{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This notebook is from [the Hands on Machine Learning using Scikit-Learn and Tensorflow 2](https://github.com/ageron/handson-ml2), 2nd edition**\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Prediction using RNNs"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Is this notebook running on Colab or Kaggle?\n",
    "IS_COLAB = \"google.colab\" in sys.modules\n",
    "IS_KAGGLE = \"kaggle_secrets\" in sys.modules\n",
    "\n",
    "# Scikit-Learn ≥0.20 is required\n",
    "import sklearn\n",
    "assert sklearn.__version__ >= \"0.20\"\n",
    "\n",
    "# TensorFlow ≥2.0 is required\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "assert tf.__version__ >= \"2.0\"\n",
    "\n",
    "if not tf.config.list_physical_devices('GPU'):\n",
    "    print(\"No GPU was detected. LSTMs and CNNs can be very slow without a GPU.\")\n",
    "    if IS_COLAB:\n",
    "        print(\"Go to Runtime > Change runtime and select a GPU hardware accelerator.\")\n",
    "    if IS_KAGGLE:\n",
    "        print(\"Go to Settings > Accelerator and select GPU.\")\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\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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",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\", legend=True):\n",
    "    plt.plot(series, \".-\")\n",
    "    if y is not None:\n",
    "        plt.plot(n_steps, y, \"bo\", label=\"Target\")\n",
    "    if y_pred is not None:\n",
    "        plt.plot(n_steps, y_pred, \"rx\", markersize=10, label=\"Prediction\")\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",
    "    if legend and (y or y_pred):\n",
    "        plt.legend(fontsize=14, loc=\"upper left\")\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",
    "                legend=(col == 0))\n",
    "save_fig(\"time_series_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: in this notebook, the blue dots represent targets, and red crosses represent predictions. In the book, I first used blue crosses for targets and red dots for predictions, then I reversed this later in the chapter. Sorry if this caused some confusion."
   ]
  },
  {
   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:03:42.738268: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:42.738469: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:42.738547: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:45.573522: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:45.573896: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:45.574148: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:996] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2023-04-04 13:03:45.574349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1635] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13868 MB memory:  -> device: 0, name: NVIDIA RTX A4500 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 2s 3ms/step - loss: 0.1313 - val_loss: 0.0564\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0298 - val_loss: 0.0183\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0132 - val_loss: 0.0106\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0091 - val_loss: 0.0082\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 1s 2ms/step - loss: 0.0074 - val_loss: 0.0071\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0065 - val_loss: 0.0063\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0059 - val_loss: 0.0057\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0054 - val_loss: 0.0052\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0050 - val_loss: 0.0049\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0047 - val_loss: 0.0046\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0045 - val_loss: 0.0044\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0042 - val_loss: 0.0040\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.0039\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0040 - val_loss: 0.0038\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0039 - val_loss: 0.0040\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0037\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0038 - val_loss: 0.0037\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0037 - val_loss: 0.0037\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0037 - val_loss: 0.0036\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 793us/step - loss: 0.0036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.003601563163101673"
      ]
     },
     "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 586us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": [],
   "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=tf.keras.optimizers.legacy.Adam(learning_rate=0.005)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 3s 11ms/step - loss: 0.1699 - val_loss: 0.1090\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0630 - val_loss: 0.0332\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 2s 11ms/step - loss: 0.0196 - val_loss: 0.0132\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 2s 11ms/step - loss: 0.0122 - val_loss: 0.0114\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0117 - val_loss: 0.0112\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0116 - val_loss: 0.0111\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 2s 11ms/step - loss: 0.0115 - val_loss: 0.0110\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 2s 11ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 2s 11ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 3s 13ms/step - loss: 0.0114 - val_loss: 0.0109\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 3s 12ms/step - loss: 0.0114 - val_loss: 0.0109\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 6ms/step - loss: 0.0109\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.01088138297200203"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qCH1IunTpEn7//XesXr0an332GXJycrBkyRIAUkCcOHEiJk6ciBs3bmDfvn1YsWIFVq9ejZs3b+KPP/6oUO22gof/5OheqHLRcaSKiCpHYCAQFWWbgaokly5dAgD079/fZNmff/5Z1eWUSZMmTaBWq/H333+bjKYJIXDo0KEKPb+HhwcaNGiAixcvIiEhwWS5/tQHrVq1Mrt9SEgInn/+eezduxdubm745ZdfzK4XEBCAoUOHYvv27QgNDcWuXbuqzaFAhio5uheq1PmZAARDFRFRKQUHBwMA/vrrL6P2NWvW4LfffrNGSaWmVqsxcOBAJCUl4dNPPzVatnr1apw9e7bC+xg+fDjy8/MxZcoUo0v9HD9+HCtXroSnpycGDBgAALh16xZOnjxp8hxpaWnQarVwdnYGIE2wP3jwoMl6WVlZyMzMhKOjo8lEd3vFw39ydC9UqXR34YQ83LqlfsAGREQEAC+88ALmzZuHsWPHYu/evQgODsa///6L3bt346mnnsKmTZusXWKJ5s6di127duG///0v9u3bZzhP1bZt29CrVy9s3769QgFl0qRJ+PXXX/H999/jzJkz6Nq1K5KTk7Fu3TrcvXsXy5Ytg7u7OwDpnFmtW7dGy5YtER4ejoCAANy8eRO///478vPzMXHiRABATk4OHnvsMTRu3Bht2rRBvXr1kJmZiW3btuHmzZuYOHGiycR7e8VQJUeFZqNKl6qpHn+MREQVFRgYiH379mHSpEnYtWsX7t69i4cffhg7duxAfHy87ENVUFAQDh06hMmTJ2PHjh3Yt28f2rRpgx07dmDDhg0AzE+eLy1nZ2fs2bMH8+bNw7p167Bo0SK4urqiU6dOmDp1Kjp06GBYt379+pgxYwb27NmDXbt2ISUlBT4+PmjdujXeeust9OrVCwBQo0YNzJs3D7t378aff/6J5ORk1KxZE02aNMHcuXPxzDPPVOxFsSEKUXj8j8pMo9HA09MT6enpFfpDN+HiAuTmIhhxUDcKxqlT+fjtt9/Qp08fODo6Wm4/VpKfb1/9AeyvT+xP1cvNzcWVK1cQEhJiOLRSnKLXybMH9tYnS/enQ4cOOHToENLT0+F274hGVbKV309p/x2lpKTA19fXop/f8n1Vqjte/4+IqFpKTEw0afvhhx9w4MABdOvWzSqBikqHh//kys0NuH0bbshEWhpg5lxrRERkh8LCwtC6dWs0a9bMcH6t6OhouLu745NPPrF2eVQChiq50p8AFNJpFVJSrFkMERFVlVdffRVbt27FkSNHkJWVhVq1auHZZ5/FtGnT0LRpU2uXRyVgqJKre6HK3z0TyOC5qoiIqovZs2dj9uzZ1i6DyoFzquTqXqiq46Y/q7r5swcTERGRPDBUydW90yr41eBZ1YmIiGwBQ5Vc3Rup8nXJAgCkpHCkioiISM4YquTqXqjyVnOkioiIyBYwVMnVvVBV01E/p8qaxRAREdGDMFTJ1b1Q5anUj1Tx8B8REZGcMVTJlf48VQqOVBEREdkChiq5uheqXAVHqoiIiGwBQ5Vc3QtVLgU8ozoRkSWsXLkSCoUCK1euNGqvX78+6tevX+HnsaQZM2ZAoVAgOjq60vZRFjNmzIBKpcJff/1l7VJkjaFKru6FKnX+/cN/Op01CyIiqjzPPvssVCoVNm7cWOJ6Go0Grq6u8PLyQk5OThVVZ3nR0dFQKBSYMWOGtUshC2Kokqt7ocpRK4WqggIFsrIcrVkREVGlGTVqFADg//7v/0pc78cff0ROTg6GDh0KFxcXi+x79+7d2L17t0Wey1LeeOMNnDlzBpGRkdYuhcqA1/6Tq3uhSpmVCXd3ICMD0GjUVi6KiKhydOnSBSEhIdi/fz+uXbtW7OG47777DsD9EGYJDRs2tNhzWYqvry98fX2tXQaVEUeq5OpeqEJmJmrVku6mpztZrx4iokqkUCgwYsQI6HS6YucqnTp1CrGxsQgPD0fbtm2Rnp6OefPmoVOnTggICICTkxMCAgIwbNgwXLp0qdT7Lm5OVWpqKl599VX4+fnB1dUVERER2Lx5c7HP891336F///6oX78+nJ2d4evri6effhp79+41Wm/GjBno3LkzAGDmzJlQKBSGW1xcnGGd4uZUbd26FZ07d4anpydcXFzQsmVLLFy4EHfv3jVaLy4uzvC6Xrx4EU8++SRq1qyJGjVqoFu3bvj3339L/RqVpLT1AMDevXvRu3dvBAQEQK1Ww8/PD48//jiWLl1qtN4///yDgQMHol69elCr1ahVqxYiIiJkf6FpjlTJVeFQ1QC4fJkjVURkQdevAxcuAI0aAYGB1q4GADB8+HDMnDkTq1atwvTp06FQGH/recWKFQDuj1KdOXMG77//Pjp37ownn3wSNWrUwNmzZ7FmzRr8+uuv+OeffxAcHFyuWrKzsxEVFYUTJ07gkUceQadOnRAfH48hQ4agR48eZrd5/fXX0bJlS3Tr1g21atXC9evX8fPPP6NHjx7YtGkT+vfvDwCIiopCXFwcVq1ahU6dOiEqKsrwHF5eXiXWtXDhQkyYMAHe3t549tlnUaNGDfzyyy+YMGEC/vzzT2zatMnkdYuLi0P79u3RvHlzjBw5EpcuXcLPP/+Mzp0748yZM/Dz8yvXa1TWen799Vc88cQT8PLyQv/+/eHv749bt27h33//xffff4/Ro0cDAI4dO4ZHH30UKpUK/fv3R3BwMO7cuYPTp09j6dKlePfdd8tdb6UTVCHp6ekCgEhPT7fsEycnCwEIAYh+fe8KQIjXXjsq8vLyLLsfK8nLyxNbtmyxm/4IYX99Yn+qXk5Ojjh9+rTIyckxXajTCZGZabgVaDQi7fp1UaDRGLWX6vbVV0IoldJ7jFIpPS7rcxR30+nK3f+CggLRtWtXAUDs2rXLaFl+fr7w8/MTarVapKSkCCGEuHPnjuF+YXv27BFKpVK89NJLRu0rVqwQAMSKFSuM2oODg0VwcLBR2/Tp0wUA8fLLLxu1b9++XQAw+zyXL1826c+ZM2dEQECAaNSokdGyvXv3CgBi+vTp5l4Kw/737t1raLt48aJwcHAQtWvXFteuXTO05+bmig4dOggAYvXq1Yb2K1euGGr96KOPjJ7/vffeEwDE3Llzze6/uHq2bt0qCgoKylXPU089JQCIY8eOmTz/7du3DffHjx8vAIgtW7aUuF5xSvx3VOS5LP35zcN/cqUfqQJQt2Y2AB7+I6rWsrOl94V7N6WHB7wCA6H08DBqL9Xt9dfvf51Yp5Mel/U5irtlZ1eomy+88AKA+3On9LZt24akpCT0798f3t7eAABPT0/D/cI6d+6M5s2bY9euXeWuY/Xq1XBycsIHH3xg1N6zZ0907drV7DYhISEmbXXq1MFTTz2FCxcu4OrVq+WuBwDWrFmDu3fvYsKECQgKCjK0q9VqzJs3DwDMHjoNCQnBO++8Y9SmH+07fPhwlddj7gsGPj4+5V5PThiq5MrZGVBKv54AD+kbgDz8R0T2rk+fPqhVqxY2b96M9PR0Q3txE9Sjo6MxYMAA+Pv7w9HR0TA36cSJE7hx40a5atBoNLhy5QpCQ0NRp04dk+WPP/642e0uX76Ml19+GQ0bNoSzszNUKhVq1qyJL7/8EgDKXY/e0aNHAcDocKHeI488AmdnZxw7dsxkWatWraBUGn/cB9475Hvnzp0qq+eZZ54BALRv3x5vvPEGNm/ejNtmLhcyePBgKJVKPPnkkxg5ciR+/PFHJCQklLvOqsQ5VXKlUEj/69No4FdDH6o4UkVUbbm6ApmZhoc6nQ4ajQYeHh4mH5glSkgAHnrI+MR3KhVw+jRQt65l6qwAR0dHPP/881i0aBHWrFmDMWPG4ObNm/j9999Rr149dOvWzbDuhg0bMGTIELi5uaFnz56oX78+XF1dDSfmLO/IkEajAQDUrl3b7HJzc5AuXryIyMhIaDQadO7cGU888QTc3d2Rn5+P//3vf9i3bx+0Wm256ilal7n9KxQK+Pn5mQ0fHh4eJm0ODtLHf0FBQZXVM2jQIGzZsgULFy7EN998g6+++goKhQKdO3fGggUL0KpVKwBAu3btEB0djTlz5mDNmjWGuXQRERGYN2+eYZK/HDFUyVmRUJWezpEqompLoQBq1Lj/WKcDCgqktrKEqsaNgaVLgVdekbZXqYAlS6R2mRg5ciQWLVqE5cuXY8yYMfj+++9x9+5dvPjii0YBcsaMGXB2dsbff/+NRo0aGT3H2rVry71/fQhJTk42uzwpKcmkbdGiRUhLS8P333+P559/HsD94Dt58mTs27ev3PUUrSspKclkAr4QAklJSWYDVGUpTz39+/dH//79kZGRgQMHDmDTpk1Yvnw5evXqhbNnzxom6j/++OP4/fffkZOTg5iYGGzduhWLFy9G3759cfLkSTRo0KBK+lhWPPwnZ/fmVfk6c6SKiCxo1CggLg7Yu1f6acFzPllCs2bN0L59e/z99984fvw4VqxYAYVCgRdffNFovUuXLuGhhx4yCVSJiYm4fPlyuffv4eGBkJAQXLx4ETdv3jRZ/ueff5q06U/hoP+Gn54QAgcPHjRZX6VSASjbSFHr1q0BwOxpFmJiYpCbm2sY7akKFanH3d0dvXr1wtKlSzFixAgkJSUhJibGZD0XFxdERUVhwYIFmDp1KnJycrBz505LdsOiGKrk7F6o8lEzVBGRhQUGAlFRsjmdQlH6uVOvvfYazpw5g27dupmMhgQHB+PixYtGI0e5ubkYM2YM8vPzK7T/F154AXl5eXj//feN2nfs2GH27Ov62opeG2/RokU4efKkyfr6Cfbx8fGlrunZZ5+Fg4MDFi5caDQ/Ky8vD5MnTwYAjBgxotTPV1FlrWf//v1mQ6R+RNDZ2RkAcOjQIeTm5pqsp/8969eTIx7+k7N7ocrL8f5EdSF4AUAisn9DhgzBW2+9hQMHDgAwfwb1sWPHYuzYsWjdujUGDhyIu3fvYufOnRBCoGXLlhU6ueWkSZOwadMmLFu2DKdOnULHjh0RHx+P9evXo2/fvvj111+N1n/11VexYsUKPP300xg8eDB8fHzwv//9D//88w/69OmD3377zWj9pk2bIiAgAGvXroVarUZgYCAUCgXGjh0LT09PszU1bNgQ8+bNw4QJExAeHo7BgwejRo0a2Lp1K86dO4f+/fsbDj1WhbLWM27cONy4cQMdOnRA/fr1oVAo8NdffyE2Nhbt27dHhw4dAADz5s3D3r170bFjR4SEhMDZ2Rn//PMPdu/ejQYNGuDJJ5+ssj6WFUeq5OxeqPJUSqEqL0+FrCxrFkREVDXc3d0xePBgANKozoABA0zWef311/HNN9/A29sby5Ytw+bNm9GpUyccOnTogSfRfJAaNWpg3759GD16NC5cuIBPP/0UZ8+exbp16zBw4ECT9Vu3bo0dO3bg4YcfxqZNm/Ddd9/B09MT27dvR9u2bU3WV6lU2LRpE9q3b48ff/wR77//PqZNm4a0tLQS6xo/fjx+/vlnhIWF4YcffsAXX3wBJycnLFiwABs3bjQ58WdlK0s9U6ZMQefOnXH8+HEsWbIEy5cvh1arxbx587Bz507DIdExY8ZgwIABuHDhAlauXImvv/4aiYmJmDp1KmJiYqp03lhZKYQQwtpF2DKNRgNPT0+kp6db/hc9ZAiwfj3EZ5/DZdIb0GoVOHcuH40b2/6FlfPz8/Hbb7+hT58+cHS0/f4A9tcn9qfq5ebm4sqVK4b/nZek3N/+kzF76xP7Yx2l/XeUkpICX19fi35+y/dVIcNIlSLr/vX/bt+u2v+FEBERUekwVMlZoev/6S9WbuY8aURERCQDDFVyVviiyrWko7S3blmxHiIiIioWQ5WcmR2p4uE/IiIiOWKokjP92ZOzsjhSRUREJHMMVXJWaKRKf2FujlQRERHJkyxDlVarxeTJkxEQEAAXFxe0a9eu1KelT0hIwODBg+Hl5QUPDw/079//gZcr+OuvvwxXNjd3xWyr4ZwqomqJZ7ohKj9r/vuRZagaMWIEFi5ciOeeew6fffYZVCoV+vTpY3L6/6IyMzPRuXNn7Nu3D1OnTsXMmTNx9OhRdOrUCSkpKWa30el0GDt2LGoUvlCpXJiZU1VMN4jIDuhPfljRS6wQVWd3794FADg4VP1FY2QXqmJjY7F27VrMnTsX8+fPx+