saver.save(sess, \"./my_reber_classifier\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 두 개의 문자열에 이 RNN을 테스트해 보죠. 첫 번째는 잘못된 것이고 두 번째는 올바른 것입니다. 이 문자열은 마지막에서 두 번째 글자만 다릅니다. RNN이 이를 맞춘다면 두 번째 문자가 항상 끝에서 두 번째 문자와 같아야 한다는 패턴을 알게 됐다는 것을 의미합니다. 이렇게 하려면 꽤 긴 단기 기억(long short-term memory)이 필요합니다(그래서 GRU 셀을 사용했습니다)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./my_reber_classifier\n",
      "\n",
      "레버 문자열일 추정 확률:\n",
      "BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVETE: 0.01%\n",
      "BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVEPE: 99.95%\n"
     ]
    }
   ],
   "source": [
    "test_strings = [\n",
    "    \"BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVETE\",\n",
    "    \"BPBTSSSSSSSSSSSSXXTTTTTVPXTTVPXTTTTTTTVPXVPXVPXTTTVVEPE\"]\n",
    "l_test = np.array([len(s) for s in test_strings])\n",
    "max_length = l_test.max()\n",
    "X_test = [string_to_one_hot_vectors(s, n_steps=max_length)\n",
    "          for s in test_strings]\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, \"./my_reber_classifier\")\n",
    "    y_proba_val = y_proba.eval(feed_dict={X: X_test, seq_length: l_test})\n",
    "\n",
    "print()\n",
    "print(\"레버 문자열일 추정 확률:\")\n",
    "for index, string in enumerate(test_strings):\n",
    "    print(\"{}: {:.2f}%\".format(string, 100 * y_proba_val[index][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "쨘! 잘 작동하네요. 이 RNN이 완벽한 신뢰도로 정확한 답을 냈습니다. :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 과 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coming soon..."
   ]
  }
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