15_processing_sequences_using_rnns_and_cnns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chapter 15 – Processing Sequences Using RNNs and CNNs**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This notebook contains all the sample code in chapter 15._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# TensorFlow ≥2.0-preview is required\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "assert tf.__version__ >= \"2.0\"\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"rnn\"\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic RNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_time_series(batch_size, n_steps):\n",
    "    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n",
    "    time = np.linspace(0, 1, n_steps)\n",
    "    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  #   wave 1\n",
    "    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n",
    "    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)   # + noise\n",
    "    return series[..., np.newaxis].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 1)\n",
    "X_train, y_train = series[:7000, :n_steps], series[:7000, -1]\n",
    "X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\n",
    "X_test, y_test = series[9000:, :n_steps], series[9000:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 50, 1), (7000, 1))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure time_series_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
    "    plt.plot(series, \".-\")\n",
    "    if y is not None:\n",
    "        plt.plot(n_steps, y, \"bx\", markersize=10)\n",
    "    if y_pred is not None:\n",
    "        plt.plot(n_steps, y_pred, \"ro\")\n",
    "    plt.grid(True)\n",
    "    if x_label:\n",
    "        plt.xlabel(x_label, fontsize=16)\n",
    "    if y_label:\n",
    "        plt.ylabel(y_label, fontsize=16, rotation=0)\n",
    "    plt.hlines(0, 0, 100, linewidth=1)\n",
    "    plt.axis([0, n_steps + 1, -1, 1])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(12, 4))\n",
    "for col in range(3):\n",
    "    plt.sca(axes[col])\n",
    "    plot_series(X_valid[col, :, 0], y_valid[col, 0],\n",
    "                y_label=(\"$x(t)$\" if col==0 else None))\n",
    "save_fig(\"time_series_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Some Baselines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive predictions (just predict the last observed value):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020211367"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = X_valid[:, -1]\n",
    "np.mean(keras.losses.mean_squared_error(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 0s 51us/sample - loss: 0.1015 - val_loss: 0.0551\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 0s 29us/sample - loss: 0.0384 - val_loss: 0.0267\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 0s 28us/sample - loss: 0.0205 - val_loss: 0.0161\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 0s 30us/sample - loss: 0.0133 - val_loss: 0.0119\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 0s 34us/sample - loss: 0.0104 - val_loss: 0.0099\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 0s 35us/sample - loss: 0.0090 - val_loss: 0.0088\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 0s 30us/sample - loss: 0.0081 - val_loss: 0.0079\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 0s 30us/sample - loss: 0.0073 - val_loss: 0.0074\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 0s 31us/sample - loss: 0.0067 - val_loss: 0.0066\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 0s 27us/sample - loss: 0.0062 - val_loss: 0.0062\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 0s 27us/sample - loss: 0.0058 - val_loss: 0.0058\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 0s 27us/sample - loss: 0.0055 - val_loss: 0.0054\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 0s 27us/sample - loss: 0.0052 - val_loss: 0.0052\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 0s 34us/sample - loss: 0.0050 - val_loss: 0.0050\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 0s 30us/sample - loss: 0.0048 - val_loss: 0.0048\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 0s 28us/sample - loss: 0.0047 - val_loss: 0.0046\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 0s 29us/sample - loss: 0.0045 - val_loss: 0.0047\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 0s 28us/sample - loss: 0.0044 - val_loss: 0.0044\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 0s 34us/sample - loss: 0.0043 - val_loss: 0.0042\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 0s 33us/sample - loss: 0.0042 - val_loss: 0.0042\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[50, 1]),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "history = model.fit(X_train, y_train, epochs=20,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 0s 15us/sample - loss: 0.0042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.004174047667533159"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "def plot_learning_curves(loss, val_loss):\n",
    "    plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n",
    "    plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n",
    "    plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
    "    plt.axis([1, 20, 0, 0.05])\n",
    "    plt.legend(fontsize=14)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.grid(True)\n",
    "\n",
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AfHXRqyCY3ngjqTW3ma55fc0+9pZWMS7Nv2tI+Zo7F+64A55/fiR33NH5pAOWeEwPVl3nZsX2YmaPHcC952UEZG351kwa3JeYKGGNzWBguiC3oJQfvrIegL+v2BWw5uKlS+Gxx+Cmm3bx2GMtDzhoj7AdTm1MoC3eeIiyqjq+feZo5owL3oi2BvExUUwa0s9mMDBdkpNfTL3HaTCudzvNxf7+AeXbpyOyi29+s2vNbXbFY3qsRav2MLR/L04b49/O2I6YPrw/G/aWUe/2hCwGE9lOHuXcByYEprm4uYEErY12aw9LPKZH2nekiuXbi7gyc5jf7nnojOkj+lNV52bLIZu31nRO71hn7aiLThzs9+bi1kavdSX5WOIxPdKruXtRhaszh4U0junDnS8Ja24zndXQp/OjCyf4vYlt5crWm9Maks/KlR07r/XxmB7H41EWrdrD7LEpDE/u3fYBATQ8uRfJCbGs3XOEG2elhzQWE5lW7Solra9zQ7K/tWcanLlz7QZSY9qUk1/M3tIqrpkxPNShICJMH96fNbttSLXpnNyCUmakJ0fMarZgicf0QItW7aFPfDTnTw6PhbKmDe/PjsMVlFXVhToUE2EOlFWx70hVUG8D8AdLPKZHKauq490vDnLZtCHEx0SFOhyAxpmEH35nY1Cn7DGRb9Uu5/MyY6QlHmPC1lvr9lNT7+HaGSNCHUoj9U7a80ruXub9LceSj2m33IJSesVEMXFw31CH0iGWeEyP8s9Ve5gwqA8nDA2fP9T1e8sAZ832YM8XZyLbqoISpg3vT0yQ1pHyl8iK1pgu2HTgKOv3lnHNjOFh1RE7a3QK0d57iaKjgjtfnIlc5TX1bNx/lJkR1swGYZx4RCRZRF4XkQoRKRCRG1rY734R+UJEjonIThG5v8n2XSJSJSLl3scHwXkHJtwsWrWHmCjh8ulDQx3KcTLTk/jjNVMBuOnU9IjrKDahsXb3ETwKmRG4gm3YJh7gUaAWSAPmAY+JyORm9hPgZiAJuAC4W0Sua7LPJaqa6H2cF8igjf/kFpTy6NLtfunzyMkvYuHnu5mZnkxyQvAWfGuvS6cNZXRqApsP2AwGpn1WFZQg8uUs55EkLBOPiCQAVwIPqmq5qi4H3gJuarqvqv5OVVerar2qbgHeBGYHN2Ljb5/lF3PtE5/yhw+2dLnD/d0NB7jhqc+orvOwqqAkbDvvz5s0iJz8YsoqbVi1aVtuQSkZaX3oGx8T6lA6TFS17b2CTESmA5+oai+fsvuAM1X1klaOE2A18ISqPu4t2wX0wkmya4D7VXVdM8fOB+YDpKamZi5atMh/byhClZeXk5jon/U9tpe62VziZkJyFGOTWh/GXO9RHlxRxYEK57PpAq4YF8PXxnTsSqW8Vnl7Ry2LC+ppmIKzM+fyZz20ZvsRNw/nVDN/ShynDQmvSUWCVQfhLlzqwaPKnR9WctqQaG6eHBfU126tDubOnZurqjPaOkd4fbq/lAiUNSkrA/q0cdxDON8tf/cpm4eTjAT4LvC+iExQ1eMmx1LVJ4EnATIyMjQrK6uzsXcb2dnZ+KMePt9ZzG8++AyPKrHR7lYnMqxze7hrwWoOVFQS5RLcHkVEuP6cme3q+8gtKGX5tsMUHqvhrXX7qaipJ2tCKiu2F1Pv9hAT7Wr3uRr4qx7acoZHeSLv3+zVJLKyMgP+eh0RrDoId+FSD3n7y6h+fzmXnnYCWUHus/RHHYRr4ikHmo537Qu02AAuInfj9PXMUdWahnJVXeGz23+LyC3AHOBt/4VrWvP08p2N64XU1nvIyS9q9ou/zu3hOy+u4YONh3jokkmcOKw///P+ZnLyS9jfjruzcwtKue7JT6lzO681Iz2JX19xIuPT+pBbUEpOfjGzRqeEbee9yyWcOymNN9bso7rOHTY3uJrw09BcHK6f5baEZR8PsBWIFpFxPmVTgbzmdhaRbwE/As5W1b1tnFtxrn5MELg9yvq9ZY0V7lFYsb2YI5W1x+1X5/Zwz8I1vJd3kJ99bRLfmD2KzPQknr/1FE4a0Z//fG0Du4srW3wdj0f57XubGpOOS2DuhIGMT3MukjPTk7hr7tiw/0M9b1IalbVuPtlRFOpQTBgL5MSgwRCWiUdVK4DXgF+KSIKIzAYuA55vuq+IzAN+DZyrqvlNto0QkdkiEisi8d6h1gOAFU3PYwJj8caDHCir5vvnjue+88Zzwykj+HxnCec/8jEfbT0MOKsmfu+ltbz7xUF+evFEvnX6qMbjY6Jc/N9100HgOy+toa6ZBdNq6z3cu2gtn+8sJcolRAnEBmBBrGA4dUwKiXHRfJB3KNShmDAWiROD+grXpjaAO4FngEKgGLhDVfNEZA7wrqo29G49DKQAK33+J7ygqt/G6RN6DBgDVANrgQtV1W4NDwJV5bGP8hmR3Ju75o5tXHDthpNH8P2X13LLM59z4QmD2FlUweaDx/jpxRO5bc7or5xneHJvfnvlFO5csJrff7CFH184sXHbseo6vv1CLiu2F3P/+RnMGpVMzs6SsG5Sa01cdBRZGal8uOkQbo+GdJE6E572H3EmBr1tzqi2dw5TYZt4VLUEuLyZ8mU4gw8anrdY+6qaB0wJSICmTZ/tLGHdniP86vITjvsCPWFoP97+zunc9891vLP+AADRLmmcLLM5F504mOtPHsETH+Uze8wAzhifyqGj1dzyzOdsLyznD1dP5Urvom6ReEOdr3MnpfHO+gOs3VNKZnpkvxfjf6u8/TszIvizEZZNbaZ7ePyjHQxIjG12lc9478SGDflIVduco+xnX5vE+LREvrNwNQ+8uo6L/7SMPSWVPPONmY1JpzuYO2EgMVFizW2mWbm7SugdG8XEwW0N8g1flnhMQGw6cJTsLYf5xmkjWxydNWt0CrHRLqIEYtrRJ9MrNoo7s8ZSVlXPyyv3UlRey0OXTuaM8amBeAsh0zc+hlmjU3g/7yDheJ+dCa1VBaVMG96f6AibGNRX5EZuwtqTH+fTOzaq1eWcM9OTWHDbLO49L6PVe3t87TtS1ThCziVQeKym1f0j1XmTB7GruJLtheWhDsWEkfKaejYdOMqMCOy/9GWJx/jd3tJK3lq3n+tPHkH/3q3PENDRYc6zRqcQF+OK6JFr7XHuxDQAPthozW3mS5E8MaivsB1cYCLX08t3IsCtp/t/1E3DVVK43wzaVYP6xTN1eH8+yDvIXXPHhjocEyYieWJQX36/4hGRP4vIV2YFEJG+IvKQiEz0Kfu+iKwXEbvy6iZKK2p56fM9XDptCEP6B+bmtki5GbSrzpuUxrq9ZRwsqw51KCZMLN1cyICEWLYdiuwmWL9+4YvIGOA/gF80s3kG8HPAdyrVx4GBwC3+jMOEzvM5BVTVufmPM8aEOpSId/5kp7lt8SZrbjOwclcJ6/aWcbi8NuKXSPf3lcb3gHWquqqZbdOBGmBjQ4GqVgHPAff5OQ4TAp/uKOKx7B1kpieRMShyh3qGizGpiQzpF8/Ty/Ij+kvG+Mcba/Y1/jvSl0hvV+IRkbEiUiciv2hS/ph35c8ZIhIH3Ai82Mzxm4DfA3FAnYioiLzi3fwSMElETuvSOzEhlVtQyk1Pf05VnZsNe8vsi9IPVu8+wqFjNewqruSGpyL7F67puspaN0C7bz8IZ+1KPKq6Hfgb8H0RGQAgIj8DvgV83XuFMwvoDyxr5hQ3A/k4M0Kf6n38wLttLXAUZ/VQE6Fy8osbZ6B2eyL711i4yMkvbryPp6bewwd5B0MckQmlDfvKOHFo3w7dfhCuOtLU9gsgCnhARG7F6a+5SVU/9G6fhTPz8/pmjl0HDAOWqGqO91EAoKoe7zGzOvkeTBhouItaiPxfY+Gi4Qbbhtkd3l63n+Ly7nnfkmndzqIKtheWc8VJw7rFwJp2D6dW1YMi8gjOlUo0cI+q+i7TOQQ4qqq1zRw+GYjFWZCtOYeB8e2NxYSfY9X1ANw4K53Lpw+N+D+McOA7dLxfrxh+9c5GvvnsSl68fRaJcXYnRE/yofd+rnO893dFuo5+erfh9NMsV9VHm2yLxxk80JyTcK6G1rawvQpneWoToZZtK6J/7xgeunSyzajsR5npSY1JfEj/eG5/Lpf5z63i79+cSVy0LRTXUyzedIgJg/owPLl3qEPxi3Y3tYnIWcATwKfAbBGZ2mSXYqCln7nTgR2qerSF7cmArXwVoVSV5duKmD1mgCWdADprQhr/c9UUPtlRzPdeWovbY/O49QQlFbWs2lXCeZO6x9UOtH9U20nAGzgDDLKA3TiLr/naDMSISHPTBE/CZxh1M0YBW9oTiwk/2wvLOXi0mjnjBoQ6lG7vipOG8dOLJ/LuFwf59gu5PLp0m4126+aWbC7Eo3DupEGhDsVv2kw8IjIWeBf4APiOtw/nF8BFInKGz64fe/97cjOnOQJMFZHzRWSWiDT2PItIf5z+nY+bOc5EgGXbnIvV0y3xBMVtc0bz9elDWbzxEP/z/laufeJTFn6+m9p6Z3XW3IJSHl263RJSN/HhxkMM6hvPCUP7hjoUv2m1j0dEBuEknE3APO8INHBu+vwh8BvgNABV3SUinwOX4Cxb7etnwNM4V03xwBxguXfbxUAt8HpX34wJjWXbDjNqQALDkrpH+3MkGDswAcHpOK33KD9+bQO/eDuPMakJbDlYjtujxES5+OnXJnrXPRJcAtsOHeOL/UeZPjyJE4f1IyZKiHIJMVEuNh44St6+Mk4dM+C4wSG5BaW8s6OWPqNKbdBIkFXXufl422GuOGloxC5z3ZxWE4+qHgS+shaxqrqBiV89gseA/xORu1S10mf/L4BTWniZG4F/Nl2OWkSScZLVeTj9Pz9W1eZuThWcBHibt+hp4AH13gAhItO8ZRNxEuitqtrSIAfTQTX1bnLyS7h6RvdZiC0SzBo9gLiY7dTVe4iOcvHds8dSVF7H/9uwv/F+qlq3h5+9mdfs8c99WtDK2bcyNjWB0amJuEQal+F+Z1dOxN8/Emk+2VFEZa27WzWzgf9np34e50roTpyZClrlTQpzgROa2fwozpVQGjAN+JeIrPMuZ+1rPs4S2VNxfgAuxrlZ9XERiQXeBB4B/oozj9ybIjKuhWHfpoNWFxyhqs7N6WOtmS2YWpql++Ipg5n3VA61bg/RLhf/efEExqb2wa3KW2v38drqfSjOWkYXnTiYuRkDcXuUDzcdYvHGQzQMV6j3KAXFlRSUVDQmsoZpWizxBM/ijYUkxkUza3RkL4PQlF8Tj6q6ReRbOMOn22MQ8E3vzAiNRCQBuBI4QVXLgeUi8hZwE/CjJue4BfiDqu71HvsH4HacCUizcN7jI94roD+JyH3AWcB7nXiLponl2w8T5RJOHWM3jAab71Br37IFtze/bERiXDT/2nCAunoPMdEuvjl7VOP2MQMT+Xjb4cZtf7hmGpnpSeQWlHL9UznU1ntwidiNwUHk8f4gOHN8avcbOq+qYffAGX5d1aTsPuDtZvYtA07xeT4DOOb99/eBd5vs/w7wg2bOMx9YBazq3bu34lw92aONx6Cb/qhp834b8jjs0b5H7JAJ2nfW1Ro7ZEK7t8UOmaBD73peB3/r0ZDH35MesYPHa/oD72jCpKyQx9KBx6r2fMeH6zo4iTgJxVcZ0NyUx033LQMSvX0/7T6Pqj6pqjNUdcawYcNCnnzD4bF06dJWt5eU1xA/dDw/+sblIY81lPUQSY+afZso+3QRNfs2tXtbzb5N3JiZRmxqOlsPHg35e+gpn4WH//4mUS5h78r3Q/6+21sH7RWuiaccaDp2sC9wrB379gXK1amFjpzHdNAnO4pRhTnjUkMdigmw04ZEE+0SXsndG+pQeozFGw9x8sjkNpePj0Thmni2AtEiMs6nbCrQ3BCdPO+25vbLA6bI8eMQp7RwHtNBy7Ydpk98NFOH9Qt1KCbA+sYJcycM5LU1+6h3e9o+wHRJQXEFWw+Vc043mq3AV1gmHlWtwLkX6JcikiAis4HLcEbNNfUccK+IDBWRITiTmD7r3ZYNuIF7RCRORO72li8JZPw9gaqybFsRp41JIToqLD9Gxs+uzhzG4WM1fLT1cKhD6fYWeycF7U7T5PgK52+MO3EmDi0EFgJ3qGqeiMwj3DUJAAAgAElEQVQREd8Fx5/AWednA/AF8C9vGeoMmb4cZz2gIzjrB12uNpS6y3YWVbDvSBWnWzNbjzF3wkBSEmL55yprbgu0D7vZpKBNhe3c6qpagpM0mpYvwxk00PBcce4d+mEL51kDZAYozB5r+XZnmpw5dv9OjxET5eLy6UN57tNdlFTUkpzQ/foewsGRylpW7irljjPHhDqUgAnnKx4Txj7eWsTw5F6kp3TPX2SmeVfPGEadW3ljzb5Qh9JtPbN8J26PMjy5+64UY4nHdFid27mD/fSxqd1q/ijTtgmD+nLi0H42ui1AcgtK+ctS5376n7+V120nerXEYzps3Z4jlNfUc4bNRt0jXT1jmDOh6P6mt8iZrlq+7TANyyw1TFHUHVniMR328bYiXAKnjbHE0xNdOnUIsVEuG2QQAPExztQ4LoGYaFe3naLIEo/psOXbDnPisP706x0T6lBMCPTvHcu5k9J4c+2+xjWAjH/kH66gd0wU3ztnfLeeCdwSj+mQj7ceZs3uI4wbmBDqUEwIXTVjGKWVdfx706FQh9JtNMwSfs6kNO45e1y3TTpgicd0QG5BKbf+YyUKvLX2QLft+DRtO2NcKml94/hnNxtk0NrqrQ0L4gXqc796dynFFbWcN7l73jTqK2zv4zHhJye/mHq30/Pp9tjaLD1ZlEu44qRhPJ69g9+9t5mzJ6ZF/Gcht6CUeU/lUFPvITpK+O7Z4xg1wLllMP9wOX9aso16d+AWxHv/i4PERrnIyhjo1/OGI0s8pt1mjU6hYb3l7tzxadpn0uA+KPBY9g6eWbEz4vskcvKLqPb2WdW5ld9/sLXZ/QKxIJ6q8sHGQ8wem0JiXPf/Wu7+79D4zaTBfXEBM0Yl88MLJkT0l4zput0lVYCzCEt3WJ30aFU94Py2io128ZsrpjBpSF9EYOP+o9z/yjrq3IoEYEG8LYeOsbukkjuyuu9sBb4s8Zh2W7vnCG6F/zhzdER/wRj/mDU6hSiX4PYoMVGRfQVcUFzB8zkFnDi0L+dPHsSpYwYc9xkfn+bMm3bvCznsPubBSbf+80HeIUTg7Indv5kNbHCB6YDcghIAThphScc4y2w/+LWJAHz3nMgdheX2KPf9cx1RIjxx0wzuPqv595KZnsSPTolnWHIvvvvSWo5V1/kthvfzDnLSiCQG9on32znDmSUe024rd5UyPi2xWy5MZTrnhpPTSYiNYm9pVahD6bSnl+ezclcpD106mSH9W58frVe08Mi10zlQVs3P3/TPsl57SyvJ23+U83vAaLYGlnhMu7g9yuqCUmaMTA51KCaMxEa7OG3sALK3HO7Q0sfhYsvBY/z+/a2cNymNK04a2q5jMtOT+M5ZY3ltzT7eXNv1yVIb1t45d9KgLp8rUljiMe2y5eAxjtXUM3NkZDanmMDJykhl35Eqdhwub3vnMFJb7+HeRWvpEx/Nr684sUMT3t49dywnjejPT9/4gr2llV2K44O8Q4xPS2TUgJ5zU7YlHtMuq7z9OzPS7YrHHK/hvpPsLZG1Mulflmwjb/9R/uvrJzIgMa5Dx0ZHuXjk2umowr0vr8Pt6dzVXmlFLZ/vKuG8HnS1A5Z4TDut2lXKoL7xDEvqvmuEmM4Z2r8X4wYmRtSS2C+v3M2fl27nzPEDuOCEzn3pj0jpzS8vm8znu0r4xt8/79SMBv/eXIjboz1itgJfYZd4RCRZRF4XkQoRKRCRG1rZ934R+UJEjonIThG5v8n2XSJSJSLl3scHgX8H3dOqXSVkjkyy9XdMs84cn8pn+SVU1taHOpQ2fb6zmB+9ugFVyMkv6dIUOOnJvXEJLNtWxLVPfMpnHVzG4IO8gwzuF8+JQ/t1OoZIFHaJB3gUqAXSgHnAYyIyuYV9BbgZSAIuAO4Wkeua7HOJqiZ6H+cFKujubN+RKvaXVTMzQofLmsDLyhhIrdvDpzvCf/2YV1fvbbwLp97dtTVvcnaWNP673qPc9eJq1u890q5jq2rdfLztMOdNSutxP+jCKvGISAJwJfCgqpar6nLgLeCm5vZX1d+p6mpVrVfVLcCbwOzgRdwzrNrl7d+xEW2mBTNHJdE7Nioi+nnKq52rsig/rHkza3QKsdEu51xRzs20X//rJ/zuvc1U17lbPfbjbYeprvNw3uSe1b8DIOE0BFJEpgOfqGovn7L7gDNV9ZI2jhVgNfCEqj7uLdsF9MJJsGuA+1V1XQvHzwfmA6SmpmYuWrSo62+oFdtL3WwucTMhOYqxSVEBfa3OKi8vJzExkefyavhkfz2Pnt2bKFfP+mUGX9ZDT9aeOngkt5p95R5+d0avsP0Fr6rc/3EV/eOEaalRHf77a64efP+WBye6eGlzLcv21TMkQTh/ZAzHarXZ13lqfQ1rCuv501m9iY6gv6vWPgtz587NVdUZbZ0j3KbMSQSarqdbBvRpx7EP4SSYv/uUzcNJRgJ8F3hfRCao6leuhVX1SeBJgIyMDM3Kyupo7M36eMth/vXFfgb360ViXDSFx2rYfOAoy7cX4VGIjXaz8PbwnFwxOzubrKwsfrP2Y2aOjuPss04JdUgh0VAPPVl76mBPfAEPvvEF6SfMZHRqeCbqTQeOUvT+Mu694ARuOGVEh49vrh6ymuxz8bmQvaWQHyxax9/zagGIknoumTqEE4f1I7VPHAMSY1m3dBUjU/uSNOaEsPz7b4k//h6CmnhEJBs4s4XNK4DvAH2blPcFjrVx3rtx+nrmqGpNQ7mqrvDZ7b9F5BZgDvB2xyLvnP+3/gB3vrj6uLK4aBfxMa7GddVr6z28vHJ32H7wyqrq2HLoGBedODjUoZgwlzU+FXCGVYdr4vnQe7PmOQGeEy0rYyA3zhrBn/69HQXcqry1bh9vNLnhdNOBo8z7W2CWWQhnQe3jUdUsVZUWHqcDW4FoERnnc9hUoMW5KUTkW8CPgLNVta1VqRTn6ifgSipq+ckbGxqfuwTuOWssm391Ac9842TiY5x2YQFeW72PdzccCEZYHbZ6dymqMMNuHDVtGJ7cm9GpCWSH8bDqxZsOMW14fwb2DfycaGeMH0ic9+88PsbFP//jVNb+7FwWf/8Mrp0xrGGFkcaZvXuSsBpcoKoVwGvAL0UkQURmA5cBzze3v4jMA34NnKuq+U22jRCR2SISKyLx3qHWA3CurAKqqtbNt55dSXlNPbFRzgcvNtrFmRkDEREy05NYcNss7j0vg39882SmDu/PXS+uZuHnuwMdWoet2lVClEuYNrx/qEMxESBr/EBy8oupqm29Yz0UDpZVs35vGedOCs49M75/5wtum0XmyGT6945lXFofrpk5ojEp9cS1rcKtjwfgTuAZoBAoBu5Q1TwAEZkDvKuqDdfxDwMpwEqfzswXVPXbOP1CjwFjgGpgLXChqgb0p0W928N3Fq5h3d4jPDYvk9Q+ceTkFzNrdMpxl9KZ6UmNz2eMSuLOBav58WsbKK2s5Y4zx4RN5+zKXaWcMKQvvWPD8aNiwk1WRirPrNhJzs5i5obZSpqLNznNbOcFKfHA8X/nTcsX3Dar2e+GniDsvk1UtQS4vIVty3AGIDQ8H9XKefKAKX4PsBWqyoNv5vHhpkP88rLJjXdEt/Wh6h0bzVM3z+C+f67jd+9tYfOBo2QM6sOs0QNC+oGs9yjr9hzhxlnpIYvBRJaTRyUTH+Pioy2Hwy/xbDxEekpvxg4Mj/6nlpJSTxB2iSeS/WXJdhZ+vps7s8Zw86kjO3RsTJSL/71mGrX1Ht5adwBZd4C4mO0h7XTcddRDTb3HJgY17RYfE8Wpo1PI3lIItHTfd/Adq67j0x1F3HLqyLBpTejJwqqPJ1LlFpRyxwu5/GHxVq6YPpT7z8/o1HlcLuGEoc6gvnDodNxW6qw/n2kTg5oOyMoYyK7iSnYVVYQ6lEYfby2izq1B698xrbMrni7KLSjl+idzqHV7cAlcO3N4l35RzRo9gLjo7dTUewKytntHbC11M2pAAql9OjZzr+nZsjKcYdUfbT3MyDCZ6n/xxoMk9Y7psU1b4caueLpo6eZCat3OlYEAq7ow4SA47b4v3j6L9OTe9O8VE7LRZKrKtlK3/aGaDktPSWBwv3j+8cmuLk3A6S91bg9LNhdy1oQ0oqPsKy8c2P+FLlBVVu5ymsJcfhwWmZmexI8vmkBRRS1LNhd2+XydseNwBeV1WP+O6bDcglIKj9WQX1TBvKdyQp58Vu4s4Wh1PedOCq/BDj2ZJZ5mlNVou/5YFq3aw2c7S7n51HR+0DBW309XCOdMTCOtbxwv5BT45XwdlVtgE4OazsnJL25cBrsmDG6OXLzpELHRLuaMSw1pHOZLlniaUVqj3NDGL7X8w+U89NZGThuTwkOXTOauuWP92iwVHeXiupkj+HjbYXYXd21p3c5474uDxLrgSEVt0F/bRLaGGZsbhPKqWVVZvPEQp48dQEKcdWmHC0s8Laip9/DGmn3Nbqtze/jey2uJjXbxx2um4QrQzLLXnzwClwgLPg/uVU/urhKytxym1gPznv4s5E0lJrI03Bx5+bQhKFBUHrofL5sPHmNvaZWNZgszlnhaIMDClbtZtHLPV7Y98uFW1u8t4zdXnMigfoGb82lQv3jOmTiQf67aS0198KYgeXv9gcaFskI9pNtEpsz0JP5wzTTSU3rz1LL8tg8IkMXeSUHPDvCkoKZjLPE0IylOeOabM5k1KoUfvrqen7y+gdp6Z+TaZ/nF/DV7B9fOGM6FQZix+cZZ6ZRU1PLuhoMBf60GDcsXu+iZ80gZ/4hyCbeePoo1u4809hkG24cNk4L2CfykoKb9LPE0o1+cMDdjIM9+cyb/ceZoFny2m+ufyuGttfu4/blVpPWJ42eXTApKLLPHDGBkSu+gDjJYv7eMiYP7cMW4mB43Xbvxr6syh9GvVwx/W7Yz6K+9OO8Q6/eWMWlI05VWTKhZ4mlFdJSLH184kUdvOIkv9pVxz0trOVpdT0