jRo7Fnzx4EBwdj0qRJJW67ePFiXLhwAdu2bcOkSZPw9ttvY8eOHUhMTMSCBQvMbrN06VLEx8fjpZdeqozuVIzRSJV0l4f/iOyXo6Mj1Go10tPTOVpFVE4ajQYqlcrwn5SqJLtr/23cuBEqlQqjR482tDk7O2PUqFGYOnUq4uPjERQUVOy2ERERiIiIMLQ1bdoUXbt2xfr16zFnzhyj9VNTU/Hee+/hgw8+MFzQUVaMRqp4+I+oOvD19UVCQgKuX78OT09PODo6mr30iE6nQ15eHnJzc2V9duuysLc+sT9VSwiBrKwsaDQa+Pv7V/klewAZhqqjR4+icePGJqeMj4yMBAAcO3bMbKjS6XQ4fvw4Ro4cabIsMjISO3bsQEZGBtzd3Q3t06ZNQ506dfDKK6/gww8/LFV9Wq0WWq3W8Fij0QCQhustPmSvVsMRgMjMhJdXPgBHaDQKZGbmQ6227K6qmv61sqfDHPbWJ/bHOlxcXODn54e0tDRcv3692PWEEMjNzYWzs7NVPjwqg731if2pegqFAu7u7nB1dX3gv/XKeC+QXahKTEyEv7+/Sbu+7caNG2a3S01NhVarfeC2TZo0AQDDBR1/++23Mg0Rzp07FzNnzjRp37FjB1xdXUv9PKXhnJqKngBERgYOx+6AUtkPOp0S69fvgY9PrkX3ZS2l/QKCLbG3PrE/1qNUKmU5IkAkVwUFBaU+dJ6dnW3x/csuVOXk5EBtZhhGf1HEnJycYrcDUOptx40bh969e6NHjx5lqm/KlCkYP3684bFGo0FQUBB69Ohh+Qsq3xsFU+p06BnVEe7ueUhPd0aLFl3QqpVld1XV8vPzsXPnTnTv3l22F7ctK3vrE/sjb/bWH8D++sT+yFtxX2CrCNmFKhcXF6PDa3q5ubmG5cVtB6BU265btw4HDx7EyZMny1yfWq02G9wcHR0t/0fm5XX/+bVaeHo6ID3dGenpjrCDv2cAlfS6WZm99Yn9kTd76w9gf31if+SpMvogu1Dl7++PhIQEk/bExEQAQEBAgNntvL29oVarDeuVtO0777yDQYMGwcnJCXFxcQCAO3fuAADi4+ORl5dX7H6qlEoFuLgAOTlAZiY8PKRfFyerExERyY/sQlWrVq2wd+9eaDQao8NpMTExhuXmKJVKtGjRAkeOHDFZFhMTgwYNGhgmqcfHx2PNmjVYs2aNyboPP/wwWrZsiWPHjlW8M5bg5lYoVElzthiqiIiI5Ed2MyAHDhyIgoICLF261NCm1WqxYsUKtGvXzvDNv2vXruHs2bMm2x4+fNgoWJ07dw579uzBoEGDDG2bN282uQ0ZMgQAsHr1aixatKgyu1g2906roMjKgqdnHgCGKiIiIjmS3UhVu3btMGjQIEyZMgXJyckIDQ3FqlWrEBcXh+XLlxvWGzZsGPbt22c0y/+1117DsmXL0LdvX0ycOBGOjo5YuHAh/Pz8MGHCBMN6AwYMMNmvfmSqd+/e8NWfvlwO9Gd6z8yEh4c0X4yhioiISH5kF6oAabRo2rRp+P7775GWlobw8HBs27YNHTt2LHE7d3d3REdH4+2338asWbOg0+kQFRWFRYsWoZb+lOS2Rn8C0KwseHhwpIqIiEiuZBmqnJ2dMX/+fMyfP7/YdaKjo822BwYGYsOGDWXe54wZMzBjxowyb1fpCp1V3dNTGqmS0zWfiYiISCK7OVVURKE5VRypIiIiki+GKrkrNFLFOVVERETyxVAld0aH/6SRqpQUoKDAijURERGRCYYquSs0Ud3dXQpVQgCpqVasiYiIiEwwVMldoZEqBwcBLy/pFBI8BEhERCQvDFVyp5+onpkJANCfQovfACQiIpIXhiq5KzRSBQC1anGkioiISI4YquSu0Jwq4P5IFUMVERGRvDBUyZ3JSJX0kKGKiIhIXhiq5M5kThUP/xEREckRQ5Xc8fAfERGRTWCokrsaNaSfhlAljVTx239ERETywlAld5xTRUREZBMYquROP6cqJwcoKOApFYiIiGSKoUru9CNVABy0WqM5VUJYqSYiIiIywVAld2o1oFIBABxycw2hKj8f0GisWBcREREZYaiSO4XCMFqlysmBqyvg6iot4iFAIiIi+WCosgX3QpVDbi6A+5PV+Q1AIiIi+WCosgXFhCqOVBEREckHQ5UtKHT4D2CoIiIikiOGKlvAkSoiIiLZY6iyBQxVREREssdQZQuKhCpe/4+IiEh+GKpsQTFzqvjtPyIiIvlgqLIF9y6qzMN/RERE8sVQZQv0I1VaLQCGKiIiIjliqLIF+jlVPKUCERGRbDFU2YJivv2XlQXcy1lERERkZQxVtkB/+O9eqPLwABwdpUUcrSIiIpIHhipbUGSkSqG4f1oFfgOQiIhIHhiqbEGROVUA51URERHJDUOVLSgyUgUwVBEREckNQ5UtKDKnCmCoIiIikhuGKlvAkSoiIiLZY6iyBYVDlRAAeP0/IiIiuWGosgX3QpVCpwOKnKuK3/4jIiKSB4YqW+Dqev9+ZiYAHv4jIiKSG4YqW6BSQeiDFUMVERGRLDFU2QpnZ+nn5csAGKqIiIjkhqHKFixfDqSmAgAc+vYFli83hKq0NCA/34q1EREREQCGKvm7fh0YPRqKew8VOh3wyivwzr4Oxb3Ge3mLiIiIrIihSu4uXAB0OuO2ggKorlyEt7f0kIcAiYiIrI+hSu4aNQKURX5NKhUQGsp5VURERDLCUCV3gYHA0qUQ94KVAIAlS4DAQIYqIiIiGWGosgWjRqFg3Trpvp8fMGoUAH4DkIiISE4YqmyE6NwZAKBISgJSUgAwVBEREckJQ5Wt8PBAVu3a0v0TJwDwUjVERERywlBlQzTBwdKde6GKF1UmIiKSD4YqG5JRJFTx8B8REZF8MFTZkKIjVQxVRERE8sFQZUM09etLd06cAHQ6hioiIiIZYaiyIZn+/hBOTkBWFhAXZzRRvehJ14mIiKhqMVTZEOHgADRtKj04ccIwUb2gAEhPt15dRERExFBlc0RYmHTnxAmo1YC7u/SQhwCJiIisi6HKxhhC1fHjADhZnYiISC4YqmyMaNFCusNvABIREckKQ5WNMYxUXbgA5OYyVBEREckEQ5WtCQgAataUZqefOcNQRUREJBMMVbZGoQAKHQLk9f+IiIjkgaHKFoWHSz+PH+f1/4iIiGRClqFKq9Vi8uTJCAgIgIuLC9q1a4edO3eWatuEhAQMHjwYXl5e8PDwQP/+/XH58mWjdXJycjBq1CiEhYXB09MTbm5uaNmyJT777DPk5+dXRpcsy8xIFUMVERGRdTlYuwBzRowYgY0bN+Ktt95Co0aNsHLlSvTp0wd79+5Fhw4dit0uMzMTnTt3Rnp6OqZOnQpHR0csWrQInTp1wrFjx+Dj4wNAClWnTp1Cnz59UL9+fSiVShw8eBBvv/02YmJisGbNmqrqavkUDlXjpLsMVURERNYlu1AVGxuLtWvXYv78+Zg4cSIAYNiwYQgLC8OkSZNw8ODBYrddvHgxLly4gNjYWERERAAAevfujbCwMCxYsABz5swBAHh7e+N///uf0bavvvoqPD098eWXX2LhwoWoU6dOJfXQAvTfAExMhL/jbQC+DFVERERWJrvDfxs3boRKpcLo0aMNbc7Ozhg1ahQOHTqE+Pj4EreNiIgwBCoAaNq0Kbp27Yr169c/cN/1712w+M6dO+Wuv0q4uwP3avW/LZ2v6tYtQAgr1kRERFTNyW6k6ujRo2jcuDE8PDyM2iMjIwEAx44dQ1BQkMl2Op0Ox48fx8iRI02WRUZGYseOHcjIyIC7/rouAPLy8qDRaJCTk4MjR47gk08+QXBwMEJDQ4utT6vVQqvVGh5rNBoAQH5+fqXOx9I/t/6nKiwMyrg4eF47BqAzcnOB9PR81KhRaSVYVNH+2AN76xP7I2/21h/A/vrE/shbZfRDdqEqMTER/v7+Ju36ths3bpjdLjU1FVqt9oHbNmnSxNC+adMmDB061PC4bdu2+O677+DgUPzLMnfuXMycOdOkfceOHXB1dS12O0vRT9hv6uKCJgCSd/8OR8dxyM9XYf36vfDzy6n0GiyptF9AsCX21if2R97srT+A/fWJ/ZGn7Oxsiz+n7EJVTk4O1Gq1Sbuzs7NheXHbASjTtp07d8bOnTtx584d7N69G//++y+ysrJKrG/KlCkYP3684bFGo0FQUBB69OhhMrpmSfn5+di5cye6d+8OR0dHKDIzgQ0bUE+TDj8/Ja5fB1q06IK2bW3jGGDR/tgDe+sT+yNv9tYfwP76xP7IW0pKisWfU3ahysXFxejwml5ubq5heXHbASjTtn5+fvDz8wMADBw4EHPmzEH37t1x4cKFYieqq9Vqs8HN0dGxSv7IDPtp3RoAoDx1CrUbCVy/rkBamgNs7e+8ql63qmRvfWJ/5M3e+gPYX5/YH3mqjD7IbqK6v78/EhMTTdr1bQEBAWa38/b2hlqtLte2egMHDkRmZiZ+/vnnspZd9Ro3BpycgKwstHC7AoCnVSAiIrIm2YWqVq1a4fz584YJ4HoxMTGG5eYolUq0aNECR44cMVkWExODBg0aGE1SN0d/eDA9Pb0clVcxBwegWTMAQEvl/W8AEhERkXXILlQNHDgQBQUFWLp0qaFNq9VixYoVaNeuneGbf9euXcPZs2dNtj18+LBRsDp37hz27NmDQYMGGdpu374NYeb8A99++y0AacK6Tbh3EtAm+VKo4vX/iIiIrEd2c6ratWuHQYMGYcqUKUhOTkZoaChWrVqFuLg4LF++3LDesGHDsG/fPqNw9Nprr2HZsmXo27cvJk6cCEdHRyxcuBB+fn6YMGGCYb0ffvgB33zzDQYMGIAGDRogIyMDf/zxB3bu3IknnngCXbp0qdI+l9u9UFU/gyNVRERE1ia7UAUAq1evxrRp0/D9998jLS0N4eHh2LZtGzp27Fjidu7u7oiOjsbbb7+NWbNmQafTISoqCosWLUIt/UXyAHTo0AEHDx7Ejz/+iKSkJDg4OKBJkyZYuHAhxo4dW9nds5x7oSrg1nEADFVERETWJMtQ5ezsjPnz52P+/PnFrhMdHW22PTAwEBs2bCjx+du2bVuqM6zLXng4AMDz1gU4Iwe3bpn/ZiQRERFVPtnNqaIy8PcHvL2h0OnwEM5wpIqIiMiKGKpsmUJhOATYAicYqoiIiKyIocrWFQpVGg2Ql2fleoiIiKophipbd29eVUtIk9V5WgUiIiLrYKiydfdGqsJ5AlAiIiKrYqiydc2bAwD8dDfhg9sMVURERFbCUGXr3N2BkBAAnKxORERkTQxV9uDevCqGKiIiIuthqLIH+nlVOM6J6kRERFbCUGUPeK4qIiIiq2Oosgf3QlVznMLtZJ2ViyEiIqqeKhSq4uPjsWfPHmRnZxvadDod5s2bh8ceewzdunXDr7/+WuEi6QEaNUKBoxpuyIJD/BVrV0NERFQtVeiCytOmTcPWrVtx8+ZNQ9vs2bMxffp0w+N9+/bh4MGDiIiIqMiuqCQODsgObgb3i0fhe+M4gIbWroiIiKjaqdBI1YEDB9CtWzc4OjoCAIQQ+PLLL9G0aVNcu3YNsbGxqFGjBubPn2+RYql4dx+SDgHWTTth5UqIiIiqpwqFquTkZAQHBxseHzt2DLdu3cLYsWMRGBiItm3bYsCAATh8+HCFC6WSqVpLoSo0+wQKCqxcDBERUTVUoVCl0+mg092fGB0dHQ2FQoEuXboY2urWrWt0eJAqh2ukFKrCcAJpaVYuhoiIqBqqUKiqV68eYmNjDY+3bNkCf39/NGnSxNB28+ZNeHl5VWQ3VAoOD0snAG2EC7gdn2PlaoiIiKqfCoWqp59+GgcOHMDAgQPx/PPP46+//sLTTz9ttM7p06fRoEGDChVJpVCnDtKUPlBBh+wjp61dDRERUbVToVA1ceJEREREYNOmTVizZg1atGiBGTNmGJZfvXoVsbGxiIqKqmCZ9EAKBa64SYcAdf9ysjoREVFVq9ApFTw8PPC///0PJ0+eBAA89NBDUKlURuts2rQJbdu2rchuqJRu+LTAw5poOJ5lqCIiIqpqFQpVemFhYWbbg4ODjb4dSJUrtW4L4ArgHsdQRUREVNUqdPgvIyMDly9fRn5+vlH7unXr8Nxzz+Gll17C0aNHK1QglV5mA2myum/icStXQkREVP1UaKRq0qRJ+OGHH5CUlGQ4AejXX3+NN954A0IIAMCPP/6Iv//+G02bNq14tVSigqbNAQAe2UnArVtArVpWroiIiKj6qNBI1b59+9CtWze4uroa2j766CPUrVsX+/fvx/r16yGE4BnVq4hXoBsu4d43LU/wECAREVFVqlCoSkxMREhIiOHxmTNnEB8fj3HjxqFDhw4YOHAg+vXrh/3791e4UHqwWrWAE5C+AchQRUREVLUqFKq0Wi2cnJwMj/ft2weFQoEePXoY2ho0aICEhISK7IZKqVYt4DikeVUMVURERFWrQqEqMDAQx4/fnxS9bds2eHt7Izw83NCWkpICNze3iuyGSqnwSJU4zsnqREREValCE9V79+6Nr776ChMnToSzszO2b9+OYcOGGa1z/vx51KtXr0JFUun4+hY6/HfqFKDTAcoK5WYiIiIqpQp94k6ZMgX16tXDwoULMWfOHPj5+eGDDz4wLE9OTsaBAwfQsWPHChdKD+bqCtxwCUUu1FBkZwOXL1u7JCIiomqjQiNVderUwalTp7B7924AQMeOHeHh4WFYfvv2bcyfPx89e/asWJVUat61HXD6ajM8jKPSvKrQUGuXREREVC1U+IzqLi4u+M9//mN2WbNmzdCsWbOK7oLKoFYt4PjVcClUHT8OPPmktUsiIiKqFixymRoASEhIwLFjx6DRaODh4YFWrVqhbt26lnp6KiWeVoGIiMg6KhyqLl68iDFjxmDPnj0my7p27YrFixcjlIegqgxDFRERkXVUKFTFx8ejQ4cOSE5ORtOmTdGxY0f4+/vj5s2b2L9/P3bt2oXHH38csbGxCAoKslTNVIJatYAd+lB18SKQkwO4uFi3KCIiomqgQqFq5syZSE5OxuLFi/HKK69AoVAYLV+yZAnGjBmDDz74AMuWLatQoVQ6vr7ATdSBRu0LD+1t4PRpoE0ba5dFRERk9yp0SoU//vgDTzzxBF599VWTQAUAr7zyCp544gn8/vvvFdkNlYF0DWUFrrjdG63iSUCJiIiqRIVCVXJyMsLCwkpcJywsDLdu3arIbqgMpFAFnFFxXhUREVFVqlCoqlWrFk6fPl3iOqdPn0Yt/Sc9VTr9S31Mx1BFRERUlSoUqnr27IlffvkFy5cvN7v8u+++w9atW9GrV6+K7IbKQB+qYrIYqoiIiKpShSaqT58+HVu3bsXo0aPx6aefolOnTvDz80NSUhL279+PU6dOwcfHB9OnT7dUvfQA+lB1OKc5hEIBRVISkJwM1K5t3cKIiIjsXIVCVb169XDgwAG88soriI6OxqlTp4yWd+7cGd988w1Pp1CFPDwAR0cgK98Nd+s1gOPVS9JoVdeu1i6NiIjIrlX45J+NGjXCnj17EB8fb3JG9aCgIMybNw87duwwXB+QKpdCIZ1WITERyAppAS+GKiIioiphscvUBAUFmR2ROnv2LKKjoy21GyqFWrWkUJUS0AJe2MJ5VURERFWgQhPVSZ7086oSfMOlOwxVRERElY6hyg7pQ9WVGve+AXjyJFBQYL2CiIiIqgGGKjukD1UXRCjg7Cxd/+/yZesWRUREZOcYquyQr6/0MzlFBTRrJj3gIUAiIqJKxVBlh/QjVbduAWjBk4ASERFVhTJ/+69Pnz5lWv8EP8yrnFGoepyT1YmIiKpCmUPV9u3by7wThUJR5m2o/PSh6vZt3B+pOn7cavUQERFVB2UOVVeuXKmMOsiCzB7+u3gRyM4GXF2tVhcREZE9K3OoCg4Orow6yIL0oSo1Fbjr4wcHX19p2Or0aaBtW+sWR0REZKc4Ud0OeXtLl6sBgJRUBRDOeVVERESVjaHKDqlUUrACihwC5LwqIiKiSsNQZad4WgUiIqKqxVBlp8x+A5ChioiIqNIwVNkpo5Gq5s2lSVbJydKNiIiILI6hyk4ZhaoaNYCGDaUGjlYRERFVCoYqO2UUqgBOViciIqpkDFV2Sn9RZZNQxZEqIiKiSiHLUKXVajF58mQEBATAxcUF7dq1w86dO0u1bUJCAgYPHgwvLy94eHigf//+uHz5stE68fHxmDlzJiIjI1GzZk34+voiKioKu3btqozuWEWxI1UMVURERJVClqFqxIgRWLhwIZ577jl89tlnUKlU6NOnD/76668St8vMzETnzp2xb98+TJ06FTNnzsTRo0fRqVMnpKSkGNb7+eefMW/ePISGhmLWrFmYNm0aMjIy0L17d6xYsaKyu1cljL79B9w/AeipU0BBgVVqIiIismdlvkxNZYuNjcXatWsxf/58TJw4EQAwbNgwhIWFYdKkSTh48GCx2y5evBgXLlxAbGwsIiIiAAC9e/dGWFgYFixYgDlz5gAAOnfujGvXrsFXf4wMwKuvvopWrVrh/fffx4svvliJPawaJiNVDRsCLi5ATg5w6RLQuLHVaiMiIrJHshup2rhxI1QqFUaPHm1oc3Z2xqhRo3Do0CHEx8eXuG1ERIQhUAFA06ZN0bVrV6xfv97Q1rx5c6NABQBqtRp9+vTB9evXkZGRYcEeWUfhkSohIJ1mvVkzqZGHAImIiCxOdiNVR48eRePGjeHh4WHUHhkZCQA4duwYgoKCTLbT6XQ4fvw4Ro4cabIsMjISO3bsQEZGBtzd3Yvd982bN+Hq6gpXV9di19FqtdBqtYbHGo0GAJCfn4/8/PySO1cB+ucu7T48PQHAEXfvArdu5aNmTUDVvDmUf/+NgmPHoOvXr9JqLY2y9scW2Fuf2B95s7f+APbXJ/ZH3iqjH7ILVYmJifD39zdp17fduHHD7HapqanQarUP3LZJkyZmt7948SI2bdqEQYMGQaVSFVvf3LlzMXPmTJP2HTt2lBjGLKW0E/YBwNm5D3JzHbFs2f8QFpaKBg4OaAEgadcuHG7btvKKLIOy9MdW2Fuf2B95s7f+APbXJ/ZHnrKzsy3+nLILVTk5OVCr1Sbtzs7OhuXFbQegXNtmZ2dj0KBBcHFxwUcffVRifVOmTMH48eMNjzUaDYKCgtCjRw+T0TVLys/Px86dO9G9e3c4Ojo+cP0VKxTIzZXC4fvvd8DXXxeg7yA18N138L99G3369Km0WkujrP2xBfbWJ/ZH3uytP4D99Yn9kbfCX2CzFNmFKhcXF6PDa3q5ubmG5cVtB6DM2xYUFOCZZ57B6dOn8fvvvyMgIKDE+tRqtdng5ujoWCV/ZKXZz/XrwJgx9x/rdAq89poD/nO4NQIAKC5dgmNennSmdSurqtetKtlbn9gfebO3/gD21yf2R54qow+ym6ju7++PxMREk3Z9W3Ghx9vbG2q1uszbvvzyy9i2bRtWrlyJLl26VKR02bhwAdDpjNsKCoDz6X7SDHYhgNOnrVMcERGRnZJdqGrVqhXOnz9vmACuFxMTY1hujlKpRIsWLXDkyBGTZTExMWjQoIHJJPV33nkHK1aswKJFizB06FDLdEAGGjUClEV+s0olEBoKngSUiIioksguVA0cOBAFBQVYunSpoU2r1WLFihVo166d4Zt/165dw9mzZ022PXz4sFGwOnfuHPbs2YNBgwYZrTt//nx88sknmDp1Kt58881K7FHVCwwEli6VzqKg17at1G44CShDFRERkUXJbk5Vu3btMGjQIEyZMgXJyckIDQ3FqlWrEBcXh+XLlxvWGzZsGPbt2wchhKHttddew7Jly9C3b19MnDgRjo6OWLhwIfz8/DBhwgTDeps3b8akSZPQqFEjPPTQQ/jhhx+MaujevTv8/Pwqv7OVaNQooGdP4PffgdGjgSNHgIsXgVCOVBEREVUK2YUqAFi9ejWmTZuG77//HmlpaQgPD8e2bdvQsWPHErdzd3dHdHQ03n77bcyaNQs6nQ5RUVFYtGgRaunPhgng33//BQBcuHABL7zwgsnz7N271+ZDFSCNTL38MrBlC/Dbb8BHHwHfvnIvVB0/btXaiIiI7I3sDv8B0ikQ5s+fj8TEROTm5iI2NhY9e/Y0Wic6OtpolEovMDAQGzZsQHp6OjIyMrB161aEhoYarTNjxgwIIYq9RUVFVWb3qty770o/V68Grns2BxQK6fo1SUnWLYyIiMiOyDJUkWU9+igQFQXk5wMff+l6b8Y6eAiQiIjIghiqqon33pN+LlsG5DbmvCoiIiJLY6iqJrp0Adq1A3Jzgb/ucF4VERGRpTFUVRMKxf25VSv/5kgVERGRpTFUVSP/+Y90mqrY3Huh6tQp6VTrREREVGEMVdWIQgFMnQpcQkNkw0U6FnjpkrXLIiIisgsMVdXMwIFAaGMVTqG51MBDgERERBbBUFXNqFTAf/8LnIB0CDD/b05WJyIisgSGqmro+eeB615SqLr2G0eqiIiILIGhqhpydARaviBdWFl56gTy861cEBERkR1gqKqmekyQRqqC717C2uVZVq6GiIjI9jFUVVMuwbWR5VYbSgj8POcUz6xARERUQQxV1Zi6rTRa5Rl/Ahs3WrkYIiIiG8dQVY05tJJCVQucwJw5gBBWLoiIiMiGMVRVZ+HSZPVWqhM4fhzYts3K9RAREdkwhqrqrIU0UtXW6TgAgdmzOVpFRERUXgxV1VmzZoBCAbec2whWJyEmBtizx9pFERER2SaGqurM1RUIDQUAvNNLOgnorFnWLIiIiMh2MVRVd/fmVT0bfgKOjkB0NHDwoHVLIiIiskUMVdXdvXlVNeNPYNgwqWn2bCvWQ0REZKMYqqq7e6EKx4/jv/8FlErgt9+Ao0etWxYREZGtYaiq7vSh6vRphIYUYMgQ6eGcOdYriYiIyBYxVFV3DRpIE9Zzc4GLFzF1qtT800/AmTPWLY2IiMiWMFRVdyoV0Ly5dP/ECYSFAf37S+er+ugj65ZGRERkSxiq6P4hwBPSaRXefVd6+H//B1y5YqWaiIiIbAxDFRlNVgeAiAige3egoAD4+GMr1kVERGRDGKrIZKQKuD9a9d13wI0bVqiJiIjIxjBUkeEEoLh8GcjKAgB07Ag89hiQlwd88okVayMiIrIRDFUE1KoF+PlJs9NPnQIAKBTAe+9Ji5csAW7ftmJ9RERENoChiiRmDgH27Am0aQNkZwOffmqdsoiIiGwFQxVJikxWB6TRKv15q778EkhPt0JdRERENoKhiiT6eVWFRqoAYMAAoFkzKVB99VXVl0VERGQrGKpIUvjwnxCGZqUSmDJFur9okWEeOxERERXBUEWSZs2kBHX7NpCUZLTomWekq9ncvg0sW2al+oiIiGSOoYokLi5AaKh0v9C8KgBwcAAmT5buz58PaLVVXBsREZENYKii+8x8A1Bv+HCgbl3pRKCrVlVxXURERDaAoYruK2ayOgCo1cDEidL9efOAu3ersC4iIiIbwFBF95UwUgUAL78M+PpKJ15fu7YK6yIiIrIBDFV0X+FQFRdnsrhGDeDtt6X7c+YAOl3VlUZERCR3DFV039690s/8fKBhQ2D5cpNVXn8d8PQEzpwBNm+u4vqIiIhkjKGKJNevA6++ev+xTge88orUXoinJ/DGG9L92bONTmlFRERUrTFUkeTCBdPjeQUFwGuvAdeuGTW/9Rbg6gocPQps3151JRIREckZQxVJGjWSTv5Z1Nat0vmrxowB4uMBSJPV9YNaHK0iIiKSMFSRJDAQWLoUUKmkxyoVMGkS0KWLNMfqm2+kcPX668D165gwAXByAg4cAPbvt27pREREcsBQRfeNGiV962/vXunnvHnA7t1AdDQQFQXk5QGLFwMNGyJgzht4e3ACAGm0ioiIqLpjqCJjgYFSgAoMvN/WqZMUtPbuBTp2lMLVV19hzvqG+FwxDid33sDXX5vMaSciIqpWGKqo9KKipFGrPXuAxx+HMk+LseILXEYD5L32JtrVSzR3FgYiIqJqgaGKykahADp3Bvbtw60fd+EvPAZnaPEmPsdF0QCZL72FX5YkIiGBE9iJiKh6Yaii8lEocNKvKx7Hn+iGnTiAR+GCXLyJz9D91QZYFzgeDWvcxMMPA888A7z/PvB//wccPgykp1u7eCIiIstzsHYBZLukszAosFvXDbvRFd2xEzMxHY/gfxiPRXg15xt8fXQMPj46CevgZ7Stn58DfHwew88/q9C0KdCkCdC4MdCggXTx5uJcvy6dUqtRI+NpX0RERNbGUEXlpj8LwyuvAAUFCuxR9cCQb7rjkaAd0L0/Ha6xMZiAhRjn+DX2PPQ6Fru+g9i42rh5E0hKUiApyRenTxs/p1IJhIRIAatx4/thq3Fj6USjr74qnaNUqZT2PWpU5fSN4Y2IiMqKoYoqZNQooGdP4OJF6TRWgYEKAD2h7NED+OMPYPp0OMbGoufxT9DTdTHw+uvIePUdXDiZhf99vx1ZAb1x9FYwzp0Dzp8HMjOBS5ek2++/F79fnQ54+WXp+oPe3oCLC+DsLP0sfL+sbU5OwHffAaNH21d4Y0iUN/5+ys/e/g3xb6F85PK6MVRRhQUGmvkjViiAXr2kxPX778D06cCRI8D8+XD/7DO0zs/Hw0JAKJVQLF0KrBkFIYCbN2EIWOfP379/8aLpVXSEAH79tXL7ptMBL70ELFwIuLtL4Uutvv+z8H0nJyXi4x/C0aNKuLoaLzO33fbtwJw598PbrFnA0KHSfZXqwT/1982dCL+w5csZEuW8H/5+yq+qXjt72w9gX38LVfm6PYhCCH5HqyI0Gg08PT2Rnp4ODw+PSttPfn4+fvvtN/Tp0weOjo6Vtp9KIwTw22/AlCnAiROmyzt3BurWlYadvL0BH5/79729EZ/ljVZdfZAmPCHufb9CqQRmzJCCSm4ukJMj3fT39T/d7lyHb9oFXFQ0wjVdoNEy/X1b/1dQXPACzH8xICREGplzcJBujo737+tvKpUOt28noW5dPzg5KU2WF745Okq/1t9+k15LhQJ44gmgdWtpf/rXt7ifpVlH//Pff4EdO+7vp2dPoFUr6X5JN52uABcunEeTJo3h4KB64PqxscCGDff3M3AgEBEhPdbp7v8sfL+sPzUaYPVq49dBoQCGDZNCvEIh/S7N1SdEAa5cuYyGDRsY+mNuXX3b338DP/98vz8DBgBt2tx/bc293g9qM7f82DFpkLrw7yc83Py/saJtBQUFuHz5Cho0CIFSqSpxm4wM6cO06Gv34ouAm5v5fZTm767o/YwMYM0a0/0888z9/RS3bUGBDvHx8QgKCoJCYfy/n6LbZGUBP/1kup8hQ6T96H+PhX/H5b1/9CiwbVvZ/60WFBTg4sVLaNiwIZRK1QPXP37c+N9qjx5AixbmX4OiSrvM3N+BSiWdv/pBIS4lJQW+vr4W/fxmqKoghqoy2rMH6Nq13JvroEAaaiIV3vBs4IPaTbyLDWLw8ZHOCP/eeyX+F0YI6XymubnSYceICONRMaUSWLUK8PCQ1tFq7/8sfN/h5lXknvgTd0MeR7JTsNGyoj/T0oAbN0z7p5+kr9NJ17MuOjoHAHVxHY1wARfQCAng8QEioqL27pVOrViSyghVPPxHVatxYymlFE0t8+ZJ91NSgNTU+7fCj7OyoISAD1Lhg1Tg8kXgchn2rT+W98470jCAiwvg6gqFiwvUrq5Qu7jgYVdXnGnvgl2HXJEtXJCrcEXPAS6ISHEFsqX14eoC+LoatoeLC7BtG8Ty96DQ6SBilVB8+aUU3hwdpf+iFXH9OhAcbPwyqFTSYc6i/7syCljfLYfTG6Ol/SiVyFywFDnPjjIsL/ozIUHKsEVf7g0bgJo1gbt3i7+pEuOQfHAnPNv0QKZXsNl18vOln1euABs3mr7k/fpJA5D6l8DcT6/M66h15wJueTVCuntgieteu2Z+P08/Lb1u+tETczf39KvAhYMQoY/hjlu9EtdNTAR27TLdT48eQEDA/f/1Fx09UCqBmlnXUTv9Am7XbIQMz0CT5YW3zcgAPv/cdHRi3DjpT7TwyFbhm04HuN25irtn/oSyyeNIdw82u47+fkKC+UPlffrc70/R11p/v2bWddTWXMAtT9PfT9H78fHSaEtRgwcDQUFm/ykYtUl92g/HZh2R6RVssrzwNhoN8PXXpq/d669L/wEqum3h+54Z1+GTegEp3tLvqKR1NRpg0SLT/UyYAHh6mt9Gf9819Sqy/92DGq26INs72KQvhR+npwMffWS6n4kTpf6YG+3U33dPl/pzy6sR7rgFml1Hfz8+HvjlF9PX9Ikn7r/3FPdv0DPjKgrO/gWHhzpA4xFc7Hol/VsdPBioV8/8a6DnmSEdXUjxlv7mzNFvp9FIl6YtOlIVGmp2M2MJCaVYqYwEVUh6eroAINLT0yt1P3l5eWLLli0iLy+vUvdTJb79VuhUKiEA6ee335Zuu9xcIRIThTh5Uoj9+4XYskWI774TYv58IaZMEeKVV4QYNEiIrl2FaNVKiNq1S/rcrLqbSiWEs7MQ7u5CeHtLddWtKzS+9cU5NBIn0UwcRUuRHNxGiPbthXj8cSG6dBGiZ08h+vYVYsAAqV8DBgihUBg/t0IhxGuvCfH++0J8+KEQH30kxIIFQnz+uRDffCPE8uUietRqMVTxo3gKG0V/5S/i97d+F2LXLiH27RPi0CEhjhwR4t9/hTh9WogLF4S4elWITz4ROqVS+h0plUJ8+aUQWVlC5OQIodUKcfeuEDqd4VcTHy/EvdWNuh0f/+C/BcOGSuUD/xYqsh+j/lTifsrSH/0m9/45iFL/c5Bpf8q8H51O+lvKyxPi66+N+7R0qdHfWHHllee1k+PvqEr6U1Ag4i9phasiW7hBIzyRJnxwSwQoE0VC7HUh4uKEuHhRiHPnhDh1Snpf+OcfIWJjhZgyxbg/770nLT91Slr/8mUhrl0T4sYNIW7dEtdPpgl3RYZwQq5Q4q4AdLL7N3RHobD45zcP/1UQD/+VT/6VK4j5v/9Du+eeg2NISOXsxNxwkFIpTfjw9ASys6VJVUV/mmsr6Wd6OnDnTuX0wRbcG3YpgBJ5d5UogAo6KKF2UULtrLw/LFN4wpd+tNLcBSODgqT1iompWVkCmnQBBaSbh7uAi7qEWFtQIE1YKcrHRzreqlLpJ5Hd/6lS4fYdB1y5pkI+HKCDCg2aOCAg0My6+p9aLbBli+lQw1NPSaOZhYcQC98KCpCTrUN2pg41nHVwdjS/juF+bi5w8qRpfxo3lkZGi5GWBiTcABSQ6gsIAGp6lfB7zc+XZhgXFRws/f70QyBFbvq+KCHdXNQ6OKlM1zN7bLs4SqXp7d6Qn06hRIFQQumghMrBzHqF19fppMk2RTVrJk3OLLpNob/X3HwlsnOVcK2hhLNrCftRKqXf0c8/G+9DoZCGBtVq099rod+vNlv6e3BR66BWFZj/G9Df12qlYeKivLzu/+3rh5ULCkqeqFQFClSOUDmZmcipvy8EcNnM4YfwcOmr2eaGA+/91OYrkJur/zJQMUNo+vtaLXD4MDQAPAHOqZIThqryqbL+LF+uP5GW9Aa5ZInlvxZS3LG8Y8eA2rXvHyczdyvLslu3pK8IFv3QfvFF6Z0kL8/885S1PTdX+klE1YNCUfgbKqb38/OB5GTT7WrWlNbRv1/p37PKEpitqDJCFedUkX0zPZGW5fdx7yyo4pVXoCgogFCpoFiyBAgLs/y+goOtFxJPnwb8/c2Ptpj73/SD2hMTpa+gFR1J3LwZ8PN78Nf5Snu7eVOasVp0P9u3A7Vq3f9ffHE/S1pW+GdKivnQO3Wq9MUJc6N1xbWVtE5KCjBihGl/fvxR6o+l3LolneOj6H5++gmoU6fkkZrS3ApPLktMBFq2NP2b++cfaV/F/b0VM1pW7C0pSZqAZ+6bKD4+ZXuukv7eU1OBDz4w/VuYORPw9TX9nRb+fT/ofuG2lBRg0CDT/uzaJU1mLBqOzAUm/fOVpLj3hOPHzb+n6l+bomHrQfcTEqSvOxbtz4oV0u9HT/+6Fv1Z2mW3b0tnkq6MMSWLHUi0oNzcXDFp0iTh7+8vnJ2dRWRkpNixY0eptr1+/boYNGiQ8PT0FO7u7qJfv37i0qVLJustXrxYDBw4UAQFBQkAYvjw4eWqlXOqysfe+iOEEHmXL4s/P/xQ5F2+XLk7io8XYu/eUkxOqIDyznsrx37KPhmifPthf8q3nyrpz7192VWf2J9y76eq+nNHqbT457csQ9UzzzwjHBwcxMSJE8WSJUvEI488IhwcHMSff/5Z4nYZGRmiUaNGonbt2mLevHli4cKFIigoSAQGBorbt28brRscHCy8vb1Fr169hIODA0NVFbO3/ghhf32yq5Ao2J9yq6L+CGF/fWJ/yqmK+nP7338t/vktu8N/sbGxWLt2LebPn4+JEycCAIYNG4awsDBMmjQJBw8eLHbbxYsX48KFC4iNjUVERAQAoHfv3ggLC8OCBQswZ84cw7r79u1DvXr1oFAo4Fb4THFEJAkMREqLFpV/zQezp+SvnP2wP+XbT5Vd98Pe+sT+lHs/VdKfunUt/pQPOJBa9TZu3AiVSoXRo0cb2pydnTFq1CgcOnQI8fHxJW4bERFhCFQA0LRpU3Tt2hXr1683Wjc4OBgKcyfIICIiIioH2Y1UHT16FI0bNzaZiR8ZGQkAOHbsGIKCgky20+l0OH78OEaOHGmyLDIyEjt27EBGRgbc3d0rVJ9Wq4VWqzU81mg0AKRvs+VX4jem9M9dmfuoSvbWH8D++sT+yJu99Qewvz6xP/JWGf2QXahKTEyEv7+/Sbu+7Ya5a3sASE1NhVarfeC2TZo0qVB9c+fOxcyZM03ad+zYAVdX1wo9d2ns3Lmz0vdRleytP4D99Yn9kTd76w9gf31if+QpOzvb4s8pu1CVk5MDtf4CaIU4Ozsblhe3HYBybVsWU6ZMwfjx4w2PNRoNgoKC0KNHj0o/T9XOnTvRvXt3uzlPlT31B7C/PrE/8mZv/QHsr0/sj7ylpKRY/DllF6pcXFyMDq/p5ebmGpYXtx2Acm1bFmq12mxwc3R0rJI/sqraT1Wxt/4A9tcn9kfe7K0/gP31if2Rp8rog+wmqvv7+yMxMdGkXd8WEBBgdjtvb2+o1epybUtERERUUbILVa1atcL58+cNE8D1YmJiDMvNUSqVaNGiBY4cOWKyLCYmBg0aNKjwJHUiIiKi4sguVA0cOBAFBQVYunSpoU2r1WLFihVo166d4Zt/165dw9mzZ022PXz4sFGwOnfuHPbs2YNBgwZVTQeIiIioWpLdnKp27dph0KBBmDJlCpKTkxEaGopVq1YhLi4Oy5cvN6w3bNgw7Nu3D6LQtXtee+01LFu2DH379sXEiRPh6OiIhQsXws/PDxMmTDDaz9atW/Hvv/8CkCbfHT9+HLNmzQIA9OvXD+Hh4VXQWyIiIrIXsgtVALB69WpMmzYN33//PdLS0hAeHo5t27ahY8eOJW7n7u6O6OhovP3225g1axZ0Oh2ioqKwaNEi1CpyodGffvoJq1atMjw+evQojh49CgAIDAxkqCIiIqIykWWocnZ2xvz58zF//vxi14mOjjbbHhgYiA0bNjxwHytXrsTKlSvLWSERERGRMdnNqSIiIiKyRQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBbAUEVERERkAQxVRERERBYgy1Cl1WoxefJkBAQEwMXFBe3atcPOnTtLtW1CQgIGDx4MLy8veHh4oH///rh8+bLZdZcvX46HHnoIzs7OaNSoEb744gtLdoOIiIiqEVmGqhEjRmDhwoV47rnn8Nlnn0GlUqFPnz7466+/StwuMzMTnTt3xr59+zB16lTMnDkTR48eRadOnZCSkmK07pIlS/DSSy+hefPm+OKLL/DII49g3LhxmDdvXmV2jYiIiOyUg7ULKCo2NhZr167F/PnzMXHiRADAsGHDEBYWhkmTJuHgwYPFbrt48WJcuHABsbGxiIiIAAD07t0bYWFhWLBgAebMmQMAyMnJwbvvvou+ffti48aNAICXX34ZOp0OH374IUaPHo2aNWtWck+JiIjInshupGrjxo1QqVQYPXq0oc3Z2RmjRo3CoUOHEB8fX+K2ERERhkAFAE2bNkXXrl2xfv16Q9vevXuRkpKC1157zWj7119/HVlZWfj1118t2CMiIiKqDmQ3UnX06FE0btwYHh4eRu2RkZEAgGPHjiEoKMhkO51Oh+PHj2PkyJEmyyIjI7Fjxw5kZGTA3d0dR48eBQC0bdvWaL02bdpAqVTi6NGjeP75583Wp9VqodVqDY/T09MBAKmpqcjPzy9DT8smPz8f2dnZSElJgaOjY6Xtp6rYW38A++sT+yNv9tYfwP76xP7IW2pqKgBACGGx55RdqEpMTIS/v79Ju77txo0bZrdLTU2FVqt94LZNmjRBYmIiVCoVateubbSek5MTfHx8it0HAMydOxczZ840aQ8JCSm+U0RERCRLKSkp8PT0tMhzyS5U5eTkQK1Wm7Q7Ozsblhe3HYBSbZuTkwMnJyezz+Ps7FzsPgBgypQpGD9+vOGxTqdDamoqfHx8oFAoit2uojQaDYKCghAfH28yimeL7K0/gP31if2RN3vrD2B/fWJ/5C09PR316tWDt7e3xZ5TdqHKxcXF6PCaXm5urmF5cdsBKNW2Li4uyMvLM/s8ubm5xe4DkEJb0eDm5eVV7PqW5uHhYRd/zHr21h/A/vrE/sibvfUHsL8+sT/yplRabnq57Caq+/v7IzEx0aRd3xYQEGB2O29vb6jV6lJt6+/vj4KCAiQnJxutl5eXh5SUlGL3QURERFQc2YWqVq1a4fz589BoNEbtMTExhuXmKJVKtGjRAkeOHDFZFhMTgwYNGsDd3d3oOYque+TIEeh0umL3QURERFQc2YWqgQMHoqCgAEuXLjW0abVarFixAu3atTN88+/atWs4e/asybaHDx82Ckvnzp3Dnj17MGjQIENbly5d4O3tja+//tpo+6+//hqurq7o27dvZXStQtRqNaZPn252zpgtsrf+APbXJ/ZH3uytP4D99Yn9kbfK6I9CWPK7hBYyePBgbN68GW+//TZCQ0OxatUqxMbGYvfu3ejYsSMAICoqCvv27TP6KmRGRgZat26NjIwMTJw4EY6Ojli4cCEKCgpw7Ngx1KpVy7Du4sWL8frrr2PgwIHo2bMn/vzzT6xevRqzZ8/G1KlTq7zPREREZNtkGapyc3Mxbdo0/PDDD0hLS0N4eDg+/PBD9OzZ07COuVAFANevX8fbb7+NHTt2QKfTISoqCosWLUJoaKjJfpYtW4YFCxbgypUrCAoKwhtvvIE333yzUr/FR0RERPZJlqGKiIiIyNbIbk4VERERkS1iqCIiIiKyAIYqGTt8+DDeeOMNNG/eHDVq1EC9evUwePBgnD9/3tqlWczs2bOhUCgQFhZm7VIq5J9//kG/fv3g7e0NV1dXhIWF4fPPP7d2WeVy4cIFPPPMMwgMDISrqyuaNm2KDz74ANnZ2dYurUSZmZmYPn06evXqBW9vbygUCqxcudLsumfOnEGvXr3g5uYGb29vvPDCC7h161bVFlwKpemTTqfDypUr0a9fPwQFBaFGjRoICwvDrFmzDCc+louy/I708vPz0axZMygUCnzyySdVU2gplaU/Op0OX3/9NVq1agUXFxf4+PigS5cu+Pfff6u26BKUpT/r169H+/bt4eXlBR8fH3Tq1Am//vpr1Rb8AGX5DLXYe4Ig2Xr66adFnTp1xNixY8WyZcvEhx9+KPz8/ESNGjXEiRMnrF1ehcXHxwtXV1dRo0YN0bx5c2uXU25//PGHcHJyEu3atRMLFy4US5cuFZMnTxbvvPOOtUsrs2vXrgkvLy8RHBws5s6dK5YsWSJGjBghAIh+/fpZu7wSXblyRQAQ9erVE1FRUQKAWLFihcl68fHxwtfXVzRs2FB89tlnYvbs2aJmzZqiZcuWQqvVVn3hJShNnzIyMgQA0b59ezFr1iyxdOlS8eKLLwqlUimioqKETqezTvFmlPZ3VNiCBQtEjRo1BAAxf/78qim0lMrSn+HDhwsHBwcxcuRIsWzZMvHpp5+K4cOHix07dlRt0SUobX8+//xzAUD07dtXfP3112LRokWiZcuWAoD46aefqr7wYpT2M9SS7wkMVTJ24MABk1/o+fPnhVqtFs8995yVqrKcIUOGiC5duohOnTrZbKhKT08Xfn5+4sknnxQFBQXWLqfCZs+eLQCIkydPGrUPGzZMABCpqalWquzBcnNzRWJiohBCiMOHDxf7gTBmzBjh4uIirl69amjbuXOnACCWLFlSVeWWSmn6pNVqxYEDB0y2nTlzpgAgdu7cWRWllkppf0d6SUlJwtPTU3zwwQeyDFWl7c+6desEALFp06YqrrBsStufRo0aiYiICKPAnp6eLtzc3GT1n6/SfoZa8j2Bh/9k7NFHHzW58HOjRo3QvHlznDlzxkpVWcb+/fuxceNGfPrpp9YupULWrFmDpKQkzJ49G0qlEllZWdDpdNYuq9z0VzLw8/Mzavf394dSqSz2QuRyoFarUadOnQeu99NPP+E///kP6tWrZ2jr1q0bGjdujPXr11dmiWVWmj45OTnh0UcfNWl/8sknAUBW7xWl/R3p/fe//0WTJk3w/PPPV2JV5Vfa/ixcuBCRkZF48sknodPpkJWVVQXVlV1p+6PRaFC7dm2j0w95eHjAzc2txGvnVrXSfoZa8j2BocrGCCGQlJQEX19fa5dSbgUFBRg7dixeeukltGjRwtrlVMiuXbvg4eGBhIQENGnSBG5ubvDw8MCYMWNkN5+lNKKiogAAo0aNwrFjxxAfH49169bh66+/xrhx41CjRg3rFlhBCQkJSE5ORtu2bU2WRUZG4ujRo1aoqnLcvHkTAGz2vSI2NharVq3Cp59+atPnDtRoNIiNjUVERASmTp0KT09PuLm5oUGDBrIL8aUVFRWF7du344svvkBcXBzOnj2L119/Henp6XjzzTetXV6Jin6GWvo9wcEiVVKV+b//+z8kJCTggw8+sHYp5fbNN9/g6tWr2LVrl7VLqbALFy7g7t276N+/P0aNGoW5c+ciOjoaX3zxBe7cuYMff/zR2iWWSa9evfDhhx9izpw5+OWXXwzt7777LmbNmmXFyixDf3F1f39/k2X+/v5ITU2FVqu1i8twfPzxx/Dw8EDv3r2tXUqZCSEwduxYDBkyBI888gji4uKsXVK5Xbp0CUIIrF27Fg4ODvj444/h6emJzz77DM888ww8PDzQq1cva5dZJp9//jlu376NcePGYdy4cQCk8L5792488sgjVq6uZEU/Qy39nsBQZUP0/xt45JFHMHz4cGuXUy4pKSl4//33MW3aNKPLBtmqzMxMZGdn49VXXzV82++pp55CXl4elixZgg8++ACNGjWycpVlU79+fXTs2BFPP/00fHx88Ouvv2LOnDmoU6cO3njjDWuXVyE5OTkAYPYN0tnZ2bCOrYeqOXPmYNeuXVi8eDG8vLysXU6ZrVy5EidOnMDGjRutXUqFZWZmApDe+/73v/+hXbt2AIB+/fohJCQEs2bNsrlQ5erqiiZNmiAwMBD/+c9/kJGRgUWLFuGpp57Cn3/+afYKJnJg7jPU0u8JDFU24ubNm+jbty88PT2xceNGqFQqa5dULu+99x68vb0xduxYa5diEfr5A0OHDjVqf/bZZ7FkyRIcOnTIpkLV2rVrMXr0aJw/fx6BgYEApJCo0+kwefJkDB06FD4+Plausvz0vy+tVmuyTH+4Vk5zQspj3bp1eO+99zBq1CiMGTPG2uWUmUajwZQpU/DOO+8gKCjI2uVUmP7vKSQkxBCoAMDNzQ1PPPEEfvjhB9y9excODrbzcTxo0CA4ODhg69athrb+/fujUaNGePfdd7Fu3TorVmdecZ+hln5P4JwqG5Ceno7evXvjzp072L59OwICAqxdUrlcuHABS5cuxbhx43Djxg3ExcUhLi4Oubm5yM/PR1xcHFJTU61dZpnofxdFJ3bXrl0bAJCWllblNVXE4sWL0bp1a0Og0uvXrx+ys7Ntfs6RfohfP+RfWGJiIry9vW16lGrnzp0YNmwY+vbti2+++cba5ZTLJ598gry8PAwZMsTwHnH9+nUA0r+nuLg45OXlWbnK0ivuPQKQ3ify8/NlO3HdnMuXL2P79u3o16+fUbu3tzc6dOiAAwcOWKmy4pX0GWrp9wSGKpnLzc3FE088gfPnz2Pbtm1o1qyZtUsqt4SEBOh0OowbNw4hISGGW0xMDM6fP4+QkBCbmyvWpk0bAFLfCrtx4wYA2NwhzqSkJBQUFJi05+fnAwDu3r1b1SVZVN26dVGrVi0cOXLEZFlsbCxatWpV9UVZSExMDJ588km0bdsW69evt6mRj8KuXbuGtLQ0NG/e3PAe8fjjjwOQDmuGhITg9OnTVq6y9AICAlCnTh2T9whAep9wdnaGu7u7FSorn6SkJAAo9n1Cbu8RD/oMtfR7AkOVjBUUFGDIkCE4dOgQNmzYIPsJgA8SFhaGzZs3m9yaN2+OevXqYfPmzRg1apS1yyyTwYMHAwCWL19u1P7tt9/CwcHB8G06W9G4cWMcPXrU5IzDP/74I5RKJcLDw61UmeU8/fTT2LZtG+Lj4w1tu3fvxvnz5zFo0CArVlZ+Z86cQd++fVG/fn1s27bNpg9hjhs3zuQ9YsmSJQCAESNGYPPmzQgJCbFylWUzZMgQxMfHY+fOnYa227dv4+eff0aXLl2gVNrOR3FoaCiUSiXWrVsHIYSh/fr16/jzzz/RunVrK1ZnrLSfoZZ8T1CIwq8Kycpbb72Fzz77DE888YThw7swuZ67payioqJw+/ZtnDx50tqllMuoUaPw3XffYfDgwejUqROio6OxYcMGTJkyBXPmzLF2eWWyf/9+dOnSBT4+PnjjjTfg4+ODbdu24ffff8dLL72EZcuWWbvEEn355Ze4c+cObty4ga+//hpPPfWU4U1+7Nix8PT0RHx8PFq3bg0vLy+8+eabyMzMxPz58xEYGIjDhw/L7vDfg/qkVCrRvHlzJCQkYM6cOahbt67R9g0bNpTVf8hK8zsqKi4uDiEhIZg/fz4mTpxY1SWXqDT9SUpKQuvWrZGZmYnx48fD09MT33zzDeLj43Ho0CG0bNnSyr24rzT9efnll/Htt9+ic+fOeOqpp5CRkYHFixcjMTERe/bsQceOHa3cC0lpP0Mt+p5QzhOVUhXo1KmTAFDszV7Y8hnVhRAiLy9PzJgxQwQHBwtHR0cRGhoqFi1aZO2yyi0mJkb07t1b1KlTRzg6OorGjRuL2bNni/z8fGuX9kDBwcHF/nu5cuWKYb2TJ0+KHj16CFdXV+Hl5SWee+45cfPmTesVXoIH9Ul/aZHibsOHD7d2F4yU9ndUmL6PcjujuhCl78+lS5fEk08+KTw8PISLi4vo0qWLiI2NtV7hxShNf/Lz88UXX3whWrVqJdzc3ISbm5vo3Lmz2LNnj3WLL6Isn6GWek/gSBURERGRBdjOgVwiIiIiGWOoIiIiIrIAhioiIiIiC2CoIiIiIrIAhioiIiIiC2CoIiIiIrIAhioiIiIiC2CoIiIiIrIAhioiIiIiC2CoIiKqBPXr10f9+vWtXQYRVSGGKiKSrbi4OCgUihJvDC5EJBcO1i6AiOhBGjZsaLiifFFeXl5VWwwRUTEYqohI9kJDQzFjxgxrl0FEVCIe/iMiu6FQKBAVFYXr169j6NCh8PX1haurKx577DHs2rXL7Da3b9/GW2+9hZCQEKjVatSuXRuDBw/GyZMnza6fl5eHRYsWISIiAu7u7nBzc0OzZs0wfvx4pKWlmayfmZmJN998EwEBAVCr1QgPD8fGjRtN1ktPT8f777+PZs2awc3NDR4eHggNDcXw4cNx9erVir0wRFQlFEIIYe0iiIjMiYuLQ0hICHr27Int27c/cH2FQoHw8HDcuXMHtWrVQrdu3XDr1i2sW7cOubm52LhxIwYMGGBY/9atW3jkkUdw6dIlREVFoX379rhy5Qo2btwItVqNP/74Ax06dDCsn5OTg+7du+PAgQNo1KgRevXqBbVajQsXLmDnzp04cOAAWrVqBUCaqJ6fn4/g4GCkpaWhW7duyM7Oxtq1a5GTk4Pt27ejR48eAAAhBB555BHExMTgscceQ2RkJJRKJa5evYpdu3Zhw4YN6Natm0VfWyKyPIYqIpItfagqaU5V+/bt0atXLwBSqAKAZ599Fj/88IPh8fHjxxEREQFPT09cvXoVLi4uAICRI0dixYoVmDJlCubMmWN4zt9++w19+/ZFaGgozp07B6VSGtSfOHEiFixYgBdeeAErVqyASqUybJOeng6VSgU3NzcAUqi6evUq+vfvj/Xr18PJyQkAsHv3bnTr1s0oKJ44cQLh4eEYMGAANm/ebNQ/rVaL/Px8w/MSkXwxVBGRbOlDVUnefPNNfPrppwCkUKVSqXDp0iUEBwcbrffSSy9h+fLl2LhxI55++mnk5eXB09MTNWrUwLVr1+Dq6mq0fo8ePbBz507s378fjz/+OO7evQtvb28olUpcuXIFNWvWLLEufai6fPmySR/q16+PjIwMpKSkALgfqoYOHYo1a9aU5qUhIhninCoikr2ePXtCCGH2pg9UevXq1TMJVADw+OOPAwCOHj0KADh79ixyc3MRGRlpEqgAoHPnzgCAY8eOGdbPyMhARETEAwOVnpeXl9lQGBgYiDt37hgeP/TQQwgPD8ePP/6Ijh07YuHChfjnn3+g0+lKtR8ikgeGKiKyK35+fiW2p6enAwA0Gk2J6/v7+xutp9+ubt26pa7F09PTbLuDg4NRYHJwcMCePXvwxhtv4OLFi5gwYQLatGmDOnXq4IMPPkBBQUGp90lE1sNQRUR2JSkpqcR2fdDx8PAocf2bN28arac/H1ZCQoLFai3Mx8cHX3zxBRISEnD69Gl8+eWX8Pb2xvTp0/Hxxx9Xyj6JyLIYqojIrly7ds3sKQj+/PNPAEDr1q0BAE2bNoWzszMOHz6M7Oxsk/Wjo6MBwPBtviZNmsDDwwOHDx82e+oES1EoFHjooYfw+uuvY+fOnQCAX375pdL2R0SWw1BFRHaloKAAU6dOReHv4Bw/fhzff/89atWqhT59+gAAnJycMHToUNy+fRtz5841eo7t27fjjz/+QGhoKB577DEA0iG6V155Benp6XjzzTdNDsmlp6cjMzOzXDXHxcUhLi7OpF0/iubs7Fyu5yWiqsVv/xGRbJXmlAoA8N///hfOzs4lnqcqJycHP/30k8l5qtq3b4/Lly+jS5cuaNeuHeLi4rBhwwY4OTmZnKcqNzcXPXr0wJ9//olGjRqhd+/eUKvVuHz5MrZv346//vrL6DxV+j4UFRUVhX379hmC35YtW/DUU08hMjISzZo1Q506dZCQkIAtW7YgMzMTmzdvRr9+/Sr8ehJRJRNERDJ15coVAeCBt7S0NCGEEABEp06dRHx8vBgyZIjw9vYWzs7O4pFHHhE7duwwu49bt26JcePGieDgYOHo6Ch8fX3FwIEDxYkTJ8yun5ubKz755BPRqlUr4eLiItzc3ESzZs3EhAkTDHUIIURwcLAIDg42+xydOnUShd9+4+PjxX//+1/Rvn17Ubt2beHk5CTq1asnnnrqKXHo0KFyvXZEVPU4UkVEdkOhUKBTp06G+VBERFWJc6qIiIiILIChioiIiMgCGKqIiIiILMDB2gUQEVkKp4gSkTVxpIqIiIjIAhiqiIiIiCyAoYqIiIjIAhiqiIiIiCyAoYqIiIjIAhiqiIiIiCyAoYqIiIjIAhiqiIiIiCzg/wGynoqRRUsGLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 7ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "  5/219 [..............................] - ETA: 7s - loss: 0.2693 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:04:50.263686: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:637] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219/219 [==============================] - 9s 37ms/step - loss: 0.0254 - val_loss: 0.0053\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 8s 34ms/step - loss: 0.0045 - val_loss: 0.0036\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 8s 37ms/step - loss: 0.0034 - val_loss: 0.0030\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0032 - val_loss: 0.0030\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0030 - val_loss: 0.0030\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0030 - val_loss: 0.0028\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 8s 34ms/step - loss: 0.0029 - val_loss: 0.0028\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0030 - val_loss: 0.0029\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0029 - val_loss: 0.0026\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0028 - val_loss: 0.0028\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0028 - val_loss: 0.0031\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0029 - val_loss: 0.0026\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0027 - val_loss: 0.0026\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0027 - val_loss: 0.0025\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 8s 36ms/step - loss: 0.0027 - val_loss: 0.0026\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 8s 35ms/step - loss: 0.0027 - val_loss: 0.0025\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 13ms/step - loss: 0.0025\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0025295214727520943"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 11ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0225 - val_loss: 0.0055\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0045 - val_loss: 0.0039\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0034 - val_loss: 0.0030\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0031 - val_loss: 0.0030\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0030 - val_loss: 0.0031\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0030 - val_loss: 0.0027\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0029 - val_loss: 0.0030\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0030 - val_loss: 0.0027\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0029 - val_loss: 0.0026\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0028 - val_loss: 0.0028\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0029 - val_loss: 0.0027\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0028 - val_loss: 0.0027\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0028 - val_loss: 0.0026\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0027 - val_loss: 0.0027\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0027 - val_loss: 0.0026\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 11ms/step - loss: 0.0026\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0025954328011721373"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A3bt3l3ob999/P1lZWYwePdrpq3527tzJ/PnzCQ0N5dZbbwXg2LFj/Prrry7rOHXqFDabDX9/fyD3BPvNmze79Dtz5gxpaWn4+Pi4nOjuqXT4T0REPMZ9993H1KlTGTZsGBs2bCA6Opqff/6ZdevWcfvtt7Ns2bKKnmKhpkyZwtq1a/n3v/9NTEyM4zpVK1eupEePHqxatapUAWXkyJF88cUXfPjhh+zatYsuXbqQlJTEJ598QnZ2NnPmzCE4OBjIvWZWy5Ytad68Oc2aNSMyMpIjR47w1VdfkZWVxYgRIwBIT0/nhhtuoGHDhlxzzTXUrl2btLQ0Vq5cyZEjRxgxYoTLifeeSqFKREQ8RlRUFDExMYwcOZK1a9eSnZ3N1VdfzerVq4mPj3f7UFWrVi22bNnCqFGjWL16NTExMVxzzTWsXr2aJUuWAPmfPF9U/v7+rF+/nqlTp/LJJ58wc+ZMAgMD6dChA2PGjKFdu3aOvnXq1GH8+PGsX7+etWvXcuLECSpXrkzLli15+umn6dGjBwCVKlVi6tSprFu3jm+//ZakpCQuu+wyGjVqxJQpU7jrrrtK96RcRCzGufv/pNhSUlIIDQ0lOTm5VH/oF5KVlcWXX35Jr1698PHxKbPtlBdPqwc8rybVU/4yMjI4cOAAdevWdRxaKcj535PnCTytJrPradeuHVu2bCE5OZmgoCATZlg8F8vvp6j/jk6cOEGVKlVMff9232dFRETkEpSYmOjS9tFHH7Fp0ya6du1aIYFKikaH/0RERNxI06ZNadmyJVdeeaXj+lobN24kODiYV199taKnJ4VQqBIREXEjjzzyCJ9//jk//PADZ86coWrVqtx9992MHTuWxo0bV/T0pBAKVSIiIm5k0qRJTJo0qaKnISWgc6pERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAkUqkRE5JIwf/58LBYL8+fPd2qvU6cOderUKfV6zDR+/HgsFgsbN24ss20Ux/jx47FarXz33XcVPRW3plAlIiIV7u6778ZqtbJ06dJC+6WkpBAYGEhYWBjp6enlNDvzbdy4EYvFwvjx4yt6KmIihSoREalwQ4YMAeD//u//Cu338ccfk56ezoABAwgICDBl2+vWrWPdunWmrMssTzzxBLt27aJ169YVPRUpBn33n4iIVLjOnTtTt25dvvnmGw4ePFjg4bj3338f+CeEmeHyyy83bV1mqVKlClWqVKnoaUgxaU+ViIhUOIvFwqBBg7Db7QWeq/Tbb7+xdetWmjVrxrXXXktycjJTp06lQ4cOREZG4uvrS2RkJAMHDuTPP/8s8rYLOqfq5MmTPPLII1SvXp3AwEBatWrF8uXLC1zP+++/T58+fahTpw7+/v5UqVKFO+64gw0bNjj1Gz9+PJ06dQJgwoQJWCwWxy0uLs7Rp6Bzqj7//HM6depEaGgoAQEBNG/enBkzZpCdne3ULy4uzvG8/vHHH9x2221cdtllVKpUia5du/Lzzz8X+TkqTFHnA7BhwwZ69uxJZGQkfn5+VK9enRtvvJHZs2c79fvpp5/o27cvtWvXxs/Pj6pVq9KqVSu3/6Jp7akSEbkUHToE+/ZBgwYQFVXRswHg/vvvZ8KECXzwwQeMGzcOi8XitHzevHnAP3updu3axQsvvECnTp247bbbqFSpErt372bhwoV88cUX/PTTT0RHR5doLmfPnqVjx4788ssvXHfddXTo0IH4+HjuvPNOunfvnu+Yxx9/nObNm9O1a1eqVq3KoUOH+PTTT+nevTvLli2jT58+AHTs2JG4uDg++OADOnToQMeOHR3rCAsLK3ReM2bMYPjw4YSHh3P33XdTqVIlPvvsM4YPH863337LsmXLXJ63uLg42rZtS5MmTRg8eDB//vknn376KZ06dWLXrl1Ur169RM9RcefzxRdfcPPNNxMWFkafPn2IiIjg2LFj/Pzzz3z44YcMHToUgB07dnD99ddjtVrp06cP0dHRnD59mt9//53Zs2fzn//8p8TzLXOGlEpycrIBGMnJyWW6nczMTGPFihVGZmZmmW6nvHhaPYbheTWpnvKXnp5u/P7770Z6errrQrvdMNLSHLeclBTj1KFDRk5KilN7kW5vvWUYXl6GAbn3b71V/HUUdLPbS1x/Tk6O0aVLFwMw1q5d67QsKyvLqF69uuHn52ecOHHCMAzDOH36tOPnc61fv97w8vIyHnzwQaf2efPmGYAxb948p/bo6GgjOjraqW3cuHEGYDz00ENO7atWrTKAfNezf/9+l3p27dplREZGGg0aNHBatmHDBgMwxo0bl99T4dj+hg0bHG1//PGH4e3tbVSrVs04ePCgoz0jI8No166dARgLFixwtB84cMAx15dfftlp/c8//7wBGFOmTMl3+wXN5/PPPzdycnJKNJ/bb7/dAIwdO3a4rP/48eOOn5999lkDMFasWFFov4IU+u/ovHWZ/f6tw38iIheDs2chKMhx8woJISwqCq+QEKf2It0efxzs9tz12u25j4u7joJuZ8+Wqsz77rsP+OfcqTwrV67k6NGj9OnTh/DwcABCQ0MdP5+rU6dONGnShLVr15Z4HgsWLMDX15cXX3zRqf2mm26iS5cu+Y6pW7euS1uNGjW4/fbb2bdvH3/99VeJ5wOwcOFCsrOzGT58OLVq1XK0+/n5MXXqVIB8D53WrVuX5557zqktb2/ftm3byn0++X3AoHLlyiXu504UqkRExG306tWLqlWrsnz5cpKTkx3tBZ2gvnHjRm699VYiIiLw8fFxnJv0yy+/cPjw4RLNISUlhQMHDlC/fn1q1KjhsvzGG2/Md9z+/ft56KGHuPzyy/H398dqtXLZZZfx5ptvApR4Pnm2b98O4HS4MM91112Hv78/O3bscFnWokULvLyc3+6j/j7ke/r06XKbz1133QVA27ZteeKJJ1i+fDnHjx93Gdu/f3+8vLy47bbbGDx4MB9//DEJCQklnmd50jlVIiIXg8BASEtzPLTb7aSkpBASEuLyhlmohAS44op/9lQBWK3w++9Qs6Y58ywFHx8f7r33XmbOnMnChQt59NFHOXLkCF999RW1a9ema9eujr5LlizhzjvvJCgoiJtuuok6deoQGBjouDBnSfcMpaSkAFCtWrV8l+d3DtIff/xB69atSUlJoVOnTtx8880EBweTlZXF999/T0xMDDabrUTzOX9e+W3fYrFQvXr1fMNHSEiIS5u3d+7bf05OTrnNp1+/fqxYsYIZM2bwzjvv8NZbb2GxWOjUqRPTp0+nRYsWALRp04aNGzcyefJkFi5c6DiXrlWrVkydOtVxkr87UqgSEbkYWCxQqdI/j+12yMnJbStOqGrYEGbPhocfzh1vtcK77+a2u4nBgwczc+ZM5s6dy6OPPsqHH35IdnY2DzzwgFOAHD9+PP7+/vz44480aNDAaR2LFi0q8fbzQkhSUlK+y48ePerSNnPmTE6dOsWHH37IvffeC/wTfEeNGkVMTEyJ53P+vI4ePepyAr5hGBw9ejTfAFVWSjKfPn360KdPH1JTU9m0aRPLli1j7ty59OjRg927dztO1L/xxhv56quvSE9PJzY2ls8//5xZs2bRu3dvfv31V+rVq1cuNRaXDv+JiFxqhgyBuDjYsCH33sRrPpnhyiuvpG3btvz444/s3LmTefPmYbFYeOCBB5z6/fnnn1xxxRUugSoxMZH9+/eXePshISHUrVuXP/74gyNHjrgs//bbb13a8i7hkPcJvzyGYbB582aX/larFSjenqKWLVsC5HuZhdjYWDIyMhx7e8pDaeYTHBxMjx49mD17NoMGDeLo0aPExsa69AsICKBjx45Mnz6dMWPGkJ6ezpo1a8wsw1QKVSIil6KoKOjY0W0up3C+vHOnHnvsMXbt2kXXrl1d9oZER0fzxx9/OO05ysjI4NFHHyUrK6tU27/vvvvIzMzkhRdecGpfvXp1vldfz5vb+d+NN3PmTH799VeX/nkn2MfHxxd5TnfffTfe3t7MmDHD6fyszMxMRo0aBcCgQYOKvL7SKu58vvnmm3xDZN4eQX9/fwC2bNlCRkaGS7+833NeP3ekw38iIuJ27rzzTp5++mk2bdoE5H8F9WHDhjFs2DBatmxJ3759yc7OZs2aNRiGQfPmzUt1ccuRI0eybNky5syZw2+//Ub79u2Jj49n8eLF9O7dmy+++MKp/yOPPMK8efO444476N+/P5UrV+b777/np59+olevXnz55ZdO/Rs3bkxkZCSLFi3Cz8+PqKgoLBYLw4YNIzQ0NN85XX755UydOpXhw4fTrFkz+vfvT6VKlfj888/Zs2cPffr0cRx6LA/Fnc+TTz7J4cOHadeuHXXq1MFisfDdd9+xdetW2rZtS7t27QCYOnUqGzZsoH379tStWxd/f39++ukn1q1bR7169bjtttvKrcbi0p4qERFxO8HBwfTv3x/I3atz6623uvR5/PHHeeeddwgPD2fOnDksX76cDh06sGXLlgteRPNCKlWqRExMDEOHDmXfvn289tpr7N69m08++YS+ffu69G/ZsiWrV6/m6quvZtmyZbz//vuEhoayatUqrr32Wpf+VquVZcuW0bZtWz7++GNeeOEFxo4dy6lTpwqd17PPPsunn35K06ZN+eijj3jjjTfw9fVl+vTpLF261OXCn2WtOPMZPXo0nTp1YufOnbz77rvMnTsXm83G1KlTWbNmjeOQ6KOPPsqtt97Kvn37mD9/Pm+//TaJiYmMGTOG2NjYcj1vrLgshmEYFT2Ji1lKSgqhoaEkJyeX6S86KyuLL7/8kl69euHj41Nm2ykvnlYPeF5Nqqf8ZWRkcODAAcf/zgtT4k//uTFPq0n1VIyi/js6ceIEVapUMfX9232fFREREZGLiEKViIiIiAkUqkRERERMoFAlIiIiYgKFKhERERETKFSJiIiImMAtQ5XNZmPUqFFERkYSEBBAmzZtinxZ+oSEBPr3709YWBghISH06dPngl9X8N133zm+2Ty/b8wWESlPutKNSMlV5L8ftwxVgwYNYsaMGdxzzz28/vrrWK1WevXq5XL5//OlpaXRqVMnYmJiGDNmDBMmTGD79u106NCBEydO5DvGbrczbNgwKp37RaUiIhUg7+KHpf2KFZFLWXZ2NgDe3uX/pTFuF6q2bt3KokWLmDJlCtOmTWPo0KGsX7+e6OhoRo4cWejYWbNmsW/fPlauXMnIkSN55plnWL16NYmJiUyfPj3fMbNnzyY+Pp4HH3ywLMoRESkyHx8f/Pz8SE5O1t4qkRJKSUnBarU6/pNSntzuu/+WLl2K1Wpl6NChjjZ/f3+GDBnCmDFjiI+Pp1atWgWObdWqFa1atXK0NW7cmC5durB48WImT57s1P/kyZM8//zzvPjii44vdBQRqUhVqlQhISGBQ4cOERoaio+PT75fPWK328nMzCQjI8Otr25dHJ5Wk+opX4ZhcObMGVJSUoiIiCj3r+wBNwxV27dvp2HDhi6XjG/dujUAO3bsyDdU2e12du7cyeDBg12WtW7dmtWrV5OamkpwcLCjfezYsdSoUYOHH36Yl156qUjzs9ls2Gw2x+OUlBQgd3d9We6yz1u3pxwW8LR6wPNqUj0VIyAggOrVq3Pq1CkOHTpUYD/DMMjIyMDf379C3jzKgqfVpHrKn8ViITg4mMDAwAv+Wy+L1wK3C1WJiYlERES4tOe1HT58ON9xJ0+exGazXXBso0aNABxf6Pjll18WaxfhlClTmDBhgkv76tWrCQwMLPJ6SqqoJ+xfLDytHvC8mlRPxfHy8nLLPQIi7ionJ6fIh87Pnj1r+vbdLlSlp6fj5+fn0p73pYjp6ekFjgOKPPbJJ5+kZ8+edO/evVjzGz16NM8++6zjcUpKCrVq1aJ79+5l/oXKa9asoVu3bm77ZbDF4Wn1gOfVpHrcm6fVA55Xk+pxbwV9gK003C5UBQQEOB1ey5ORkeFYXtA4oEhjP/nkEzZv3syvv/5a7Pn5+fnlG9x8fHzK5Y+svLZTXjytHvC8mlSPe/O0esDzalI97qksanC7UBUREUFCQoJLe2JiIgCRkZH5jgsPD8fPz8/Rr7Cxzz33HP369cPX15e4uDgATp8+DUB8fDyZmZkFbkdEREQkP24Xqlq0aMGGDRtISUlxOpwWGxvrWJ4fLy8vrrrqKn744QeXZbGxsdSrV89xknp8fDwLFy5k4cKFLn2vvvpqmjdvzo4dO0pfjIiIiFwy3O4MyL59+5KTk8Ps2bMdbTabjXnz5tGmTRvHJ/8OHjzI7t27XcZu27bNKVjt2bOH9evX069fP0fb8uXLXW533nknAAsWLGDmzJllWaKIiIh4ILfbU9WmTRv69evH6NGjSUpKon79+nzwwQfExcUxd+5cR7+BAwcSExPjdJb/Y489xpw5c+jduzcjRozAx8eHGTNmUL16dYYPH+7od+utt7psN2/PVM+ePalSpUqZ1SciIiKeye1CFeTuLRo7diwffvghp06dolmzZqxcuZL27dsXOi44OJiNGzfyzDPPMHHiROx2Ox07dmTmzJlUrVq1nGYvIiIilyK3DFX+/v5MmzaNadOmFdhn48aN+bZHRUWxZMmSYm9z/PjxjB8/vtjjRERERMANz6kSERERuRgpVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAkUqkRERERMoFAlIiIiYgKFKhERERETKFSJiIiImEChSkRERMQEClUiIiIiJlCoEhERETGBQpWIiIiICRSqREREREygUCUiIiJiAoUqERERERMoVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAkUqkRERERMoFAlIiIiYgKFKhERERETKFSJiIiImEChSkRERMQEClUiIiIiJlCoEhERETGBQpWIiIiICRSqREREREygUCUiIiJiAoUqERERERMoVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZwy1Bls9kYNWoUkZGRBAQE0KZNG9asWVOksQkJCfTv35+wsDBCQkLo06cP+/fvd+qTnp7OkCFDaNq0KaGhoQQFBdG8eXNef