llHZsPtrpEkN+4XMK8U9JZVVDK5oNHA/56O4sq2HzwGFdnDudrY2It6Zgu6R0bzbxTRvB+3sGgDpLJLSjljgW5ALyau9f6KcOMJZ52uHjKYG44+cvVCt3u4PZ7XJU5jNhoV1Cuet7Pc5r0zj+h560DbwLjltNGEuUSnlkRvKuenPxi6r2rLdYH+e/VtM0STzt9beqQxsXbgt3vkZQQy9emDOb11fsor6kP6Gu998VBpgzrx9D+vQL6OqbnSOsbzyVTh7Bo1R7KKuuC8poNM34I1k8ZjizxtNNXFnUKchPUjbPSqah1tzjE2x8OlFWxds8Rzp9sVzvGv247fTSVtW5eDNJihw0/0K6ZMcz6KcOQ3VHVAaFcP2P68P5MGtyXpz7Op6yqNiBr9bz/hdPMdqE1sxk/mzSkL7PHpvDsJzu59fRRx91gGghLNhXSJy6aX11+YsBfy3Sc/R+JECLCGeMGUFBSyR8+2Mq8v/l/Dqz38g4yPi2R0anhsVCW6V5umzOaQ0dr+NeG/QF9HY9HWbKlkDMyUi3phKmw+78iIski8rqIVIhIgYjc0Mq+D4lInYiU+zxG+2yfJiK5IlLp/e+04LyLwIiPjQLAo/6/sbO4vIbPd5ZwgTWzmQA5c1wqYwcm8n8fbuPRpdsCNtJsw74yDh+r4ewJdtNouAq7xAM8CtQCacA84DERaW0pw5dVNdHnkQ8gIrHAm8ALQBLwD+BNb3lEmjMulZgoZ3oel8u/a/V8uOkQHrXRbCZwXC7h3InOInGBumoH+PfmQlziLEhnwlNYJR4RSQCuBB5U1XJVXQ68BdzUidNl4fRhPaKqNar6J5xBLmf5K95gcwY4nEJyQiwDEuOYMqyf38793hcHGZ7ci0mD7WY7EzjxMYG7am+wZPMhThqRRHJCxP7G7PbCbXDBeMCtqlt9ytYBZ7ZyzCUiUgIcAP6iqo95yycD67VhfnbHem/5e01PIiLzgfkAqampZGdnd/pNBNrNGcIjq6v5xQv/5pz0mC6fr7JO+XhrJeemR/PRRx81lpeXl4d1PQSL1YP/6iDhmBuXOInHJRB3pIDs7L1dD9CrtNrDF/uquGp8TED+n9lnwT91EG6JJxEoa1JWBvRpYf9FwJPAIeAU4FUROaKqCzt6LlV90nsuMjIyNCsrqzPxB8WZqnxe9hn/KjjKD6+dTd/4riWfN9fuw61ruf3Ck48bKZednU0410OwWD34rw6ygIGj9nHPS2u57uR0brv8hC6f09eLn+0GNjD/4lMZn9bS10bn2WfBP3UQ1KY2EckWEW3hsRwoB5q29fQFmp2fRlU3qup+VXWr6ifA/wFXeTd36FyRRET4z4smcqSqjr8u3dHl8733xUEG9oljeoiW2TY9y6XThjJzZBIrthdxfINE1/170yGGJfVi3EAbmRnOgpp4VDVLVaWFx+nAViBaRMb5HDYVyGvvS+D04+A9ZoqI+C6WM6UD5wprJwztx9enD+WZFTvZW9r5ObCqat1kbznM+ZMHBWxdIWOaumbGcPKLKli5y3+DC6pq3SzfXsQ5E9M4/s/ehJuwGlygqhXAa8AvRSRBRGYDlwHPN7e/iFwmIkniOBm4B2ckG0A24AbuEZE4EbnbW74koG8iiO47LwMBfv/+lk6f4+Nth6mqc3OBjWYzQXTxlMEkxkXz0kr/zWTwaX4RNfUezrJh1GEvrBKP151AL6AQWAjcoap5ACIyR0TKffa9DtiO03z2HPBbVf0HgKrWApcDNwNHgG8Bl3vLu4Uh/Xtx25xRvLF2P+v3HunUOd774iD9e8dw8qhkP0dnTMt6x0Zz6bQh/L8NBzha7Z/52z7cVEhCbBSnjLbPcrgLu8SjqiWqermqJqjqCFV90WfbMlVN9Hl+vaqmeO/fmeAdMu17rjWqmqmqvVT1JFVdE8z3EgzfPnMMKQmx/Ne/NnW4vfyz/GL+teEAJw3vT0xU2H0UTDd37YzhVNd5eGtt12cyUFWWbCpkzrhU4qKj/BCdCST7tolwfeJj+N654/lsZwn3LlrX7hvycgtKufHpz6it97Bse5GtV2KCbsqwfkwY1IeXm1levqPy9h/l4NFqzrIlriOCJZ5uICMtEQFeX7OPa5/4lE+2F7W6/8b9R/nBorXUuZ0rJI9Hbb0SE3QiwrUzh7NhXxl5+5ve+dAxSzYXIgJzbbaCiGCJpxtYuauUhkE89R7lm8+u5HfvbabwaPVx+x0oq+K+f67j4j8v4/CxGqJdEpL1hYxp8PXpQ4mNdrGoi1c9/95cyNRh/UntE+enyEwghdsNpKYTZo1OITbaRV29h6goF9OG9+exj3bw1LJ8Lp06lBkjk3hz7T5WF5QCwu1zRnNX1li2Hy4nJ7+YWaNTbL0SExL9e8dyweRBvL5mHz++aGLjlDodUXismnV7jvCDc8cHIEITCJZ4uoGGRep8k0hBcQXPLN/Jws/38OpqZ0oSl8CjN0znwhMHNx5nCceE2rUzh/PWuv28n3eQy6YN7fDx2ZsPA3D2xDR/h2YCxBJPN9E0iaSnJPCLy06gb68Y/rJke+OdtflFFSGL0ZjmnDo6heHJvXjp8z2dSjyv5O6hT1w0VbWBXRbe+I/18XRzWRkDiYtxWV+OCVsul3BN5nA+zS+moLhjP4w+3VHE57tKOVZTz7ynP7PRmRHCEk8319AMd+95Gbb2vAlbV80YhgAPvLq+Q8nj9x98OZF9oJZZMP5niacHyExP4q65Yy3pmLC1/0g1IpCTX9LuBeJe/Gw3uQWlRNnozIhjfTzGmJDLyS+mYeKN6joPy7YdbvWH0ic7ivjZm19w5vhU7po7hpW7Sm10ZgSxxGOMCblZo1OIi3FRU+dBgXfWH+CmWemkJH71vpydRRXc8cJqRg5I4M83TKdvfAwnj7IrnUhiTW3GmJBr6Iu87/wMHrgggz0llVz1+KfsLj5+yY+yqjpu/cdKXAJP3zKjy4sgmtCwxGOMCQsNfZF3ZI3lxdtPobSyliseW8GGvc50OvVuD3e/uJo9JZU8fmMm6SkJIY7YdJYlHmNM2MlMT+aVb59GXHQU1z35KU8vz+eaJz5l2bYi/uvyEznFBhFENEs8xpiwNHZgIq/deRoDEuP41TubWL37CFEuYYwtax3xLPEYY8JWWt94Lps+5MsCtZnUuwNLPMaYsHbm+IHE2+wb3UrYJR4RSRaR10WkQkQKROSGVvZ9V0TKfR61IrLBZ/suEany2f5BcN6FMcZfbPaN7icc7+N5FKgF0oBpwL9EZJ2q5jXdUVUv9H0uItnAkia7XaKqHwYoVmNMENhM6t1LWF3xiEgCcCXwoKqWq+py4C3gpnYcOxKYAzwfyBiNMcZ0TVglHmA84FbVrT5l64DJ7Tj2ZmCZqu5sUr5ARA6LyAciMtVfgRpjjOmccGtqSwSaLr5eBvRpx7E3Aw83KZsHrMZZiua7wPsiMkFVjzQ9WETmA/MBUlNTyc7O7ljk3VB5ebnVA1YPYHXQwOrBT3WgqkF7ANmAtvBYDkwHKpsc8wPg7TbOezpQDiS2sd9mnD6fVuMcP368GtWlS5eGOoSwYPVgddDA6qH1OgBWaTtyQVCveFQ1q7Xt3j6eaBEZp6rbvMVTga8MLGjiFuA1VS1vKwScqx9jjDEhElZ9PKpaAbwG/FJEEkRkNnAZrQwYEJFewNXAs03KR4jIbBGJFZF4EbkfGACsCNgbMMYY06awSjxedwK9gEJgIXCHeodSi8gcEWl6VXM5Tj/Q0iblfYDHgFJgH3ABcKGq2m3PxhgTQuE2uABVLcFJJs1tW4YzAMG3bCFOgmq6bx4wJRAxGmOM6bxwvOIxxhjTjVniMcYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1Rhl3hE5G4RWSUiNSLybDv2/76IHBSRMhF5RkTifLaNFJGlIlIpIptF5JyABm+MMaZNYZd4gP3Aw8Azbe0oIucDPwLOBkYCo4Ff+OyyEFgDpAA/AV4RkVQ/x2uMMaYDwi7xqOprqvoGUNyO3W8BnlbVPFUtBX4FfANARMYDJwE/V9UqVX0V2ABcGZjIjTHGtEd0qAPoosnAmz7P1wFpIpLi3ZavqseabJ/c3IlEZD4w3/u0RkS+CEC8kWYAUBTqIMKA1YPVQQOrh9brIL09J4j0xJMIlPk8b/h3n2a2NWwf2tyJVPVJ4EkAEVmlqjP8G2rksXpwWD1YHTSwevBPHQS1qU1EskVEW3gs78Qpy4G+Ps8b/n2smW0N249hjDEmZIKaeFQ1S1WlhcfpnThlHjDV5/lU4JCqFnu3jRaRPk2253X+HRhjjOmqsBtcICLRIhIPRAFRIhIvIi01CT4H3Coik0QkCfgp8CyAqm4F1gI/957j68AU4NV2hPFkV99HN2H14LB6sDpoYPXghzoQVfVHIH4jIg8BP29S/AtVfUhERgAbgUmqutu7/73AA0AvnKTybVWt8W4biZOITgF2A3ep6oeBfxfGGGNaEnaJxxhjTPcWdk1txhhjujdLPMYYY4LKEo8PEUkWkddFpEJECkTkhlDHFAytzY8nImd757mr9M57164bxCKNiMSJyNPe/+/HRGSNiFzos72n1MMLInJARI6KyFYRuc1nW4+oA18iMk5EqkXkBZ+yG7yfkwoReUNEkkMZYyB5b4GpFpFy72OLz7ZO14MlnuM9CtQCacA84DERaXamg26m2fnxRGQA8BrwIJAMrAJeDnp0wREN7AHOBPrhvOdF3olme1I9/DcwUlX7ApcCD4tIZg+rA1+PAisbnni/D54AbsL5nqgE/hqa0ILmblVN9D4yoOv1YIMLvEQkASgFTvAOxUZEngf2qeqPQhpckIjIw8AwVf2G9/l84Buqepr3eQLOVBnTVXVzyAINEhFZjzPpbAo9sB5EJAPIBr4L9KeH1YGIXAdcgTOSdqyq3igiv8ZJzDd49xkDbAJSmkzP1S2ISDbwgqr+rUl5l+rBrni+NB5wNyQdrxbndushJuPUAQCqWgHsoAfUiYik4Xwm8uhh9SAifxWRSmAzcAD4f/S8OugL/BL4QZNNTethB04ryfjgRRd0/y0iRSKyQkSyvGVdqgdLPF9qaW63Ps3s21P0yDoRkRhgAfAP76/5HlUPqnonznubg9O8VkMPqwOcme6fVtU9Tcp7Wj08gLPczFCcG0ff9l7ddKkeLPF8yeZ2+6oeVyci4gKex/n1dre3uMfVg6q6VXU5MAy4gx5UByIyDTgH+N9mNveYegBQ1c9U9Ziq1qjqP4AVwEV0sR4ifXZqf9oKRIvIOFXd5i3r6XO75eGseQQ0tuuPoZvWiYgI8DROZ+lFqlrn3dSj6qGJaL58rz2lDrJwFpbc7XwkSMSZvmsS8B4+80OKyGggDuf7oydQQGgyT2aH60FV7eF9AC/hrFqaAMzGuXScHOq4gvC+o4F4nBFNz3v/HQ2keuvgSm/Zb4GcUMcbwHp4HMgBEpuU94h6AAYC1+H9ogXOByqAy3pKHXjroTcwyOfxe+AVbx1MBo7iNEMmAC8AL4U65gDVQ3/vZ6Dh+2Ce9/OQ0dV6CPmbC6cHzjDRN7yVuxu4IdQxBel9P4TzS8b38ZB32zk4ncxVOCOcRoY63gDVQbr3fVfjNCM0POb1lHrwfrF+BBzxfqlsAG732d7t66CFenkIZ2RXw/MbvN8PFTgLUSaHOsYAfh5W4jSfHfH+KDvXH/Vgw6mNMcYElQ0uMMYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFBZYnHmAghIn1F5CERmRjqWIzpCks8xkSOGcDPgZhQB2JMV1jiMSZyTMdZomBjqAMxpitsyhxjIoCIbAImNCl+VXOh87IAAADhSURBVFWvCkU8xnSFJR5jIoCIzMSZPT0P+LW3+ICqFoQuKmM6x9bjMSYyrMNZlO3PqpoT6mCM6Qrr4zEmMkwGYoHVoQ7EmK6yxGNMZDgJZ72gtaEOxJiussRjTGSYDuxQ1aOhDsSYrrLEY0xkmIQNozbdhA0uMCYyHAFOEpHzgTJgm6oWhzgmYzrFhlMbEwFE5ATgaWAKEA/MUdXloY3KmM6xxGOMMSaorI/HGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFBZYnHGGNMUFniMcYYE1SWeIwxxgSVJR5jjDFB9f8BQAUsKG7wR94AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using a Simple RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 4s 513us/sample - loss: 0.0960 - val_loss: 0.0482\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 3s 400us/sample - loss: 0.0366 - val_loss: 0.0293\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 3s 388us/sample - loss: 0.0251 - val_loss: 0.0216\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 3s 372us/sample - loss: 0.0196 - val_loss: 0.0175\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 3s 367us/sample - loss: 0.0165 - val_loss: 0.0150\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 3s 364us/sample - loss: 0.0145 - val_loss: 0.0133\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 3s 390us/sample - loss: 0.0132 - val_loss: 0.0123\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 3s 361us/sample - loss: 0.0123 - val_loss: 0.0116\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 3s 364us/sample - loss: 0.0118 - val_loss: 0.0112\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 3s 364us/sample - loss: 0.0116 - val_loss: 0.0110\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 3s 362us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 3s 395us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 3s 396us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 3s 397us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 3s 391us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 3s 392us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 3s 391us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 3s 387us/sample - loss: 0.0114 - val_loss: 0.0110\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 3s 392us/sample - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 3s 364us/sample - loss: 0.0114 - val_loss: 0.0108\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(1, input_shape=[None, 1])\n",
    "])\n",
    "\n",
    "optimizer = keras.optimizers.Adam(lr=0.005)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)\n",
    "history = model.fit(X_train, y_train, epochs=20,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 0s 179us/sample - loss: 0.0108\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.010848855562508107"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep RNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 10s 1ms/sample - loss: 0.0502 - val_loss: 0.0093\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0069 - val_loss: 0.0055\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0050 - val_loss: 0.0044\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0048 - val_loss: 0.0040\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0041 - val_loss: 0.0037\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0041 - val_loss: 0.0043\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0038 - val_loss: 0.0036\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0037 - val_loss: 0.0032\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0036 - val_loss: 0.0036\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0035 - val_loss: 0.0031\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0034 - val_loss: 0.0032\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0034 - val_loss: 0.0036\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0033 - val_loss: 0.0031\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0033 - val_loss: 0.0030\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0033 - val_loss: 0.0031\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0033 - val_loss: 0.0029\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0032 - val_loss: 0.0029\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0032 - val_loss: 0.0029\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0032 - val_loss: 0.0027\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 9s 1ms/sample - loss: 0.0031 - val_loss: 0.0029\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
    "    keras.layers.SimpleRNN(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "history = model.fit(X_train, y_train, epochs=20,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 1s 561us/sample - loss: 0.0029\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0028896138425916432"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the second `SimpleRNN` layer return only the last output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 7s 992us/sample - loss: 0.0212 - val_loss: 0.0053\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 6s 825us/sample - loss: 0.0041 - val_loss: 0.0034\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0034 - val_loss: 0.0031\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 6s 922us/sample - loss: 0.0033 - val_loss: 0.0030\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 7s 932us/sample - loss: 0.0031 - val_loss: 0.0030\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 6s 894us/sample - loss: 0.0031 - val_loss: 0.0028\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 6s 866us/sample - loss: 0.0030 - val_loss: 0.0028\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 6s 892us/sample - loss: 0.0030 - val_loss: 0.0028\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 7s 950us/sample - loss: 0.0030 - val_loss: 0.0031\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 7s 982us/sample - loss: 0.0030 - val_loss: 0.0027\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 6s 887us/sample - loss: 0.0029 - val_loss: 0.0027\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 6s 876us/sample - loss: 0.0030 - val_loss: 0.0029\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 6s 909us/sample - loss: 0.0029 - val_loss: 0.0029\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 6s 877us/sample - loss: 0.0029 - val_loss: 0.0030\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 7s 960us/sample - loss: 0.0029 - val_loss: 0.0028\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 7s 975us/sample - loss: 0.0029 - val_loss: 0.0029\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 6s 924us/sample - loss: 0.0028 - val_loss: 0.0025\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 6s 831us/sample - loss: 0.0028 - val_loss: 0.0027\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "history = model.fit(X_train, y_train, epochs=20,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 1s 334us/sample - loss: 0.0027\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.00267703290656209"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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efvpppk6dyscff0xBQQGlpaU1+rW9evVqrrnmmiplgwYNYu7cuRWvN27cyK233srChQvZuHEjZWVl7Nmzhy+++KJG67R69eqKDXm5IUOGMGHChCplPXv2rHiekZFBq1at2LRpU1TL2Lp1K19//TWDBw/+xnJeeeUVAH74wx9y//33c+yxx/Ld736XM844g/POO4+MjAyOPPJILrvsMkaMGMHw4cMZPnw43//+96Pee4qlRO/BTAOKgTzgUuBPZhZpYIUrgfOBXkBP4BzgqsoVzKw5MB5YGc+ARQ4bgwax57nn4K67YP78uCaXcmPGjOHZZ59l69atzJw5kxYtWnDeeedVzM/Pz+fSSy/lrLPO4vnnn2f58uXceeedFBcXxzSOyy67jOXLlzNlyhQWL17MihUraNOmTa2WE2m45Opl6enp35gfKt97PIjy39EHWk6HDh1Ys2YNDz74IDk5OVx33XX079+/4tzTrFmzyM/PZ8iQITzzzDN07dq1yp5koiQswZhZNnAhcKu7F7j7IuA54McRqo8CJrv7WndfB0wGRler8ztgKsGekIhEITRgAIwfn5DkAnDRRRfRqFEj/vKXvzBjxgx+8pOfVNn4LlmyhA4dOvCb3/yG/v3706VLl4hXnh1I9+7dWbJkSZWy6q8XLVrEtddey1lnnUWPHj3Izs5mw4YNFfNTU1NJTU2lrKzsoMuqfqhr0aJFHH/88TWK+UBatmzJEUcccdDlZGVlce655zJlyhSWLFnCO++8U2W9e/fuzbhx43j11VcZPHgwTzzxRMxijFYiD5F1BcrcvfJF5W8DJ0eo2yM8r3K9ij0dM/s20A+4GvjBgRZqZlcS7BGRl5fHwoULaxN7hYKCgjq3ESvJFAskVzyHWyxNmzZl165ddY6lrKwsJu3UxEUXXcRtt93G9u3bufjii6ssv2PHjnzxxRfMmDGDvn37Mm/evIpDaOX1CgsLD/j6yiuv5JprrqFHjx4MGjSIZ555hrfeeotWrVpV1OncuTOPPfYY3bt3Z9euXdx6661kZmZSVFRUUaddu3a89NJL9OrVi4yMDJo3b/6NZV1zzTX87Gc/o3v37gwbNoyXX36ZOXPm8PTTT7Nr1y5KS0sB2Lt3b5X1dHcKCwv3+9kXFRURCoUq5v/yl79k4sSJtGnThp49ezJ79myWLFnC5MmT2bVrF7NmzQKgb9++NG7cmLlz55Kenk7r1q1ZsWIFs2bN4swzz6RNmzZ8/PHHvPvuuwwZMmS/yy8sLIzP/0ttx1qu6QQMBTZUK7sCWBihbhlwXKXXXQAHDEgFlgGDwvMWAmOiiaFv377RDE99QIfbWO+xlEzxHG6xHGjM9JpI9Fjv7u5vvfWWA37SSSdFjOemm27yVq1aeU5Ojl944YX+wAMPeGpqakWdP//5z960adP9vnZ3v+uuu/yII47w7Oxsv/TSS/23v/2td+rUqWL+f/7zH+/fv79nZmZ6p06d/Mknn/Ru3br5XXfdVVHnqaee8s6dO3taWlrFeyMta9q0ad6xY0dPT0/3zp07+/Tp0yvmlZSUOODPPPNMlfe0bdvW//CHP+z3M/rNb37jvXr1qni9fft2v/32271t27aenp7u3/rWt/y5556rmP+3v/3NBwwY4E2bNvXs7Gzv37+/v/DCC+7uvn79ej///PP9qKOO8oyMDG/fvr2PGzfOS0pK9rv8A32/gGVe2+1+bd9Y4wXBicCeamU3AM9HqLsD+Hal132BXeHnvwRmVJqnBJMkkimewy2WQznBHEgyxdOQY4lXgknkSf41QJqZdalU1ovIJ+lXhudFqjccuMDMNpjZBuAkYLKZPRCHmEVEpJYSdg7G3Xeb2d+BO81sDNAbGEmQIKp7ArjezF4kODR2A3B/eN5ooFGlun8H/kpwKbOIiCSJRN/JfzUwA9gEbAF+4e4rzWwo8JK754TrPQx0BN4Nv54eLsPdt1du0MyKgZ3uHt+7xkREpEYSmmDcfSvB/S3Vy18Dciq9duDX4elgbQ6LYYgiIhIj6ipG5BDhVTuyEImJeH6vlGBEDgGpqamUlJTUdxhyGCopKdlv5551pQQjcgho1qwZGzdujLq7EZFohEIhNm7cSNOa9KpdA+quX+QQ0KpVK9auXcsHH3xQp3YKCwtp1KjRwSsmSDLF01Bjyc7O3m/v0XWlBCNyCEhJSaF9+/Z1bmfhwoWceOKJMYgoNpIpHsUSezpEJiIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicaEEIyIicZHQBGNmLczsGTPbbWafm9kl+6lnZjbRzLaEp/vMzMLzWpnZ6+Hy7WaWb2aDE7keIiJycGkJXt40oBjIA3oDL5jZ2+6+slq9K4HzgV6AA/OAT4CHgALgcuDD8LyRwPNmdqS7lyZkLURE5KAStgdjZtnAhcCt7l7g7ouA54AfR6g+Cpjs7mvdfR0wGRgN4O6F7v6Bu4cAA8qA5kCLBKyGiIhEydw9MQsyOxFY7O5ZlcpuBE5293Or1d0BnO7ub4Rf9wMWuHuTSnXeAY4D0oHp7n7FfpZ7JcEeEXl5eX3nzJlTp/UoKCggJyenTm3ESjLFAskVj2KJLJligeSKR7FEdsopp7zl7v1q9WZ3T8gEDAU2VCu7AlgYoW4ZcFyl110IDodZtXqNgB8Bo6KJoW/fvl5XCxYsqHMbsZJMsbgnVzyKJbJkisU9ueJRLJEBy7yW2/1EnoMpAHKrleUCu6KomwsUhFe2grsXArPNbLWZrXD3t2MZsIiI1F4iryJbA6SZWZdKZb2A6if4CZf1iqJeuXSgY50jFBGRmElYgnH33cDfgTvNLDt8afFIYFaE6k8A15tZWzNrA9wAzAQws4FmNsTMMswsy8xuJrgq7Y2ErIiIiEQl0ZcpXw3MADYBW4BfuPtKMxsKvOTu5We1HibYI3k3/Hp6uAwgE5ganl8SrnO2u69PzCqIiEg0Eppg3H0rwf0t1ctfA3IqvXbg1+Gpet1/U/XwmYiIJCF1FSMiInGhBCMiInGhBCMiInFRpwQTvoprhJl1iFVAIiJyeKhRgjGzmWZ2dfh5BrAU+D/gAzM7Mw7xiYjIIaqmezDfBZaEn58HNAFaA7eHJxEREaDmCaY5wT0sAGcAf3P3TcAc4PhYBiYiIoe2miaYDcAJZpZKsDfzr3B5DsFNjyIiIkDNb7ScATwNrCfo8Xh+uHwA8H4M4xIRkUNcjRKMu99pZiuB9sBcdy8OzyoFJsY6OBEROXTVuKsYd/9bhLLHYxOOiIgcLmp6mfIPzOz0Sq8nmNlaM3vZzI6KfXgiInKoqulJ/tvLn5hZH+AWgp6N04HJsQtLREQOdTU9RNYB+CD8/ALgWXe/z8z+D3g5ppGJiMghraZ7MIUEN1cCDGffZco7KpWLiIjUeA/mNWCymS0C+gEXhcu7Al/GMjARETm01XQP5hqgmCCx/LzSKJJnokNkIiJSSU3vg1kLnBuhfGzMIhIRkcNCrYZMNrNTCfoec2CVuy+IaVQiInLIq1GCMbO2wDNAX4LuYgDamNky4IJKh8xERKSBq+k5mKkEfZB1dvej3f1ooEu4bGqsgxMRkUNXTQ+RnQYMc/dPywvc/RMzu5Z9HV+KiIjUbcjkSkIxakdERA4TNU0w84GpZnZ0eYGZtQf+CLwSy8BEROTQVtMEcy3QGPjEzD43s8+Aj4Es4Jcxjk1ERA5hNb0P5kugj5mdBhwHGLAK+Aj4PfCDmEcoIiKHpFrdB+Pu84B55a/NrBdwYayCEhGRQ1+sTvKLiIhUoQQjIiJxoQQjIiJxEdU5GDN77iBVcmMQi4iIHEaiPcm/JYr5nx6kjoiINCBRJRh3/2m8AxERkcOLzsGIiEhcJDTBmFkLM3vGzHaHewK4ZD/1zMwmmtmW8HSfmVl4Xlcz+4eZfW1mW83sZTPrlsj1EBGRg0v0Hsw0giGX84BLgT+ZWY8I9a4Ezgd6AT2Bc4CrwvOaAc8B3cLtLAX+Ed+wRUSkphKWYMwsm+Bu/1vdvcDdFxEkih9HqD4KmOzua919HTAZGA3g7kvd/VF33+ruJcAfgG5m1jIhKyIiIlExd0/MgsxOBBa7e1alshuBk9393Gp1dwCnu/sb4df9gAXu3iRCu+cDf3L3o/az3CsJ9ojIy8vrO2fOnDqtR0FBATk5OXVqI1aSKRZIrngUS2TJFAskVzyKJbJTTjnlLXfvV6s3u3tCJmAosKFa2RXAwgh1y4DjKr3uAjjhhFipvB2wDvhRNDH07dvX62rBggV1biNWkikW9+SKR7FElkyxuCdXPIolMmCZ13K7n8hzMAV884bMXGBXFHVzgYLwygJgZkcA/wc86O6zYxyriIjUUSITzBogzcy6VCrrBayMUHdleF7EembWnCC5POfud8chVhERqaOEJRh33w38HbjTzLLNbDAwEpgVofoTwPVm1tbM2gA3ADMBzCwXeBl43d3HJSR4ERGpsURfpnw1weiXm4DZwC/cfaWZDTWzgkr1HgaeB94F3gNeCJcBXAD0B35qZgWVpvYJWwsRETmoWg04VlvuvpXg/pbq5a8BOZVeO/Dr8FS97uPA43EMU0REYkBdxYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFwowYiISFw0qARTVlbfEYiINBwNKsEUFNR3BCIiDYcSjIiIxIUSjIiIxEWDSjC7d0NhYX1HISLSMDSoBOMOb75Z31GIiDQMDSrBACxaVN8RiIg0DA0qwTRqBK+9Vt9RiIg0DA0qweTkwOuv634YEZFEaFAJpkkT2LkT3nuvviMRETn8JTTBmFkLM3vGzHab2edmdsl+6pmZTTSzLeHpPjOzSvMfMbMPzCxkZqOjXX5OTvCow2QiIvGX6D2YaUAxkAdcCvzJzHpEqHclcD7QC+gJnANcVWn+28DVwH9qsvCMDDj6aCUYEZFESFiCMbNs4ELgVncvcPdFwHPAjyNUHwVMdve17r4OmAyMLp/p7tPcfT5Q47tahgwJEox7bdZCRESiZZ6gLa2ZnQgsdvesSmU3Aie7+7nV6u4ATnf3N8Kv+wEL3L1JtXqLgOnuPvMAy72SYI+IvLy8vj/60atMmdKVJ59cQps2Nb/rsqCggJzyY231LJligeSKR7FElkyxQHLFo1giO+WUU95y9361erO7J2QChgIbqpVdASyMULcMOK7S6y6AE06IlcoXAaOjjaFv377+7rvu4D5zptfKggULavfGOEimWNyTKx7FElkyxeKeXPEolsiAZV7L7X4iz8EUALnVynKBXVHUzQUKwitbJ8cfD82b6zyMiEi8JTLBrAHSzKxLpbJewMoIdVeG5x2sXo2lpMDgwbqjX0Qk3hKWYNx9N/B34E4zyzazwcBIYFaE6k8A15tZWzNrA9wAzCyfaWYZZtYIMCDdzBqZWdTrMnQofPABbNpUhxUSEZEDSvRlylcDWcAmYDbwC3dfaWZDzaxyZ/oPA88D7wLvAS+Ey8r9H7AXOAl4JPz8O9EGMXRo8Ki9GBGR+ElL5MLcfSvB/S3Vy18Dciq9duDX4SlSO8PqEkffvvv6Jfve9+rSkoiI7E+D6iqmXEYGDBigPRgRkXhqkAkGgsNky5drlEsRkXhpsAlmyJCgV+X8/PqORETk8NRgE8ygQcElyzpMJiISHw02weTmQu/euuFSRCReGmyCgeA8zJIlUFxc35GIiBx+GnSCGTIE9u6F/9So038REYlGg04wuuFSRCR+GnSCycuDLl10HkZEJB4adIKB4DDZokUQCtV3JCIih5cGn2CGDoWtW+H99+s7EhGRw4sSTPg8jA6Y526PAAAVPElEQVSTiYjEVsNKMBs2fOPW/U6doHVrJRgRkVhrWAlm3To49dQqScZs33kYERGJnYaVYAAKC+EXv4D33qsoGjoUPv8cvvyyHuMSETnMNLwEk5YWDGf5rW/B+efD0qU6DyMiEgcNK8G0bQuvvgpr18JttwXPBwyg940jODvrFV571es7QhGRw0bDSjCtWwfdKLdsCbffHhwXmzQJW72Kf+4dzpgZg3j/vud0U4yISAw0rARTXZMmcMMNLHnqE65OeYjmJZs47uaRfNmyF4v/60lWLCulsLC+gxQROTQ17AQTtiC/EY/YVXRlDZfyF3Zsd0568DKa9O/GdY0fpkfnIs4/H265BebNO5Lly4NOMkVEZP/S6juAZDBsGGRkQHFxGs9kXMpVL/6IL959nmZT7+FPH/2crevuYHrBDcx7vjf9Qku5+p4dvGGD6NgRevSA44/f97hjRzAEwLBhwdE4EZGGSgmGIBHMnw8LF5YnhhQYNhKuOQ8WLKDFPffw6/k3clO4vqems3jgdSwN9ee9t1vwrxdb8JfSlmylBXtoDBhm0L49tGoFWVnQuPG+6YRd+ZyweSFrOw9jc5dBVeaVT59+CitXBrENHBiUZWUFU6NGwWicB5OfX3mdav/5xKqdQ06DXXGR2FCCCRs0KMI2xCy4MfPUU+Hqq7E//SkoLithyOv3MSRCO0VksJUWbPUWFH7dkuLCFmy3FmylBZu9JY0Kt/PTHVNIpZSS/HSu5Y/8h74U0ogiMikis8rzhx7KBOwbyzk5I59hLOL3uZmsbjaoIvmUJ7M9e/Z14pmSAmedFVzjYFZ16rgxn67rF/Jh22F83mYQKSlV53/1FcydC2VlkJoKP/tZ0AN1RgZkZlZ9XLOmJSUlkee99x68+SYMHgz9+wdtV19W5Wl/8954A/797+Dm2P79g/UrK9s3hUKQujSfFrOfZv2WTPb0GhSxzvLl8NZbQRu9e4OVlpC+dyepu3eStmcnjVcsJu/e67CyEjw9g/WP/R8lA4dWiW358mBvdejQYL3S04MpNTWYH63a5DF3KC0NBssrKQkelywJPp+TTw4+n4yMarHk59P+ySeDP8p+FhRVLFFUiradA8VzOP5AisXnEnU7ScDcG86luf369fNly5bV7s35+TB8OKGiIlIyM2HOHDj22KCnzPD0+X+2MPeRrTQt20or28qpvbfStHTLvjp79tRq0SUpGXhGI0rTMilNzcRDTu6u9YDjGBuadGFvSjYeAg85oRAUl0BZafC3NZyUlPCGEQcHcLJ8Dx1Cn2I4IVJ4J/VENlpr9pLFXhqzlywKQlkUhBqHy4JpD42/8bwLH3Iiy1lKf5bThxAplJFKiJSDTuX1+vEmQ3mNxZzEcvqQRmnFlE7JAV+Xl/XgPe5iAmmUUkYqj/IzttOMXHYecMri4FdzfE0rvuIoNtD6gI8FNCE9w0hPh8Ep+Qwumc+ynOG812RQRRLKyAge9+4N9lTLfwh06xZsV8qTRvljrz35DNi7kIUMY1HZIKy0mCbsqphy2RnxdS67aJa6k458wrCy+aQQIkQK72b2Z2d6Szw1FVJSITWFkrJUNm9LpZRUQpbKEXmpZGSl4JaKp6QSSkmlaeEm+n/5d8zLcEvlnXZnsrdRc9JCxaSFikkvK6KssJgdm4pJp5hMimmVW0SmBfNTy8L1SvaQWbbv/2FL43YUZLaiMC2HotRsdpZl89nmbAo8m72WTV6nbDKbZ1OaGUxljYKpVcFn5G1dxZedhrGu23BCTZqSnplS8Tl/9hlMnBgk47S04O6E7t0j/5B599136N27Z8R5KSmwalXwo+Jb3wr+TpV/sJQ/b7Y6nyNWLmR9t2Fs6jioyg+bjz6C++8HLymlVdp2rv3xNrq02kbW3q002ruNRnu30nLt23Rd9BgWKsVT03lv5G/Y3ONkClu0oahVW7xxNh9+CBMmBOuUng733hvElJoaeUpJ2f+85cvh7bdhxIj9JKr8fNqddNK6te7tarPtUoKpifx8Ppkxg46XX167XxaFhTBvHvzgB8GWIy0t+PZ36hTMKyqCwkI+WV3Ew38sJK2siMapRYy+uJC2rYr21fnPf4JvRbnjjgt2K8qZsXUrvL7YKjZcg4cYLVrsmw/AmjXB1q1chw7BJdx79wbJcO9eSguC52mU1f5zq2eh1DRKsppS0jiX0sa5bNiTy8ebctlBLrvIpd3xuRzTK5eSRrkUZwWPjTavpfdTvw72YFJS+XjwT/DUdBpt/4qsHRuwr74id+8GMvnmeNvFaVnsym5NUXoOrbesgvDGeGXrEexKb4GVlYa3TGXsLSijaE8ZaZSSShk5WcGURhmplJJGGY1KdnLkro+DHwdAWUo6aaGSqNa9JK0RRRlNoKyM7KKtGMHvi61Z7diWmQehEBYqIyVURllxGaHSMlIJprSUEOkpZaR48DrFy8gK7SGDoop2CqwJ21JaUkwGJZZBMRnsDWVSGAqeF5NBKDWDsrRMSiyjYupRuoK+xfmk4IQwPkg/gfXpHWjsu2kc2k1G6W4ale0mm/Ipuh9nIYwdNGUbzfc7bafZN8o68REnkU8+A3mb3qRTEnHKoHi/845jNTdzH6mUEiKVFzibEtJpzjZasLXisSk7D7gOTqRjFoEd5LKeNqynDetoG/H5VxxFCRkMJJ9hBD9KlhBsjIwQjdlDDgXkUEA2u8m1ApqnF/A/txXQrW0B7N4NBQVBF/OzZtGvtJRl7jXYJ99HCaaGFi5cyLBhw+oWSF0PMVTfm5o/v3aHGMLtUFwc/KQ+QDuvzi/hlIF7+fa39iWfikT02GPw+OP7fob/4Adw3nnB61CIj9aE+J+JIUKlITLSQlx/XYhOx4T2/fQLhYLE+9JLwbEfMzjnnOC4XlpaxbTm03Ru++80CkvTsLQ0/vveNI7vlV6lDqtWwc9/jpeUYBkZQZsnn1zlmFWUq33ADzA/H4af6mQXb+Po9A3M+p+v6NH8q6BD1a/Cj0uWwCef7HtT8+bBSblKPyELClN5/8M0Sj3YQzihZyq5zcPz09KCx08+gdWrgzbMgmNyp50WXGafmwtNmrDqyyZcPS6XbaVNKExvwl/+0YT+pzYJfuJG+Z2J6nOJolJN2tlfPN9oY16IQb33Bhu+3buD6cEH4ZFHIBTCU1IoO+0MSnv2xbduw7dtg23b2LNuG5s/3EYzD1JJpB8E8eCAN86h7Kh2hJq3wJs1Z0tZc559tQVbQs3ZkdaCn1zbnA69m1PWtAVluc0pzW1BykcfcORPzoDiIkjPZMM9j1LaIo/UDetI3bie1I3r2f3her56ax1HeZBSMvjmj43i7Gak79kR/D9hFGc3I7W0iLSimh9F6QdKMNFImgQTC1HsTUXbTp0P5ka58YpVsovmGHad9jSjFO06JfKHwMHaicnnEsNzMAeKJy6fS++9EE4+FdMTT8Df/hZsjFNS4Nxzg6n8OFt6Ou9/nM74CensKc3A09KZfH863zoxvUod3n0XLruMihORtfk/iOJzqdLOd0IM6rY16Mh3/fp90z//CUuXBpXNgpONQ4dCTk7F9OH6bG6dmMP20hwK03L446M59Dope1+dFStgxAj67t3rb7nX7pYWd28wU9++fb2uFixYUOc2YiWZYvHFi/3jMWPcFy+uUxt+zz11ayMsaT6bWHwu4XZi8dkkzecSVud4YvG5LF7snpXlZSkp7llZ+20rqkUly98pvE6emlq3dVq82NvCWq/lNldXkUlsDBrEF0VFdKzL3lTES/kOcbH4XMLtHHafTSzE4nMJ36fw2UH2GqJaVLL8nb5578V+qx0w3EGDWAcbahuGEoyISKx+CCSTJEh26ipGRETiQglGRETiQglGRETiQglGRETiIqEJxsxamNkzZrbbzD43s0v2U8/MbKKZbQlP95ntu1POzHqb2Vtmtif82DtxayEiItFI9B7MNKAYyAMuBf5kZj0i1LsSOB/oBfQEzgGuAjCzDOAfwF+A5sDjwD/C5SIikiQSlmDMLBu4ELjV3QvcfRHwHPDjCNVHAZPdfa27rwMmA6PD84YRXF49xd2L3H0qQdc9p8Z5FUREpAYSeR9MV6DM3ddUKnsbODlC3R7heZXr9ag07x33Kn3cvBMu/3/VGzKzKwn2iAAKzOyD2oVfoRWwuY5txEoyxQLJFY9iiSyZYoHkikexRNattm9MZILJAXZUK9sBNImi7g4gJ3wepibt4O6PAI/UJuBIzGyZu/eLVXt1kUyxQHLFo1giS6ZYILniUSyRmVmtO3BM5DmYAiC3WlkusCuKurlAQXivpSbtiIhIPUlkglkDpJlZpYFL6AWsjFB3ZXhepHorgZ6VryojuBAgUjsiIlJPEpZg3H038HfgTjPLNrPBwEhgVoTqTwDXm1lbM2sD3ADMDM9bCJQB15pZppldEy5/JZ7xVxKzw20xkEyxQHLFo1giS6ZYILniUSyR1TqWhI4HY2YtgBnAacAWYJy7P2VmQ4GX3D0nXM+AicCY8FunAzeXn9g3sxPDZccDq4GfufvyhK2IiIgcVIMacExERBJHXcWIiEhcKMGIiEhcKMFEIXwxwaPh/tN2mdlyMzszCeLqYmaFZvaXJIjlYjNbHe5n7uPwebX6iOMYM3vRzLaZ2QYze8DMEnK/l5ldY2bLzKzIzGZWmzfczN4P95+3wMw61EcsZjbQzOaZ2VYz+9rM5prZUfURS7U6t5mZm9mIeMZysHjMrLGZPWhmm81sh5m9Wo+x/CD8P7XLzFaZ2flxjuWA27nafIeVYKKTBnxJ0OtAU+BW4H/N7Jh6jAmCvt3erOcYMLPTCC7K+CnBDa/fAT6pp3AeBDYBRwG9Cf5mVydo2euB/ya4kKWCmbUiuILyVqAFsAx4uj5iIei/7xHgGKADwf1jj9VTLACYWSfgIuCrOMcRTTyPEPyNuocfr6uPWMysLUF/i9cT3Od3E/CUmR0Zx1j2u52r7XdYQyZHIXyJ9e2Viv5pZp8CfYHP6iMmM7sY2A4sBjrXRwyV3AHc6e5Lwq/X1WMsxwIPuHshsMHM/h/7uhmKK3f/O4CZ9QPaVZr1PWClu88Nz78d2Gxmx7n7+4mMxd1fqlzPzB4A/h2PGA4WSyUPADcT/DiIu/3FY2bdgPOAdu6+M1z8Vn3EEn6+vdLf6wUz2w10IvgBFY9YDrSda0ktvsPag6kFM8sj6FutXm7uNLNc4E6C+4PqlZmlAv2AI8zsIzNbGz4slVVPIf0RuDh8qKMtcCYR+qhLsCp964X/kT8mQYnvIL5DPd6kbGbfB4rd/cX6iqGSAcDnwB3hQ2TvmtmF9RTLMmC1mZ1nZqnhw2NFBP0uJkS17VytvsNKMDVkZunAk8Dj8fr1GYW7gEfd/ct6Wn5leUA6wSGOoQSHpU4EfltP8fyb4Eu/E1hL8I/6bD3FUq5G/eclipn1BCYQHH6pj+XnAPcAY+tj+RG0A04g+Nu0Aa4BHjez7okOxN3LCG44f4ogsTwFXBXesMddhO1crb7DSjA1YGYpBD0PFBN8+eojht7ACOAP9bH8CPaGH+9396/cfTPwe+CsRAcS/vu8THCsOJugR9rmBOeH6lPS9Z9nZp2Bl4Bfuftr9RTGHcAsd/+0npZf3V6gBPhvdy92938DC4DTEx1I+GKH+wiGJ8kgOC8y3RIwuOJ+tnO1+g4rwUTJzAx4lOAX+4XuXlJPoQwjOEH7hZltAG4ELjSz/9RHMO6+jWBPIRnu2G0BHE1wDqbI3bcQnMBOeLKrpkrfehaMjdSJ+jvE2gH4F3CXu0fqqilRhhN0+bQh/F0+muCk8s31FE/CDj9FoTfwqrsvc/eQu78JvEHw4zJuDrCdq9V3WAkmen8iuLLkXHffe7DKcfQIwR+2d3h6CHgB+G49xvQY8EszO9LMmhMc8vhnooMI7z19CvzCzNLMrBnB4HVvH/idsRFeZiMgFUg1s0YWXCL9DHCCmV0Ynj+BYEyjuB1i3V8s4fNSrwDT3P2heC0/mlgIEswJ7PsurycYuXZaPcXzKvAFMD5cZzDBD7qX6yGWN4Gh5XssFnSPNZT4J8H9bedq9x12d00HmQgu53SgkGBXsXy6NAliux34Sz3HkE5wBdB2YAMwFWhUT7H0JugQdRvBgE1zgSMT+LfwatPt4XkjgPcJDsMsBI6pj1iA28LPK3+PC+rrc6lW7zNgRD3/nXoA+cBuYBVwQT3Gcg3wEcFhqE+AG+IcywG3c7X5DqsvMhERiQsdIhMRkbhQghERkbhQghERkbhQghERkbhQghERkbhQghERkbhQghE5RFgwXspF9R2HSLSUYESiYGYzwxv46tOSg79bpGHSeDAi0fsX8ONqZcX1EYjIoUB7MCLRK3L3DdWmrVBx+OoaM3shPKTs52Z2WeU3m9m3zOxfZrbXgiGLZ5pZ02p1RoXHISkys432zSGGW1gwzPFuM/skwjImhJddFO5E8ol4fBAi0VCCEYmdO4DnCPpDewR4IjxSIWbWmGDgswLg28AFwElUGirXzK4CHiboPLQnQS/Q1XurnQD8g6Bn26eBGeHekQkPjnUjwRDRXYBzgKVxWE+RqKgvMpEohPckLiPoCLCyae5+s5k5MN3dr6j0nn8BG9z9MjO7AphEMBzvrvD8YQTjjXRx94/MbC1Bx6Xj9hODA/e6+/jw6zSCgdWudPe/mNn1BL0Rn+D1N5yESAWdgxGJ3qvAldXKtld6nl9tXj5wdvh5d4LuzSsP0LQYCAHHm9lOoC0w/yAxVHTX7u6lZvY1cGS4aC7wK+BTM3uZYI/pOXcvOkibInGhQ2Qi0dvj7h9VmzZH+V5j/4OyeXh+NKrvmTjh/2MPhtDuRrAXsxOYDLwVHhxKJOGUYERiZ2CE16vDz1cBvcys8hjmJxH8D652943AOoJBuGrN3Qvd/QV3vw7oTzC+yeC6tClSWzpEJhK9TDNrXa2szN2/Dj//npm9STAY00UEyWJAeN6TBBcBPGFmE4DmBCf0/+7uH4Xr3A38wcw2EoxS2hgY7u6TownOzEYT/E+/QXAxwQ8J9ng+rOF6isSEEoxI9EYAX1UrWwe0Cz+/HbiQYETPr4GfejCWOu6+x8y+C0whuLKrkOBqsF+VN+TufzKzYuAGYCKwFXixBvFtB24muJggnWCv6Xvu/mkN2hCJGV1FJhID4Su8vu/uf63vWESShc7BiIhIXCjBiIhIXOgQmYiIxIX2YEREJC6UYEREJC6UYEREJC6UYEREJC6UYEREJC7+P98W1BsjNQ36AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting Several Steps Ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(43) # not 42, as it would give the first series in the train set\n",
    "\n",
    "series = generate_time_series(1, n_steps + 10)\n",
    "X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n",
    "X = X_new\n",
    "for step_ahead in range(10):\n",
    "    y_pred_one = model.predict(X[:, step_ahead:])[:, np.newaxis, :]\n",
    "    X = np.concatenate([X, y_pred_one], axis=1)\n",
    "\n",
    "Y_pred = X[:, n_steps:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure forecast_ahead_plot\n"
     ]
    },
    {
     "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": [
    "def plot_multiple_forecasts(X, Y, Y_pred):\n",
    "    n_steps = X.shape[1]\n",
    "    ahead = Y.shape[1]\n",
    "    plot_series(X[0, :, 0])\n",
    "    plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"ro-\", label=\"Actual\")\n",
    "    plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"bx-\", label=\"Forecast\", markersize=10)\n",
    "    plt.axis([0, n_steps + ahead, -1, 1])\n",
    "    plt.legend(fontsize=14)\n",
    "\n",
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "save_fig(\"forecast_ahead_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an RNN that predicts all 10 next values at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 10)\n",
    "X_train, Y_train = series[:7000, :n_steps], series[:7000, -10:, 0]\n",
    "X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, -10:, 0]\n",
    "X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 7s 978us/sample - loss: 0.0670 - val_loss: 0.0341\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 6s 838us/sample - loss: 0.0267 - val_loss: 0.0195\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 6s 833us/sample - loss: 0.0189 - val_loss: 0.0195\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 6s 847us/sample - loss: 0.0152 - val_loss: 0.0129\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 6s 850us/sample - loss: 0.0136 - val_loss: 0.0125\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 7s 1ms/sample - loss: 0.0126 - val_loss: 0.0128\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 6s 803us/sample - loss: 0.0121 - val_loss: 0.0107\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 5s 776us/sample - loss: 0.0112 - val_loss: 0.0109\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 6s 808us/sample - loss: 0.0109 - val_loss: 0.0106\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 6s 787us/sample - loss: 0.0114 - val_loss: 0.0100\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 6s 788us/sample - loss: 0.0105 - val_loss: 0.0109\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 5s 773us/sample - loss: 0.0104 - val_loss: 0.0095\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 6s 789us/sample - loss: 0.0098 - val_loss: 0.0098\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 5s 784us/sample - loss: 0.0099 - val_loss: 0.0104\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 5s 786us/sample - loss: 0.0096 - val_loss: 0.0089\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 5s 785us/sample - loss: 0.0096 - val_loss: 0.0088\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 5s 779us/sample - loss: 0.0094 - val_loss: 0.0092\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 6s 787us/sample - loss: 0.0090 - val_loss: 0.0083\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 5s 774us/sample - loss: 0.0090 - val_loss: 0.0113\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 5s 784us/sample - loss: 0.0098 - val_loss: 0.0082\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20),\n",
    "    keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n",
    "Y_pred = model.predict(X_new)[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9j4+wqxDtBAJFpJdb2SAgtRbXKtY+EK72A6Xyu2JgLfsx1IPV6dmEBgUwoHNV/fcel8a0YXV6Dk6nSQ1h8A1Op/JG4h56d2jBuH4dPH+DKk4MrcnjSpawIHCS5+/lA2wpRKpaAMwDnheR5iIyCpgAfFC1rYhMEJEIsRgB/BrLUw4gCSgDfi0iISLyoKt8cdV+DJ5hVXoOQ6MiCApovLfWiJg2nCgqZfthM9E1+IbF24+y48hJ7o/rgcPhhbNz1RyKnRD0H7aX9mTnzhqu8SNsKUQuHgBCgaPAHOB+VU0VkdEiku/W7jZgN9Zy27+AP6nq+wCqWgxMBO4CjgP3ABNd5QYPk1dYwo4jJ73utl2V8vutTjdRuA2Nj6oyPWk3XSNCGT+ws3du4nYoFtcCz41XFQGwcKF3btmY2FaIVDVHVSeqanNV7a6qH7nKl6lqC7d2U1Q1Uq3zQ33VctF272e9qg5V1VBVHaKq6xt7LBcKazNzUPX++aGqdI0Io0vrUFZnmH0iQ+OTnJbD+r3H+eWVPQj05kqA++HXq68mat18Bg10smDBea+0PbYVIoP/sTo9h+AAB4O7tW70e5fvE6lJIW5oZN5I2k3bFiHcOrRro93z5W7/IPFwXyZEb+LHH61jRlXxpxTiRogMHmNVeg6DurWiWVANUYW9yIiYNmTlF7PnWEGj39tw4fLJmn0s25XFdRd1bNT3/fDbezE54DM6r12I0wlffVW5vjxx3vDhjWZSgzBCZPAIK3ZnsWn/cbpFeC/a9rm41HV63bhxGxqLlMxcfjdvEwCfrN3XqCnr48cKc3+zmqcO3k/b8OJKy3P+mL3VCJGhwaRk5vKLf67BqfDlpoON+g9ZTnRkGO1ahrDKOCwYGokfth2h/MRASVnjp6yPfz6euRH/RX6+8vXXcOqUf4oQGCEyeIDktGyKXbHeypza6P+QACLCiJg2rEoz+0SGxuFkUSkAjvrElPMEISHE/2YIzzmf4vRpuCdsDpPHZTN32iK/EiEwQmTwABe5Dq8KPvqHdDEypg2HTxQxd0exT2ZlhgsHp1NZtusY/Tq15DdX92H2fSN9k7K+bVt+zT8IoISPmcL9zunEvzahIqurv2CEyNBgdh6xjnXddVm07/4hgRYhQQB8k1F6Jg+MweAFktOyycgu5JdjevCr+J4+e8/z4ous5DIcKO04ygzuJ7FwBDz5pG/sqSdGiAwNwulUPkjOZHh0BM9NGOC7f0jgwPFCoJ55YAyGOjB79V5ahQZx7UUdfWpHYmYsk5nLL3iXY7TnDe5nMnNJzIw9/8U2wgiRoUEs2XmMvTmF3HVZtK9N4bIebQlwhVcJCvDdEqGhaZOVf5rvUg8zaUhXnxxVKCcxESY7PmEuk/kv3gKgiFDmMpnJjk/8KmurESJDg3h/ZQbtW4ZwzQDffjMEGBoVwf/e0B+AB3y5XGJo0nyWsp+SMmXKiG4+s6HCO+7x9cSHrWYQG2nLMb7nKuLDVjP38fV+lULcCJGh3mRkFZC04xi3X9qd4EB7vJXuGBlF6xAh9WCer00xNEFUlTmr9zI8OoJejZCBuCbWrHG5aL8wDmbOxNGlMz/hBxbJVehbM4l/YZxfpRC3x6eHwS/5MDmTQIdw+4ju52/cSAQ4hOEdA0jccYwTRSW+NsfQxFjpclK4/VLfvucfe8ztnFBCAuzfz1X99nNIO7HtktsB/0ohboTIUC8Ki0uZu3Yf117UkfbhzXxtTiUu7RRIcamT71OP+NoUQxNjzup9tAoN4rqLOvnalLMY93NrqfD7d/b62JK6Y4TIUC8WbDjIiaJS7r482temnEWPVg66tA7li00HfW2KoQmRnX+ab7cc5uYhXXzqpFATUdOuoRe7WPS5/+XlMkJkqDOqyvs/ZtCvUzjDbOgQICKMH9SZ5buyyCkwqacMnmHeugMUlzmZYqOl6Eq0bs24mD0kZURTUlTma2vqhBEiQ52ZvWov2w+f5Mrebamchd0+jB/UiVKn8s2Ww742xdAESMnI4Y2k3fTt2JLePnRSOB/jJoWTry1Y9aZ/pV0zQmSoEymZufzvgi0AvLciw7bRC/p3Cie2XXO+2GiW5wwNIyUzl9veTia3sIQ9x/Jt+54HiH/kEhyU8f37/vW+t60QiUgbEZkvIgUikikit9fQ7lER2SIiJ0UkXUQerVKfISKnRCTf9fiucUbQNFm01bcRh2uLiDB+YGeS07M5eqLI1+YY/Jg5qzMpKbPe9E4fBfWtLRGdQxnWNoNFm9tDkf+8720rRMB0oBjoACQAM0RkQDXtBLgLiACuBR4UkduqtBnvSiXeQlWv9qbRTZ3Drg91n0UcrgPjB3VCFb7afMjXphj8lPV7c1m44RACBPjBex7gqnGwqmwYeZ9+72tTao0thUhEmgOTgKdVNV9VlwMLgTurtlXVl1V1naqWquoOYAEwqnEtvjAoLC5l8fajXBrTxrcRh2tJz/Yt6dcp3CzPGerFnmP53PPeGjq0CuHtu4fxP37wngcYd180ZQSyZMZWX5tSawJ9bUAN9AbKVHWnW9lG4MpzXSTWzvlocAVeOsNsEXEA64FHVXVjNddOA6YBtGvXjqSkpPpbb3Py8/PrNb7Fe0vIO1XCT9oX0lv2czJ9P0npnrevobiPb0DLYj7dWcIn/1lMuzBbfu+qM/V9/fwBu4wtt8jJH5KLKHUqDw4IJPDINgYIDX7Pe3N8c+Z0o2/fkwwYkEeYYwTf/RjGeIeD0+3bk3bffRwdN47161uzfXtLpkzZ5xUb6o2q2u6BJSaHq5RNBZLOc91zWIIV4lY2CggFwoDfA4eB1ufqp3fv3tqUSUxMrPM1ZWVOjf9Lot7w92XqdDo9b5QHcR/f3uwCjfrdlzojabfvDPIw9Xn9/IW6jO27LYf19cU7dW1GjkdtWLLjqA77v++0z5P/0U37jnu0b2++dosXq7Ztq7r4ie/1WvlG+7JVFaxHWJgufuJ7q36x10xQYK3W4zPfrl8R84HwKmXhQI0ntUTkQay9outV9XR5uaquUNVTqlqoqi8Cx7GEzlAHluw8RtqxAu69Isa2LtvV0a1NGL3at+Dd5em29nYy1I2ZS/cw9YO1/OXbnR7JPaWq7Dh8ksc/28Td767mWH4xTqUi87A/EB9vxZ+b/NIlxOgettOP/XQBILFwBJNfusS2KcTtujS3EwgUkV6qustVNghIra6xiNwDPA6MUdX95+lbsRwcDHXgneXpdAgP4acX2y+0yblIycwlPauAUqdy+9vJfDTV/mv8hnNTWFzK64t3A9Y/c7Er91RdX9eUjBzmrT9A/ulSNuw7TmZ2YaX6Mmf9+vUl8fEw13krk/gUgEWMI4pMJjOXuc5biY9f7GMLq8eWQqSqBSIyD3heRO4DBgMTgMurthWRBOCPQLyqplWp6w50A9ZgOWY8BLQFVnh3BE2L7YdPsHx3Fo9e08c2UbZrS3JaNk5ribbeH1gGe/Haol2cKColKEAq3KpHxrSpUx8pmblMnplMmesswqCurZg68SI6hjfjwTnrKCl1+oWHXHXER6XxaeYkxvEDr/AbDtORuUwmPsqGG7ou7Pyp8gDW3s5RYA5wv6qmishoEcl3a/cHIBJY43ZW6E1XXUtgBpALHMBy775OVe17EMCG/HN5Bs2CHLaKsl1bRsZGVoinQ8QvP1gMZ9h68ATvLE9nyohufDztMsb2bY9TYf/xU3XqZ+bSPRUiFCBw9YCO3DEyinH9OzD7vpF+4yFXLS+8wNiw1QwglS1czH8xg/jQVfDCC762rEZsOSMCUNUcYGI15cuAFm7PY87RRyow0CsGXiBk5Z9m/oYD3DK0KxHNg31tTp0ZGhXB7PtG8vt5m8gpKPbPDxYDAGVO5Yn5m2kdGsTvru1L67Bg3r5rGDe/sYL/+3IrV/ZuR+uw879Hf9yTxaKtR3CItUZfdeYzNCrCv98nCQkkbu1AxosxoDCdBxk7IJ/4hARfW1Yjdp4RGWzA7OS9FJc6uWdUjXpve4ZGRTBlRHey8ovZn1t4/gsMtuSjVZls2Hecp2/oXyE4AQ7hjzdfTG5hCX/6Zvt5+0jPKuD+D9cR064F798zwr9nPjWQmAiTZ47j9X9a39fvHZDM5LWPkjj/uI8tqxkjRIYaSU7L4q2le7ike2t6tm9x/gtszKUx1jfe1ek5PrbEUB+Oniji5W92cEXPtkwY3LlS3YDOrbhnVDRzVu875+ubV1jCve+twSHwzt3DGN2rHb9qYinlK1KIz4W77oJ27eBoz8uZy8+YfEeQbVOHGyEyVEtKZi53vrOawuIyUg+c8HvX5z4dWxLeLNAIkR+SkpnLne+u5lRpGX+YeFG1xwceuao3XVqH8sjcDfz9h51nvV9Xp2dz4+vLycwp4M07hhIV2byxzG803EUoPh5EYMwYWLqxNfE3tWau3MbkW522FCMjRIZq+XFPVoVHUrkbqz8T4BBGxLRhlREivyIlM5cpM1ey4/BJUMiuIb9UWHAgP788mgO5p3j1+13cNnMlf/zPNt5bkc6zC7dw28xkMnMKcYgQGNA0P/bWrOGsc0JjxkBGBuz9+f8SX/Alc2/5hDVrfGZijdjWWcHgW0pdB/n8Ibhpbbk0JpJF245y9ESR7dKbG85GVZmxZA/Fri9EqnpO93v3w6clZcrMpWlntSmPnt2UluPKeeyxs8vGjLF+Ljs5mISxY4lf+Ajx6ROBkEa17Xx4/KuBiPxDRL6opjxcRJ4VkX5uZY+IyCZXHDiDTSgtc/LFxkNEtQnjf67q3WQ2c0e4zpqYWZH9OXqyiHveW1Ph3VabyNcjYyNpFuTAIRAS6GDWXcNIeWocH9w7gmaBDr+Jnu1JLr4YWrWCpUuBxx+HQ4egc2dwOCA6GmbP9rWJgIdnRCLSA/gl1Rw8BYYBzwDz3MreBH4H3A3805O2GOrPwo0HScsq4M07hnDtRf4VSeFcDOgcTvPgAFalZzN+UOfzX2BoVFIyc/lyTzFb2c2sZekUnC7luRsHcFHncJLTcxgZG3nOL0TlrvrJadmV2o7u1Y7ZU88uvxAICIArrnAJ0Zij1sZRjuuLWGYmTJtm/e5j125PL809DGxU1bXV1F0CnAYqYpOr6ikR+RfwW4wQ2YLSMif/WLybfp3Cubp/R1+b41ECAxwMjW5jHBZsSEpmLglvJ1NU6oRdO4htG8bcX46kZ3srLffQ6NpFTqjpDJDfnw1qAGPGwFdfwdHHX6W9K8pIBYWF8OSTPheiWi2JiUhPESkRkeeqlM9wZUYdJiIhwB3AR9Vcvw34C9bCZImIqIh86qr+GOgvItXNogxupGTmMj1xt1c92BZsOEh6VgH//ZNeOBxNLyTfpTFt2Hkkn5waNr0NviE5LZvTpdYejwATL+laIUKGhlGxT7S/hrOAe/c2njE1UCshUtXdwCzgERFpCyAi/wvcA9zkmgGNBFoDy6rp4i4gDfgCuMz1+I2rbgNwAiv8jqEGyr8xvvLdjgZFGy5f/qjuems2tIv+ncK5ZkCHhppsSy517ROZWZG9GOEWKy4kyMGonm19aE3TYsgQCAuDpS2vr75Bd9+H7qqLk8BzQADwOxG5F2u/505VXeSqH4kVDHdTNdduBLoCi1U12fXIBFBVp+uakfUcwwVBcloWRaVOnAqnS+rnTm25wibz2a6SasXs8w0Hycgu5OFxvfwq1UNdGNi1NSGBDlal+7c7elMjr7AEBUZ2CmgyzjF2ITgYLrsMlraZYClS1UobxKCrtRCp6mHgNawI1m8Bv1bVuW5NOgMnVLW6NY8BQDCwrobuj7muN9RAcemZtV0FCk6X1LmP71IPU1zmRIGiEicfrMwoTx5YMRsa0Dmcq/o3zdkQQHCggyHdI8yMyGa8vzKDjuHNuO/iECNCXmDMGNi4tw3H//pPiIqynBaCgyEoCMaN87V5dXbf3oW1z7NSVadXqWuG5YxQHUOwPj831FB/CivStqEasvJP86+VGfTu0IJHxvVicLfWvLkkja82Hap1H6rKWtcMSFyPzzccZNKMH1mTkcNrP+wiM7uQ8YM6N9nZUDmXxrZh66ET5J2qu5gbPM/uo/ks25XFHSO7E9gE9yXtwJgxVqrWFV0mWydcnU7YsAFKS+HlhcMZAAAgAElEQVT++61KH1JrIRKRsVgzoZXAKBEZVKVJNlDTV5lLgD2qeqKG+jZAVm1tudB4ZmEqBafLmH77EP57XG/muJK7Pfzv9STuOFqrPr7cdIiUzFx+fnkUk3oF8e9fjuRPky7mwPFT3PrmyopEY68tOjs8SlNjREwbVCEl08yK7MAHKzMIDnBwmx+mGfEXLr3UmvwsXepW2K8f/N//wfz50L69T88W1dZrbgjwOZbDQhywFysZnTvbgSAR6VpNF/1xc9uuhhhgR21sudD4NvUwX206xENje9Krg+VFFBocwDs/H07vDi25/8MUPlyZeU5vurzCEp77IpWLu7Tiqev7c0OPYEbERPKz4d1J+m08o3ud2RguKfX/cD7nY0j3CIIDHKxKM0Lka04WlfBpyn5uGNiJti3sddq/KREaCiNGVBEigI4dLQHKyrJmReVnixpZjM4rRCLSE/ga+A54yLUH9BzwUxEZ49a0fIgjqunmODBIRK4RkZEiUnG0WURaA73drje4yDtVwtOfb6Ffp3D+K65HpbrwZkH8654RRDYP5qkFW87pTffH/2wjt7CEF2+++Kw4W6HBATw8rjfNgi6ck+fNggIY1K2VibBgA+atO0BBcRl3Xx7ta1OaPGPGwNq1UFDgVvj009YynTvlZ4sakXMKkYh0xBKgbUCCy8MN4F9YM6CXytuqagawGhhfTVf/CxzBmlWtBPq51V0PFAPz6zWCJswfv9pGdkExf75lIEHVBGqMbBFSESGg3Jtuxe5jldqs3JPNv9fu474rYrioS6tq71N+Ir0p5mapiRExbdh8II+C06W+NuWCxelU3l+ZwaBurRnUrbWvzWnyjBljbQklJ7sV1nSGqJHPFp1TiFT1sKrGqmqcqp52Ky9T1X6qWvUQ6gzgZhEJq9LPFlW9VFVDVVVUdblb9R3AJ1XTd4tIGxGZLyIFIpIpIrdXZ6NY/ElEsl2Pl8Vtt11EBotIiogUun4OPvefxB78c3k6/167jxsHda5RQACu6t+REFcqbAX+vXY/6/das6KikjKenL+Zbm1CeXhc73Peb2hURJPLzXIuLo2JpMypPPtFapPfE7MrK/ZkkXasgJ9fHuVrU5o0L79spYi4/HJrFa7S8pzrDFEicbzMo2eVNxaeDjb6AXAAeKA2jV2iEI+11FeV6VgzpQ5AAjBDRAZU024aVkrxQVhpwW/AineHiAQDC4APsRwp3gcWuMpty+LtR3j+S2tL7evNh875QTk0KoKPpo7k0Wv68PQN/VCnMmnGj/x6zjrufGcVaVkF/PGmiwkNDmgs8/2C8qgRn67d36ADwob68/6PGbRtEcxPL2468QztyPDhVp6ilBQYPLiKEL3wAokh1zKZuQzHlR8iJKTRzxZ5VIhUtQwr2kJt8zF3BH7hitxQgYg0ByYBT6tqvmsGtRC4s5o+7gZeUdX9qnoAeAX4uasuDiue3muqelpV/47luTy2TgNrRPJOlfD4Z5spd6YsKTu/80D5bObeK2L59pExjOvXgYUbD7EmI5cAEcKCTbaPqmzcZ6VNVqwlzW9TD/vWoAuMvdmF/LD9KFNGdCck0HxJ8ibx8VaeosmTISbGWpo77VrfSuycwOTg+czt8GviZYkVJbVzZ7i92gUoryHqY//x6hCRS4AfVTXUrey3wJWqOr5K2zzgalVd5Xo+DEhU1ZYi8oir7jq39l+66l+p0s80rNkVYWFhQwsLa6ulnkOCQmg/+f8I6dQb1AniQJ1lHPn4SYoPbq91P+Ejb6X16DsQRwDqLOP4sg85kfyJFy33P4I796XDbS8ggUGAgLOM/M3fk/fjXAJaRtKs+8UU7d1cp7+7ofZE3vAbmve7kmPz/sCpPat9bc4FQhzWNn0rYBRWjIG5wGQgCYBfAO8CN2LFY6sHKao6rK4X2fWrcgsgr0pZHlBdFMSqbfOAFq59olr3o6ozgZkAffr00R07Gteb/HRpGfe9v5YVu7OYfvsQ2oc3OxO2/pVtdeorJTOXhFnJlJQ6CQoJYt7HbzE06kwQjKSkJOLi4jw8AvtQ2/GlZOaSnJZNr/YtWLYri38HB9N6yHWAoKoEBzps6bzh76/fqrRsfjbT2jHvPuXZSn9jfx/b+fD1+D7/HG66CUaPXsG2beUZXd1yh5eWQr9+LGzeHNatszaV6kB9D8PbVYjygfAqZeHAyVq0DQfyVVVFpC79+IzSMicPfbSeZbuy+PMtA7nOtWZe3w/AmvKyGCrjnhrg6gEduT+uBw/MTmHDPuu7S/mZKvP38yz/Xruv4nfzN25cJk60zqwuWwZPPVU5rTgAgYHwzDNw553WQddJkxrFLrtmRt0JBIpIL7eyQUBqNW1TXXXVtUsFBkplmR5YQz8+YW1GDhNeX8F3W4/w7Pj+3Dqsm0f6vdC84DxB59ahPH3DAMqjzFwIZ6p8wdET1gbFhXJuzU4kJlpnVwFef916fhZTpkDfvpYglZU1il22FCJVLcDK5Pq8iDQXkVHABCyvvKr8C/gfEekiIp2x0ku856pLAsqAX4tIiIg86Cpf7E37a0tKZi63zUwm9dAJAh3CxV3NWQpfMzQqgqev7w/AvVfEGCH3MIXFpaRk5nJV/w4X1Lk1O5CYaDksfPQRNGsGcXHW87PEKCAAnn0WUlPPRF7wcugfWwqRiwewAqEeBeYA96tqqoiMdi25lfMW1r7aZmAL8JWrDFcUiIlY+ZCOY3n0TawhQnijk5yWTanTchZR1SYfWsdf+PmoaPp2bMmirUexozOPP/P91iOcKinjvitizIy9ESkXoblzYfx4uPFGWL7cEqVqxaikxIrQ3Uihf2wrRKqao6oTVbW5qnZX1Y9c5ctUtYVbO1XVx1S1jevxmLp9eqjqelUd6jpMO0RV1/tiPNUxoLO1fSWYJQo7ISJMHR3LjiMnWbrLxOL1JF9sPEinVs0YXsvU34aG4y5C5XtCd9xhaUxx8RnX7kpi9NRTZ0fk9mLoH9sK0YVAUYkVMWnKiO5micJmjB/UmQ7hIcxaluZrU5oMxwuLWbLzGOMHdW6Saejtypo1lUUI4JprIDISPvzwzDmjNWvcLmrk0D929Zq7IEhOy6ZZkINnbxxAcKD5TmAnggMd3H15NC9/s4Nth07Qr1NV58u6U+4ufqF6Mn695TAlZcqNg0wOzMbkscfOLgsOhp/9DN59F06csMSokgdd9+7WclxVvBT6x3z6+ZDktGyGRbUxImRTEkZEERoUwKxl6Q3uKyUzlylvJ58zSnpTZ8GGA8S2a16xJG3wLXfcAUVFlpf2WbzwwtlpxYOCvBb6x3wC+oicgmK2Hz7JyFizVm5XWoUFMXlYVxZuPMCRE0UN6uvz9QcoLnXiVGtJ9qvNtc+u2xQ4nFfEqvQcbrwAMgD7CyNHQmystTx3FgkJMHPmmbTiYWHWYdcePapp3HCMEPmI1emWh5xxULA391wRQ6lTef/HjHr3cezkab7adKgiRTtY0dUf/ng9CzceOGdSw6bCl5sOoopZlrMRItas6Icf4ODBahokJJxJK37ggLUsd/vt1lqehzFC5COS03IIDQpgoDk7ZGuiIptzTf+OzF61l8LiuucuKi518qvZ6ygsKeXPtw7kt9f04Z8/H8Yvr+zB11sO8+s5G/jztzu4/e2mvVy3YMNBLu7Sith2Lc7f2OB1ylNDJCRYznFz5pzdJjHRagdA69aWr3d6OnTq5PGzRUaIfERyWjbDoiPM/pAfMHVMDHmnSnhozvo6i8UfvtrK6owc/jRpILcM7cav4nsS37cDj1/Xl/tGx1TMkE6XOlm07YjnjbcBacfy2XwgjwmDzWzILpSnhjhwwEohXnV5rtzle/hwt8L0dGufqLDQ42eLzKegDzizP2SW5fwDQQR+2HaUW9/8kSfnb2b93lxKy5ykZObWuLQ2d+0+/rUyk/uuiGHC4C5n1Y/t24GQIEdFSKFPU/aRnlVwVjt/Z+HGg4jADQONENkF99QQI0bAhg2wZYtVV925I8A6Q1RSUrkjD50tMu7bPuDM/pBxVPAHktOyEazcRU6F2av2MnvVXkKDAjhdWoYqBAU4+OcvhjOqZ1tSMnP5fP0B5qzZy+U9Inn8ur7V9usenLZdi2Be+mYHt8z4kffvGXHOrLz+REpGDu+vzKB/p5Z0bNXM1+YY3CgXo1tvtVbaZs+GnBz4+GMrSvdZAVFdZ4gSiWMNw3mMP1cqbwhGiHxA+f7QxV3M/pA/MDI2kuBAh5VWI9DBGwlDOFXs5O1leyoidReXOblj1iq6R4axP/cUZU5FgKljYgkMqHnhwT0C+LDoNtz5zmpum5nMb6/uTUFxmV+fOUrJzGXKrFUUlzrJL7JizPnrWJoq8fHwySdw7bVWEFSwQs1VS/fuJGbGMJm5zGVypfKGYpbmfIDZH/Ivymcu5UE6x/btwPUDO/H0DQNoFuQgQCA4QLhlWFcEKHPFDxSBrQdr72EU264Fn91/ORFhQTz7xVb+8q1/nzlKTsumpNSKHuJ0mliKdiU+Hm67DfLzrccVV8Att5wdfy4xYVaFCMW7EukRFuaRs0Xmk7CRMftD/kl1aTXcBWrOtMv48y2DeGXy4DPiVI/4gR1bNWPiJdZ+kmJ53fnrB/jI2EjKjwyZWIr2JTER/vMf+M1vIDQUvvnG2vq54QarvLzN5JnjmPvERuKj0q1vWVFR1lmjhIQG22CW5hqZVWlmf6gp4b60Vv68oUkJ4/q0Z+bSNE6XOlGFYX66nDWwaysCHQ4u6hLOk9f3N8tyNqSqY8L111u58AYOhCVLLDGaMgW++668zTh4IcPjdpgZUSOTnJZtzg81cRqalHBoVAQfTR3JTYM7o8Di7Uc9a2Ajsf3QSYrLnPxilMnrZEeq846Lj4fPPrNSEb3+uhUY9aOP4O67q3Fe8CBmRtTIJKflMCw6gqBzbGAbDOUzrdCQQN5amsaY3u0Y1bOtr82qEymZOUD9U94bvEeNLtqc8aabONEKqiAC06dbsyVviZH5NGxEsvNPs+OI2R8y1J6nru9HbLvm/GbuRnILbJHPsdaszcylU6tmdG4d6mtTDFWoLjVEVUSsiD6//a0VHPWmm2pILe4BjBA1IqvTrW+IRogMtSUsOJC/33YJ2QWneWL+Zr/KGLvOuGvblsceq1mEymdL8+fDW2/BM89YfgkRETVkc/UAthMiEWkjIvNFpEBEMkXk9nO0fVREtojISRFJF5FHq9RniMgpEcl3Pb7z/ghq5sz+UNM4rGhoHC7q0orfXt2Hr7cc5s/fbufLPcW2d+k+ePwUB/OKjBD5GdUt2U2fbkXyyciwluuqE6NKcenqge2ECJgOFAMdgARghogMqKGtAHcBEcC1wIMicluVNuNVtYXrcbW3jK4NiduP0SE8hE3783xphsEPmTo6lou7hPNGUhqf7Sqx/fmictuGRRnvUH+iuiW74cPhr3+F0aOt6At/+1vlbK7VxqWrI7YSIhFpDkwCnlbVfFVdDiwE7qyuvaq+rKrrVLVUVXcAC4BRjWdx7UncfoS9uYVkZhfa/kPEYD8cDqlwVlCgxObni1IycwkNCqBvp5a+NsVQB6pbsit3XkhNtWKdfvghPOpaezqX00NdsJvXXG+gTFV3upVtBK4834ViZdsaDbxVpWq2iDiA9cCjqrqxhuunAdMA2rVrR1JSUt2tPwevrj0FuA4pljiZs2gNJ3sEe/QetSU/P9/j47MTTXV8bYvKXDHvFIcIIcczSUra72uzqmXJllNEtYQVy5bW6bqm+tqV46/jE4GnnmrNE09czNdfB/Dcc6m0alXCc8/155lntiJynIYMy25C1AKoum6VB9Tma9WzWDO8f7qVJQDrsJbw/hv4VkT6qurxqher6kxgJkCfPn00Li6urrbXSFFJGfuW/oBDnAjWKfMp44b7bP08KSkJT47PbjTV8cUBpW328NLX23n02r7cN8Y72TIbSmFxKXu/+477r+xBXFyfOl3bVF+7cvx5fHFxcPHFcN118MorAwgJsRwa4uMHN7jvRl2aE5EkEdEaHsuBfKBqQvtw4OR5+n0Qa6/oelU9XV6uqitU9ZSqFqrqi8BxrFlTozJ//QGOF5bwvzf0r4hXZjZxDfXhnlExtAyCjTbeZ9y4L48yp5r3eBNk3DjrcGt+vuXO7alzRY06I1LVuHPVu/aIAkWkl6ruchUPAlLPcc09wOPAGFU93zqFciZbc6NQ5lTeWrKHgV1bcffl0Yg06u0NTYzgQAeXdgrk+61HyDtVQqvQIF+bdBbr9lr7n5d0N9FDmhqJibBgATzxhBVm7vbbPSNGtnJWUNUCYB7wvIg0F5FRwATgg+rai0gC8EfgKlVNq1LXXURGiUiwiDRzuXa3BVZ4dxSV+WbLYTKyC/mvK3sYETJ4hFFdAikudfLVpkO+NqVa1mbk0Kt9C1qH+WYP1OAd3B0TXnjhTGI9T5wrspUQuXgACAWOAnOA+1U1FUBERotIvlvbPwCRwBq3s0JvuupaAjOAXOAAlnv3daraaK5GqsqbS/YQ07Y51wzo2Fi3NTRxosMd9Gzfgnnr7Oeo4HQq6/YeN8tyTYya4tJ5Sozs5qyAquYAE2uoW4bl0FD+POYc/aQCAz1uYB1YsTubzQfyeOnmiwlwmNmQwTOICDcP6cLL3+wgM7uAqMjmvjapgrSsfPJOlRghakLUJi5deX19seOMqMkwY8lu2rcM4aYhXXxtiqGJMXFwF0Rg3roDvjalEmszrP0hI0RNh/PFpSsXI/dDrnXFCJGX2LT/OCt2Z3PvFTGEBNaUe9dgqB+dW4dyeY9I5q3fb6v4cymZubRpHkxMW/vM0gwN41xx6cqJj7fa1RcjRF7izSV7aNkskNsvbXg+d4OhOm6+pCv7ck6x1kZROlIycxnSPcI45hjqhBEiL/DFxgP8Z/Nhru7fgZbN7Odea2gaXHtRR8KCA2zjtJBTUExaVoFZljPUGSNEHiYlM5eH/21FEfpy0yETU87gNZqHBHLtRR35ctMhikrKfG0O6zLN/pChfhgh8jDLdx2jzGmt2ZeW2TswpcH/mTSkKyeLSnn0040+/9KzNjOXoAAxaU4MdcYIkadxrY07xIopZ5LgGbxJcKD1L/zFxkM+j+q+ZMdR2rYIIfXgCZ/ZYPBPjBB5mE37jhPZPJj/uaq3iSln8Dqr03MqYlb5MjXEyj1ZbDt8ksN5RT4XRIP/YYTIg+QUFLNk5zFuGdqVB8f2MiJk8DojYyMrZkUi4rMZ+Kzl6YB/5Eoy2A8jRB7kq82HKHUqEwabA6yGxmFoVAQfTR1Jt4hQ2jQP4pJujR9otLjUyYa9xxGBALMkbagHtgvx488sWH+A3h1a0M9kpTQ0IkOjIvjtNX3474838OOebK7o1bZR7z9v3X6yC4p5+oZ+FJU4GRkbaVYDDHXCCJGH2JdTyNrMXB69po85zGdodK4Z0JFWoUF8vGZvowpRaZmTN5KsNCf3jIox731DvTBLcx5i4caDAEwY3NnHlhguRJoFBXDTJV34LvUIOQXFjXbfLzYdZG9OIQ/G9zQiZKg3Rog8gKry+foDDI+OoGtEmK/NMVyg3DaiG8VlzkaLtOB0Kq8v3k3fji0Z169Do9zT0DQxQuQBth46wa6j+dxonBQMPqRvx3AGd2vNx2v2NUog1K+3HGbPsQIeHNsTh0lzYmgARog8wIINBwl0CNdf3MnXphgucKaM6Mbuo/leP8ejqvxj8S5i2zXnuovM+97QMIwQNZAyp7Jww0Gu7N2ONs1NamSDb7lhYGeaBwfw8Zp9Xr3PD9uOsv3wSX4V19MkfTQ0GNsJkYi0EZH5IlIgIpkicvs52j4rIiVuacLzRSTWrX6wiKSISKHr52BP27sqPZvDJ4qYcIlZljP4nuYhgdw4uDNfbjrIiaISr9wjJSOHpxZsoX3LYG40zjkGD2A7IQKmA8VAByABmCEiA87R/t+q2sLtkQYgIsHAAuBDIAJ4H1jgKvcYCzccpHlwAFeZzVqDTfjZ8O4UlThZuOGgx/tOycxlyturOJxXRG5hCZv253n8HoYLD1sJkYg0ByYBT6tqvqouBxYCd9ajuzisc1KvqeppVf07IMBYT9mbnJbF/PUHGBYdQWiwycJqsAeDuraib8eWfLxmr8f7Tk7LprjMCVhecyaUj8ET2O1Aa2+gTFV3upVtBK48xzXjRSQHOAS8rqozXOUDgE1a2X1ok6v8m6qdiMg0YBpAu3btSEpKOqehu3PLeGl1EaUKK3ZlMWv+D/SM8A8xys/PP+/4/BkzPhgaUcLsbcXc+8a3XNYp0GPvzcDc0orfAwRCjmeSlOQ5d3Hz2l2Y2E2IWgBV5/p5QE0xc+YCM4EjwKXAZyJyXFXn1LUvVZ3p6os+ffpoXFzcOQ3dsngXpS69VOB06yji4nqe8xq7kJSUxPnG58+Y8YFjxzFmb1vND3tLWXHI6bFI8KFp2bA6mYmDO3PnZdEeD+VjXrsLk0YVIhFJoubZzQrgISC8Snk4cLK6C1R1q9vTH0Xkb8AtwBwgvy591RVX7juTd8hgSzYfPPMdrDwatidEY+muYwQ4hOcnXkR4s6AG9VVSUsL+/fspKiqqKGvVqhXbtm1rqJm2xZ/HFxAQQOvWrWnbti0Oh2d3dRpViFQ17lz1rj2iQBHppaq7XMWDgNTa3gIq0rOkAr8REXFbnhuI5QzRIMqcysKNB+kaEcptw7txWY+2JsijwVaMjI0kOMBBcZkTh8Nz6SGW7crikm6tGyxCAPv376dly5ZER0dXhAc6efIkLVs23aDB/jo+VaWkpIQjR46wf/9+unfv7tH+beWsoKoFwDzgeRFpLiKjgAnAB9W1F5EJIhIhFiOAX2N5ygEkAWXAr0UkREQedJUvbqidX246yO6j+fz+un4m75DBlgyNimD21EtpGRJI/07hHnmP5hQUs/lAHmN6t/OAhVBUVERkZKSJUecHiAjBwcF06dKFgoICj/dvKyFy8QAQChzFWmK7X1VTAURktIjku7W9DdiNtdz2L+BPqvo+gKoWAxOBu4DjwD3ARFd5vSktc/Laol307diS6y7q2JCuDAavMjy6Db8YFc2mA3kcPH6qwf0t352FKoz2YHRvI0L+haeX5Mqxm7MCqpqDJSDV1S3DckIofz7lPH2tB4Z60r7PNxwkPauAt+4cauJrGWzPLUO78ffFu5m3bj8Pju3VoL6W7jxGq9AgBnZt/OR7hqaNHWdEtqWkzMnff9jFgM7hXN3fHGA12J/ukWFcGtOGT1P2NygQqqqybNcxrujZ1oT0MXgcI0R1YN66/ezNKeR/ruptlhQMfsOtw7qRkV3Imoz6B0LdeSSfIydOM6Z342Z/NdSNpKQkRISsrCxfm1InjBDVkuJSJ3//YTeDurVmbN/2vjbHYKg1P724I82DA/hkbf0DoS7deQyA0b0846jQFFi/fj0BAQGMGjWqTtc9++yzXHTRRV6yyj8xQlRL/vLdDg4cP8WNgzqZ2ZDBrwgLDuSGgZ35avMhCk6Xnv+Cali66xg927egc+tQD1vXQGbPhuhocDisn7NnN9qt3377bR544AG2bNnit2eD7IIRolqwKi2bmUvTAPjztzu8nuvFYPA0tw7rSmFxGV9tPlTna4tKylidnsMYu82GZs+GadMgMxNUrZ/TpjWKGJ06dYqPPvqIqVOncsstt/DOO+9Uqj948CAJCQlERkYSFhbG4MGDSUxMZPbs2Tz33HOkpqYiIogI7733HmB5EH766aeV+omOjuYvf/lLxfNXX32VgQMH0rx5c7p06cJ9993H8ePHvT5eb2M7rzk78t6PGRW/e/KUusHQWAyNiiC2bXM+XbufycO61ena1ek5nC51Mtrb+0MPP0xoSgoE1DIuXnIynD5duaywEO69F95+u3Z9DB4Mr71WNzuBTz/9lKioKAYOHMidd97J5MmTefHFFwkKCqKgoIArr7yS9u3bM3/+fLp06cLGjRsBuPnmm9m9ezdffvllRcy5Vq1a1fq+DoeD1157jdjYWDIzM3nooYd46KGH+OCDao9a+g1GiM6D06ls2n8cwYTzMfgvIsItw7ry8jc7yMgqILpt81pfu3TnMYIDHIyMsdn7vqoIna/cg8yaNYs777SSAlx55ZWEhYWxcOFCJk2axEcffcThw4dZuXIlbdta4t2jRw/AiqzQokULAgMD6dix7ucQH3744Yrfo6Ojefnll5kwYQLvv/++1874NAZGiM7Dom1HOHC8iIfH9SIowBIhMxsy+COThnTlz9/s4PF5m3j0mr61fh8v25XF8JhGSHXy2mucqksInOhoazmuKlFR4MUI17t372bFihXMmTMHsEQ+ISGBWbNmMWnSJNavX8/AgQMrRMiTLF68mBdffJFt27aRl5dHWVkZxcXFHD58mM6d/TdJoRGic6CqvLlkD93ahPJgfE8CA/z3G4fBsD/3FCKQnJZDwqzkWkXkPpxXxI4jJ7l5SN9GsrIOvPCCtSdUWHimLCzMKvcis2bNoqysrFK8tfIzWvv27av3eS0ROevakpIzWXYzMzO5/vrrmTp1Ks8//zyRkZGsW7eOKVOmUFzcoIAxPsd8sp6DNRm5rNt7nKmjY40IGfye5LRsyj/nTpc4a5XUbtkuG7ttJyTAzJnWDEjE+jlzplXuJUpLS3n//fd58cUX2bBhQ8Vj48aNDBw4kH/+858MGTKETZs21XiWJzg4mLKysrPK27Vrx6FDZ5xJjhw5Uun52rVrKS4u5q9//SuXXXYZvXv35uBBz2fh9QVmRnQO3lyyhzbNg7l1aN02dw0GOzIyNpKQIAdFJU4UiIoMO+81S3dl0bZFCP062TRidEKCV4WnKl999RVZWVlMnTqVyMjKe2a33XYbM2bMIDU1lZdeeomJEyfy4osv0rVrVzZv3kzLli0ZNmwY0dHRZGZmsm7dOrp3707LlrdIsWQAABIuSURBVC0JCQlh7NixTJ8+ncsvv5yAgACeeOIJmjVrVtF/r169cDqdvPbaa9x8880kJyfzWj0cLeyI+ZpfAzsOn2Tx9qP8/PJokwbc0CQYGhXB7PtGcn9cD8KCA/jXj5k4nTUvIzmdStL2I3RoGcK6vf7vIuwJ3nnnHeLj488SIYBbb72VzMxMVqxYwZIlS+jSpQvjx49nwIABPPPMMxXnDydNmsRPf/pTfvKTn9CuXbuKvaZXXnmF2NhY4uLiuOWWW7jvvvto3/7M4fmBAwfyt7/9jVdffZX+/fsza9asSq7d/ow0JP5UU6VPnz56/fP/5pvUw/z4+FhahwX72iSP0tSzRJrxnZ9P1u7j0U838fyEAdx1WXS1bV74aitvL0tHgJAgh8eyvJazbds2+vXrV6nMX/P11JamML7qXrdyRCRFVYfVtU8zI6qGUics2HiQKSO6NzkRMhgAbhnalTG92/HS19vZl1NYqU5VeW3RTt5elm4958z5OYPBGxghqoYTxYoA914R42tTDAavICK8ePPFOER4fN6mCm+t0jInT8zfzGuLdhHXux3NghwEmPNzBi9jnBWq4USxcn3PtvaLq2UweJAurUP5/U/78uT8Lfx7zT4mDO7CQ3PWs2jbEX4V34PfXt2HdXuPk5yWbc7PGbyKEaIaWJmWTUpmrvnnMzRppgzvzpcbD/H8F1v52w+7OJRXVGnfaGhUhPkfMHgd2y3NiUgbEZkvIgUikikit5+j7dciku/2KBaRzW71GSJyyq3+u9raUVpm1sQNTR+HQ7hjZHcKS8o4lFdEUIAwoHPtY58ZDJ7AjjOi6UAx0AEYDHwlIhtVNbVqQ1W9zv25iCQBi6s0G6+qi+pqhFkTN1woZGQX4hBwquWybYL6GhobW82IRKQ5MAl4WlXzVXU5sBC4sxbXRgOjgQaHoY0IEY+7qhoMdmVkbCTBgcYpweA77DYj6g2UqepOt7KNwJW1uPYuYJmqplcpny0iDmA98KiqbjxfR61CxIiQ4YKh/KCrcUow+Aq7CVELIK9KWR5QmxNgdwF/qFKWAKwDBPhv4FsR6auqZx0TF5FpwDSwYj4leTF6r6/Jz8834/NjvDW+AQL/397dB0dR5gkc//7IC4kBRCBENyuBEAgvFpAoKIdwWLDCnlW3etlLWcErQsGhIMVxvqAlJwTUXaMrt7incBRQIp5oCjyvLN6qrkisxDcQzIIEFFkJ4gGnHkqAECnyuz+6M0yGCS/DTHp6+H2qukg/T3fz/DI9+c10P/08DV8fpir0o1yMXH/99TQ0NLQqO3fu3AVliSQR4jtz5kz0zz9VbbcFqMJ5Pi7cUgMUAKdD9nkUeO8Sx70TOAl0usR2+3DuGV20nf3799dEVllZ6XUTYsri84e6uroLyk6cOHHJ/crLVbduvfg2W7c628Wby4kv3oV73VoAn2oEuaFd7xGp6lhVlTaWO4EvgWQR6Re021Dggo4KISYD76jqyUs1AefbkTHGp4YPh+JiqKwMX19Z6dQPHx6b/7+0tDQwzXfwUltbG5v/sB1UVVUhIm2OGB5rcdVZQVVPAe8Ai0QkQ0RGAb/hIh0QRCQd+HvgtZDyXiIySkRSRSRNRB4HegAfxCwAY0zM3XUXVFSET0YtSaiiwtkuVsaPH8+RI0daLbfccktExwqec+haFVeJyDUTSAf+F1gLzFC367aIjBaR0G899+LcRwr9fNQZWAocB74FJgK/VlV7OMgYnwuXjNorCQF07NiRG2+8sdWSnJxMU1MTc+bMISsri7S0NO644w5qamoC+1VXVyMibNy4kREjRpCamsqWLVsAeO+997j11ltJS0ujT58+zJs3r9WEdz///DNPPfUUOTk5dOzYkdzcXF5++WXAufc0depU+vTpQ3p6Ov369eOFF16gubk5sP/u3bsZN24cXbp0oXPnzgwdOpTKykoOHjzIXe4vLDMzExGhtLQ0tr/AEPHWWQFV/T+c5BKurhqnQ0Nw2VqchBW67R5gSCzaaIyJvjlzYMeOdJKuYNaVX/wCJkyAm26CI0dg4EBYuNBZLsewYRDNKX3mzp1LRUUFq1atIjc3l8WLFzNx4kT279/PTTfdFNjuiSee4KWXXiIvL4/OnTuzZcsWJk2axJIlSxgzZgyHDh3ioYceoqmpKTDVw+TJk6murmbJkiUUFBRQX1/PN998A0BzczPZ2dlUVFSQmZnJtm3bmD59Ot27d2fq1KkAlJSUMHToULZt20ZycjK7d+8mLS2Nm2++mfXr11NUVMSePXvo1q0b6entO7xZ3CUiY4y5XDfc4CShQ4egVy9nvT1s3ryZTp3OfyYePXo069atY+nSpaxYsYJ77rkHgGXLlrF161ZeeeUVnn32fKfesrIy7r777sD6c889x+OPP86UKVMA6Nu3L+Xl5TzwwAO8+OKLfPXVV7z11lts2rSJiRMnApCbmxvYPyUlhUWLFgXWe/fuzc6dO1m7dm0gEdXX1/PYY48xYIAz7XteXl5g+27dugHQs2dPevToEZ1f0hWwRGSMiQt//CM0NDRe0Xw9LZfjnn4ali6FBQtif1kOYMyYMSxfvjywnp6ezoEDBzh79iyjRo0KlCclJTFy5Ejq6upa7X/bba2n7NmxYwfbtm2jvLw8UNbc3ExjYyNHjx7ls88+o0OHDoFLaOEsW7aMFStWUF9fT2NjI2fPniUnJydQ/8gjjzBt2jRWr17NuHHjKCoqCiQlr8XjPSJjjLmk4HtCixa13YEhFq677jry8vICS3Z2dmAqjZaZWIOFlmVkZLRab25uZsGCBdTW1gaWXbt2sX//fjIzMwPHbsvbb7/NnDlzKC0tZcuWLdTW1jJz5sxW95jKysqoq6vj3nvv5cMPP2TIkCGsWrUq0l9BVFkiMsb4TriOCRfrTdce8vLySE1NbdU54dy5c3z00UcMGjToovsWFhayb9++