/11srKyyqIkERER8XDeFT2B/AwaNIilS5fy9NNP06BBA+bPn0+vXr3YsGED7dq1K3BcWloanTp1Ijk5mTFjxuDj48PMmTPp0KEDO3bsoHLlykBuqPrtt9/o1asXderUwcvLi82bN/PMM88QGxvLwoULy6tUERER8RBuF6q2bt3KokWLmDZtGiNGjABg4MCBNG3alJEjR7J58+YCx86aNYt9+/axdetWWrVqBUDPnj1p2rQp06dPZ/LkyQCEh4fz/fffO4195JFHCA0N5c0332TGjBnUqFGjjCoUERERT+R2h/+WLl2K1Wpl6NChjjZ/f3+GDBnCli1biI+PL3Rsq1atHIEKoHHjxnTp0oXFixdfcNt16tQB4PTp0yWev4iIiFya3G5P1fbt22nYsCEhISFO7a1btwZgx44d1KpVy2Wc3W5n586dDB482GVZ69atWb16NampqQQHBzvaMzMzSUlJIT09nR9++IFXX32V6Oho6tevX+D8bDYbNpvN8TglJQWArKysMj0fK2/dnnLOl6fVA55Xk+pxb55WD3heTarHvZVFHW4XqhITE4mIiHBpz2s7fPhwvuNOnjyJzWa74NhGjRo52pctW8aAAQMcj6+99lref/99vL0LflqmTJnChAkTXNpXr15NYGBggePMUtQT9i8WnlYPeF5Nqse9eVo94Hk1qR73dPbsWdPX6XahKj09HT8/P5d2f39/x/KCxgHFGtupUyfWrFnD6dOnWbduHT///DNnzpwpdH6jR4/m2WefdTxOSUmhVq1adO/e3WXvmpmysrJYs2YN3bp1w8fHp8y2U148rR7wvJpUj3vztHrA82pSPe7txIkTpq/T7UJVQECA0+G1PBkZGY7lBY0DijW2evXqVK9eHYC+ffsyefJkunXrxr59+wo8Ud3Pzy/f4Obj41Muf2TltZ3y4mn1gOfVpHrcm6fVA55Xk+pxT2VRg9udqB4REUFiYqJLe15bZGRkvuPCw8Px8/Mr0dg8ffv2JS0tjU8//bS40xYREZFLnNuFqhYtWrB3717HCeB5YmNjHcvz4+XlxVVXXcUPP/zgsiw2NpZ69eo5naSen7zDg8nJySWYuYiIiFzK3C5U9e3bl5ycHGbPnu1os9lszJs3jzZt2jg++Xfw4EF2797tMnbbtm1OwWrPnj2sX7+efv36OdqOHz+OYRgu237vvfeA3BPWRURERIrD7c6patOmDf369WP06NEkJSVRv359PvjgA+Li4pg7d66j38CBA4mJiXEKR4899hhz5syhd+/ejBgxAh8fH2bMmEH16tUZPny4o99HH33EO++8w6233kq9evVITU3l66+/Zs2aNdx888107ty5XGsWERGRi5/bhSqABQsWMHbsWD788ENOnTpFs2bNWLlyJe3bty90XHBwMBs3buSZZ55h4sSJ2O12OnbsyMyZM6lataqjX7t27di8eTMff/wxR48exdvbm0aNGjFjxgyGDRtW1uWJiIiIB3LLUOXv78+0adOYNm1agX02btyYb3tUVBRLliwpdP3XXnttka6wLiIiIlJUbndOlYiIiMjFSKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiYoVaiKj49n/fr1nD171tFmt9uZOnUqN9xwA127duWLL74o9SRFRERE3F2pvlB57NixfP755xw5csTRNmnSJMaNG+d4HBMTw+bNm2nVqlVpNiUiIiLi1kq1p2rTpk107doVHx8fAAzD4M0336Rx48YcPHiQrVu3UqlSJaZNm2bKZEVERETcValCVVJSEtHR0Y7HO3bs4NixYwwbNoyoqCiuvfZabr31VrZt21bqiYqIiIi4s1KFKrvdjt1udzzeuHEjFouFzp07O9pq1qzpdHhQRERExBOVKlTVrl2brVu3Oh6vWLGCiIgIGjVq5Gg7cuQIYWFhpdmMiIiIiNsrVai644472LRpE3379uXee+/lu+++44477nDq8/vvv1OvXr1STVJERETE3ZXq038jRoxg9erVLFu2DIBmzZoxfvx4x/K//vqLrVu38u9//7tUkxQRERFxd6UKVSEhIXz//ff8+uuvAFxxxRVYrVanPsuWLePaa68tzWZERERE3F6pQlWepk2b5tseHR3t9OlAEREREU9VqnOqUlNT2b9/P1lZWU7tn3zyCffccw8PPvgg27dvL9UERURERC4GpdpTNXLkSD766COOHj3quADo22+/zRNPPIFhGAB8/PHH/PjjjzRu3Lj0sxURERFxU6XaUxUTE0PXrl0JDAx0tL388svUrFmTb775hsWLF2MYhq6oLiIiIh6vVHuqEhMT6dGjh+Pxrl27iI+P55VXXqFdu3YALF26lG+++aZ0sxQRERFxc6XaU2Wz2fD19XU8jomJwWKx0L17d0dbvXr1SEhIKM1mRERERNxeqUJVVFQUO3fudDxeuXIl4eHhNGvWzNF24sQJgoKCSrMZEREREbdXqsN/PXv25K233mLEiBH4+/uzatUqBg4c6NRn79691K5du1STFBEREXF3pQpVo0eP5vPPP2fGjBkARERE8OKLLzqWJyUlsWnTJp544onSzVJERETEzZUqVNWoUYPffvuNdevWAdC+fXtCQkIcy48fP860adO46aabSjdLERERETdX6iuqBwQE8K9//SvfZVdeeSVXXnllaTchIiIi4vZM+ZoagISEBHbs2EFKSgohISG0aNGCmjVrmrV6EREREbdW6lD1xx9/8Oijj7J+/XqXZV26dGHWrFnUr1+/tJsRERERcWulClXx8fG0a9eOpKQkGjduTPv27YmIiODIkSN88803rF27lhtvvJGtW7dSq1Yts+YsIiIi4nZKFaomTJhAUlISs2bN4uGHH8ZisTgtf/fdd3n00Ud58cUXmTNnTqkmKiIiIuLOShWqvv76a26++WYeeeSRfJc//PDDfPnll3z11Vel2YyIiIiI2yvVFdWTkpJo2rRpoX2aNm3KsWPHSrMZEREREbdXqlBVtWpVfv/990L7/P7771StWrU0mxERERFxe6UKVTfddBOfffYZc+fOzXf5+++/z+eff06PHj1KsxkRERERt1eqc6rGjRvH559/ztChQ3nttdfo0KED1atX5+jRo3zzzTf89ttvVK5cmXHjxpk1XxERERG3VKpQVbt2bTZt2sTDDz/Mxo0b+e2335yWd+rUiXfeeUeXUxARERGPV+qLfzZo0ID169cTHx/vckX1WrVqMXXqVFavXu34fkARERERT2Ta19TUqlUr3z1Su3fvZuPGjWZtRkRERMQtlepEdRERERHJpVAlIiIiYgKFKhERERETKFSJiIiImEChSkRERMQExf70X69evYrV/5dffinuJkREREQuOsUOVatWrSr2RiwWS7HHiIiIiFxMih2qDhw4UBbzEBEREbmoFTtURUdHl8U8RERERC5qOlFdRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJjALUOVzWZj1KhRREZGEhAQQJs2bVizZk2RxiYkJNC/f3/CwsIICQmhT58+7N+/36lPfHw8EyZMoHXr1lx22WVUqVKFjh07snbt2rIoR0RERC4BbhmqBg0axIwZM7jnnnt4/fXXsVqt9OrVi++++67QcWlpaXTq1ImYmBjGjBnDhAkT2L59Ox06dODEiROOfp9++ilTp06lfv36TJw4kbFjx5Kamkq3bt2YN29eWZcnIiIiHqjYX1NT1rZu3cqiRYuYNm0aI0aMAGDgwIE0bdqUkSNHsnnz5gLHzpo1i3379rF161ZatWoFQM+ePWnatCnTp09n8uTJAHTq1ImDBw9SpUoVx9hHHnmEFi1a8MILL/DAAw+UYYUiIiLiidxuT9XSpUuxWq0MHTrU0ebv78+QIUPYsmUL8fHxhY5t1aqVI1ABNG7cmC5durB48WJHW5MmTZwCFYCfnx+9evXi0KFDpKammliRiIiIXArcbk/V9u3badiwISEhIU7trVu3BmDHjh3UqlXLZZzdbmfnzp0MHjzYZVnr1q1ZvXo1qampBAcHF7jtI0eOEBgYSGBgYIF9bDYbNpvN8TglJQWArKwssrKyCi+uFPLWXZbbKE+eVg94Xk2qx715Wj3geTWpHvdWFnW4XahKTEwkIiLCpT2v7fDhw/mOO3nyJDab7YJjGzVqlO/4P/74g2XLltGvXz+sVmuB85syZQoTJkxwaV+9enWhYcwsRT1h/2LhafWA59Wketybp9UDnleT6nFPZ8+eNX2dbheq0tPT8fPzc2n39/d3LC9oHFCisWfPnqVfv34EBATw8ssvFzq/0aNH8+yzzzoep6SkUKtWLbp37+6yd81MWVlZrFmzhm7duuHj41Nm2ykvnlYPeF5Nqse9eVo94Hk1qR73du4H2MzidqEqICDA6fBanoyMDMfygsYBxR6bk5PDXXfdxe+//85XX31FZGRkofPz8/PLN7j5+PiUyx9ZeW2nvHhaPeB5Nake9+Zp9YDn1aR63FNZ1OB2oSoiIoKEhASX9sTERIACQ094eDh+fn6OfkUd+9BDD7Fy5Ur+7//+j86dO5dm6iIiInIJc7tP/7Vo0YK9e/c6TgDPExsb61ieHy8vL6666ip++OEHl2WxsbHUq1fP5ST15557jnnz5jFz5kwGDBhgTgEiIiJySXK7UNW3b19ycnKYPXu2o81mszFv3jzatGnj+OTfwYMH2b17t8vYbdu2OQWrPXv2sH79evr16+fUd9q0abz66quMGTOGp556qgwrEhERkUuB2x3+a9OmDf369WP06NEkJSVRv359PvjgA+Li4pg7d66j38CBA4mJicEwDEfbY489xpw5c+jduzcjRozAx8eHGTNmUL16dYYPH+7ot3z5ckaOHEmDBg244oor+Oijj5zm0K1bN6pXr172xYqIiIjHcLtQBbBgwQLGjh3Lhx9+yKlTp2jWrBkrV66kffv2hY4LDg5m48aNPPPMM0ycOBG73U7Hjh2ZOXMmVatWdfT7+eefAdi3bx/33Xefy3o2bNigUCUiIiLF4pahyt/fn2nTpjFt2rQC+2zcuDHf9qioKJYsWVLo+sePH8/48eNLMUMRERERZ253TpWIiIjIxUihSkRERMQEClUiIiIiJlCoEhERETGBQpWIiIiICRSqREREREygUCUiIiJiAoUqERERERMoVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAkUqkRERERMoFAlIiIiYgKFKhERERETKFSJiIiImEChSkRERMQEClUiIiIiJlCoEhERETGBQpWIiIiICRSqREREREygUCUiIiJiAoUqERERERMoVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpMYhgVPQMRERGpSApVJjl4sKJnICIiIhVJocokP/9c0TMQERGRiqRQZRKFKhERkUubQpVJduyo6BmIiIhIRVKoMsmOHTpZXURE5FKmUGWS48chMbGiZyEiIiIVRaHKRD/9VNEzEBERkYqiUGUihSoREZFLl0KViRSqRERELl0KVSZSqBIREbl0KVSZKD4ejh2r6FmIiIhIRVCoMkm9ern327dX7DxERESkYihUmaRFi9x7HQIUERG5NClUmaR589x7hSoREZFLk0KVSRSqRERELm0KVSbJC1V//gmnT1foVERERKQCKFSZJDwcoqNzf9aXK4uIiFx6FKpMdPXVufc6BCgiInLpUagykUKViIjIpUuhykQKVSIiIpcuhSoT5YWq3bvhzJmKnYuIiIiUL4UqE9WoARERYBjw888VPRsREREpT24Zqmw2G6NGjSIyMpKAgADatGnDmjVrijQ2ISGB/v37ExYWRkhICH369GH//v0u/d5++2369etH7dq1sVgsDBo0yJS56xCgiIjIpcktQ9WgQYOYMWMG99xzD6+//jpWq5VevXrx3XffFTouLS2NTp06ERMTw5gxY5gwYQLbt2+nQ4cOnDhxwqnv1KlTWb9+PU2aNMHb29u0uStUiYiIXJrMSxMm2bp1K4sWLWLatGmMGDECgIEDB9K0aVNGjhzJ5s2bCxw7a9Ys9u3bx9atW2nVqhUAPXv2pGnTpkyfPp3Jkyc7+sbExDj2UgUFBZk2f4UqERGRS5Pb7alaunQpVquVoUOHOtr8/f0ZMmQIW7ZsIT4+vtCxrVq1cgQqgMaNG9OlSxcWL17s1Dc6OhqLxWL6/PNC1W+/QUaG6asXERERN+V2e6q2b99Ow4YNCQkJcWpv3bo1ADt27KBWrVou4+x2Ozt37mTw4MEuy1q3bs3q1atJTU0lODi4VPOz2WzYbDbH45SUFACysrLIysqiRg2oXNmbEycs7NiRzTXXGKXaXp6srCyn+4udp9UDnleT6nFvnlYPeF5Nqse9lUUdbheqEhMTiYiIcGnPazt8+HC+406ePInNZrvg2EaNGpVqflOmTGHChAku7atXryYwMBCAqKjrOHGiGgsW/MrRo3+VanvnK+oJ+xcLT6sHPK8m1ePePK0e8LyaVI97Onv2rOnrdLtQlZ6ejp+fn0u7v7+/Y3lB44ASjS2O0aNH8+yzzzoep6SkUKtWLbp37+7Yu/bdd178/DNkZ19Fr15NSr1NyE3Ua9asoVu3bvj4+JiyzorkafWA59Wketybp9UDnleT6nFv53+AzQxuF6oCAgKcDq/lyfj7BKWAgIACxwElGlscfn5++QY3Hx8fxx9Z3ildO3ZY8fGxlnqbBW3HE3haPeB5Nake9+Zp9YDn1aR63FNZ1OB2J6pHRESQmJjo0p7XFhkZme+48PBw/Pz8SjTWbHknq+/cCR5y6FlEREQuwO1CVYsWLdi7d6/jBPA8sbGxjuX58fLy4qqrruKHH35wWRYbG0u9evVKfZJ6UdWrByEhYLPBrl3lskkRERGpYG4Xqvr27UtOTg6zZ892tNlsNubNm0ebNm0cn/w7ePAgu3fvdhm7bds2p2C1Z88e1q9fT79+/cqnAMDLC1q2zP1Z16sSERG5NLjdOVVt2rShX79+jB49mqSkJOrXr88HH3xAXFwcc+fOdfQbOHAgMTExGMY/lyx47LHHmDNnDr1792bEiBH4+PgwY8YMqlevzvDhw5228/nnn/Pz31/Ql5WVxc6dO5k4cSIAt9xyC82aNStVHVdfDTExuaHKpG/AERERETfmdqEKYMGCBYwdO5YPP/yQU6dO0axZM1auXEn79u0LHRccHMzGjRt55plnmDhxIna7nY4dOzJz5kyqVq3q1Pd///sfH3zwgePx9u3b2b59OwBRUVGmhCrQnioREZFLhVuGKn9/f6ZNm8a0adMK7LNx48Z826OioliyZMkFtzF//nzmz59fwhleWF6o2rEDcnLAau6HAEVERMTNuN05VZ6iUSMICIAzZ2DfvoqejYiIiJQ1haoyYrVC3gcVdQhQRETE8ylUlSGdVyUiInLpUKgqQwpVIiIilw6FqjJ0bqg658oPIiIi4oEUqsrQlVeCry8kJ8OBAxU9GxERESlLClVlyNcXrroq92cdAhQREfFsClVlTOdViYiIXBoUqsqYQpWIiMilQaGqjOlkdRERkUuDQlUZu+qq3AuBHjsGCQkVPRsREREpKwpVZSwgIPdTgKBDgCIiIp5Moaoc6LwqERERz6dQVQ4UqkRERDyfQlU5UKgSERHxfApV5aB5c7BYck9UP3q0omcjIiIiZUGhqhwEB0PDhrk/b99esXMRERGRsqFQVU50CFBERMSzKVSVE4UqERERz6ZQVU4UqkRERDybQlU5adky9/7AATh1qmLnIiIiIuZTqDLLBb6D5rLLoG7d3J91srqIiIjnUagyS5MmMHduoV10CFBERMRzKVSZxTDg4Yfh0KECuyhUiYiIeC6FKjPl5MD77+fe50OhSkRExHMpVJlt3DioXx+mT4fTp50W5Z2svncvpKWV/9RERESk7ChUmcXLC3r1gsqVIS4ORoyAqCh44oncFAVUrw41a+YeKfz554qdroiIiJhLocosv/4KX3wB8fEwZw40bQpnzsBbb0GjRrmBa/Vqrm5pADoEKCIi4mkUqsxSs2bufUAAPPgg7NwJa9fCzTfnfpvyV1/BTTcx9/smPMw7/Bp7pmLnKyIiIqZSqCorFgt06QKffZZ7+O/JJyE4mKrHd/EOjzL141qkPjYKDh6s6JmKiIiICRSqykP9+vD663DoEOtveY0/qUeY/RTBb7+CvW496N8fNm3KPdlKRERELkoKVeXoUEoI3VY+RUP2cgufso7OeNlzYMkSaNcOWreGjz6CzMyKnqqIiIgUk0JVOdq3D+x2sGPlc26hK+toxs+8xxAy8IMffoD77iOnVjS89BIkJVX0lEVERKSIFKrKUYMGuVdeONevlma8EPEeURxiDJNIIBJr0hF44QWya9bm9G0PYGzfAYcOUeWXXwq9YruIiIhUHIWqchQVBbNng9Wa+9hqzb36QkICrN1eBf8JY7j96jgGsJBYWuOdbSNsxXwsV7fEu149bhg7Fu/69S/4HYMiIiJS/hSqytmQIbnXBt2wIfd+yJDcDwq2aAEvvACxP/owLX4A29+O5Zm2W/jMcgsGYPl7vMVux3jwQfb1HMaZz9ZBenqF1SIiIiL/8K7oCVyKoqJyb4Utf+QR4JG2nP3iaSz/+sxpuQVosOpNWPUmmRZfkupfT9DNnQm7owu0agU+PmU6fxEREXGlPVVuLrC564lYdrxYFXQ7CUTia2QStW8jYTNegBtuwFbpMpJv6IUx7VWOfvUTG9bm6DQsERGRcqBQ5e7+PhHL+PtELMNqxeu92fRI/R9puw4xd+QeXq3/Nkvpy3Eq45d1htDNX2EZ+RzVe11D825V2VrrDtbe9ha2Hbt0LSwREZEyosN/F4MhQ8ju3JnY//s/2txzDz516wLQqLGFRlMbwtSGHD/+CF+utPPzR79g/WY9N2atpwMxhHOK21kGK5bBCjhiqUFspc78Wq0LcfU6Q506VK0K1arl3qI4ROTZfQS3bEB4s6giHUk8dCj3chENGhR+WPP8QVV++QWaNYO/6ynisBJtq/iD3FgJ6vG0p0BExB0pVF0soqI4cdVVBb4jVqkCAwd5waDmfP11c3r0eAYr2VzLD3RmPZ1Zzw1sooZxhD5pC+mTthD2w37q/r20M9U5wp08hxU7OXgxlNksv2yII3CdG77yft62DWbMyL3+lpcXzJwJgwblfrLRy8v53pJ3tv3cuXgPHcoNdjvGuHG5H4kcMuSCT8HcuTB0aO62rBY7c97O5oGBOZCTA9nZufc55z3++GN4/vl/JljEbZVXCCn2ds59EopYTwmGlJjCm5Q3/c2JO1Go8kBNmuS+eebYvYmlLbG05RXrGHZuzSD4t+9h/XoCt6wjbN9W6tkPUI+5PIjzZRqs2JnDQ3Q5tY70UwF47bFjJQcv7HiR+7MFO9eRw//y2uw5eD1lZ8tTrn3zfg4gneb87PJpxr8emojFYsFKDlay/77PwZtsrEYOXuRwr5HN/eTgTQ4YwCN/34rq723Fj3uP5PB6nAmrydnLapIeXpOM8EhsVWqSVSWCb2N9ef/93COlFgs88QT07Ane3rk3qzX/n+12SEysxIEDud+rXVhfLy94/33nsPPWW3D3nTlkHTlBTsIR7IePYBw5guXIESxJR/GJ389l36xwPHe59TzE3o9/JLny5aQFViM1oBop/tU47VuN0z5VSTrty1tv/XPU126Hhx6CXbtyg7Gvb8G3wJMJpMQc5IfMBKzRdQrt6+sLCxbAww97VnjTdko2qLzq8cT/MHjadi41FsPQSTalkZKSQmhoKMnJyYSEhJTZdrKysvjyyy/p1asXPkU4Jjd3bu4bXE5O7pv5u+/m82KTmgrffQfr18Onn+b+C/MAeZHMAPwp/lf+HKUaCdQkgZocJtLx87ltJwnnnwtd5KrJIRqwj300IIFzX6UMQkihBkcKvFXnKDU4QjWSckOjSU4RRhLVnG5Hqe7SlkQ1ThOG8fdploOZy2yGOu21fJ/iv1vVqgVBQeDnB/7+zvf5tRWlz/r1zntHx43L/frMvMBa0M2IP8CPi/6PNvf+cwi9MMV9wzaM3B2k2XGHyNm9j8zoBtiqRpGdDVlZBd8+/zx3D29eiH/qKejVy3Vv7/l7fr0OH+DPVctofPPtWKPrFt7XCxYuhGeecd6rPGBA7muE3Q45GVkYZ85inE3HfiYd0tMJWvkxVd6djMWwY1i8ODx0PEm9BpHlF0SmXzA5Fu/csXnryIGvvoI33/ynnocegg4dnHckF3TzO/YXqT9twP+qTiQHRxfaNzkZFi1yPk3UYoHBg+Gyywr/WwhNPUTlk/s4E9mAzGpRF/zbWbUKpk7957mbMAHuuit3mY/PP/3yfs67z447wNb/+z9a31M2f3MOxUxIJd1Ocd+HSjC1Eg8qyXZOnDhBlSpVTH3/VqgqJXcNVZD7R/bHH7nf53zBP7JDhyA6OvdfWR6LBUaMyH2FyudV+lSyF/95wUq2kbsfKgcrWLyY8oqVsMpe2A0v7BYrhsWLHLwwLFY4eZKw4YOxnPNnZ3h5ET9zKZnhEdi9vMnBit1iJcfiTbaR+/PRE948NsxKlpG3L8sbw2Jl6qtWgsK8ybJbycyxkpntRVa2hexs8D9+iEenRuNl/FOT3eLF4nav452ZQUhqAqFpCYSdSSAs/TDhGYfxsRcthGVY/DlqjSTRqyaJXjUJyz5Bh+y1eGFgx8JOmpOBnyM0+WMr0noB7Fg4ThWOWmpwzFqD49YanPCpQZa3P0+cnogXhlPfVZFDCCCdy7KTCMtMItSWREhGElajeOEs2+LNaZ+qpFrDqJO+yyky2rHwctAk/iKaUznBnMoO4XhWCCn8c8vEr1jby1NwGDWDgR82HmI2r/GMIyS+YJ3Ep7798bYauW+IVgMfbyN3b6LVAMMg/hBYMLD8/XxbMIisYWAYkJNtkJWVe5+Tbfz9Rm9wG8sYy0THdiYzhs+5GQsGXthNu+/COh7mXcff28cMYActCCC9xLeShPkM/EglmDSC8r0vbNn593fwP97kCcdz9yhvs4CBjr3WeXu9z72VpO1frOQZZjq28woj+ZQ+ZOPteG05//5Cy/L+M3Ku8/9j8nTAbBZVGoK31SDAO4sA7yz8rf/ce+VksX9vFj4435pfkUWgTxbexjk3svC2/7385Aa6Hf3Q8bfwWdTjbK52KxnWSmR4B5FhrYTNuxIZ1kpkegeSYbOwcaPzXC0WuPXW3Jd6H5/cPc8+Pq4/h6b+xZkda6hyXTfskdGF9vX1hS++gEmTnP8DdPvtf//LNPK/v2zZXGpNHJp7FMPLi4P/mc2JW4cU2N8w4LPPYMqU4ofEEzt3UqV5c4Uqd+LOoarYirR7q9RDYO5cjIcfxpKTg2G1YinSoJJvq8iD7HYO/3KCf7VMIMJIIJLD1CSBKEsC93RMIOBEAhw+DMePX3Cu+TFCQrBXq4G9ag3Sgmrw4ZoaJJ6zv+q4V3UWf1OD2tdUxepfwO+4qPXY7XD6NCQl8cW8JD54NYkq9iQiLEfp2z6JKyon5X63ZN7t9OkS1XQuG75OISuVYBpeG4IlJASbfwg23xAyfENI9wnhrHfurereTVzz3Wt4GXbseBHT/El2Ve+AJT0dS0Y6Xhln8cpMx2pLx5qZjnH2LLbTzmEgkLMEeaXjTzr+Ru4tkLMEkI4/GU4hVIrmLAFk400IqS7LMvHGl+wKmJV7yzonaNmxEEya039MDCAHL7yxF7SKcpFGJc6cc0sjyOlxfm1pBNGaWIYyxxHeZvMQm7kBXzLxw1bofVH6+GEjgLPU4pDL85ZAJDb8ycabLHwcYTbvdn5bDt50/5cPgcHn7XI8d7fi77+TvGoVYaBQ5U48KlRBMXdvlXgIWQcOuHyasay2VdxBF8wtGRmQmJj7/UIJCRATA2+/7bqil16CLl2gRg2oXh0CA4u3HZPqKdIQmy03LCYlwW+/wcCBrsdVbrop93hVSkruoeOUlNxbWlqR5uBu7H7+4O2NkfcSbrE4frYbFtLO5P5s8E97cIgFLy+wWCzgZfl72