VsmtZUlOTqawsJDm5mYq2wispqaG22+/nVmzZlFYWEheXh4HDhy4YLt+/foxe/ZsNmzYwNSpU1mxYgUAqampgfZ6wRKRMcZXLtY7zstklJGRwYwZM3jyySfZuHEje/fuZcaMGRw7doyZM2dedN/58+fz5ptvMn/+fD7//HP27dvHunXrmDt3LuAkkOLiYqZNm8b69ev5+uuvqa6uZs0a58mW/v37s3PnTjZt2sT+/ft55plneP/99wPHb2xs5OGHH6aqqoqDBw/yySefUFNTE0iQOTk5iAgbNmzgu+++4+TJSz2SGV2WiIwxvrJ9+8W7aLcko+3b27ddAOXl5RQXFzNlyhSGDRvGrl272Lx5c6sec+FMmDCBDRs2UFlZyYgRIxgxYgTPP/88vXr1Cmzz+uuvU1JSwuzZsxkwYAClpaX89JMzItqDDz5IcXExJSUlDB8+nIMHD/Loo48G9k1KSuL48eNMnjyZ/Px87rvvPkaOHMnixYsByM7OZuHChcybN4+srCxmzZoVg99O2+RS1x6vRfn5+frFF1943YyYqaqqYuzYsV43I2YsPn/Yu3cvAwcObFXW0NBwRZ0V/CYR4gv3urUQkR2qelvYyouwb0TGGGM8ZYnIGGOMpywRGWOM8ZQlImOMMZ6yRGSM8Yx1lvKXWL1eloiMMZ5ISkqyKRB8prGxkZSUlKgf1xKRMcYTXbt25dixY62mKjDxSVU5ffo03377LT179oz68W3QU2OMJ3r06MHhw4cJfmbvzJkzpKWlediq2PJzfCkpKWRlZdGlS5eoH9sSkTHGEx06dGg1cgA4D+sWFBR41KLYS/T4ImWX5owxxnjKEpExxhhPxV0iEpFZIvKpiDSJyGuXsf0/i8hREflJRFaJSMegut4iUikip0Vkn4iMj2njjTHGXLG4S0TA/wDPApecsUlEJgBPAuOA3kAuEDxb/VrgM6A7MA9YJyKZUW6vMcaYqxB3iUhV31HVd4EfLmPzycBKVd2jqseBZ4BSABHpDxQCC1S1UVXXA7uBoti03BhjTCT83mtuMPBfQet/BrJEpLtb9xdVbQipHxzuQCIyHZjurjaJyOcxaG+86AF873UjYsji869Ejg0SP778SHbyeyLqBPwUtN7yc+cwdS312eEOpKrLgeUAIvJpJHNq+IXF52+JHF8ixwbXRnyR7Neul+ZEpEpEtI2l5tJHuMBJIPjpqpafG8LUtdQ3YIwxJm60ayJS1bGqKm0sd0ZwyD3A0KD1ocAxVf3BrcsVkc4h9Xsij8AYY0y0xV1nBRFJFpE0IAlIEpE0EWnrEuLrwFQRGSQiNwD/ArwGoKpfArXAAvcY9wFDgPWX0YzlVxtHnLP4/C2R40vk2MDiC0vibRh2ESkDFoQUL1TVMhHpBdQBg1T1kLv9I8ATQDpOknlIVZvcut44iel24BDwsKr+d+yjMMYYc7niLhEZY4y5tsTdpTljjDHXFktExhhjPGWJKIiIdBOR/xSRUyJSLyIlXrfpalxs3D4RGeeOv3faHY8vx6NmRkREOorISvd1ahCRz0Tk10H1vo4PQETeEJEjInJCRL4UkWlBdb6PD0BE+onIGRF5I6isxH1dT4nIuyLSzcs2Rsp9XOWMiJx0ly+C6nwfo4jcLyJ73RgOiMhot/yKz01LRK29AvwMZAGTgKUiEnYkBp8IO26fiPQA3gGeBroBnwJvt3vrrk4y8A3w18D1OLFUuAPdJkJ8AL8HeqtqF+BvgWdF5NYEig+c99z2lhX3/fbvwD/gvA9PA69607SomKWqndwlHxIjRhH5FVAOTMEZQGAM8JdIz03rrOASkQzgOHCL2/UbEVkDfKuqT3rauKskIs8Cv1TVUnd9OlCqqn/lrmfgDDtSoKr7PGvoVRKRXTiD3nYnweITkXygCvgnoCsJEJ+I3A/8HU5P2DxVfUBEfoeTfEvcbfoCe4HuIcN1xT0RqQLeUNUVIeW+j1FEPsQZ53NlSHlEf1vsG9F5/YFzLUnI1ebYdD43GCc2AFT1FHAAH8cqIlk4r+EeEig+EXlVRE4D+4AjwEYSID4R6QIsAh4NqQqN7QDOVYr+7de6qPq9iHwvIh+IyFi3zNcxikgScBuQKSJfichhEfk3EUknwnPTEtF5bY1N1znMtn6XULGKSArwH8Bq91NXwsSnqjNx2j0a55JHE4kR3zM4n6i/CSlPhNhaPIEzNU02zoOe77nffvweYxaQAvwW57wcBhTgDCgQUWyWiM67lsamS5hYRaQDsAbnE+Ustzhh4gNQ1XOqWgP8EpiBz+MTkWHAeOBfw1T7OrZgqvqJqjaoapOqrgY+AP4G/8fY6P77J1U9oqrfA4u5itgsEZ33JZAsIv2CyhJ1bLpWY/S513H74rNYRUSAlTif0IpU9axblRDxhZHM+Tj8HN9YnIksD4nIUeAxoEhEdnJhbLlAR5z3p98pIPg8Rnfut8M48YSK7NxUVVvcBXgLZ1bXDGAUzlfKwV636yriSQbScHpfrXF/TgYy3diK3LJy4GOv2xtBfMuAj4FOIeW+jw/oCdyPc6kjCZgAnAJ+4/f4gOuAG4OWPwDr3LgGAydwLvlkAG8Ab3nd5ghi7Oq+Zi3vuUnu65efCDHi3N/b7p6nNwDVOJdbIzo3PQ8onhac7obvuifMIaDE6zZdZTxlOJ9agpcyt248zg3wRpzeWL29bu8VxpbjxnMG53JAyzIpQeLLBN4HfnT/aO0G/jGo3tfxhcRahtO7rGW9xH3/ncKZ+LKb122M8PXbjnNJ6kecD0y/SpQYce4RverGdhR4GUhz66743LTu28YYYzxl94iMMcZ4yhKRMcYYT1kiMsYY4ylLRMYYYzxlicgYY4ynLBEZY4zxlCUiY4wxnrJEZIxPiEgXESkTkYFet8WYaLJEZIx/3AYswHmq3ZiEYYnIGP8owJkGos7rhhgTTTbEjzE+ICJ7gQEhxetV9bdetMeYaLJEZIwPiMhwnNHh9wC/c4uPqGq9d60yJjqSvW6AMeay/BlnYrw/qerHXjfGmGiye0TG+MNgIBXY6XVDjIk2S0TG+EMhzvxLtV43xJhos0RkjD8UAAdU9YTXDTEm2iwRGeMPg7Bu2yZBWWcFY/zhR6BQRCYAPwH7VfUHj9tkTFRY921jfEBEbgFWAkOANGC0qtZ42ypjosMSkTHGGE/ZPSJjjDGeskRkjDHGU5aIjDHGeMoSkTHGGE9ZIjLGGOMpS0TGGGM8ZYnIGGOMpywRGWOM8dT/A60l53RHJoJiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an RNN that predicts the next 10 steps at each time step. That is, instead of just forecasting time steps 50 to 59 based on time steps 0 to 49, it will forecast time steps 1 to 10 at time step 0, then time steps 2 to 11 at time step 1, and so on, and finally it will forecast time steps 50 to 59 at the last time step. Notice that the model is causal: when it makes predictions at any time step, it can only see past time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 10)\n",
    "X_train = series[:7000, :n_steps]\n",
    "X_valid = series[7000:9000, :n_steps]\n",
    "X_test = series[9000:, :n_steps]\n",
    "Y = np.empty((10000, n_steps, 10))\n",
    "for step_ahead in range(1, 10 + 1):\n",
    "    Y[..., step_ahead - 1] = series[..., step_ahead:step_ahead + n_steps, 0]\n",
    "Y_train = Y[:7000]\n",
    "Y_valid = Y[7000:9000]\n",
    "Y_test = Y[9000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 50, 1), (7000, 50, 10))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 7s 997us/sample - loss: 0.0743 - last_time_step_mse: 0.0648 - val_loss: 0.0470 - val_last_time_step_mse: 0.0343\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 6s 849us/sample - loss: 0.0440 - last_time_step_mse: 0.0310 - val_loss: 0.0412 - val_last_time_step_mse: 0.0267\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 6s 852us/sample - loss: 0.0397 - last_time_step_mse: 0.0258 - val_loss: 0.0379 - val_last_time_step_mse: 0.0232\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 6s 861us/sample - loss: 0.0373 - last_time_step_mse: 0.0225 - val_loss: 0.0361 - val_last_time_step_mse: 0.0214\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 6s 876us/sample - loss: 0.0359 - last_time_step_mse: 0.0211 - val_loss: 0.0355 - val_last_time_step_mse: 0.0205\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 7s 929us/sample - loss: 0.0345 - last_time_step_mse: 0.0196 - val_loss: 0.0335 - val_last_time_step_mse: 0.0182\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 6s 925us/sample - loss: 0.0337 - last_time_step_mse: 0.0189 - val_loss: 0.0324 - val_last_time_step_mse: 0.0170\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 6s 856us/sample - loss: 0.0327 - last_time_step_mse: 0.0180 - val_loss: 0.0317 - val_last_time_step_mse: 0.0170\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 6s 823us/sample - loss: 0.0320 - last_time_step_mse: 0.0173 - val_loss: 0.0310 - val_last_time_step_mse: 0.0158\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 6s 818us/sample - loss: 0.0312 - last_time_step_mse: 0.0165 - val_loss: 0.0302 - val_last_time_step_mse: 0.0151\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 6s 834us/sample - loss: 0.0306 - last_time_step_mse: 0.0162 - val_loss: 0.0297 - val_last_time_step_mse: 0.0151\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 6s 825us/sample - loss: 0.0301 - last_time_step_mse: 0.0156 - val_loss: 0.0293 - val_last_time_step_mse: 0.0149\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 6s 834us/sample - loss: 0.0296 - last_time_step_mse: 0.0153 - val_loss: 0.0291 - val_last_time_step_mse: 0.0148\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 6s 821us/sample - loss: 0.0293 - last_time_step_mse: 0.0153 - val_loss: 0.0291 - val_last_time_step_mse: 0.0146\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 6s 843us/sample - loss: 0.0286 - last_time_step_mse: 0.0146 - val_loss: 0.0281 - val_last_time_step_mse: 0.0138\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 6s 854us/sample - loss: 0.0282 - last_time_step_mse: 0.0144 - val_loss: 0.0273 - val_last_time_step_mse: 0.0131\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 6s 822us/sample - loss: 0.0275 - last_time_step_mse: 0.0137 - val_loss: 0.0272 - val_last_time_step_mse: 0.0138\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 6s 835us/sample - loss: 0.0271 - last_time_step_mse: 0.0136 - val_loss: 0.0264 - val_last_time_step_mse: 0.0127\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 6s 836us/sample - loss: 0.0266 - last_time_step_mse: 0.0133 - val_loss: 0.0262 - val_last_time_step_mse: 0.0124\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 6s 845us/sample - loss: 0.0262 - last_time_step_mse: 0.0129 - val_loss: 0.0261 - val_last_time_step_mse: 0.0125\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "def last_time_step_mse(Y_true, Y_pred):\n",
    "    return keras.metrics.mean_squared_error(Y_true[:, -1], Y_pred[:, -1])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
    "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep RNN with Batch Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.1939 - last_time_step_mse: 0.1922 - val_loss: 0.0876 - val_last_time_step_mse: 0.0834\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 7s 987us/sample - loss: 0.0536 - last_time_step_mse: 0.0448 - val_loss: 0.0553 - val_last_time_step_mse: 0.0467\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 7s 933us/sample - loss: 0.0472 - last_time_step_mse: 0.0377 - val_loss: 0.0450 - val_last_time_step_mse: 0.0353\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 7s 929us/sample - loss: 0.0438 - last_time_step_mse: 0.0338 - val_loss: 0.0423 - val_last_time_step_mse: 0.0331\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 6s 917us/sample - loss: 0.0408 - last_time_step_mse: 0.0304 - val_loss: 0.0394 - val_last_time_step_mse: 0.0285\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 6s 914us/sample - loss: 0.0385 - last_time_step_mse: 0.0277 - val_loss: 0.0381 - val_last_time_step_mse: 0.0274\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 6s 908us/sample - loss: 0.0365 - last_time_step_mse: 0.0253 - val_loss: 0.0363 - val_last_time_step_mse: 0.0244\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 6s 909us/sample - loss: 0.0350 - last_time_step_mse: 0.0236 - val_loss: 0.0348 - val_last_time_step_mse: 0.0242\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 6s 913us/sample - loss: 0.0339 - last_time_step_mse: 0.0223 - val_loss: 0.0333 - val_last_time_step_mse: 0.0212\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 6s 917us/sample - loss: 0.0330 - last_time_step_mse: 0.0215 - val_loss: 0.0345 - val_last_time_step_mse: 0.0241\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 7s 931us/sample - loss: 0.0321 - last_time_step_mse: 0.0206 - val_loss: 0.0319 - val_last_time_step_mse: 0.0203\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 6s 912us/sample - loss: 0.0316 - last_time_step_mse: 0.0199 - val_loss: 0.0314 - val_last_time_step_mse: 0.0200\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 6s 905us/sample - loss: 0.0312 - last_time_step_mse: 0.0195 - val_loss: 0.0313 - val_last_time_step_mse: 0.0195\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 6s 908us/sample - loss: 0.0307 - last_time_step_mse: 0.0190 - val_loss: 0.0339 - val_last_time_step_mse: 0.0227\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 6s 909us/sample - loss: 0.0303 - last_time_step_mse: 0.0183 - val_loss: 0.0306 - val_last_time_step_mse: 0.0184\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 6s 896us/sample - loss: 0.0297 - last_time_step_mse: 0.0178 - val_loss: 0.0292 - val_last_time_step_mse: 0.0172\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 6s 887us/sample - loss: 0.0294 - last_time_step_mse: 0.0174 - val_loss: 0.0291 - val_last_time_step_mse: 0.0168\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 6s 906us/sample - loss: 0.0291 - last_time_step_mse: 0.0172 - val_loss: 0.0286 - val_last_time_step_mse: 0.0160\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 6s 903us/sample - loss: 0.0288 - last_time_step_mse: 0.0169 - val_loss: 0.0294 - val_last_time_step_mse: 0.0174\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 6s 895us/sample - loss: 0.0282 - last_time_step_mse: 0.0161 - val_loss: 0.0289 - val_last_time_step_mse: 0.0171\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep RNNs with Layer Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import LayerNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LNSimpleRNNCell(keras.layers.Layer):\n",
    "    def __init__(self, units, activation=\"tanh\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.state_size = units\n",
    "        self.output_size = units\n",
    "        self.simple_rnn_cell = keras.layers.SimpleRNNCell(units,\n",
    "                                                          activation=None)\n",
    "        self.layer_norm = LayerNormalization()\n",
    "        self.activation = keras.activations.get(activation)\n",
    "    def get_initial_state(self, inputs=None, batch_size=None, dtype=None):\n",
    "        if inputs is not None:\n",
    "            batch_size = tf.shape(inputs)[0]\n",
    "            dtype = inputs.dtype\n",
    "        return [tf.zeros([batch_size, self.state_size], dtype=dtype)]\n",
    "    def call(self, inputs, states):\n",
    "        outputs, new_states = self.simple_rnn_cell(inputs, states)\n",
    "        norm_outputs = self.activation(self.layer_norm(outputs))\n",
    "        return norm_outputs, [norm_outputs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 17s 2ms/sample - loss: 0.1638 - last_time_step_mse: 0.1463 - val_loss: 0.0718 - val_last_time_step_mse: 0.0670\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0620 - last_time_step_mse: 0.0533 - val_loss: 0.0551 - val_last_time_step_mse: 0.0432\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0520 - last_time_step_mse: 0.0402 - val_loss: 0.0485 - val_last_time_step_mse: 0.0365\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0460 - last_time_step_mse: 0.0335 - val_loss: 0.0435 - val_last_time_step_mse: 0.0300\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0419 - last_time_step_mse: 0.0294 - val_loss: 0.0399 - val_last_time_step_mse: 0.0276\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0391 - last_time_step_mse: 0.0267 - val_loss: 0.0373 - val_last_time_step_mse: 0.0246\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0370 - last_time_step_mse: 0.0251 - val_loss: 0.0364 - val_last_time_step_mse: 0.0257\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0351 - last_time_step_mse: 0.0232 - val_loss: 0.0346 - val_last_time_step_mse: 0.0235\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0340 - last_time_step_mse: 0.0220 - val_loss: 0.0329 - val_last_time_step_mse: 0.0207\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0328 - last_time_step_mse: 0.0206 - val_loss: 0.0316 - val_last_time_step_mse: 0.0190\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0318 - last_time_step_mse: 0.0199 - val_loss: 0.0310 - val_last_time_step_mse: 0.0187\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0312 - last_time_step_mse: 0.0189 - val_loss: 0.0306 - val_last_time_step_mse: 0.0185\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0305 - last_time_step_mse: 0.0182 - val_loss: 0.0296 - val_last_time_step_mse: 0.0168\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0299 - last_time_step_mse: 0.0175 - val_loss: 0.0295 - val_last_time_step_mse: 0.0169\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0295 - last_time_step_mse: 0.0170 - val_loss: 0.0288 - val_last_time_step_mse: 0.0157\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0290 - last_time_step_mse: 0.0164 - val_loss: 0.0294 - val_last_time_step_mse: 0.0180\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0285 - last_time_step_mse: 0.0160 - val_loss: 0.0289 - val_last_time_step_mse: 0.0156\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0281 - last_time_step_mse: 0.0155 - val_loss: 0.0286 - val_last_time_step_mse: 0.0171\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0278 - last_time_step_mse: 0.0154 - val_loss: 0.0272 - val_last_time_step_mse: 0.0146\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0273 - last_time_step_mse: 0.0148 - val_loss: 0.0269 - val_last_time_step_mse: 0.0145\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True,\n",
    "                     input_shape=[None, 1]),\n",
    "    keras.layers.RNN(LNSimpleRNNCell(20), return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Custom RNN Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyRNN(keras.layers.Layer):\n",
    "    def __init__(self, cell, return_sequences=False, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.cell = cell\n",
    "        self.return_sequences = return_sequences\n",
    "        self.get_initial_state = getattr(\n",
    "            self.cell, \"get_initial_state\", self.fallback_initial_state)\n",
    "    def fallback_initial_state(self, inputs):\n",
    "        return [tf.zeros([self.cell.state_size], dtype=inputs.dtype)]\n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        states = self.get_initial_state(inputs)\n",
    "        n_steps = tf.shape(inputs)[1]\n",
    "        if self.return_sequences:\n",
    "            sequences = tf.TensorArray(inputs.dtype, size=n_steps)\n",
    "        outputs = tf.zeros(shape=[n_steps, self.cell.output_size], dtype=inputs.dtype)\n",
    "        for step in tf.range(n_steps):\n",
    "            outputs, states = self.cell(inputs[:, step], states)\n",
    "            if self.return_sequences:\n",
    "                sequences = sequences.write(step, outputs)\n",