N8/Z9qwnj7luqG8373Fwt+DHfdZdi/27LVgxwuDf+6bXuWFr59rf7y8co97/fKL63a6d8/9JG1AgMvtxNkAnhwVwBnjnzia6RXAiq8DqFH3vP5+frnbym/vtdWa+1UQ1arl/t5TU53ujx9IZfjDaVQyUgkijWBSCbGk8eCdqVQyXPs77ov7N/T3Mc5srNiyc/dB2fHCv5IV/0Cr8171c2/p6RAf77q+GjVy33DP/ZBLdjZkZ2Nk55Bty8anDIOk3cuK3erD2azz91P5EF7NB4ufD3Zr7s2w/vOzd3oKVf7c6rK+tCp1MCwWfDLS8LadwTvzbJnN/WKWAoSiUOVWPC5UlRN3r6fUh07z3nwuMLhEIbE8FGdvYk5O7ptiSgpH9qZweHcKtUJTqOp3Xvg6/3bwYG6AO9+VV+a+yQUE5IaR8wLCj7sCWfRpbkCwWQK496EAOvV27ecYe/IkNG1a7N9PsUNvCf8O3HY7JRxUou0cPJgbCM+v6ZdfoHZt1xPELP/syyiPf6t5Ndlz7Ph65fDOm9kMuu+8AHbu/aFD0LGj63a+/z53+3nHyPL2nPx9gecy+1uw23MDZVoanDnDsg/PMP3FM/jbzxDilcaTQ87QqdUZOPP37e9+jltcHMTGum6/VSuIiMg91ufn53SfmunHf9/xJQM/MvHFhh/ZFl9enOpLeA2/fMdw+nTucchz6/Hyyj2+V7kyjhMU/w68ZGdz/Eg2TzychZfxz74qX0s2U17M4rLgbKe+jrF//gmLFpVJqMKQUklOTjYAIzk5uUy3k5mZaaxYscLIzMws0+2UF0+rx3jvPcNutRoG5N6/915Fz6jUMvfvN7596SUjc//+stlAfLxheHkZuWcw/X2zWnPbizB0w4Yidc1Vwt9PSbZj/L0do4y3Uy71lHBQibZTXv+GPO13VB71lPDfaommVoJBxR7ydz3JuUcXTX3/VqgqJYWqkvG0egyjHEJIOSuX31EJ3xBKotx+PyVKFMXnaX9vhqHfUYmVRz2eFuTfe8847eVl+vu3rlMlYpYLXKBV8jFkSO65WuVxDLS8fj8X+sZ0E7fjcX9v+h2VeDtlvo0CvtmjTKZWgkHFHjJkCNmtWkHz5sWb2wUoVIlIxSqvNzgRKR1PC/I1a5q+Sn2hsoiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAncMlTZbDZGjRpFZGQkAQEBtGnThjVr1hRpbEJCAv379ycsLIyQkBD69OnD/v378+07d+5crrjiCvz9/WnQoAFvvPGGmWWIiIjIJcQtQ9WgQYOYMWMG99xzD6+//jpWq5VevXrx3XffFTouLS2NTp06ERMTw5gxY5gwYQLbt2+nQ4cOnDhxwqnvu+++y4MPPkiTJk144403uO6663jyySeZOnVqWZYmIiIiHsrtvvtv69atLFq0iGnTpjFixAgABg4cSNOmTRk5ciSbN28ucOysWbPYt28fW7dupVWrVgD07NmTpk2bMn36dCZPngxAeno6//nPf+jduzdLly4F4KGHHsJut/PSSy8xdOhQLrvssjKuVERERDyJ2+2pWrp0KVarlaFDhzra/P39GTJkCFu2bCE+Pr7Qsa1atXIEKoDGjRvTpUsXFi9e7GjbsGEDJ06c4LHHHnMa//jjj3PmzBm++OILEysSERGRS4Hb7anavn07DRs2JCQkxKm9devWAOzYsYNatWq5jLPb7ezcuZPBgwe7LGvdujWrV68mNTWV4OBgtm/fDsC1117r1O+aa67By8uL7du3c++99+Y7P5vNhs1mczxOTk4G4OTJk2RlZRWj0uLJysri7NmznDhxAh8fnzLbTnnxtHrA82pSPe7N0+oBz6tJ9bi3kydPAmAYhmnrdLtQlZiYSEREhEt7Xtvhw4fzHXfy5ElsNtsFxzZq1IjExESsVivVqlVz6ufr60vlypUL3AbAlClTmDBhgkt73bp1Cy5KRERE3NKJEycIDQ01ZV1uF6rS09Px8/Nzaff393csL2gcUKSx6enp+Pr65rsef3//ArcBMHr0aJ599lnHY7vdzsmTJ6lcuTIWi6XAcaWVkpJCrVq1iI+Pd9mLdzHytHrA82pSPe7N0+oBz6tJ9bi35ORkateuTXh4uGnrdLtQFRAQ4HR4LU9GRoZjeUHjgCKNDQgIIDMzM9/1ZGRkFLgNyA1t5we3sLCwAvubLSQkxCP+mPN4Wj3geTWpHvfmafWA59Wketybl5d5p5e73YnqERERJCYmurTntUVGRuY7Ljw8HD8/vyKNjYiIICcnh6SkJKd+mZmZnDhxosBtiIiIiBTE7UJVixYt2Lt3LykpKU7tsbGxjuX58fLy4qqrruKHH35wWRYbG0u9evUIDg52Wsf5fX/44QfsdnuB2xAREREpiNuFqr59+5KTk8Ps2bMdbTabjXnz5tGmTRvHJ/8OHjzI7t27XcZu27bNKSzt2bOH9evX069fP0db586dCQ8P5+2333Ya//bbbxMYGEjv3r3LorRS8fPzY9y4cfmeM3Yx8rR6wPNqUj3uzdPqAc+rSfW4t7Kox2KY+VlCk/Tv35/ly5fzzDPPUL9+fT744AO2bt3KunXraN++PQAdO3YkJibG6aOQqamptGzZktTUVEaMGIGPjw8zZswgJyeHHTt2ULVqVUffWbNm8fjjj9O3b19uuukmvv32WxYsWMCkSZMYM2ZMudcsIiIiFze3DFUZGRmMHTuWjz76iFOnTtGsWTNeeuklbrrpJkef/EIVwKFDh3jmmWdYvXo1drudjh07MnPmTOrXr++ynTlz5jB9+nQOHDhArVq1eOKJJ3jqqafK9FN8IiIi4pncMlSJiIiIXGzc7pwqERERkYuRQpWIiIiICRSq3Ni2bdt44oknaNKkCZUqVaJ27dr079+fvXv3VvTUTDNp0iQsFgtNmzat6KmUyk8//cQtt9xCeHg4gYGBNG3alP/+978VPa0S2bdvH3fddRdRUVEEBgbSuHFjXnzxRc6ePVvRUytUWloa48aNo0ePHoSHh2OxWJg/f36+fXft2kWPHj0ICgoiPDyc++67j2PHjpXvhIugKDXZ7Xbmz5/PLbfcQq1atahUqRJNmzZl4sSJjgsfu4vi/I7yZGVlceWVV2KxWHj11VfLZ6JFVJx67HY7b7/9Ni1atCAgIIDKlSvTuXNnfv755/KddCGKU8/ixYtp27YtYWFhVK5cmQ4dOvDFF1+U74QvoDjvoaa9Jhjitu644w6jRo0axrBhw4w5c+YYL730klG9enWjUqVKxi+//FLR0yu1+Ph4IzAw0KhUqZLRpEmTip5OiX399deGr6+v0aZNG2PGjBnG7NmzjVGjRhnPPfdcRU+t2A4ePGiEhYUZ0dHRxpQpU4x3333XGDRokAEYt9xyS0VPr1AHDhwwAKN27dpGx44dDcCYN2+eS7/4+HijSpUqxuWXX268/vrrxqRJk4zLLrvMaN68uWGz2cp/4oUoSk2pqakGYLRt29aYOHGiMXv2bOOBBx4wvLy8jI4dOxp2u71iJp+Pov6OzjV9+nSjUqVKBmBMmzatfCZaRMWp5/777ze8vb2NwYMHG3PmzDFee+014/777zdWr15dvpMuRFHr+e9//2sARu/evY23337bmDlzptG8eXMDMP73v/+V/8QLUNT3UDNfExSq3NimTZtcfqF79+41/Pz8jHvuuaeCZmWeO++80+jcubPRoUOHizZUJScnG9WrVzduu+02Iycnp6KnU2qTJk0yAOPXX391ah84cKABGCdPnqygmV1YRkaGkZiYaBiGYWzbtq3AN4RHH33UCAgIMP766y9H25o1awzAePfdd8trukVSlJpsNpuxadMml7ETJkwwAGPNmjXlMdUiKervKM/Ro0eN0NBQ48UXX3TLUFXUej755BMDMJYtW1bOMyyeotbToEEDo1WrVk6BPTk52QgKCnKr/3wV9T3UzNcEHf5zY9dff73LFz83aNCAJk2asGvXrgqalTm++eYbli5dymuvvVbRUymVhQsXcvToUSZNmoSXlxdnzpzBbrdX9LRKLO+bDKpXr+7UHhERgZeXV4FfRO4O/Pz8qFGjxgX7/e9//+Nf//oXtWvXdrR17dqVhg0bsnjx4rKcYrEVpSZfX1+uv/56l/bbbrsNwK1eK4r6O8rz73//m0aNGnHvvfeW4axKrqj1zJgxg9atW3Pbbbdht9s5c+ZMOcyu+IpaT0pKCtWqVXO6/FBISAhBQUGFfndueSvqe6iZrwkKVRcZwzA4evQoVapUqeiplFhOTg7Dhg3jwQcf5Kqrrqro6ZTK2rVrCQkJISEhgUaNGhEUFERISAiPPvqo253PUhQdO3YEYMiQIezYsYP4+Hg++eQT3n77bZ588kkqVapUsRMspYSEBJKSkrj22mtdlrVu3Zrt27dXwKzKxpEjRwAu2teKrVu38sEHH/Daa69d1NcOTElJYevWrbRq1YoxY8YQGhpKUFAQ9erVc7sQX1QdO3Zk1apVvPHGG8TFxbF7924ef/xxkpOTeeqppyp6eoU6/z3U7NcEb1NmKeXm//7v/0hISODFF1+s6KmU2DvvvMNff/3F2rVrK3oqpbZv3z6ys7Pp06cPQ4YMYcqUKWzcuJE33niD06dP8/HHH1f0FIulR48evPTSS0yePJnPPvvM0f6f//yHiRMnVuDMzJH35eoREREuyyIiIjh58iQ2m80jvobjlVdeISQkhJ49e1b0VIrNMAyGDRvGnXfeyXXXXUdcXFxFT6nE/vzzTwzDYNGiRXh7e/PKK68QGhrK66+/zl133UVISAg9evSo6GkWy3//+1+OHz/Ok08+yZNPPgnkhvd169Zx3XXXVfDsCnf+e6jZrwkKVReRvP8NXHfdddx///0VPZ0SOXHiBC+88AJjx451+tqgi1VaWhpnz57lkUcecXza7/bbbyczM5N3332XF198kQYNGlTwLIunTp06tG/fnjvuuIPKlSvzxRdfMHnyZGrUqMETTzxR0dMrlfT0dIB8XyD9/f0dfS72UDV58mTWrl3LrFmzCAsLq+jpFNv8+fP55ZdfWLp0aUVPpdTS0tKA3Ne+77//njZt2gBwyy23ULduXSZOnHjRharAwEAaNWpEVFQU//rXv0hNTWXmzJncfvvtfPvtt/l+g4k7yO891OzXBIWqi8SRI0fo3bs3oaGhLF26FKvVWtFTKpHnn3+e8PBwhg0bVtFTMUXe+QMDBgxwar/77rt599132bJly0UVqhYtWsTQoUPZu3cvUVFRQG5ItNvtjBo1igEDBlC5cuUKnmXJ5f2+bDaby7K8w7XudE5ISXzyySc8//zzDBkyhEcffbSip1NsKSkpjB49mueee45atWpV9HRKLe/vqW7duo5ABRAUFMTNN9/MRx99RHZ2Nt7eF8/bcb9+/fD29ubzzz93tPXp04cGDRrwn//8h08++aQCZ5e/gt5DzX5N0DlVF4Hk5GR69uzJ6dOnWbVqFZGRkRU9pRLZt28fs2fP5sknn+Tw4cPExcURFxdHRkYGWVlZxMXFcfLkyYqeZrHk/S7OP7G7WrVqAJw6darc51Qas2bNomXLlo5AleeWW27h7NmzF/05R3m7+PN2+Z8rMTGR8PDwi3ov1Zo1axg4cCC9e/fmnXfeqejplMirr75KZmYmd955p+M14tChQ0Duv6e4uDgyMzMreJZFV9BrBOS+TmRlZbntiev52b9/P6tWreKWW25xag8PD6ddu3Zs2rSpgmZWsMLeQ81+TVCocnMZGRncfPPN7N27l5UrV3LllVdW9JRKLCEhAbvdzpNPPkndunUdt9jYWPbu3UvdunUvunPFrrnmGiC3tnMdPnwY4KI7xHn06FFycnJc2rOysgDIzs4u7ymZqmbNmlStWpUffvjBZdnWrVtp0aJF+U/KJLGxsdx2221ce+21LF68+KLa83GugwcPcurUKZo0aeJ4jbjxxhuB3MOadevW5ffff6/gWRZdZGQkNWrUcHmNgNzXCX9/f4KDgytgZiVz9OhRgAJfJ9ztNeJC76FmvyYoVLmxnJwc7rzzTrZs2cKSJUvc/gTAC2natCnLly93uTVp0oTatWuzfPlyhgwZUtHTLJb+/fsDMHfuXKf29957D29vb8en6S4WDRs2ZPv27S5XHP7444/x8vKiWbNmFTQz89xxxx2sXLmS+Ph4R9u6devYu3cv/fr1q8CZldyuXbvo3bs3derUYeXKlRf1Icwnn3zS5TXi3XffBWDQoEEsX76cunXrVvAsi+fOO+8kPj6eNWvWONqOHz/Op59+SufOnfHyunjeiuvXr4+XlxeffPIJhmE42g8dOsS3335Ly5YtK3B2zor6Hmrma4LFOPdZEbfy9NNP8/rrr3PzzTc73rzP5a7Xbimujh07cvz4cX799deKnkqJDBkyhPfff5/+/fvToUMHNm7cyJIlSxg9ejSTJ0+u6OkVyzfffEPnzp2pXLkyTzzxBJUrV2blypV89dVXPPjgg8yZM6eip1ioN998k9OnT3P48GHefvttbr/9dseL/LBhwwgNDSU+Pp6WLVsSFhbGU089RVpaGtOmTSMqKopt27a53eG/C9Xk5eVFkyZNSEhIYPLkydSsWdNp/OWXX+5W/yEryu/ofHFxcdStW5dp06YxYsSI8p5yoYpSz9GjR2nZsiVpaWk8++yzhIaG8s477xAfH8+WLVto3rx5BVfxj6LU89BDD/Hee+/RqVMnbr/9dlJTU5k1axaJiYmsX7+e9u3bV3AVuYr6Hmrqa0IJL1Qq5aBDhw4GUODNU1zMV1Q3DMPIzMw0xo8fb0RHRxs+Pj5G/fr1jZkzZ1b0tEosNjbW6Nmzp1GjRg3Dx8fHaNiwoTFp0iQjKyuroqd2QdHR0QX+ezlw4ICj36+//mp0797dCAwMNMLCwox77rnHOHLkSMVNvBAXqinvq0UKut1///0VXYKTov6OzpVXo7tdUd0wil7Pn3/+adx2221GSEiIERAQYHTu3NnYunVrxU28AEWpJysry3jjjTeMFi1aGEFBQUZQUJDRqVMnY/369RU7+fMU5z3UrNcE7akSERERMcHFcyBXRERExI0pVImIiIiYQKFKRERExAQKVSIiIiImUKgSERERMYFClYiIiIgJFKpERERETKBQJSIiImIChSoREREREyhUiYiUgTp16lCnTp2KnoaIlCOFKhFxW3FxcVgslkJvCi4i4i68K3oCIiIXcvnllzu+Uf58YWFh5TsZEZECKFSJiNurX78+48ePr+hpiIgUSof/RMRjWCwWOnbsyKFDhxgwYABVqlQhMDCQG264gbVr1+Y75vjx4zz99NPUrVsXPz8/qlWrRv/+/fn111/z7Z+ZmcnMmTNp1aoVwcHBBAUFceWVV/Lss89y6tQpl/5paWk89dRTREZG4ufnR7NmzVi6dKlLv+TkZF544QWuvPJKgoKCCAkJoX79+tx///389ddfpXtiRKRcWAzDMCp6EiIi+YmLi6Nu3brcdNNNrFq16oL9LRYLzZo14/Tp01StWpWuXbty7NgxPvnkEzIyMli6dCm33nqro/+xY8e47rrr+PPPP+nYsSNt27blwIEDLF26FD8/P77++mvatWvn6J+enk63bt3YtGkTDRo0oEePHvj5+bFv3z7WrFnDpk2baNGiBZB7onpWVhbR0dGcOnWKrl27cvbsWRYtWkR6ejqrVq2ie/fuABiGwXXXXUdsbCw33HADrVu3xsvLi7/++ou1a9eyZMkSunbtaupzKyLmU6gSEbeVF6oKO6eqbdu29OjRA8gNVQB33303H330kePxzp07adWqFaGhofz1118EBAQAMHjwYObNm8fo0aOZPHmyY51ffvklvXv3pn79+uzZswcvr9yd+iNGjGD69Oncd999zJs3D6vV6hiTnJyM1WolKCgIyA1Vf/31F3369GHx4sX4+voCsG7dOrp27eoUFH/55ReaNWvGrbfeyvLly53qs9lsZGVlOdYrIu5LoUpE3FZeqCrMU089xWuvvQbkhiqr1cqff/5JdHS0U78HH3yQuXPnsnTpUu644w4yMzMJDQ2lUqVKHDx4kMDAQKf+3bt3Z82aNXzzzTfceOONZGdnEx4ejpeXFwcOHOCyyy4rdF55oWr//v0uNdSpU4fU1FROnDgB/BOqBgwYwMKFC4vy1IiIG9I5VSLi9m666SYMw8j3lheo8tSuXdslUAHceOONAGzfvh2A3bt3k5GRQevWrV0CFUCnTp0A2LFjh6N/amoqrVq1umCgyhMWFpZvKIyKiuL06dOOx1dccQXNmjXj448/pn379syYMYOffvoJu91epO2IiHtQqBIRj1K9evVC25OTkwFISUkptH9ERIRTv7xxNWvWLPJcQkND82339vZ2Ckze3t6sX7+eJ554gj/++IPhw4dzzTXXUKNGDV588UVycnKKvE0RqTgKVSLiUY4ePVpoe17QCQkJKbT/kSNHnPrlXQ8rISHBtLmeq3LlyrzxxhskJCTw+++/8+abbxIeHs64ceN45ZVXymSbImIuhSoR8SgHDx7M9xIE3377LQAtW7YEoHHjxvj7+7Nt2zbOnj3r0n/jxo0Ajk/zNWrUiJCQELZt25bvpRPMYrFYuOKKK3j88cdZs2YNAJ999lmZbU9EzKNQJSIeJScnhzFjxnDuZ3B27tzJhx9+SNWqVenVqxcAvr6+DBgwgOPHjzNlyhSndaxatYqvv/6a+vXrc8MNNwC5h+gefvhhkpOTeeqpp1wOySUnJ5OWllaiOcfFxREXF+fSnrcXzd/fv0TrFZHypU//iYjbKsolFQD+/e9/4+/vX+h1qtLT0/nf//7ncp2qtm3bsn//fjp37kybNm2Ii4tjyZIl+Pr6ulynKiMjg+7du/Ptt9/SoEEDevbsiZ+fH/v372fVqlV89913TtepyqvhfB07diQmJsYR/FasWMHtt99O69atufLKK6lRowYJCQmsWLGCtLQ0li9fzi233FLq51NEypghIuKmDhw4YAAXvJ06dcowDMMAjA4dOhjx8fHGnXfeaYSHhxv+/v7GddddZ6xevTrfbRw7dsx48sknjejoaMPHx8eoUqWK0bdvX+OXX37Jt39GRobx6quvGi1atDACAgKMoKAg48orrzSGDx/umIdhGEZ0dLQRHR2d7zo6dOhgnPvyGx8fb/z73/822rZta1SrVs3w9fU1ateubdx+++3Gli1bSvTciUj5054qEfEYFouFDh06OM6HEhEpTzqnSkRERMQEClUiIiIiJlCoEhERETGBd0VPQETELDpFVEQqkvZUiYiIiJhAoUpERETEBApVIiIiIiZQqBIRERExgUKViIiIiAkUqkRERERMoFAlIiIiYgKFKhERERET/D9nHFw4DgCYfwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 10ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n"
     ]
    }
   ],
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure forecast_ahead_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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[0, :, 0], \"bo-\", label=\"Actual\")\n",
    "    plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"rx-\", 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 use this model to predict the next 10 values. We first need to regenerate the sequences with 9 more time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": "markdown",
   "metadata": {},
   "source": [
    "Now let's predict the next 10 values one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 1s 10ms/step\n",
      "63/63 [==============================] - 1s 8ms/step\n",
      "63/63 [==============================] - 0s 6ms/step\n",
      "63/63 [==============================] - 0s 6ms/step\n",
      "63/63 [==============================] - 1s 9ms/step\n",
      "63/63 [==============================] - 1s 11ms/step\n",
      "63/63 [==============================] - 1s 8ms/step\n",
      "63/63 [==============================] - 0s 7ms/step\n",
      "63/63 [==============================] - 0s 6ms/step\n",
      "63/63 [==============================] - 0s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "X = X_valid\n",
    "for step_ahead in range(10):\n",
    "    y_pred_one = model.predict(X)[:, np.newaxis, :]\n",
    "    X = np.concatenate([X, y_pred_one], axis=1)\n",
    "\n",
    "Y_pred = X[:, n_steps:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 10)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021216832"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(keras.metrics.mean_squared_error(Y_valid, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare this performance with some baselines: naive predictions and a simple linear model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25697407"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_naive_pred = np.tile(X_valid[:, -1], 10) # take the last time step value, and repeat it 10 times\n",
    "np.mean(keras.metrics.mean_squared_error(Y_valid, Y_naive_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.1342 - val_loss: 0.0637\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0512 - val_loss: 0.0437\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0390 - val_loss: 0.0360\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 0s 1ms/step - loss: 0.0336 - val_loss: 0.0320\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0305 - val_loss: 0.0294\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0282 - val_loss: 0.0276\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0265 - val_loss: 0.0260\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0253 - val_loss: 0.0247\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0242 - val_loss: 0.0238\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 1s 3ms/step - loss: 0.0233 - val_loss: 0.0229\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0225 - val_loss: 0.0222\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 1s 2ms/step - loss: 0.0219 - val_loss: 0.0217\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 1s 3ms/step - loss: 0.0214 - val_loss: 0.0212\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 1s 2ms/step - loss: 0.0209 - val_loss: 0.0210\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 1s 3ms/step - loss: 0.0205 - val_loss: 0.0205\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 1s 3ms/step - loss: 0.0202 - val_loss: 0.0202\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 1s 2ms/step - loss: 0.0198 - val_loss: 0.0197\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 1s 3ms/step - loss: 0.0195 - val_loss: 0.0194\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 1s 2ms/step - loss: 0.0192 - val_loss: 0.0191\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 0s 2ms/step - loss: 0.0190 - val_loss: 0.0189\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(10)\n",
    "])\n",
    "\n",
    "optimizer=tf.keras.optimizers.legacy.Adam()\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": "markdown",
   "metadata": {},
   "source": [
    "Now let's create an RNN that predicts all 10 next values at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 6s 24ms/step - loss: 0.0568 - val_loss: 0.0303\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0276 - val_loss: 0.0233\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0191 - val_loss: 0.0157\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0157 - val_loss: 0.0147\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0133 - val_loss: 0.0114\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0122 - val_loss: 0.0106\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 5s 23ms/step - loss: 0.0113 - val_loss: 0.0102\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0111 - val_loss: 0.0095\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0108 - val_loss: 0.0114\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0103 - val_loss: 0.0097\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0104 - val_loss: 0.0091\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0098 - val_loss: 0.0122\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0095 - val_loss: 0.0081\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0093 - val_loss: 0.0097\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0090 - val_loss: 0.0083\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0094 - val_loss: 0.0084\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0091 - val_loss: 0.0087\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0089 - val_loss: 0.0088\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0089 - val_loss: 0.0082\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0089 - 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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 127ms/step\n"
     ]
    }
   ],
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 37,
   "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 50, 1), (7000, 50, 10))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0523 - last_time_step_mse: 0.0421 - val_loss: 0.0425 - val_last_time_step_mse: 0.0308\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0407 - last_time_step_mse: 0.0288 - val_loss: 0.0333 - val_last_time_step_mse: 0.0194\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0335 - last_time_step_mse: 0.0214 - val_loss: 0.0312 - val_last_time_step_mse: 0.0203\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0300 - last_time_step_mse: 0.0179 - val_loss: 0.0262 - val_last_time_step_mse: 0.0130\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0262 - last_time_step_mse: 0.0135 - val_loss: 0.0235 - val_last_time_step_mse: 0.0105\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0234 - last_time_step_mse: 0.0109 - val_loss: 0.0234 - val_last_time_step_mse: 0.0111\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0220 - last_time_step_mse: 0.0101 - val_loss: 0.0202 - val_last_time_step_mse: 0.0082\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0208 - last_time_step_mse: 0.0088 - val_loss: 0.0198 - val_last_time_step_mse: 0.0081\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 5s 23ms/step - loss: 0.0203 - last_time_step_mse: 0.0085 - val_loss: 0.0197 - val_last_time_step_mse: 0.0081\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 5s 23ms/step - loss: 0.0197 - last_time_step_mse: 0.0078 - val_loss: 0.0208 - val_last_time_step_mse: 0.0111\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 5s 23ms/step - loss: 0.0197 - last_time_step_mse: 0.0080 - val_loss: 0.0194 - val_last_time_step_mse: 0.0075\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0193 - last_time_step_mse: 0.0076 - val_loss: 0.0178 - val_last_time_step_mse: 0.0060\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0187 - last_time_step_mse: 0.0069 - val_loss: 0.0194 - val_last_time_step_mse: 0.0078\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0185 - last_time_step_mse: 0.0068 - val_loss: 0.0183 - val_last_time_step_mse: 0.0069\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0187 - last_time_step_mse: 0.0071 - val_loss: 0.0177 - val_last_time_step_mse: 0.0064\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0187 - last_time_step_mse: 0.0071 - val_loss: 0.0191 - val_last_time_step_mse: 0.0083\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0183 - last_time_step_mse: 0.0066 - val_loss: 0.0173 - val_last_time_step_mse: 0.0059\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 5s 24ms/step - loss: 0.0183 - last_time_step_mse: 0.0069 - val_loss: 0.0178 - val_last_time_step_mse: 0.0067\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0178 - last_time_step_mse: 0.0063 - val_loss: 0.0188 - val_last_time_step_mse: 0.0072\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0181 - last_time_step_mse: 0.0068 - val_loss: 0.0199 - val_last_time_step_mse: 0.0083\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=keras.optimizers.legacy.Adam(learning_rate=0.01), 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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 127ms/step\n"
     ]
    }
   ],
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "219/219 [==============================] - 7s 26ms/step - loss: 0.1921 - last_time_step_mse: 0.1716 - val_loss: 0.0865 - val_last_time_step_mse: 0.0776\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0540 - last_time_step_mse: 0.0439 - val_loss: 0.0519 - val_last_time_step_mse: 0.0421\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0466 - last_time_step_mse: 0.0365 - val_loss: 0.0451 - val_last_time_step_mse: 0.0345\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0420 - last_time_step_mse: 0.0312 - val_loss: 0.0404 - val_last_time_step_mse: 0.0295\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0383 - last_time_step_mse: 0.0267 - val_loss: 0.0373 - val_last_time_step_mse: 0.0256\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0357 - last_time_step_mse: 0.0235 - val_loss: 0.0356 - val_last_time_step_mse: 0.0232\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0342 - last_time_step_mse: 0.0222 - val_loss: 0.0338 - val_last_time_step_mse: 0.0218\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0326 - last_time_step_mse: 0.0206 - val_loss: 0.0318 - val_last_time_step_mse: 0.0197\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0315 - last_time_step_mse: 0.0193 - val_loss: 0.0311 - val_last_time_step_mse: 0.0190\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0310 - last_time_step_mse: 0.0190 - val_loss: 0.0309 - val_last_time_step_mse: 0.0191\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0304 - last_time_step_mse: 0.0184 - val_loss: 0.0313 - val_last_time_step_mse: 0.0197\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0298 - last_time_step_mse: 0.0178 - val_loss: 0.0292 - val_last_time_step_mse: 0.0169\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0293 - last_time_step_mse: 0.0173 - val_loss: 0.0289 - val_last_time_step_mse: 0.0169\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0290 - last_time_step_mse: 0.0169 - val_loss: 0.0282 - val_last_time_step_mse: 0.0163\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0286 - last_time_step_mse: 0.0166 - val_loss: 0.0280 - val_last_time_step_mse: 0.0159\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0284 - last_time_step_mse: 0.0164 - val_loss: 0.0293 - val_last_time_step_mse: 0.0176\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 6s 25ms/step - loss: 0.0281 - last_time_step_mse: 0.0161 - val_loss: 0.0281 - val_last_time_step_mse: 0.0155\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0277 - last_time_step_mse: 0.0157 - val_loss: 0.0273 - val_last_time_step_mse: 0.0153\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 6s 26ms/step - loss: 0.0275 - last_time_step_mse: 0.0155 - val_loss: 0.0272 - val_last_time_step_mse: 0.0153\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 5s 25ms/step - loss: 0.0273 - last_time_step_mse: 0.0153 - val_loss: 0.0274 - val_last_time_step_mse: 0.0152\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\n",
    "model.compile(loss=\"mse\", optimizer=optimizer, 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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tensorflow.keras.layers import LayerNormalization\n",
    "from keras.layers import LayerNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "  3/219 [..............................] - ETA: 9s - loss: 0.6127 - last_time_step_mse: 0.5811  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:15:05.308735: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219/219 [==============================] - 13s 54ms/step - loss: 0.1121 - last_time_step_mse: 0.0978 - val_loss: 0.0626 - val_last_time_step_mse: 0.0526\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 16s 71ms/step - loss: 0.0583 - last_time_step_mse: 0.0486 - val_loss: 0.0546 - val_last_time_step_mse: 0.0455\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 14s 66ms/step - loss: 0.0517 - last_time_step_mse: 0.0415 - val_loss: 0.0484 - val_last_time_step_mse: 0.0380\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0461 - last_time_step_mse: 0.0355 - val_loss: 0.0437 - val_last_time_step_mse: 0.0340\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 15s 67ms/step - loss: 0.0416 - last_time_step_mse: 0.0309 - val_loss: 0.0401 - val_last_time_step_mse: 0.0301\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 14s 66ms/step - loss: 0.0390 - last_time_step_mse: 0.0280 - val_loss: 0.0375 - val_last_time_step_mse: 0.0262\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 13s 61ms/step - loss: 0.0369 - last_time_step_mse: 0.0258 - val_loss: 0.0357 - val_last_time_step_mse: 0.0239\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 14s 62ms/step - loss: 0.0354 - last_time_step_mse: 0.0239 - val_loss: 0.0349 - val_last_time_step_mse: 0.0229\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 13s 61ms/step - loss: 0.0340 - last_time_step_mse: 0.0221 - val_loss: 0.0336 - val_last_time_step_mse: 0.0218\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 14s 62ms/step - loss: 0.0336 - last_time_step_mse: 0.0216 - val_loss: 0.0333 - val_last_time_step_mse: 0.0211\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 16s 72ms/step - loss: 0.0327 - last_time_step_mse: 0.0204 - val_loss: 0.0318 - val_last_time_step_mse: 0.0194\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 14s 63ms/step - loss: 0.0320 - last_time_step_mse: 0.0195 - val_loss: 0.0315 - val_last_time_step_mse: 0.0193\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 15s 70ms/step - loss: 0.0315 - last_time_step_mse: 0.0190 - val_loss: 0.0307 - val_last_time_step_mse: 0.0182\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 13s 61ms/step - loss: 0.0309 - last_time_step_mse: 0.0184 - val_loss: 0.0308 - val_last_time_step_mse: 0.0187\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 15s 66ms/step - loss: 0.0305 - last_time_step_mse: 0.0179 - val_loss: 0.0299 - val_last_time_step_mse: 0.0173\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 13s 62ms/step - loss: 0.0300 - last_time_step_mse: 0.0174 - val_loss: 0.0296 - val_last_time_step_mse: 0.0173\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 15s 68ms/step - loss: 0.0295 - last_time_step_mse: 0.0170 - val_loss: 0.0289 - val_last_time_step_mse: 0.0160\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0290 - last_time_step_mse: 0.0163 - val_loss: 0.0281 - val_last_time_step_mse: 0.0153\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 14s 64ms/step - loss: 0.0286 - last_time_step_mse: 0.0158 - val_loss: 0.0279 - val_last_time_step_mse: 0.0149\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 14s 63ms/step - loss: 0.0282 - last_time_step_mse: 0.0154 - val_loss: 0.0274 - val_last_time_step_mse: 0.0144\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\n",