    "        if self.return_sequences:\n",
    "            return sequences.stack()\n",
    "        else:\n",
    "            return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 17s 2ms/sample - loss: 0.2280 - last_time_step_mse: 0.2059 - val_loss: 0.1155 - val_last_time_step_mse: 0.1047\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0883 - last_time_step_mse: 0.0784 - val_loss: 0.0637 - val_last_time_step_mse: 0.0526\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0543 - last_time_step_mse: 0.0407 - val_loss: 0.0480 - val_last_time_step_mse: 0.0319\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0450 - last_time_step_mse: 0.0300 - val_loss: 0.0416 - val_last_time_step_mse: 0.0261\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0403 - last_time_step_mse: 0.0253 - val_loss: 0.0386 - val_last_time_step_mse: 0.0223\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0380 - last_time_step_mse: 0.0235 - val_loss: 0.0362 - val_last_time_step_mse: 0.0204\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0356 - last_time_step_mse: 0.0208 - val_loss: 0.0347 - val_last_time_step_mse: 0.0200\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0345 - last_time_step_mse: 0.0198 - val_loss: 0.0330 - val_last_time_step_mse: 0.0182\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0330 - last_time_step_mse: 0.0190 - val_loss: 0.0347 - val_last_time_step_mse: 0.0195\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0336 - last_time_step_mse: 0.0193 - val_loss: 0.0322 - val_last_time_step_mse: 0.0182\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0320 - last_time_step_mse: 0.0181 - val_loss: 0.0316 - val_last_time_step_mse: 0.0181\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0311 - last_time_step_mse: 0.0174 - val_loss: 0.0305 - val_last_time_step_mse: 0.0168\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0305 - last_time_step_mse: 0.0172 - val_loss: 0.0299 - val_last_time_step_mse: 0.0165\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0299 - last_time_step_mse: 0.0166 - val_loss: 0.0293 - val_last_time_step_mse: 0.0157\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0292 - last_time_step_mse: 0.0159 - val_loss: 0.0292 - val_last_time_step_mse: 0.0164\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0288 - last_time_step_mse: 0.0154 - val_loss: 0.0282 - val_last_time_step_mse: 0.0146\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0295 - last_time_step_mse: 0.0156 - val_loss: 0.0436 - val_last_time_step_mse: 0.0272\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0346 - last_time_step_mse: 0.0182 - val_loss: 0.0319 - val_last_time_step_mse: 0.0170\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0306 - last_time_step_mse: 0.0159 - val_loss: 0.0305 - val_last_time_step_mse: 0.0153\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0307 - last_time_step_mse: 0.0159 - val_loss: 0.0302 - val_last_time_step_mse: 0.0161\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    MyRNN(LNSimpleRNNCell(20), return_sequences=True,\n",
    "          input_shape=[None, 1]),\n",
    "    MyRNN(LNSimpleRNNCell(20), return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0759 - last_time_step_mse: 0.0615 - val_loss: 0.0551 - val_last_time_step_mse: 0.0364\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0477 - last_time_step_mse: 0.0275 - val_loss: 0.0421 - val_last_time_step_mse: 0.0211\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0390 - last_time_step_mse: 0.0183 - val_loss: 0.0364 - val_last_time_step_mse: 0.0151\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0351 - last_time_step_mse: 0.0152 - val_loss: 0.0336 - val_last_time_step_mse: 0.0132\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0327 - last_time_step_mse: 0.0135 - val_loss: 0.0314 - val_last_time_step_mse: 0.0121\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0311 - last_time_step_mse: 0.0126 - val_loss: 0.0302 - val_last_time_step_mse: 0.0114\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0300 - last_time_step_mse: 0.0120 - val_loss: 0.0293 - val_last_time_step_mse: 0.0111\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0292 - last_time_step_mse: 0.0115 - val_loss: 0.0283 - val_last_time_step_mse: 0.0103\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0283 - last_time_step_mse: 0.0110 - val_loss: 0.0289 - val_last_time_step_mse: 0.0112\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0277 - last_time_step_mse: 0.0105 - val_loss: 0.0272 - val_last_time_step_mse: 0.0102\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0271 - last_time_step_mse: 0.0104 - val_loss: 0.0268 - val_last_time_step_mse: 0.0101\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0266 - last_time_step_mse: 0.0100 - val_loss: 0.0268 - val_last_time_step_mse: 0.0102\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0262 - last_time_step_mse: 0.0098 - val_loss: 0.0260 - val_last_time_step_mse: 0.0100\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0259 - last_time_step_mse: 0.0098 - val_loss: 0.0254 - val_last_time_step_mse: 0.0095\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 11s 2ms/sample - loss: 0.0255 - last_time_step_mse: 0.0097 - val_loss: 0.0259 - val_last_time_step_mse: 0.0112\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0252 - last_time_step_mse: 0.0095 - val_loss: 0.0250 - val_last_time_step_mse: 0.0098\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0248 - last_time_step_mse: 0.0093 - val_loss: 0.0245 - val_last_time_step_mse: 0.0091\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0245 - last_time_step_mse: 0.0091 - val_loss: 0.0247 - val_last_time_step_mse: 0.0101\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0242 - last_time_step_mse: 0.0090 - val_loss: 0.0240 - val_last_time_step_mse: 0.0090\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 12s 2ms/sample - loss: 0.0239 - last_time_step_mse: 0.0089 - val_loss: 0.0237 - val_last_time_step_mse: 0.0085\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.LSTM(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.LSTM(20, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 1s 677us/sample - loss: 0.0237 - last_time_step_mse: 0.0085\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.023689543694257735, 0.008518973]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, Y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
    "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 15s 2ms/sample - loss: 0.0742 - last_time_step_mse: 0.0667 - val_loss: 0.0528 - val_last_time_step_mse: 0.0414\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0476 - last_time_step_mse: 0.0365 - val_loss: 0.0440 - val_last_time_step_mse: 0.0326\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0418 - last_time_step_mse: 0.0303 - val_loss: 0.0394 - val_last_time_step_mse: 0.0271\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0373 - last_time_step_mse: 0.0249 - val_loss: 0.0359 - val_last_time_step_mse: 0.0226\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0328 - last_time_step_mse: 0.0181 - val_loss: 0.0316 - val_last_time_step_mse: 0.0168\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0308 - last_time_step_mse: 0.0157 - val_loss: 0.0299 - val_last_time_step_mse: 0.0146\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0298 - last_time_step_mse: 0.0149 - val_loss: 0.0289 - val_last_time_step_mse: 0.0137\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0288 - last_time_step_mse: 0.0140 - val_loss: 0.0282 - val_last_time_step_mse: 0.0136\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0280 - last_time_step_mse: 0.0135 - val_loss: 0.0274 - val_last_time_step_mse: 0.0126\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0272 - last_time_step_mse: 0.0126 - val_loss: 0.0268 - val_last_time_step_mse: 0.0126\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0269 - last_time_step_mse: 0.0126 - val_loss: 0.0262 - val_last_time_step_mse: 0.0120\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0264 - last_time_step_mse: 0.0122 - val_loss: 0.0264 - val_last_time_step_mse: 0.0128\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0261 - last_time_step_mse: 0.0120 - val_loss: 0.0260 - val_last_time_step_mse: 0.0123\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0258 - last_time_step_mse: 0.0119 - val_loss: 0.0254 - val_last_time_step_mse: 0.0116\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0256 - last_time_step_mse: 0.0117 - val_loss: 0.0258 - val_last_time_step_mse: 0.0127\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0254 - last_time_step_mse: 0.0116 - val_loss: 0.0253 - val_last_time_step_mse: 0.0116\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 13s 2ms/sample - loss: 0.0252 - last_time_step_mse: 0.0116 - val_loss: 0.0250 - val_last_time_step_mse: 0.0118\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0248 - last_time_step_mse: 0.0111 - val_loss: 0.0250 - val_last_time_step_mse: 0.0120\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0246 - last_time_step_mse: 0.0110 - val_loss: 0.0243 - val_last_time_step_mse: 0.0109\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 14s 2ms/sample - loss: 0.0245 - last_time_step_mse: 0.0110 - val_loss: 0.0246 - val_last_time_step_mse: 0.0111\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.GRU(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.GRU(20, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000/2000 [==============================] - 2s 770us/sample - loss: 0.0246 - last_time_step_mse: 0.0111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.024557729810476304, 0.011060879]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, Y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
    "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4htVp1sasu84PVaZFo3p0bFbfOCwYvM73mw6QmVPAA7GdXest+ve/W14KTutvv/kNBAefXp7zd3xViHYAwSLS1amsH5BSg2sVax8IR/u+UvFd0beG/RjqwKq0bMJDgujdprL+u4+hHZuyKi0Hu92khjB4B7tdeSdhN91aNuCqni1d23n79vCnP1leCa1agc1Gk/4xXN79gBEid6Kq+cAc4EURqS8iw4HRwKeV24rIaBGJFIshwCNYnnIAiUAp8IiIhInIQ47yhW4fxAXKyrQcBkZHEhLkubfWkI5NOV5YwraDZqJr8A4Ltx1m+6ET3B/bGZvNDWfnOnSwZkWHDpUfdB2943W2bYMdO859ua/jk0Lk4AEgHDgMzAbuV9UUERkhInlO7SYAu7CW2z4B/q6qHwOoahEwBrgTOArcA4xxlBtczLGCYrYfOuF2t+3KlN1vVZqJwm3wPKrK9MRdtIsMZ1TfNu65ydSpp3OIO7ip+CsA5s93zy09ic8KkarmqOoYVa2vqh1U9XNH+RJVbeDUbqKqRql1fqiHWi7azv2sU9WBqhquqgNUdZ2nx3KhsCYjB1X3nx+qTLvICNo2CWdVutknMniepNQc1mUe5feXdybYXSsBVeQ0iiaTfqwPiOU5nxUig/+xKi2H0CAb/ds38fi9y/aJ1KQQN3iYdxJ30axBGLcObOe+m1Q66PoqT5BALKMbL2L5cjhy5MxL/CmFuBEig8tYmZZDv/aNqRdSTcIvNzKkY1Oy8orYfeTM8PkGg7v4z+o9LNmZxfV9Wrn3fV/poOtgVjOeeNrcMhS7Hb7/vmLzssR5gwe7zyRXYoTI4BKW7cpi496jtI90X7TtszHUcXrduHEbPEVyRi5/mrMRgP+s2VP7AKe1wSmnESLEkUj8DZ/w7LfDaNasohu3P2ZvNUJkOG+SM3K5+9+rsSt8t3G/e/8hqyEmKoLmDcNYaRwWDB7i162HKDsxUFzqgZT1ZbmLSkthwADitr9H/OxS8vLghx/g5En/FCEwQmRwAUmp2RQ5Yr2V2tX9/5BVICIM6diUlalmn8jgGU4UlgBgq0tMufNBBJ59FnbtIu7QF0ydaiXMK8tR5G8iBEaIDC6gj+PwquDhf8hKDOvYlIPHC4nfXuSVWZnhwsFuV5bsPELP1g354zXdmXXfMM+mrB89Gvr0gZde4pGH7AQFwRdf+G8KcSNEhvNmxyHrWNedl8R4/h/SiQZhIQD8mF5S+zwwBkMtSErNJj27gN+P7MyDcV08/5632axZ0datrPj7Ymw2aN4c3n3XP3MTGSEynBd2u/JpUgaDYyKZOrq310QIYN/RAqCOeWAMhlowa1UmjcNDuK5PK+8ZMW4cCe3vZPxLfbn7bjtHjsA77/hnojwjRIbzYtGOI2TmFHDnJTHeNoVLOjcjyBFeJSTIe0uEhsAmK+8UP6ccZOyAdl45qlBGwuIgxh99n/jSsfxP/FUAFN7/GPFTFvidGBkhMpwXH69Ip0XDMK7t7cVvhg4GRkfylxt7AfCAN5ZLDBcEXyfvpbhUmTikvddsKPeOe3AxcbKIfkcTacYRfsm+mLhpo/1OjIwQGepMelY+iduPcNvQDoQG+8Zb6fZh0TQJE1L2H/O2KYYARFWZvSqTwTGRdPVABuLqWL3a4R03ewqoYkO5kl9ZwFVoQQFxs+7zqxTivvHpYfBLPkvKINgm3Dakw7kbe4ggmzC4VRAJ249wvLDY2+YYAowVDieF24Z69z3/5JMO7zinGHRX8wsHaMNWekJmpl+lEDdCZKgTBUUlxK/Zw3V9WtGiUT1vm1OBoa2DKSqx80vKIW+bYggwZq/aQ+PwEK7v09rbplg4xaC7igUA/MLVZ8Sm83WMEBnqxLz1+zleWMJdl8Z425Qz6NzYRtsm4Xy7cb+3TTEEENl5p/hp80FuGdDWq04KFXCKQRdNJl3ZwQLbtVa5H2GEyFBrVJWPl6fTs3UjBvmgQ4CIMKpfG5buzCIn36SeMriGOWv3UVRqZ6IPLUVXiEGHNStKDL6S4vGTvGxY7TBCZKg1s1Zmsu3gCS7v1oyKWdh9h1H9WlNiV37cfNDbphgCgOT0HN5J3EWPVg3p5kUnhSopi0F36hRXRa0nryiMlUn+FebKCJGhViRn5PKXeZsB+GhZus9GL+jVuhGdmtfn2w1mec5wfiRn5DLhgyRyC4rZfSTPZ9/zhIYS9+eh2Cjllxlp3ramVvisEIlIUxGZKyL5IpIhIrdV0+4JEdksIidEJE1EnqhUny4iJ0Ukz/H42TMjCEwWbPFwxOE6IiKM6tuGpLRsDh8v9LY5Bj9m9qoMikutN73dS0F9a0rkQ7czKGQjC+bln5Fa3JfxWSECpgNFQEtgEvCuiPSuop0AdwKRwHXAQyIyoVKbUY5U4g1U9Rp3Gh3oHHR8qHs84nAdGNWvNarw/aYD3jbF4Kesy8xl/voDCBDkB+95wsK4+opSVp7oybH5i7xtTY3xSSESkfrAWOA5Vc1T1aXAfOCOym1V9VVVXauqJaq6HZgHDPesxRcGBUUlLNx2mKEdm3on4nAt6dKiIT1bNzLLc4Y6sftIHvd8tJqWjcP44K5B/K8fvOcBrvrfvpQSzKJx/2cFR42JgVmzvG3WWRFfzN0iIhcDy1U13KnsceByVR11lusEWAu8r6rvOcrSgXAs0V0HPKGqG6q4dgowBaB58+YD4+PjXTcgHyMvL48GDRrU+rqFmcV8sqWIp4fWo1ukj7ivVoHz+L5LLeKrHcW8NjKc5hE++b2r1tT19fMHfGVsuYV2/ppUSJFdeXZoOC3ru+a9487xzZ7dnh49ThB3aC7D//4k9/Av/o9HACgNC2P744/zU9Q4tm1ryMSJe9xiQ1xcXLKqDqr1harqcw9gBHCwUtlkIPEc100FNgBhTmXDsYQoAvgzcBBocrZ+unXrpoFMQkJCra8pLbVr3OsJeuM/l6jdbne9US7EeXyZ2fka/afv9N3EXd4zyMXU5fXzF2oztp83H9S3F+7QNek5LrVh0fbDOuj//azdn/mvbtxz1KV9u/O1W7hQtVkz1YUtJ+h1/Fd7sEXV2ilSBV3YcoJVv9BtJiiwRuvwme+rXxHzgEaVyhoBJ6q7QEQewtorukFVT5WVq+oyVT2pqgWq+jJwFEvoDLVg0Y4jpB7J597LOvqsy3ZVtG8aQdcWDfjX0jTf9XYy1JoZi3cz+dM1vP7TDpfknlJVth88wVNfb+Suf63iSF4RdqU887A/EBdnxZ8bf+ifdCSNbfRkL20BSCCW8Yf+6bPZW4O9bUA17ACCRaSrqu50lPUDUqpqLCL3AE8BI1V17zn6ViwHB0Mt+HBpGi0bhfGbi3wktEkNSc7IJS0rnxK7ctsHSXw+2ffX+A1np6CohLcX7gKsf+YiR+6p2r6uyek5zFm3j7xTJazfc5SM7IIK9aX2uvXrTeLiIL7lI4w9NB2ABVxFNBmMJ574lo8QFzfbyxZWjU/OiFQ1H5gDvCgi9UVkODAa+LRyWxGZBPwNuFpVUyvVdRCR4SISKiL1HK7dzYBl7h9F4LDt4HGW7srizktifCbKdk1JSs3G7tgHLTLJ8gKCaQt2crywhJCg098nh3VsWqs+kjNyGT8jiVkrM5m3fj9NwkP465g+zLxzEPVCbP7hIVcNcW/cyFehkxDsvMEfLREKuZ24N270tmnV4qszIoAHgH8Bh4Fs4H5VTRGREcAPqlq24/dXIApY7bRk9Jmq/g/QEHgX6AwUAuuB61XVfBrVgn8vTadeiM2nomzXlGGdoggNtlFYbMcm4pcfLIbTbNl/nA+XpjFxSHvGDWzP9IRdLNx2mL1HTzKwFv3MWLybUseBuCCBa3q34vZhVpicWfcNIyk1m2GdovxqNlTOpElcAfT+3Q42l1zEs/JX4lpugQmVT7X4Dj4rRKqaA4yponwJ0MDpecez9JEC9HWLgRcIWXmnmLt+H+MGtiOyfqi3zak1A6MjmXXfMP48ZyM5+UX++cFiAKDUrjw9dxNNwkP403U9aBIRygd3DuKWd5bx/77bwuXdmtMk4tzv0eW7s1iw5RA2sdboK898BkZH+v37JKHNJNLrAXkwPeIJrtj7K3Fffgm3VRkXwOv41zqLwePMSsqkqMTOPcOr1XufZ2B0JBOHdCArr4i9uQXnvsDgk3y+MoP1e47y3I29ygUnyCb87ZaLyC0o5u8/bjtnH2lZ+dz/2Vo6Nm/Ax/cM8ZuzQbWhLHvr229bz+/9fQjjg74m4Yn/QrFv5ugyQmSolqTULN5fvJuLOzShSwvvn+04H4Z2tL7xrkrL8bIlhrpw+Hghr/64ncu6NGN0/zYV6nq3acw9w2OYvWrPWV/fYwXF3PvRamwCH941iBFdm/NggKWUL08hHg933gnNm8PhLBvxU7cxfv8/SHjqJ2+bWCVGiAxVkpyRyx0frqKgqJSUfcf93vW5e6uGNKoXbITID0nOyOWOf63iZEkpfx3Tp8rjA49d3Y22TcJ5LH49//x1xxnv11Vp2dz09lIycvJ57/aBREfV95T5HsNZhOLiQARGjoTFiyHu6UuI7/E846ddQsJPvpcaxQiRoUqW784qD/RY5sbqzwTZhCEdm7LSCJFfkZyRy8QZK9h+8AQoZFeTXyoiNJjfXRrDvtyTvPnLTibMWMHf/ruVj5al8cL8zUyYkURGTgE2EYKDAvNjb/VqzjgnNHKklSEic48Q9/ZY4u3jWH3zSz4X+sdnnRUM3qXEcZDPH4Kb1pShHaNYsPUwh48X+lx6c8OZqCrvLtpNkeMLkaqe9VyP8+HT4lJlxuLUM9qURc8OpOW4Mp588syykSOtn0uWwCQOEmdbTNzJRKswIwOmTLF+n+TdRHou/2ogIv8nIt9WUd5IRF4QkZ5OZY+JyEYRCcyvKH5KSamdbzccILppBP97dbeA2cwd4jhrYmZFvs/hE4Xc89Hqcu+2mpzrGdYpinohNmwCYcE2Zt45iORnr+LTe4dQL9i/zwbVlYsugsaNreU5nnkG7JUiRRQUWOVexqUzIhHpDPweuLSK6kHA81gHVct4D/gTcBfwb1faYqg78zfsJzUrn/duH8B1ffwrksLZ6N2mEfVDg1iZls2ofm3OfYHBoyRn5PLd7iK2sIuZS9LIP1XC1Jt606dNI5LScs55rqfMVb/yGaARXZsza7Kfnw2qI0FBcNllDiHKzKy6UXXlHsTVS3OPAhtUdU0VdRcDp4AtZQWqelJEPgEexwiRT1BSauf/Fu6iZ+tGXNOrlbfNcSnBQTYGxjQ1Dgs+SHJGLpM+SKKwxA47t9OpWQTxvx9GlxZWWu6BMTWLnFDdGaBAOBtUV0aOhO+/h8PtLqbF3rVnNujg/YPqNVoSE5EuIlIsIlMrlb/ryIw6SETCgNuBz6u4fivwOhAGFIuIishXjuovgF4iUtUsyuBEckYu0xN2udWDbd76/aRl5fOHK7tiswVeSL6hHZuy41AeOdVsehu8Q1JqNqdKrGUjAcZc3K5chAznR/k+0S3/gIiIipUREfDSS543qhI1EiJV3QXMBB4TkWYAIvIX4B7gZscMaBjQBFhSRRd3AqnAt8AljscfHXXrgeNY2VUN1VD2jfGNn7efV7ThsuWPqq63ZkM76dW6Edf2bnm+JvskQx37RGZW5FsMcYoVFxZiY3iXZl60JrAYMMDSm8WMhBkzIDr6dOWDD3rdUQFq56wwFQgC/iQi92Lt99yhqgsc9cOwguFurOLaDUA7YKGqJjkeGQCqandcM6yOY7ggSErNorDEjl3hVHHd3KktV9gkvt5ZXKWYfbN+P+nZBTx6VVe/SvVQG/q2a0JYsI2Vaf7tjh5oHCsoRoFhrYMCxjnGVwgNhUsucewTTZpk+XOfOgVt2sDaKpbqvECNhUhVDwLTgIeB94FHVNU5jWkb4LiqVrXm0RsIxcqeWhVHHNcbqqGo5HQmXQXyT9U+VMfPKQcpKrWjQGGxnU9XpJclDyyfDfVu04irewXmbAggNNjGgA6RZkbkY3y8Ip1Wjepx30VhRoTcwMiRsGEDHD3qKAgNhT/8AX79Fdat86ptUHv37Z1Y+zwrVHV6pbp6WM4IVTEA6/NzfTX1J7GyqBqqICvvFJ+sSKdbywbwVhvUAAAgAElEQVQ8dlVX+rdvwnuLUvl+44Ea96GqrHHMgMTx+Gb9fsa+u5zV6TlM+3UnGdkFjOrXJmBnQ2UM7dSULQeOc+ykb8bdutDYdTiPJTuzuH1YB4IDcF/SFxg50krTusw5Ac6UKdCgAbz+utfsKqPGQiQiV2DNhFYAw0WkX6Um2UB1X2UuBnar6vFq6psCWTW15ULj+fkp5J8qZfptA/jDVd2Y7Uju9uiX60jYfrhGfXy38QDJGbn87tJoxnYN4cvfD+PvYy9i39GT3PreivJEY9MWnBkeJdAY0rEpqpCcYWZFvsCnK9IJDbIxwQ/TjPgLQ4dCSIhjea6MJk1g8mT48kuvu3DX1GtuAPANlsNCLJCJlYzOmW1AiIi0q6KLXji5bVdBR2B7TWy50Pgp5SDfbzzAw1d0oWtLy4soPDSID383mG4tG3L/Z8l8tiLjrN50xwqKmfptChe1bcyzN/Tixs6hDOkYxW8HdyDx8ThGdD29MVx8ASSPG9AhktAgGytTjRB5mxOFxXyVvJcb+7amWYMwb5sTsISHw5AhlYQIrOU5gLfe8rhNzpxTiESkC/AD8DPwsGMPaCrwGxEZ6dS0bIhDqujmKNBPRK4VkWEiUn60WUSaAN2crjc4OHaymOe+2UzP1o34n9jOFeoa1Qvhk3uGEFU/lGfnbT6rN93