    "model.compile(loss=\"mse\", optimizer=optimizer, 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": 46,
   "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",
    "        batch_size = tf.shape(inputs)[0]\n",
    "        return [tf.zeros([batch_size, self.cell.state_size], dtype=inputs.dtype)]\n",
    "    @tf.function\n",
    "    def call(self, inputs):\n",
    "        states = self.get_initial_state(inputs)\n",
    "        shape = tf.shape(inputs)\n",
    "        batch_size = shape[0]\n",
    "        n_steps = shape[1]\n",
    "        sequences = tf.TensorArray(\n",
    "            inputs.dtype, size=(n_steps if self.return_sequences else 0))\n",
    "        outputs = tf.zeros(shape=[batch_size, 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 tf.transpose(sequences.stack(), [1, 0, 2])\n",
    "        else:\n",
    "            return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:19:47.603857: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,?,1]\n",
      "\t [[{{node Placeholder}}]]\n",
      "2023-04-04 13:19:47.691018: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,?,20]\n",
      "\t [[{{node Placeholder}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219/219 [==============================] - 16s 67ms/step - loss: 0.1450 - last_time_step_mse: 0.1322 - val_loss: 0.0623 - val_last_time_step_mse: 0.0509\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 15s 69ms/step - loss: 0.0557 - last_time_step_mse: 0.0451 - val_loss: 0.0507 - val_last_time_step_mse: 0.0401\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 15s 67ms/step - loss: 0.0474 - last_time_step_mse: 0.0367 - val_loss: 0.0446 - val_last_time_step_mse: 0.0334\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 14s 66ms/step - loss: 0.0429 - last_time_step_mse: 0.0320 - val_loss: 0.0405 - val_last_time_step_mse: 0.0285\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 14s 66ms/step - loss: 0.0406 - last_time_step_mse: 0.0286 - val_loss: 0.0383 - val_last_time_step_mse: 0.0248\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0360 - last_time_step_mse: 0.0216 - val_loss: 0.0343 - val_last_time_step_mse: 0.0197\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0338 - last_time_step_mse: 0.0191 - val_loss: 0.0321 - val_last_time_step_mse: 0.0170\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 16s 71ms/step - loss: 0.0319 - last_time_step_mse: 0.0171 - val_loss: 0.0309 - val_last_time_step_mse: 0.0158\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0308 - last_time_step_mse: 0.0159 - val_loss: 0.0302 - val_last_time_step_mse: 0.0158\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 16s 72ms/step - loss: 0.0302 - last_time_step_mse: 0.0154 - val_loss: 0.0296 - val_last_time_step_mse: 0.0147\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 14s 65ms/step - loss: 0.0296 - last_time_step_mse: 0.0149 - val_loss: 0.0295 - val_last_time_step_mse: 0.0144\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 16s 75ms/step - loss: 0.0295 - last_time_step_mse: 0.0143 - val_loss: 0.0282 - val_last_time_step_mse: 0.0139\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 16s 74ms/step - loss: 0.0281 - last_time_step_mse: 0.0131 - val_loss: 0.0280 - val_last_time_step_mse: 0.0131\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 16s 71ms/step - loss: 0.0276 - last_time_step_mse: 0.0126 - val_loss: 0.0269 - val_last_time_step_mse: 0.0125\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 12s 55ms/step - loss: 0.0269 - last_time_step_mse: 0.0117 - val_loss: 0.0255 - val_last_time_step_mse: 0.0103\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 12s 53ms/step - loss: 0.0260 - last_time_step_mse: 0.0110 - val_loss: 0.0251 - val_last_time_step_mse: 0.0104\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 12s 53ms/step - loss: 0.0253 - last_time_step_mse: 0.0103 - val_loss: 0.0250 - val_last_time_step_mse: 0.0104\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 12s 53ms/step - loss: 0.0245 - last_time_step_mse: 0.0096 - val_loss: 0.0237 - val_last_time_step_mse: 0.0091\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 12s 55ms/step - loss: 0.0241 - last_time_step_mse: 0.0091 - val_loss: 0.0234 - val_last_time_step_mse: 0.0086\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 12s 53ms/step - loss: 0.0236 - last_time_step_mse: 0.0090 - val_loss: 0.0230 - 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",
    "    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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\n",
    "model.compile(loss=\"mse\", optimizer=optimizer, 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": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:24:29.090311: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:29.091362: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:29.092012: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:29.192498: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:29.193364: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:29.194040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:29.389301: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:29.390136: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:29.391227: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:29.502526: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:29.503596: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:29.504377: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:29.936053: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:29.937077: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:29.937905: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:30.048650: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:30.049493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:30.050288: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215/219 [============================>.] - ETA: 0s - loss: 0.0803 - last_time_step_mse: 0.0660"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:24:32.964493: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:32.965565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:32.966267: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:24:33.077266: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:24:33.078008: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:24:33.078737: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219/219 [==============================] - 4s 12ms/step - loss: 0.0798 - last_time_step_mse: 0.0655 - val_loss: 0.0553 - val_last_time_step_mse: 0.0368\n",
      "Epoch 2/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0467 - last_time_step_mse: 0.0266 - val_loss: 0.0400 - val_last_time_step_mse: 0.0198\n",
      "Epoch 3/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0373 - last_time_step_mse: 0.0171 - val_loss: 0.0357 - val_last_time_step_mse: 0.0161\n",
      "Epoch 4/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0342 - last_time_step_mse: 0.0147 - val_loss: 0.0330 - val_last_time_step_mse: 0.0133\n",
      "Epoch 5/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0321 - last_time_step_mse: 0.0135 - val_loss: 0.0313 - val_last_time_step_mse: 0.0132\n",
      "Epoch 6/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0304 - last_time_step_mse: 0.0120 - val_loss: 0.0294 - val_last_time_step_mse: 0.0110\n",
      "Epoch 7/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0291 - last_time_step_mse: 0.0112 - val_loss: 0.0284 - val_last_time_step_mse: 0.0111\n",
      "Epoch 8/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0281 - last_time_step_mse: 0.0104 - val_loss: 0.0274 - val_last_time_step_mse: 0.0101\n",
      "Epoch 9/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0273 - last_time_step_mse: 0.0102 - val_loss: 0.0269 - val_last_time_step_mse: 0.0099\n",
      "Epoch 10/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0268 - last_time_step_mse: 0.0101 - val_loss: 0.0264 - val_last_time_step_mse: 0.0098\n",
      "Epoch 11/20\n",
      "219/219 [==============================] - 2s 8ms/step - loss: 0.0263 - last_time_step_mse: 0.0097 - val_loss: 0.0259 - val_last_time_step_mse: 0.0093\n",
      "Epoch 12/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0258 - last_time_step_mse: 0.0095 - val_loss: 0.0259 - val_last_time_step_mse: 0.0100\n",
      "Epoch 13/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0254 - last_time_step_mse: 0.0093 - val_loss: 0.0251 - val_last_time_step_mse: 0.0093\n",
      "Epoch 14/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0251 - last_time_step_mse: 0.0092 - val_loss: 0.0249 - val_last_time_step_mse: 0.0090\n",
      "Epoch 15/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0247 - last_time_step_mse: 0.0090 - val_loss: 0.0247 - val_last_time_step_mse: 0.0086\n",
      "Epoch 16/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0245 - last_time_step_mse: 0.0091 - val_loss: 0.0244 - val_last_time_step_mse: 0.0090\n",
      "Epoch 17/20\n",
      "219/219 [==============================] - 2s 10ms/step - loss: 0.0241 - last_time_step_mse: 0.0088 - val_loss: 0.0242 - val_last_time_step_mse: 0.0091\n",
      "Epoch 18/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0238 - last_time_step_mse: 0.0087 - val_loss: 0.0235 - val_last_time_step_mse: 0.0081\n",
      "Epoch 19/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0235 - last_time_step_mse: 0.0086 - val_loss: 0.0236 - val_last_time_step_mse: 0.0092\n",
      "Epoch 20/20\n",
      "219/219 [==============================] - 2s 9ms/step - loss: 0.0232 - last_time_step_mse: 0.0084 - val_loss: 0.0233 - val_last_time_step_mse: 0.0080\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",
    "optimizer=tf.keras.optimizers.legacy.Adam()\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=optimizer, 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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 5ms/step - loss: 0.0233 - last_time_step_mse: 0.0080\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.02327839285135269, 0.008030006662011147]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, Y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-04 13:25:13.175645: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:25:13.176571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:25:13.177468: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-04 13:25:13.284336: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-04 13:25:13.285894: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-04 13:25:13.286792: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 335ms/step\n"
     ]
    }
   ],
   "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": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "nav_menu": {},
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}