f/ruV3IJiXr7lojPibIWHBvHoVd38PitlbagXEkS/9o1NhAUfYM7afeQXlXLXpTHeNiXgGTkS1qyB/HynwuhoS6H+8Q+vxp87qxCJSCssAdoKTHJ4uAF8gjUDeqWsraqmA6uAUVV09RfgENasagXQ06nuBqAImFunEQQwf/t+K9n5Rbw2ri8hVQRqjGoQVh4hoMybbtmuIxXarNidzZdr9nDfZR3p07ZxlfcpO5EeiLlZqmNIx6Zs2neM/FMl3jblgsVuVz5ekU6/9k3o176Jt80JeEaOhJISSEpyKpw1y3JWULUeZfHnPCxGZxUiVT2oqp1UNVZVTzmVl6pqT1WtfAj1XeAWEYmo1M9mVR2qquGqKqq61Kn6duA/ldN3i0hTEZkrIvkikiEiVaYWFIu/i0i24/GqOO22i0h/EUkWkQLHz/5n/5P4Bv9emsaXa/ZwU7821QoIwNW9WhEWbL2MCny5Zi/rMq1ZUWFxKc/M3UT7puE8elW3s95vYHRkwOVmORtDO0ZRalde+DYl4PfEfJVlu7NIPZLP7y6NPndjQ5159VUrRcSll1qTngrLc888A4WFJBDLqzxhlXkh/pyrg41+CuwDHqhJY4coxGEt9VVmOtZMqSUwCXhXRHpX0W4KVkrxflhpwW/EineHiIQC84DPsBwpPgbmOcp9loXbDvHid9aW2g+bDpz1g3JgdCSfTx7GE9d257kbe6J2Zey7y3lk9lru+HAlqVn5/O3miwgPDfKU+X5BWdSIr9bsPa8Dwoa68/HydJo1COU3FwVOPENfZPBgK09RcjL0719JiDIzSSCW8cQzmNUVyj2JS4VIVUuxoi3UNB9zK+BuR+SGckSkPjAWeE5V8xwzqPnAHVX0cRfwhqruVdV9wBvA7xx1sVjx9Kap6ilV/SeW5/IVtRqYBzl2spinvt5E2amh4tJzOw+UzWbuvawTPz02kqt6tmT+hgOsTs8lSISIUJPtozIb9lgHKhRrSfOnlIPeNegCIzO7gF+3HWbikA6EBZsvSe4kLs7KUzR+PHTsaC3NnXKsbyW0+C3jiSee8cSRePoiD8efk7IDjb6EiFwMLFfVcKeyx4HLVXVUpbbHgGtUdaXj+SAgQVUbishjjrrrndp/56h/o1I/U7BmV0RERAwsKKiplroOCQmjxfj/R1jrbqB2EBtqL+XQF89QtH9bjftpNOxWmoy4HbEFofZSji75jONJ/3Gj5f5HaJsetJzwEhIcAgjYS8nb9AvHlscT1DCKeh0uojBzU63+7oaaE3XjH6nf83KOzPkrJ3ev8rY5FwixWNv0jYHhQChhxDOH8fzGSYROAXcDs+t2k2RVHVTbi3z1q3ID4FilsmNAVVEQK7c9BjRw7BPVuB9VnQHMAOjevbtu3+5Zb/JTJaXc9/Ealu3KYvptA2jRqN7psPVvbK1VX8kZuUyamURxiZ2QsBDmfPE+A6NPB8FITEwkNjbWxSPwHWo6vuSMXJJSs+naogFLdmbxZWgoTQZcDwiqSmiwzSedN/z99VuZms1vZ1g75h0mvlDhb+zvYzsX3h7fN9/AzTfDiBHL2LrVkdF1/33wTJq1HBcaSli9enx+4ACfh9c+xkBdD8P7qhDlAY0qlTUCTtSgbSMgT1VVRGrTj9coKbXz8OfrWLIzi9fG9eV6x5p5XT8Aq8vLYqiIc2qAa3q34v7YzjwwK5n1e6zvLmVnqszfz7V8uWZP+e/mb+xZxoyxPLSXLIFnny1LKz7pdODTxESrcMaM02eMPICvZkbdAQSLSFensn5AShVtUxx1VbVLAfpKRZnuW00/XmFNeg6j317Gz1sO8cKoXtw6qL1L+r3QvOBcQZsm4Tx3Y2/KosxcCGeqvMHh49YGxYVybs2XSEiALEcMm7fftp5XIDbWerz8suU95yF8UohUNR8rk+uLIlJfRIYDo7G88irzCfC/ItJWRNpgpZf4yFGXCJQCj4hImIg85Chf6E77a0pyRi4TZiSRcuA4wTbhonbmLIW3GRgdyXM39ALg3ss6GiF3MQVFJSRn5HJ1r5YX1Lk1XyAhwXJY+PxzqFfP0pvx46sQo6lT4dAheO89j9nmk0Lk4AGsQKiHsfbN7lfVFBEZ4VhyK+N9rDxHm4DNwPeOMhxRIMZg5UM6iuXRN6aaCOEeJyk1mxK75SyiqgEfWsdf+N3wGHq0asiCLYfxRWcef+aXLYc4WVzKfZd1NDN2D1ImQvHxMGoU3HQTLF1qidIZYjRyJFx5JbzySqUwDO7DZ4VIVXNUdYyq1lfVDqr6uaN8iao2cGqnqvqkqjZ1PJ5Up08PVV2nqgMdh2kHqKr3Y5476N3G2r4SzBKFLyEiTB7Rie2HTrB4p4nF60q+3bCf1o3rMbiGqb8N54+zCFl7QnD77dYSXVHRadfuCmI0dSocOQLt2nkk9I/PCtGFQGGxFTFp4pAOZonCxxjVrw0tG4Uxc0mqt00JGI4WFLFoxxFG9WsTkGnofZXVqyuKEMC110JUFHz22elzRqudzrOSnm4J0NGjHgn9Y4TIiySlZlMvxMYLN/U2IuRjhAbbuOvSGJbszGLrgeqyl9SO5Izcs0ZJD3R+2HyQ4lLlpn4mB6YnefLJiiIEVl683/7Wcuc+ftyqf/JJpwbPPAN2e8WL3Bj6xwiRF0lKzWZQdFNCg83L4ItMGhJNeEgQM5eknXdfyRm5TPwg6axR0gOdeev30al5/fIlaYN3uf12KCyEuVWFm64uxI+bQv+YT0AvkZNfxLaDJxjWyayV+yqNI0IYP6gd8zfs49DxwvPq65t1+ygqsWNXa0n2+001z64bCBw8VsjKtBxuugAyAPsLw4ZBp07W8twZVBfix02hf4wQeYlVaZaHnHFQ8G3uuawjJXbl4+Xpde7jyIlTfL/xQHmKdrCiqz/6xTrmb9h3QSzXfbdxP6qYZTkfQsSaFf36K+zfX6nypZcgIqJiWXi4Ve4GjBB5iaTUHMJDguhrzg75NNFR9bm2VytmrcykoKj2uYuKSuw8OGstBcUlvHZrXx6/tjv//t0gfn95Z37YfJBHZq/ntZ+2c9sHgb1cN2/9fi5q25hOzRucu7HB7ZSlhpg0yfJFmF05sNykSSQ8Oo9Xm/zNUiyw3LrLIjC4GCNEXiIpNZtBMZFmf8gPmDyyI8dOFvPw7HW1Fou/fr+FVek5/H1sX8YNbM+DcV2I69GSp67vwX0jOpbPkE6V2Fmw9ZDrjfcBUo/ksWnfMUb3N7MhX6EsNcS+fVaC1srLcwkJMH7GVQye82fLaeG++6zCjAy32GM+Bb3A6f0hsyznHwgi8OvWw9z63nKembuJdZm5lJTaz+oJF79mD5+syOC+yzoyun/bM+qv6NGSsBBbeUihr5L3kJblmQOEnmT+hv2IwI19jRD5Cs6pIYYMgfXrYfNmq66qc0f85S/WzGhqVanjzh9fDXoa0JzeHzKOCv5AUmo2gpW7yK4wa2Ums1ZmEh4SxKmSUlQhJMjGv+8ezPAuzUjOyOWbdfuYvTqTSztH8dT1Pars1zk4bfMGobzy43bGvbucj+8ZctasvP5EcnoOH69Ip1frhrRqXM/b5hicKBOjW2+1jgzNmgU5OfDFF5ZbdwWX7/bt4YEH4K23SBj5PKsPR1d09z5PjBB5gbL9oYvamv0hf2BYpyhCg21WWo1gG+9MGsDJIjsfLNldHqm7qNTO7TNX0iEqgr25Jym1KwJMHtmJ4KDqFx6cI4APimnKHR+uYsKMJB6/phv5RaV+HT09OSOXiTNXUlRiJ6/QijHnr2MJVOLi4D//geuus4KgAgRVl6fwz38m4d1tjP99JPE/utYOszTnBcz+kH9RNnMpC9J5RY+W3NC3Nc/d2Jt6ITaCBEKDhHGD2iFAqSN+oAhs2V/zw7Cdmjfg6/svJTIihBe+3cLrP/n3maOk1GyKS6xDkXa7iaXoq8TFwYQJkJdnPS67DMaNOzMYasLm5oy3zya+aDRxV7o27I/5JPQwZn/IP6kqrYazQM2ecgmvjevHG+P7nxanOsQPbNW4HmMutvaTFMvrzl8/wId1iip3uDKxFH2XhAT473/hj3+0PLR//NEKonDjjVZ5WZvxowuJ57dWSnEXh/0xS3MeZmWq2R8KJJyX1sqen29SwtjuLZixOJVTJXZUYZCfLmf1bdeYYJuNPm0b8cwNvcyynA9S2THhhhtg7Fjo2xcWLbLEaOJE+PlniI+4m7gTP1XsoCzsz3m6dZsZkYdJSs0254cCnPNNSjgwOpLPJw/j5v5tUGDhtsOuNdBDbDtwgqJSO3cPN3mdfJGqvOPi4uDrryElxdozioqyUkXcdRfEHf6y6o5cEPbHzIg8TFJqDoNiIgk5ywa2wVA20woPC+b9xamM7Nac4V2aedusWpGckQPUPeW9wX1U6aLtoMybbswY6wiRCEyfDje0+C1xh744szMXhP0xn4YeJDvvFNsPmf0hQ8159oaedGpenz/GbyA33yfyOdaYNRm5tG5cjzZNwr1tiqESVaWGqIwI3HYbPP64FRz15hMfkxB2XcVGEREuCftjhMiDrEqzviEaITLUlIjQYP454WKy80/x9NxNfpUxdq1x1/ZZqkoNUUbZbGnuXHj/fXj+eYiOhsgWoYwPnUtCywmWSkVHw4wZLgn743NCJCJNRWSuiOSLSIaI3HaWtk+IyGYROSEiaSLyRKX6dBE5KSJ5jsfP7h9B9ZzeHwqMw4oGz9CnbWMev6Y7P2w+yGs/beO73UU+79K9/+hJ9h8rNELkZ1S1ZDd9uuUcl54OY35bj/Gls0n41W4VOEQoIcGKX1dXfE6IgOlAEdASmAS8KyK9q2krwJ1AJHAd8JCITKjUZpSqNnA8rnGX0TUhYdsRWjYKY+PeY940w+CHTB7RiYvaNuKdxFS+3lns8+eLymwbFG28Q/2JqpbsBg+Gf/wDRoywPLXfeqtiNtcy8Ro8uO739SkhEpH6wFjgOVXNU9WlwHzgjqraq+qrqrpWVUtUdTswDxjuOYtrTsK2Q2TmFpCRXeDzHyIG38Nmk3JnBQWKffx8UXJGLuEhQfRo3dDbphhqQVVLdmXOCykp1vGhzz6DJxxrT2dzeqgNvuY11w0oVdUdTmUbgMvPdaFY2bZGAO9XqpolIjZgHfCEqm6o5vopwBSA5s2bk5iYWHvrz8Kba04CjkOKxXZmL1jNic6hLr1HTcnLy3P5+HyJQB1fs8JSR8w7xSZC2NEMEhP3etusKlm0+STRDWHZksW1ui5QX7sy/HV8IvDss014+umL+OGHIKZOTaFx42KmTu3F889vQeQo5zMsXxOiBkDldatjQE2+Vr2ANcP7t1PZJGAt1hLeH4CfRKSHqh6tfLGqzgBmAHTv3l1jY2Nra3u1FBaXsmfxr9jEjmCdMp941WCvrZ8nJibiyvH5GoE6vligpOluXvlhG09c14P7Rnb2tklVUlBUQubPP3P/5Z2Jje1eq2sD9bUrw5/HFxsLF10E118Pb7zRm7Awy6EhLq7/efft0aU5EUkUEa3msRTIAyontG8EnDhHvw9h7RXdoKqnyspVdZmqnlTVAlV9GTiKNWvyKHPX7eNoQTF/ubFXebwys4lrqAv3DO9IwxDY4MP7jBv2HKPUruY9HoBcdZV1uDUvD26++fyW45zx6IxIVWPPVu/YIwoWka6qutNR3A9IOcs19wBPASNV9VzrFMrpbM0eodSuvL9oN33bNeauS2MQ8ejtDQFGaLCNoa2D+WXLIY6dLKZxeIi3TTqDtZnW/ufFHUz0kEAjIQHmzYOnn7Y8t2+7zTVi5FPOCqqaD8wBXhSR+iIyHBgNfFpVexGZBPwNuFpVUyvVdRCR4SISKiL1HK7dzYBl7h1FRX7cfJD07AL+5/LORoQMLmF422CKSux8v/GAt02pkjXpOXRt0YAmEd7ZAzW4B2fHhJdeOp1Yr3KU7rrgU0Lk4AEgHDgMzAbuV9UUABEZISJ5Tm3/CkQBq53OCr3nqGsIvAvkAvuw3LuvV1WPuRqpKu8t2k3HZvW5tncrT93WEODENLLRpUUD5qz1PUcFu11Zm3nULMsFGNXFpXOVGPmaswKqmgOMqaZuCZZDQ9nzjmfpJwXo63IDa8GyXdls2neMV265iCCbmQ0ZXIOIcMuAtrz643YysvOJjqrvbZPKSc3K49jJYiNEAURN4tKV1dcVX5wRBQzvLtpFi4Zh3DygrbdNMQQYY/q3RQTmrN3nbVMqsCbd2h8yQhQ4nCsuXZkYOR9yrS1GiNzExr1HWbYrm3sv60hYcHW5dw2GutGmSTiXdo5izrq9PhV/Ljkjl6b1Q+nYzHdmaYbz42xx6cqIi7Pa1RUjRG7ivUW7aVgvmNuGnn+IdIOhKm65uB17ck6yxoeidCRn5DKgQ6RxzDHUCiNEbuDbDfv476aDXNOrJQ3r+Z57rSEwuK5PKyJCg3zGaSEnv4jUrHyzLGeoNUaIXExyRi6PfmlFEfpu4wETU87gNuqHBXNdn1Z8t/EAhcWl3jaHtRlmf8hQN4wQuZilO49QarfW7EtKfTswpcH/GTugHScKS3jiqw1e/9KzJiOXkCAxaU4MtcYIkatxrI3bxIopZ5LgGdxJaLD1L/zthgNej+q+aPthmjUII2X/ca/ZYPBPjBC5mI17jhJVP5T/vbqbiSlncDur0nLKY1Z5MzXEit1ZbD14goPHCr0uiAb/wwiRC8nJL2LRjiOMG9iOh67oakTI4HaGdYoqnxWJiNdm4DOXpgH+kSvJ4HsYIXIh3286QIldGd3fHGA1eIaB0ZF8PnkY7SPDaVo/hIvbez7QaFGJnfWZRxGBILMkbagDPhfix5+Zt24f3Vo2oKfJSmnwIAOjI3n82u784Yv1LN+dzWVdm3n0/nPW7iU7v4jnbuxJYbGdYZ2izGqAoVYYIXIRe3IKWJORyxPXdjeH+Qwe59rerWgcHsIXqzM9KkQlpXbeSbTSnNwzvKN57xvqhFmacxHzN+wHYHT/Nl62xHAhUi8kiJsvbsvPKYfIyS/y2H2/3bifzJwCHorrYkTIUGeMELkAVeWbdfsYHBNJu8gIb5tjuECZMKQ9RaV2j0VasNuVtxfuokerhlzVs6VH7mkITIwQuYAtB46z83AeNxknBYMX6dGqEf3bN+GL1Xs8Egj1h80H2X0kn4eu6ILNpDkxnAdGiFzAvPX7CbYJN1zU2tumGC5wJg5pz67DeW4/x6Oq/N/CnXRqXp/r+5j3veH8MEJ0npTalfnr93N5t+Y0rW9SIxu8y41921A/NIgvVu9x631+3XqYbQdP8GBsF5P00XDe+JwQiUhTEZkrIvkikiEit52l7QsiUuyUJjxPRDo51fcXkWQRKXD87O9qe1emZXPweCGjLzbLcgbvUz8smJv6t+G7jfs5Xljslnskp+fw7LzNtGgYyk3GOcfgAnxOiIDpQBHQEpgEvCsivc/S/ktVbeD0SAUQkVBgHvAZEAl8DMxzlLuM+ev3Uz80iKvNZq3BR/jt4A4UFtuZv36/y/tOzshl4gcrOXiskNyCYjbuPebyexguPHxKiESkPjAWeE5V81R1KTAfuKMO3cVinZOapqqnVPWfgABXuMrepNQs5q7bx6CYSMJDTRZWg2/Qr11jerRqyBerM13ed1JqNkWldsDymjOhfAyuwNcOtHYDSlV1h1PZBuDys1wzSkRygAPA26r6rqO8N7BRK7oPbXSU/1i5ExGZAkwBaN68OYmJiWc1dFduKa+sKqREYdnOLGbO/ZUukf4hRnl5eeccnz9jxgcDI4uZtbWIe9/5iUtaB7vsvRmcW1L+e5BA2NEMEhNd5y5uXrsLE18TogZA5bn+MaC6mDnxwAzgEDAU+FpEjqrq7Nr2paozHH3RvXt3jY2NPauhmxfupMShlwqcahJNbGyXs17jKyQmJnKu8fkzZnxg236EWVtX8WtmCcsO2F0WCT48NRtWJTGmfxvuuCTG5aF8zGt3YeJRIRKRRKqf3SwDHgYaVSpvBJyo6gJV3eL0dLmIvAWMA2YDebXpq7Y4ct+ZvEMGn2TT/tPfwcqiYbtCNBbvPEKQTXhxTB8a1Qs5r76Ki4vZu3cvhYWF5WWNGzdm69at52umz+LP4wsKCqJJkyY0a9YMm821uzoeFSJVjT1bvWOPKFhEuqrqTkdxPyClpreA8vQsKcAfRUScluf6YjlDnBeldmX+hv20iwxnwuD2XNK5mQnyaPAphnWKIjTIRlGpHZvNdekhluzM4uL2Tc5bhAD27t1Lw4YNiYmJKQ8PdOLECRo2DNygwf46PlWluLiYQ4cOsXfvXjp06ODS/n3KWUFV84E5wIsiUl9EhgOjgU+rai8io0UkUiyGAI9gecoBJAKlwCMiEiYiDznKF56vnd9t3M+uw3n8+fqeJu+QwScZGB3JrMlDaRgWTK/WjVzyHs3JL2LTvmOM7NbcBRZCYWEhUVFRJkadHyAihIaG0rZtW/Lz813ev08JkYMHgHDgMNYS2/2qmgIgIiNEJM+p7QRgF9Zy2yfA31X1YwBVLQLGAHcCR4F7gDGO8jpTUmpn2oKd9GjVkOv7tDqfrgwGtzI4pil3D49h475j7D968rz7W7orC1UY4cLo3kaE/AtXL8mV4WvOCqhqDpaAVFW3BMsJoez5xHP0tQ4Y6Er7vlm/n7SsfN6/Y6CJr2XwecYNbM8/F+5iztq9PHRF1/Pqa/GOIzQOD6FvO88n3zMENr44I/JZikvt/PPXnfRu04hrepkDrAbfp0NUBEM7NuWr5L3nFQhVVVmy8wiXdWlmQvoYXI4RolowZ+1eMnMK+N+ru5klBYPfcOug9qRnF7A6ve6BUHccyuPQ8VOM7ObZ7K+G2pGYmIiIkJWV5W1TaoURohpSVGLnn7/uol/7JlzRo4W3zTEYasxvLmpF/dAg/rOm7oFQF+84AsCIrq5xVAgE1q1bR1BQEMOHD6/VdS+88AJ9+vRxk1X+iRGiGvL6z9vZd/QkN/VrbWZDBr8iIjSYG/u24ftNB8g/VXLuC6pg8c4jdGnRgDZNwl1s3XkyaxbExIDNZv2cNctjt/7ggw944IEH2Lx5s9+eDfIVjBDVgJWp2cxYnArAaz9td3uuF4PB1dw6qB0FRaV8v+lAra8tLC5lVVoOI31tNjRrFkyZAhkZoGr9nDLFI2J08uRJPv/8cyZPnsy4ceP48MMPK9Tv37+fSZMmERUVRUREBP379ychIYFZs2YxdepUUlJSEBFEhI8++giwPAi/+uqrCv3ExMTw+uuvlz9/88036du3L/Xr16dt27bcd999HD161O3jdTc+5zXni3y0PL38d1eeUjcYPMXA6Eg6NavPV2v2Mn5Q+1pduyoth1Mldka4e3/o0UcJT06GoBrGxUtKglOnKpYVFMC998IHH9Ssj/79Ydq02tkJfPXVV0RHR9O3b1/uuOMOxo8fz8svv0xISAj5+flcfvnltGjRgrlz59K2bVs2bNgAwC233MKuXbv47rvvymPONW7cuMb3tdlsTJs2jU6dOpGRkcHDDz/Mww8/zKefVnnU0m8wQnQO7HZl496jCCacj8F/ERHGDWrHqz9uJz0rn5hm9Wt87eIdRwgNsjGso4+97yuL0LnKXcjMmTO54w4rKcDll19OREQE8+fPZ+zYsXz++eccPHiQFStW0KyZJd6dO3cGrMgKDRo0IDg4mFatan8O8dFHHy3/PSYmhldffZXRo0fz8ccfu+2MjycwQnQOFmw9xL6jhTx6VVdCgiwRMrMhgz8ydkA7XvtxO0/N2cgT1/ao8ft4yc4sBnf0QKqTadM4WZsQODEx1nJcZaKjwY0Rrnft2sWyZcuYPXs2YIn8pEmTmDlzJmPHjmXdunX07du3XIRcycKFC3n55ZfZunUrx44do7S0lKKiIg4ePEibNv6bpNAI0VlQVd5btJv2TcN5KK4LwUH++43DYNibexIRSErNYdLMpBpF5D54rJDth05wy4AeHrKyFrz0krUnVFBwuiwiwip3IzNnzqS0tLRCvLWyM1p79uyp83ktETnj2uLi01l2MzIyuOGGG5g8eTIvvvgiUVFRrF27lokTJ1JUdF4BY7yO+WQ9C6vTc1mbeZTJIzoZETL4PUmp2ZR9zp0qttcoqd2SnT7stj1pEsyYYc2ARKyfM2ZY5W6ipKSEjz/+mJdffpn169eXPzZs2EDfvn3597//zYABA9i4cWO1Z3lCQ0MpLS09o7x58+YcOHDameTQoUMVnq9Zs4aioiL+8Y9/cMkll9CtWzf270W7TvcAABKvSURBVHd9Fl5vYGZEZ+G9RbtpWj+UWwfWbnPXYPBFhnWKIizERmGxHQWioyLOec3inVk0axBGz9Y+GjF60iS3Ck9lvv/+e7Kyspg8eTJRURX3zCZMmMC7775LSkoKr7zyCmPGjOHll1+mXbt2bNq0iYYNGzJo0CBiYmLIyMhg7dq1dOjQgYYNGxIWFsYVV1zB9OnTufTSSwkKCuLpp5+mXr165f137doVu93OtGnTuOWWW0hKSmJaHRwtfBHzNb8ath88wcJth/ndpTEmDbghIBgYHcms+4Zxf2xnIkKD+GR5BnZ79ctIdruSuO0QLRuGsTbT/12EXcGHH35IXFzcGSIEcOutt5KRkcGyZctYtGgRbdu2ZdSoUfTu3Zvnn3++/Pzh2LFj+c1vfsOVV15J8+bNy/ea3njjDTp16kRsbCzjxo3jvvvuo0WL04fn+/bty1tvvcWbb75Jr169mDlzZgXXbn9Gzif+VKDSvXt3veHFL/kx5SDLn7qCJhGh3jbJpQR6lkgzvnPznzV7eOKrjbw4ujd3XhJTZZuXvt/CB0vSECAsxOayLK9lbN26lZ49e1Yo89d8PTUlEMZX1etWhogkq+qg2vZpZkRVUGKHeRv2M3FIh4ATIYMBYNzAdozs1pxXftjGnpyCCnWqyrQFO/hgSZr1nNPn5wwGd2CEqAqOFykC3HtZR2+bYjC4BRHh5VsuwibCU3M2lntrlZTaeXruJqYt2Elst+bUC7ERZM7PGdyMcVaoguNFyg1dmvleXC2DwYW0bRLOn3/Tg2fmbubL1XsY3b8tD89ex4Kth3gwrjOPX9OdtZlHSUrNNufnDG7FCFE1rEjNJjkj1/zzGQKaiYM78N2GA7z47Rbe+nUnB44VVtg3Ghgdaf4HDG7H55bmRKSpiMwVkXwRyRCR287S9gcRyXN6FInIJqf6dBE56VT/c03tKCk1a+KGwMdmE24f1oGC4lIOHCskJEjo3abmsc8MBlfgizOi6UAR0BLoD3wvIhtUNaVyQ1W93vm5iCQCCys1G6WqC2prhFkTN1wopGcXYBOwq+WybYL6GjyNT82IRKQ+/P/27j24iipP4Pj3R94GUB4hOhkJxPC2gERBWYTBghFmrdrRzWzKCs4QChYFKYb1gZasEBBnjK7s4KzCUkiJOqIpcN21eFVtkViJLxCIIAFFRoK4wKqLECBEivz2j+5cbi4JhMu96duX36eqi/Q53c355fbN797u0+dQADylqidVtQr4L+C3bdi3FzAKuOJhaLukSMS7qhoTq27P6UZyonVKMN6JtW9EfYFzqvplUNlnwC/asO/vgEpV/Tqk/C8i0gHYATymqp9d6kDXpoglIXPVaHrQ1TolGK/EWiLqCBwPKTsOtOUJsN8Bi0LKJgLbAQF+D2wSkf6qesFj4iIyDZgGzphPFVEcvddrJ0+etPh8LFrxDRKo+/oQFaEf5aLk2muvpa6urlnZuXPnLiiLJ/EQ35kzZyJ//qlquy1ABc7zcS0tVUAecDpkn0eA9y5x3DuAk0DHS2y3F+ee0UXb2bdvX41n5eXlXjchqiw+f6ipqbmg7MSJE5fcr7RUdfPmi2+zebOzXaxpS3yxrqXXrQnwqYaRG9r1HpGqjlFVaWW5A/gSSBSRPkG7DQEu6KgQYhLwjqqevFQTcL4dGWN8atgwKCyE8vKW68vLnfphw6Lz/xcXFwem+Q5eqquro/MftoOKigpEpNURw6MtpjorqOop4B1goYiki8hI4NdcpAOCiKQB/wC8GlLeU0RGikiyiKSKyGNAd+CDqAVgjIm6O++EsrKWk1FTEiorc7aLlnHjxnH48OFmy8033xzWsYLnHLpaxVQics0A0oD/BVYD09Xtui0io0Qk9FvPPTj3kUI/H3UClgLHgG+BCcCvVNUeDjLG51pKRu2VhABSUlK4/vrrmy2JiYk0NDQwe/ZsMjMzSU1N5fbbb6eqqiqwX2VlJSLC+vXrGT58OMnJyWzatAmA9957j1tuuYXU1FR69+7N3Llzm01499NPP/Hkk0+SnZ1NSkoKOTk5vPjii4Bz72nKlCn07t2btLQ0+vTpw3PPPUdjY2Ng/127djF27Fg6d+5Mp06dGDJkCOXl5Rw4cIA73V9YRkYGIkJxcXF0f4EhYq2zAqr6fzjJpaW6SpwODcFlq3ESVui2u4HB0WijMSbyZs+GbdvSSLiMWVd+9jMYPx5uuAEOH4YBA2DBAmdpi6FDIZJT+syZM4eysjJWrlxJTk4OixcvZsKECezbt48bbrghsN3jjz/OCy+8QG5uLp06dWLTpk1MnDiRJUuWMHr0aA4ePMiDDz5IQ0NDYKqHSZMmUVlZyZIlS8jLy6O2tpZvvvkGgMbGRrKysigrKyMjI4MtW7Ywbdo0unXrxpQpUwAoKipiyJAhbNmyhcTERHbt2kVqaio33ngja9eupaCggN27d9O1a1fS0tp3eLOYS0TGGNNWXbo4SejgQejZ01lvDxs3bqRjx/OfiUeNGsWaNWtYunQpK1as4O677wZg2bJlbN68mZdeeolFi8536i0pKeGuu+4KrD/zzDM89thjTJ48GYCbbrqJ0tJS7r//fp5//nm++uor3nrrLTZs2MCECRMAyMnJCeyflJTEwoULA+u9evVi+/btrF69OpCIamtrefTRR+nf35n2PTc3N7B9165dAejRowfdu3ePzC/pMlgiMsbEhD/9Cerq6i9rvp6my3FPPQVLl8L8+dG/LAcwevRoli9fHlhPS0tj//79nD17lpEjRwbKExISGDFiBDU1Nc32v/XW5lP2bNu2jS1btlBaWhooa2xspL6+niNHjrBjxw46dOgQuITWkmXLlrFixQpqa2upr6/n7NmzZGdnB+offvhhpk6dyqpVqxg7diwFBQWBpOS1WLxHZIwxlxR8T2jhwtY7METDNddcQ25ubmDJysoKTKXRNBNrsNCy9PT0ZuuNjY3Mnz+f6urqwLJz50727dtHRkZG4Nitefvtt5k9ezbFxcVs2rSJ6upqZsyY0eweU0lJCTU1Ndxzzz18+OGHDB48mJUrV4b7K4goS0TGGN9pqWPCxXrTtYfc3FySk5ObdU44d+4cH330EQMHDrzovvn5+ezdu7dZcmtaEhMTyc/Pp7GxkfJWAquqquK2225j5syZ5Ofnk5uby/79+y/Yrk+fPsyaNYt169YxZcoUVqxYAUBycnKgvV6wRGSM8ZWL9Y7zMhmlp6czffp0nnjiCdavX8+ePXuYPn06R48eZcaMGRfdd968ebz55pvMmzePzz//nL1797JmzRrmzJkDOAmksLCQqVOnsnbtWr7++msqKyt5/XXnyZa+ffuyfft2NmzYwL59+3j66ad5//33A8evr6/noYceoqKiggMHDvDJJ59QVVUVSJDZ2dmICOvWreO7777j5MlLPZIZWZaIjDG+snXrxbtoNyWjrVvbt10ApaWlFBYWMnnyZIYOHcrOnTvZuHFjsx5zLRk/fjzr1q2jvLyc4cOHM3z4cJ599ll69uwZ2Oa1116jqKiIWbNm0b9/f4qLizl+3BkR7YEHHqCwsJCioiKGDRvGgQMHeOSRRwL7JiQkcOzYMSZNmkS/fv249957GTFiBIsXLwYgKyuLBQsWMHfuXDIzM5k5c2YUfjutk0tde7wa9evXT7/44guvmxE1FRUVjBkzxutmRI3F5w979uxhwIABzcrq6uouq7OC38RDfC29bk1EZJuq3tpi5UXYNyJjjDGeskRkjDHGU5aIjDHGeMoSkTHGGE9ZIjLGeMY6S/lLtF4vS0TGGE8kJCTYFAg+U19fT1JSUsSPa4nIGOOJ6667jqNHjzabqsDEJlXl9OnTfPvtt/To0SPix7dBT40xnujevTuHDh0i+Jm9M2fOkJqa6mGrosvP8SUlJZGZmUnnzp0jfmxLRMYYT3To0KHZyAHgPKybl5fnUYuiL97jC5ddmjPGGOMpS0TGGGM8FXOJSERmisinItIgIq+2Yft/EpEjInJcRFaKSEpQXS8RKReR0yKyV0TGRbXxxhhjLlvMJSLgf4BFwCVnbBKR8cATwFigF5ADBM9WvxrYAXQD5gJrRCQjwu01xhhzBWIuEanqO6r6LvBDGzafBLyiqrtV9RjwNFAMICJ9gXxgvqrWq+paYBdQEJ2WG2OMCYffe80NAv4zaP0zIFNEurl1f1XVupD6QS0dSESmAdPc1QYR+TwK7Y0V3YHvvW5EFFl8/hXPsUH8x9cvnJ38nog6AseD1pt+7tRCXVN9VksHUtXlwHIAEfk0nDk1/MLi87d4ji+eY4OrI75w9mvXS3MiUiEi2spSdekjXOAkEPx0VdPPdS3UNdXXYYwxJma0ayJS1TGqKq0sd4RxyN3AkKD1IcBRVf3BrcsRkU4h9bvDj8AYY0ykxVxnBRFJFJFUIAFIEJFUEWntEuJrwBQRGSgiXYB/Bl4FUNUvgWpgvnuMe4HBwNo2NGP5lcYR4yw+f4vn+OI5NrD4WiSxNgy7iJQA80OKF6hqiYj0BGqAgap60N3+YeBxIA0nyTyoqg1uXS+cxHQbcBB4SFX/O/pRGGOMaauYS0TGGGOuLjF3ac4YY8zVxRKRMcYYT1kiCiIiXUXkP0TklIjUikiR1226Ehcbt09Exrrj7512x+PL9qiZYRGRFBF5xX2d6kRkh4j8Kqje1/EBiMgbInJYRE6IyJciMjWozvfxAYhIHxE5IyJvBJUVua/rKRF5V0S6etnGcLmPq5wRkZPu8kVQne9jFJH7RGSPG8N+ERnlll/2uWmJqLmXgJ+ATGAisFREWhyJwSdaHLdPRLoD7wBPAV2BT4G32711VyYR+Ab4BXAtTixl7kC38RAfwB+BXqraGfg7YJGI3BJH8YHzntvatOK+3/4d+C3O+/A08LI3TYuImara0V36QXzEKCK/BEqByTgDCIwG/hruuWmdFVwikg4cA252u34jIq8D36rqE5427gqJyCLg56pa7K5PA4pV9W/c9XScYUfyVHWvZw29QiKyE2fQ227EWXwi0g+oAH4PXEccxCci9wF/j9MTNldV7xeRP+Ak3yJ3m5uAPUC3kOG6Yp6IVABvqOqKkHLfxygiH+KM8/lKSHlYf1vsG9F5fYFzTUnI1erYdD43CCc2AFT1FLAfH8cqIpk4r+Fu4ig+EXlZRE4De4HDwHriID4R6QwsBB4JqQqNbT/OVYq+7de6iPqjiHwvIh+IyBi3zNcxikgCcCuQISJficghEfk3EUkjzHPTEtF5rY1N16mFbf0urmIVkSTgL8Aq91NX3MSnqjNw2j0K55JHA/ER39M4n6i/CSmPh9iaPI4zNU0WzoOe77nffvweYyaQBPwG57wcCuThDCgQVmyWiM67msami5tYRaQD8DrOJ8qZbnHcxAegqudUtQr4OTAdn8cnIkOBccC/tlDt69iCqeonqlqnqg2qugr4APhb/B9jvfvvn1X1sKp+DyzmCmKzRHTel0CiiPQJKovXsemajdHnXse9CZ/FKiICvILzCa1AVc+6VXERXwsSOR+Hn+MbgzOR5UEROQI8ChSIyHYujC0HSMF5f/qdAoLPY3TnfjuEE0+o8M5NVbXFXYC3cGZ1TQdG4nylHOR1u64gnkQgFaf31evuz4lAhhtbgVtWCnzsdXvDiG8Z8DHQMaTc9/EBPYD7cC51JADjgVPAr/0eH3ANcH3Q8i/AGjeuQcAJnEs+6cAbwFtetzmMGK9zX7Om99xE9/XrFw8x4tzf2+qep12ASpzLrWGdm54HFEsLTnfDd90T5iBQ5HWbrjCeEpxPLcFLiVs3DucGeD1Ob6xeXrf3MmPLduM5g3M5oGmZGCfxZQDvAz+6f7R2Af8YVO/r+EJiLcHpXda0XuS+/07hTHzZ1es2hvn6bcW5JPUjzgemX8ZLjDj3iF52YzsCvAikunWXfW5a921jjDGesntExhhjPGWJyBhjjKcsERljjPGUJSJjjDGeskRkjDHGU5aIjDHGeMoSkTHGGE9ZIjLGJ0Sks4iUiMgAr9tiTCRZIjLGP24F5uM81W5M3LBEZIx/5OFMA1HjdUOMiSQb4scYHxCRPUD/kOK1qvobL9pjTCRZIjLGB0RkGM7o8LuBP7jFh1W11rtWGRMZiV43wBjTJp/hTIz3Z1X92OvGGBNJdo/IGH8YBCQD271uiDGRZonIGH/Ix5l/qdrrhhgTaZaIjPGHPGC/qp7wuiHGRJolImP8YSDWbdvEKeusYIw//Ajki8h44DiwT1V/8LhNxkSEdd82xgdE5GbgFWAwkAqMUtUqb1tlTGRYIjLGGOMpu0dkjDHGU5aIjDHGeMoSkTHGGE9ZIjLGGOMpS0TGGGM8ZYnIGGOMpywRGWOM8ZQlImOMMZ76fzqXCduYWwNlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using One-Dimensional Convolutional Layers to Process Sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "1D conv layer with kernel size 4, stride 2, VALID padding:\n",
    "\n",
    "              |-----2-----|     |-----5---...------|     |-----23----|\n",
    "        |-----1-----|     |-----4-----|   ...      |-----22----|\n",
    "  |-----0----|      |-----3-----|     |---...|-----21----|\n",
    "X: 0  1  2  3  4  5  6  7  8  9  10 11 12 ... 42 43 44 45 46 47 48 49\n",
    "Y: 1  2  3  4  5  6  7  8  9  10 11 12 13 ... 43 44 45 46 47 48 49 50\n",
    "  /10 11 12 13 14 15 16 17 18 19 20 21 22 ... 52 53 54 55 56 57 58 59\n",
    "\n",
    "Output:\n",
    "\n",
    "X:     0/3   2/5   4/7   6/9   8/11 10/13 .../43 42/45 44/47 46/49\n",
    "Y:     4/13  6/15  8/17 10/19 12/21 14/23 .../53 46/55 48/57 50/59\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 8s 1ms/sample - loss: 0.0681 - last_time_step_mse: 0.0608 - val_loss: 0.0477 - val_last_time_step_mse: 0.0397\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 7s 962us/sample - loss: 0.0411 - last_time_step_mse: 0.0338 - val_loss: 0.0367 - val_last_time_step_mse: 0.0287\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 7s 966us/sample - loss: 0.0339 - last_time_step_mse: 0.0259 - val_loss: 0.0310 - val_last_time_step_mse: 0.0217\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 7s 941us/sample - loss: 0.0284 - last_time_step_mse: 0.0186 - val_loss: 0.0260 - val_last_time_step_mse: 0.0151\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 7s 943us/sample - loss: 0.0248 - last_time_step_mse: 0.0140 - val_loss: 0.0243 - val_last_time_step_mse: 0.0137\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 6s 928us/sample - loss: 0.0234 - last_time_step_mse: 0.0126 - val_loss: 0.0228 - val_last_time_step_mse: 0.0118\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 7s 939us/sample - loss: 0.0227 - last_time_step_mse: 0.0119 - val_loss: 0.0222 - val_last_time_step_mse: 0.0112\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 7s 974us/sample - loss: 0.0221 - last_time_step_mse: 0.0112 - val_loss: 0.0217 - val_last_time_step_mse: 0.0109\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 7s 946us/sample - loss: 0.0217 - last_time_step_mse: 0.0109 - val_loss: 0.0215 - val_last_time_step_mse: 0.0108\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 6s 925us/sample - loss: 0.0212 - last_time_step_mse: 0.0105 - val_loss: 0.0210 - val_last_time_step_mse: 0.0102\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 7s 934us/sample - loss: 0.0210 - last_time_step_mse: 0.0105 - val_loss: 0.0208 - val_last_time_step_mse: 0.0098\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 7s 973us/sample - loss: 0.0207 - last_time_step_mse: 0.0101 - val_loss: 0.0206 - val_last_time_step_mse: 0.0100\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 7s 949us/sample - loss: 0.0204 - last_time_step_mse: 0.0099 - val_loss: 0.0205 - val_last_time_step_mse: 0.0099\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 7s 962us/sample - loss: 0.0202 - last_time_step_mse: 0.0098 - val_loss: 0.0200 - val_last_time_step_mse: 0.0094\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 7s 930us/sample - loss: 0.0200 - last_time_step_mse: 0.0096 - val_loss: 0.0200 - val_last_time_step_mse: 0.0094\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 7s 949us/sample - loss: 0.0197 - last_time_step_mse: 0.0094 - val_loss: 0.0200 - val_last_time_step_mse: 0.0094\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 7s 932us/sample - loss: 0.0196 - last_time_step_mse: 0.0093 - val_loss: 0.0195 - val_last_time_step_mse: 0.0093\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 7s 951us/sample - loss: 0.0193 - last_time_step_mse: 0.0090 - val_loss: 0.0195 - val_last_time_step_mse: 0.0090\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 7s 968us/sample - loss: 0.0190 - last_time_step_mse: 0.0088 - val_loss: 0.0188 - val_last_time_step_mse: 0.0084\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 7s 930us/sample - loss: 0.0187 - last_time_step_mse: 0.0085 - val_loss: 0.0186 - val_last_time_step_mse: 0.0081\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Conv1D(filters=20, kernel_size=4, strides=2, padding=\"valid\",\n",
    "                        input_shape=[None, 1]),\n",
    "    keras.layers.GRU(20, return_sequences=True),\n",
    "    keras.layers.GRU(20, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10))\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train[:, 3::2], epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid[:, 3::2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WaveNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "C2  /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\.../\\ /\\ /\\ /\\ /\\ /\\\n",
    "   \\  /  \\  /  \\  /  \\  /  \\  /  \\  /  \\       /  \\  /  \\  /  \\\n",
    "     /    \\      /    \\      /    \\                 /    \\\n",
    "C1  /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\ /\\  /\\ /.../\\ /\\ /\\ /\\ /\\ /\\ /\\\n",
    "X: 0  1  2  3  4  5  6  7  8  9  10 11 12 ... 43 44 45 46 47 48 49\n",
    "Y: 1  2  3  4  5  6  7  8  9  10 11 12 13 ... 44 45 46 47 48 49 50\n",
    "  /10 11 12 13 14 15 16 17 18 19 20 21 22 ... 53 54 55 56 57 58 59\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "7000/7000 [==============================] - 3s 451us/sample - loss: 0.0668 - last_time_step_mse: 0.0546 - val_loss: 0.0376 - val_last_time_step_mse: 0.0249\n",
      "Epoch 2/20\n",
      "7000/7000 [==============================] - 2s 302us/sample - loss: 0.0325 - last_time_step_mse: 0.0202 - val_loss: 0.0296 - val_last_time_step_mse: 0.0175\n",
      "Epoch 3/20\n",
      "7000/7000 [==============================] - 2s 286us/sample - loss: 0.0284 - last_time_step_mse: 0.0170 - val_loss: 0.0269 - val_last_time_step_mse: 0.0153\n",
      "Epoch 4/20\n",
      "7000/7000 [==============================] - 2s 305us/sample - loss: 0.0264 - last_time_step_mse: 0.0149 - val_loss: 0.0256 - val_last_time_step_mse: 0.0138\n",
      "Epoch 5/20\n",
      "7000/7000 [==============================] - 2s 303us/sample - loss: 0.0251 - last_time_step_mse: 0.0135 - val_loss: 0.0244 - val_last_time_step_mse: 0.0128\n",
      "Epoch 6/20\n",
      "7000/7000 [==============================] - 2s 301us/sample - loss: 0.0244 - last_time_step_mse: 0.0129 - val_loss: 0.0238 - val_last_time_step_mse: 0.0122\n",
      "Epoch 7/20\n",
      "7000/7000 [==============================] - 2s 309us/sample - loss: 0.0238 - last_time_step_mse: 0.0124 - val_loss: 0.0233 - val_last_time_step_mse: 0.0117\n",
      "Epoch 8/20\n",
      "7000/7000 [==============================] - 2s 307us/sample - loss: 0.0231 - last_time_step_mse: 0.0116 - val_loss: 0.0227 - val_last_time_step_mse: 0.0113\n",
      "Epoch 9/20\n",
      "7000/7000 [==============================] - 2s 326us/sample - loss: 0.0227 - last_time_step_mse: 0.0113 - val_loss: 0.0228 - val_last_time_step_mse: 0.0114\n",
      "Epoch 10/20\n",
      "7000/7000 [==============================] - 2s 349us/sample - loss: 0.0225 - last_time_step_mse: 0.0112 - val_loss: 0.0222 - val_last_time_step_mse: 0.0108\n",
      "Epoch 11/20\n",
      "7000/7000 [==============================] - 2s 317us/sample - loss: 0.0221 - last_time_step_mse: 0.0107 - val_loss: 0.0225 - val_last_time_step_mse: 0.0116\n",
      "Epoch 12/20\n",
      "7000/7000 [==============================] - 2s 306us/sample - loss: 0.0219 - last_time_step_mse: 0.0104 - val_loss: 0.0215 - val_last_time_step_mse: 0.0101\n",
      "Epoch 13/20\n",
      "7000/7000 [==============================] - 2s 296us/sample - loss: 0.0216 - last_time_step_mse: 0.0102 - val_loss: 0.0213 - val_last_time_step_mse: 0.0099\n",
      "Epoch 14/20\n",
      "7000/7000 [==============================] - 2s 306us/sample - loss: 0.0215 - last_time_step_mse: 0.0102 - val_loss: 0.0214 - val_last_time_step_mse: 0.0100\n",
      "Epoch 15/20\n",
      "7000/7000 [==============================] - 2s 320us/sample - loss: 0.0212 - last_time_step_mse: 0.0098 - val_loss: 0.0212 - val_last_time_step_mse: 0.0102\n",
      "Epoch 16/20\n",
      "7000/7000 [==============================] - 2s 300us/sample - loss: 0.0210 - last_time_step_mse: 0.0096 - val_loss: 0.0209 - val_last_time_step_mse: 0.0096\n",
      "Epoch 17/20\n",
      "7000/7000 [==============================] - 2s 298us/sample - loss: 0.0210 - last_time_step_mse: 0.0096 - val_loss: 0.0205 - val_last_time_step_mse: 0.0091\n",
      "Epoch 18/20\n",
      "7000/7000 [==============================] - 2s 299us/sample - loss: 0.0206 - last_time_step_mse: 0.0093 - val_loss: 0.0207 - val_last_time_step_mse: 0.0096\n",
      "Epoch 19/20\n",
      "7000/7000 [==============================] - 2s 303us/sample - loss: 0.0205 - last_time_step_mse: 0.0092 - val_loss: 0.0206 - val_last_time_step_mse: 0.0093\n",
      "Epoch 20/20\n",
      "7000/7000 [==============================] - 2s 330us/sample - loss: 0.0204 - last_time_step_mse: 0.0091 - val_loss: 0.0207 - val_last_time_step_mse: 0.0095\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.InputLayer(input_shape=[None, 1]))\n",
    "for rate in (1, 2, 4, 8) * 2:\n",
    "    model.add(keras.layers.Conv1D(filters=20, kernel_size=2, padding=\"causal\",\n",
    "                                  activation=\"relu\", dilation_rate=rate))\n",
    "model.add(keras.layers.Conv1D(filters=10, kernel_size=1))\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=20,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the original WaveNet defined in the paper: it uses Gated Activation Units instead of ReLU and parametrized skip connections, plus it pads with zeros on the left to avoid getting shorter and shorter sequences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "class GatedActivationUnit(keras.layers.Layer):\n",
    "    def __init__(self, activation=\"tanh\", **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.activation = keras.activations.get(activation)\n",
    "    def call(self, inputs):\n",
    "        n_filters = inputs.shape[-1] // 2\n",
    "        linear_output = self.activation(inputs[..., :n_filters])\n",
    "        gate = keras.activations.sigmoid(inputs[..., n_filters:])\n",
    "        return self.activation(linear_output) * gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wavenet_residual_block(inputs, n_filters, dilation_rate):\n",
    "    z = keras.layers.Conv1D(2 * n_filters, kernel_size=2, padding=\"causal\",\n",
    "                            dilation_rate=dilation_rate)(inputs)\n",
    "    z = GatedActivationUnit()(z)\n",
    "    z = keras.layers.Conv1D(n_filters, kernel_size=1)(z)\n",
    "    return keras.layers.Add()([z, inputs]), z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "n_layers_per_block = 3 # 10 in the paper\n",
    "n_blocks = 1 # 3 in the paper\n",
    "n_filters = 32 # 128 in the paper\n",
    "n_outputs = 10 # 256 in the paper\n",
    "\n",
    "inputs = keras.layers.Input(shape=[None, 1])\n",
    "z = keras.layers.Conv1D(n_filters, kernel_size=2, padding=\"causal\")(inputs)\n",
    "skip_to_last = []\n",
    "for dilation_rate in [2**i for i in range(n_layers_per_block)] * n_blocks:\n",
    "    z, skip = wavenet_residual_block(z, n_filters, dilation_rate)\n",
    "    skip_to_last.append(skip)\n",
    "z = keras.activations.relu(keras.layers.Add()(skip_to_last))\n",
    "z = keras.layers.Conv1D(n_filters, kernel_size=1, activation=\"relu\")(z)\n",
    "Y_proba = keras.layers.Conv1D(n_outputs, kernel_size=1, activation=\"softmax\")(z)\n",
    "\n",
    "model = keras.models.Model(inputs=[inputs], outputs=[Y_proba])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7000 samples, validate on 2000 samples\n",
      "Epoch 1/2\n",
      "7000/7000 [==============================] - 4s 546us/sample - loss: 0.1300 - last_time_step_mse: 0.1261 - val_loss: 0.1230 - val_last_time_step_mse: 0.1200\n",
      "Epoch 2/2\n",
      "7000/7000 [==============================] - 3s 410us/sample - loss: 0.1222 - last_time_step_mse: 0.1178 - val_loss: 0.1217 - val_last_time_step_mse: 0.1189\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[last_time_step_mse])\n",
    "history = model.fit(X_train, Y_train, epochs=2,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter we explored the fundamentals of RNNs and used them to process sequences (namely, time series). In the process we also looked at other ways to process sequences, including CNNs. In the next chapter we will use RNNs for Natural Language Processing, and we will learn more about RNNs (bidirectional RNNs, stateful vs stateless RNNs, Encoder–Decoders, and Attention-augmented Encoder-Decoders). We will also look at the Transformer, an Attention-only architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exercise solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See Appendix A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Embedded Reber Grammars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to build a function that generates strings based on a grammar. The grammar will be represented as a list of possible transitions for each state. A transition specifies the string to output (or a grammar to generate it) and the next state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "default_reber_grammar = [\n",
    "    [(\"B\", 1)],           # (state 0) =B=>(state 1)\n",
    "    [(\"T\", 2), (\"P\", 3)], # (state 1) =T=>(state 2) or =P=>(state 3)\n",
    "    [(\"S\", 2), (\"X\", 4)], # (state 2) =S=>(state 2) or =X=>(state 4)\n",
    "    [(\"T\", 3), (\"V\", 5)], # and so on...\n",
    "    [(\"X\", 3), (\"S\", 6)],\n",
    "    [(\"P\", 4), (\"V\", 6)],\n",
    "    [(\"E\", None)]]        # (state 6) =E=>(terminal state)\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",
    "        index = np.random.randint(len(grammar[state]))\n",
    "        production, state = grammar[state][index]\n",
    "        if isinstance(production, list):\n",
    "            production = generate_string(grammar=production)\n",
    "        output.append(production)\n",
    "    return \"\".join(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate a few strings based on the default Reber grammar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTXXTTVPXTVPXTTVPSE BPVPSE BTXSE BPVVE BPVVE BTSXSE BPTVPXTTTVVE BPVVE BTXSE BTXXVPSE BPTTTTTTTTVVE BTXSE BPVPSE BTXSE BPTVPSE BTXXTVPSE BPVVE BPVVE BPVVE BPTTVVE BPVVE BPVVE BTXXVVE BTXXVVE BTXXVPXVVE "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_string(default_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Now let's generate a few strings based on the embedded Reber grammar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTBPVVETE BTBTSSSSSSSXXVVETE BPBTSSSXXTTTTVPSEPE BTBPTTVVETE BPBTXXTVVEPE BTBTXSETE BPBTSSSSSXXTTVPXVPXTTTVVEPE BPBTSSXXTVPSEPE BPBPTTTTTTTVPSEPE BTBTSXSETE BPBPTVPXVVEPE BPBPVVEPE BPBPTVVEPE BTBPTTVPXTTVPSETE BTBTSSXSETE BTBTXXTTVVETE BPBTSXSEPE BPBPTVPSEPE BTBPVVETE BPBTXXTTTVPXTVVEPE BPBPTTVPXTVVEPE BTBPVVETE BPBPTVPXVPXTVVEPE BTBPVVETE BPBTSXSEPE "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_string(embedded_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, now we need a function to generate strings that do not respect the grammar. We could generate a random string, but the task would be a bit too easy, so instead we will generate a string that respects the grammar, and we will corrupt it by changing just one character:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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 = np.random.choice(sorted(set(chars) - set(good_char)))\n",
    "    return good_string[:index] + bad_char + good_string[index + 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a few corrupted strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTTTXXVVETE BPBTXXSPXTVVEPE BTBTXSPTE BPTTSXXTVPXVVEPE PPBPVPSEPE BTBPTVETE BPTTSSSSSXSEPE BPBSVPSEPE BTBPVVESE BPBTXSEPS BEBTXSETE XPBTXXTVPSEPE BTBPVVEPE BTXPTVVETE BTBPVXETE BVBTXSETE BPTTXXVPXVPSEPE BTBPXVPSETE STBPTTVPXVPXTVPSETE BPBPTVPSESE BPBPVEEPE ETBTXSETE BTBTXSVTE BPBTXXVPSEPP BTBTXXVPSETS "
     ]
    }
   ],
   "source": [
    "for _ in range(25):\n",
    "    print(generate_corrupted_string(embedded_reber_grammar), end=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be continued..."
   ]
  }
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